Everything VecNet (Vector-Borne Disease Network) Digital Library - Search and Curate Documents and Data fascinator cc9210861edec0b2c3c1eaedc7783295 2018-09-25T06:22:28Z ["<p>About VecNet</p><p>Our Work</p><p>Although malaria remains both treatable and preventable, the number of infected cases remains high at 350-500 million people worldwide each year. Up to one million of these cases end in death, 85 percent of which occur in children under five years old. Recent global efforts have contributed to declines in malaria-related sickness and death. However, the combined effort of these strategies is still insufficient to eliminate the disease--there is a need for new approaches to eradicate malaria.</p><p>Strategies To Fight Malaria</p><p>Simulation models use existing data to predict malaria intervention outcomes. They can be run on their own or alongside field experiments. Their use within a project can reduce costs and save time. However, historically their use has been restricted by their availability: by the requirement of advanced mathematical modelling knowledge and expensive modelling software. To address this gap and improve access to simulation modelling programs, VecNet are developing simplified user interfaces for existing simulation programs. These interfaces will be available freely to VecNet users to improve malaria eradication strategies. VecNet also provides user-friendly interfaces to data and information storage, and these can be linked to the simulation modelling interfaces. The combined benefits of the VecNet tool inventory are; accessible information and accessible simulation modelling programs-to create intervention simulations that will inform local malaria eradication strategies.</p><p>VecNet Formation</p><p>VecNet was founded in 2011 as a consortium of institutions assembled to address the concerns and recommendations of the Malaria Eradication Research Agenda(malERA) initiative. The initiative, funded by the Bill & Melinda Gates Foundation, forms a collection of 12 reviews highlighting the outcomes of consultations with more than 250 experts on malaria from 36 countries. The malERA series of consultations concluded that the first step in malaria elimination requires access to all relevant data, following this, the means to analyze that data. VecNet was formed as a new portal for malaria information and user-friendly analysis tools. Once completed, this achievement will extend present vector control interventions, whilst enabling the incorporation of additional interventions to achieve elimination.</p><p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p><p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as:</p><ul><li>Global Access - Open Access</li><li>VecNet Users Only - Available to those with a VecNet login, or</li><li>Private Access - The depositor assigns access to named users.</li></ul>", "<p>About VecNet</p> <p>Our Work</p> <p> Although malaria remains both treatable and preventable, the number of infected cases remains high at 350-500 million people worldwide each year. Up to one million of these cases end in death, 85 percent of which occur in children under five years old. Recent global efforts have contributed to declines in malaria-related sickness and death. However, the combined effort of these strategies is still insufficient to eliminate the disease--there is a need for new approaches to eradicate malaria.</p> <p>Strategies To Fight Malaria</p> <p>Simulation models use existing data to predict malaria intervention outcomes. They can be run on their own or alongside field experiments. Their use within a project can reduce costs and save time. However, historically their use has been restricted by their availability: by the requirement of advanced mathematical modelling knowledge and expensive modelling software. To address this gap and improve access to simulation modelling programs, VecNet are developing simplified user interfaces for existing simulation programs. These interfaces will be available freely to VecNet users to improve malaria eradication strategies. VecNet also provides user-friendly interfaces to data and information storage, and these can be linked to the simulation modelling interfaces. The combined benefits of the VecNet tool inventory are; accessible information and accessible simulation modelling programs-to create intervention simulations that will inform local malaria eradication strategies.</p> <p>VecNet Formation</p> <p>VecNet was founded in 2011 as a consortium of institutions assembled to address the concerns and recommendations of the Malaria Eradication Research Agenda(malERA) initiative. The initiative, funded by the Bill & Melinda Gates Foundation, forms a collection of 12 reviews highlighting the outcomes of consultations with more than 250 experts on malaria from 36 countries. The malERA series of consultations concluded that the first step in malaria elimination requires access to all relevant data, following this, the means to analyze that data. VecNet was formed as a new portal for malaria information and user-friendly analysis tools. Once completed, this achievement will extend present vector control interventions, whilst enabling the incorporation of additional interventions to achieve elimination.</p> <p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p> <p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as: <ul> <li>Global Access - Open Access</li> <li>VecNet Users Only - Available to those with a VecNet login, or</li> <li>Private Access - The depositor assigns access to named users.</li> </ul></p>", "full", "<p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p><p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as:</p><ul><li>Global Access - Open Access</li><li>VecNet Users Only - Available to those with a VecNet login, or</li><li>Private Access - The depositor assigns access to named users.</li></ul>", "<p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p> <p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as: <ul> <li>Global Access - Open Access</li> <li>VecNet Users Only - Available to those with a VecNet login, or</li> <li>Private Access - The depositor assigns access to named users.</li> </ul> </p>", "brief", "<p>https://www.vecnet.org/images/vecnet_logo_small.jpg</p>", "<p>https://www.vecnet.org/images/vecnet_logo_small.jpg</p>", "logo", ""] ["<p>About VecNet</p><p>Our Work</p><p>Although malaria remains both treatable and preventable, the number of infected cases remains high at 350-500 million people worldwide each year. Up to one million of these cases end in death, 85 percent of which occur in children under five years old. Recent global efforts have contributed to declines in malaria-related sickness and death. However, the combined effort of these strategies is still insufficient to eliminate the disease--there is a need for new approaches to eradicate malaria.</p><p>Strategies To Fight Malaria</p><p>Simulation models use existing data to predict malaria intervention outcomes. They can be run on their own or alongside field experiments. Their use within a project can reduce costs and save time. However, historically their use has been restricted by their availability: by the requirement of advanced mathematical modelling knowledge and expensive modelling software. To address this gap and improve access to simulation modelling programs, VecNet are developing simplified user interfaces for existing simulation programs. These interfaces will be available freely to VecNet users to improve malaria eradication strategies. VecNet also provides user-friendly interfaces to data and information storage, and these can be linked to the simulation modelling interfaces. The combined benefits of the VecNet tool inventory are; accessible information and accessible simulation modelling programs-to create intervention simulations that will inform local malaria eradication strategies.</p><p>VecNet Formation</p><p>VecNet was founded in 2011 as a consortium of institutions assembled to address the concerns and recommendations of the Malaria Eradication Research Agenda(malERA) initiative. The initiative, funded by the Bill & Melinda Gates Foundation, forms a collection of 12 reviews highlighting the outcomes of consultations with more than 250 experts on malaria from 36 countries. The malERA series of consultations concluded that the first step in malaria elimination requires access to all relevant data, following this, the means to analyze that data. VecNet was formed as a new portal for malaria information and user-friendly analysis tools. Once completed, this achievement will extend present vector control interventions, whilst enabling the incorporation of additional interventions to achieve elimination.</p><p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p><p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as:</p><ul><li>Global Access - Open Access</li><li>VecNet Users Only - Available to those with a VecNet login, or</li><li>Private Access - The depositor assigns access to named users.</li></ul>", "<p>About VecNet</p> <p>Our Work</p> <p> Although malaria remains both treatable and preventable, the number of infected cases remains high at 350-500 million people worldwide each year. Up to one million of these cases end in death, 85 percent of which occur in children under five years old. Recent global efforts have contributed to declines in malaria-related sickness and death. However, the combined effort of these strategies is still insufficient to eliminate the disease--there is a need for new approaches to eradicate malaria.</p> <p>Strategies To Fight Malaria</p> <p>Simulation models use existing data to predict malaria intervention outcomes. They can be run on their own or alongside field experiments. Their use within a project can reduce costs and save time. However, historically their use has been restricted by their availability: by the requirement of advanced mathematical modelling knowledge and expensive modelling software. To address this gap and improve access to simulation modelling programs, VecNet are developing simplified user interfaces for existing simulation programs. These interfaces will be available freely to VecNet users to improve malaria eradication strategies. VecNet also provides user-friendly interfaces to data and information storage, and these can be linked to the simulation modelling interfaces. The combined benefits of the VecNet tool inventory are; accessible information and accessible simulation modelling programs-to create intervention simulations that will inform local malaria eradication strategies.</p> <p>VecNet Formation</p> <p>VecNet was founded in 2011 as a consortium of institutions assembled to address the concerns and recommendations of the Malaria Eradication Research Agenda(malERA) initiative. The initiative, funded by the Bill & Melinda Gates Foundation, forms a collection of 12 reviews highlighting the outcomes of consultations with more than 250 experts on malaria from 36 countries. The malERA series of consultations concluded that the first step in malaria elimination requires access to all relevant data, following this, the means to analyze that data. VecNet was formed as a new portal for malaria information and user-friendly analysis tools. Once completed, this achievement will extend present vector control interventions, whilst enabling the incorporation of additional interventions to achieve elimination.</p> <p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p> <p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as: <ul> <li>Global Access - Open Access</li> <li>VecNet Users Only - Available to those with a VecNet login, or</li> <li>Private Access - The depositor assigns access to named users.</li> </ul></p>", "full", "<p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p><p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as:</p><ul><li>Global Access - Open Access</li><li>VecNet Users Only - Available to those with a VecNet login, or</li><li>Private Access - The depositor assigns access to named users.</li></ul>", "<p>The Vector-Borne Disease Network Digital Library contains curated data, tagged citations, articles and reports on entomology, epidemiology, demography and interventions to support malaria eradication.</p> <p>The VecNet Digital Library is a is a tool developed to improve the availability of publications and datasets. VecNet users are encourages to upload documents to the Digital Library. The access level is nominated during the upload process as: <ul> <li>Global Access - Open Access</li> <li>VecNet Users Only - Available to those with a VecNet login, or</li> <li>Private Access - The depositor assigns access to named users.</li> </ul> </p>", "brief", "<p>https://www.vecnet.org/images/vecnet_logo_small.jpg</p>", "<p>https://www.vecnet.org/images/vecnet_logo_small.jpg</p>", "logo", ""] #WhiteProverbs Australia tweets fascinator 74029c5d5d3075ba6fd94e278078a952 2017-12-11T09:31:06Z ["<p>A collection of Tweets from Australian users who contributed to #WhiteProverbs meme in January 2014.</p>", "<p>A collection of Tweets from Australian users who contributed to #WhiteProverbs meme in January 2014.</p>", "brief", "<p>The data contained here were collected through digital ethnographic methods. Australian twitter users were found who contributed to the global meme #WhiteProverbs and their tweets & usernames were downloaded. </p>", "<p>The data contained here were collected through digital ethnographic methods. Australian twitter users were found who contributed to the global meme #WhiteProverbs and their tweets & usernames were downloaded. </p>", "full", "<p>This dataset consists of a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "<p>This dataset consists of a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "note", "A collection of Tweets from Australian users who contributed to #WhiteProverbs meme in January 2014."] ["<p>A collection of Tweets from Australian users who contributed to #WhiteProverbs meme in January 2014.</p>", "<p>A collection of Tweets from Australian users who contributed to #WhiteProverbs meme in January 2014.</p>", "brief", "<p>The data contained here were collected through digital ethnographic methods. Australian twitter users were found who contributed to the global meme #WhiteProverbs and their tweets & usernames were downloaded. </p>", "<p>The data contained here were collected through digital ethnographic methods. Australian twitter users were found who contributed to the global meme #WhiteProverbs and their tweets & usernames were downloaded. </p>", "full", "<p>This dataset consists of a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "<p>This dataset consists of a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "note", "A collection of Tweets from Australian users who contributed to #WhiteProverbs meme in January 2014."] (Pachycephala griseiceps) - current and future species distribution models fascinator a2b4857b2bc4c18453ea02ad3553e28e 2017-12-11T09:31:56Z ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Pachycephala griseiceps).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Pachycephala griseiceps).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Pachycephala griseiceps).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Pachycephala griseiceps).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] (Pachycephala griseiceps) - occurrence records filtered for species distribution modelling fascinator 8a85af36c79b956e984a2ec28137e2aa 2017-12-11T09:33:02Z ["<p>(Pachycephala griseiceps) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>(Pachycephala griseiceps) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of (Pachycephala griseiceps) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of (Pachycephala griseiceps) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] ["<p>(Pachycephala griseiceps) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>(Pachycephala griseiceps) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of (Pachycephala griseiceps) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of (Pachycephala griseiceps) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] (Peltohyas australis) - current and future species distribution models fascinator 138e98a663ad7c1bb4e7b5be4c1bd4fe 2017-12-11T09:33:40Z ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Peltohyas australis).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Peltohyas australis).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Peltohyas australis).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for (Peltohyas australis).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] (Peltohyas australis) - occurrence records filtered for species distribution modelling fascinator 5f0e4e302537d6c7ff3a069e758ca88b 2017-12-11T09:31:34Z ["<p>(Peltohyas australis) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>(Peltohyas australis) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of (Peltohyas australis) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of (Peltohyas australis) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] ["<p>(Peltohyas australis) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>(Peltohyas australis) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of (Peltohyas australis) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of (Peltohyas australis) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] A comparative study on sexual reproduction of scleractinian and alcyonacean corals fascinator 13e7ffa9296b256056f07f5a84f87da2 2018-01-02T04:00:04Z ["<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis.</p>", "<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis.</p>", "brief", "<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. Corals were collected over the sampling period and sex determined during the annual coral spawning event. After spawning weight and height were recorded and new corals were tagged before returning to a marked location. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis over a 13 months period, starting in January 2014 and ending in February 2015. During this period, coral tissue samples were collected once a month and samples for histology were promptly fixed for 24h in 4% formaldehyde solution. Histological sections were stained with Haematoxylin-Eosin and analysed for gonad development. Gonads were identified using a 10x magnification of a compound stereomicroscope. Only the diameter where nucleus was clearly visible was measured under appropriate magnification using the cellSens software (cellSens standard v1.8) to ensure that the full size of the oocytes was recorded. Gonads were grouped in different size classes relative to the final size of the oocytes. For A. millepora and L. pauciflorum the size groups were determined as following: Stage I oocytes (< 50 µm), Stage II oocytes (50 – 99 µm), Stage III oocytes (100 – 199 µm) and Stage 4 oocytes (> 200 µm). F. concinna oocytes were significantly smaller than Acropora and Lobophytum oocytes (p < 0.05). The size groups were therefore adjusted for this species: Stage I oocytes (< 25 µm), Stage II oocytes (25 – 49 µm), Stage III oocytes (50 – 74 µm) and Stage 4 oocytes (> 75 µm). In order to test for possible correlation of oocyte development with water temperature, data for daily average water temperatures at 6.1 m depth for the sampling period at Orpheus Island were downloaded from the AIMS data centre for relay pole 2 (http://data.aims.gov.au/aimsrtds/datatool.xhtml?site=9).</p><p>Data files included in this dataset are as follows:</p><ul><li>Fungia_concinna_sex_weight_height_categories.xlsx (summaries of sex allocation, weight and height of tagged corals over the 4 year study period</li><li>Fungia_concinna_sex_weight_height_categories.pzf (statistical analyses of Fungia concinna sex allocation, weight and height)</li><li>OIRS monthly sampling.xlsx (oocyte measurements from all 3 coral species investigated for onset of gametogenesis over 13 months</li><li>Oocyte Overview Combined.pzf (analysed oocyte measurements from all 3 coral species investigated)</li><li>Lobophytum Oocytes.pzfx (analysed oocyte counts per size group in Lobophytum pauciflorum)</li><li>Acropora Oocytes.pzfx (analysed oocyte counts per size group in Acropra millepora)</li><li>Gametes_SexDetermination.png (histological overview of gamete production and size and time point of first observation in all 3 coral species studied)</li></ul>", "<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. Corals were collected over the sampling period and sex determined during the annual coral spawning event. After spawning weight and height were recorded and new corals were tagged before returning to a marked location. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis over a 13 months period, starting in January 2014 and ending in February 2015. During this period, coral tissue samples were collected once a month and samples for histology were promptly fixed for 24h in 4% formaldehyde solution. Histological sections were stained with Haematoxylin-Eosin and analysed for gonad development. Gonads were identified using a 10x magnification of a compound stereomicroscope. Only the diameter where nucleus was clearly visible was measured under appropriate magnification using the cellSens software (cellSens standard v1.8) to ensure that the full size of the oocytes was recorded. Gonads were grouped in different size classes relative to the final size of the oocytes. For A. millepora and L. pauciflorum the size groups were determined as following: Stage I oocytes (< 50 µm), Stage II oocytes (50 – 99 µm), Stage III oocytes (100 – 199 µm) and Stage 4 oocytes (> 200 µm). F. concinna oocytes were significantly smaller than Acropora and Lobophytum oocytes (p < 0.05). The size groups were therefore adjusted for this species: Stage I oocytes (< 25 µm), Stage II oocytes (25 – 49 µm), Stage III oocytes (50 – 74 µm) and Stage 4 oocytes (> 75 µm). In order to test for possible correlation of oocyte development with water temperature, data for daily average water temperatures at 6.1 m depth for the sampling period at Orpheus Island were downloaded from the AIMS data centre for relay pole 2 (http://data.aims.gov.au/aimsrtds/datatool.xhtml?site=9).</p><p>Data files included in this dataset are as follows:</p><ul><li>Fungia_concinna_sex_weight_height_categories.xlsx (summaries of sex allocation, weight and height of tagged corals over the 4 year study period</li><li>Fungia_concinna_sex_weight_height_categories.pzf (statistical analyses of Fungia concinna sex allocation, weight and height)</li><li>OIRS monthly sampling.xlsx (oocyte measurements from all 3 coral species investigated for onset of gametogenesis over 13 months</li><li>Oocyte Overview Combined.pzf (analysed oocyte measurements from all 3 coral species investigated)</li><li>Lobophytum Oocytes.pzfx (analysed oocyte counts per size group in Lobophytum pauciflorum)</li><li>Acropora Oocytes.pzfx (analysed oocyte counts per size group in Acropra millepora)</li><li>Gametes_SexDetermination.png (histological overview of gamete production and size and time point of first observation in all 3 coral species studied)</li></ul>", "full", "<p>This dataset contains 9 files in MS Excel (.xlsx), Prism (.pzfx and .pzf) format and Portable Network Graphics (.png) formats. MS Excel files are also saved in Open Document Format (.ods). Prism files can be viewed using the free Prism viewer, see <a href="http://graphpad.com/support/faqid/788/">http://graphpad.com/support/faqid/788/</a> for details.</p>", "<p>This dataset contains 9 files in MS Excel (.xlsx), Prism (.pzfx and .pzf) format and Portable Network Graphics (.png) formats. MS Excel files are also saved in Open Document Format (.ods). Prism files can be viewed using the free Prism viewer, see <a href="http://graphpad.com/support/faqid/788/">http://graphpad.com/support/faqid/788/</a> for details.</p>", "note", ""] ["<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis.</p>", "<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis.</p>", "brief", "<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. Corals were collected over the sampling period and sex determined during the annual coral spawning event. After spawning weight and height were recorded and new corals were tagged before returning to a marked location. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis over a 13 months period, starting in January 2014 and ending in February 2015. During this period, coral tissue samples were collected once a month and samples for histology were promptly fixed for 24h in 4% formaldehyde solution. Histological sections were stained with Haematoxylin-Eosin and analysed for gonad development. Gonads were identified using a 10x magnification of a compound stereomicroscope. Only the diameter where nucleus was clearly visible was measured under appropriate magnification using the cellSens software (cellSens standard v1.8) to ensure that the full size of the oocytes was recorded. Gonads were grouped in different size classes relative to the final size of the oocytes. For A. millepora and L. pauciflorum the size groups were determined as following: Stage I oocytes (< 50 µm), Stage II oocytes (50 – 99 µm), Stage III oocytes (100 – 199 µm) and Stage 4 oocytes (> 200 µm). F. concinna oocytes were significantly smaller than Acropora and Lobophytum oocytes (p < 0.05). The size groups were therefore adjusted for this species: Stage I oocytes (< 25 µm), Stage II oocytes (25 – 49 µm), Stage III oocytes (50 – 74 µm) and Stage 4 oocytes (> 75 µm). In order to test for possible correlation of oocyte development with water temperature, data for daily average water temperatures at 6.1 m depth for the sampling period at Orpheus Island were downloaded from the AIMS data centre for relay pole 2 (http://data.aims.gov.au/aimsrtds/datatool.xhtml?site=9).</p><p>Data files included in this dataset are as follows:</p><ul><li>Fungia_concinna_sex_weight_height_categories.xlsx (summaries of sex allocation, weight and height of tagged corals over the 4 year study period</li><li>Fungia_concinna_sex_weight_height_categories.pzf (statistical analyses of Fungia concinna sex allocation, weight and height)</li><li>OIRS monthly sampling.xlsx (oocyte measurements from all 3 coral species investigated for onset of gametogenesis over 13 months</li><li>Oocyte Overview Combined.pzf (analysed oocyte measurements from all 3 coral species investigated)</li><li>Lobophytum Oocytes.pzfx (analysed oocyte counts per size group in Lobophytum pauciflorum)</li><li>Acropora Oocytes.pzfx (analysed oocyte counts per size group in Acropra millepora)</li><li>Gametes_SexDetermination.png (histological overview of gamete production and size and time point of first observation in all 3 coral species studied)</li></ul>", "<p>This dataset contains a collection of sex-allocation, weight and height over a 4 year period (2011-2014) of Fungia concinna. Corals were collected over the sampling period and sex determined during the annual coral spawning event. After spawning weight and height were recorded and new corals were tagged before returning to a marked location. In addition, this dataset contains a collection of oocyte measurements of two scleractinian corals, Acropora millepora and Fungia concinna, and one alcyonacean corals Lobophytum pauciflorum to identify the onset of gametogenesis over a 13 months period, starting in January 2014 and ending in February 2015. During this period, coral tissue samples were collected once a month and samples for histology were promptly fixed for 24h in 4% formaldehyde solution. Histological sections were stained with Haematoxylin-Eosin and analysed for gonad development. Gonads were identified using a 10x magnification of a compound stereomicroscope. Only the diameter where nucleus was clearly visible was measured under appropriate magnification using the cellSens software (cellSens standard v1.8) to ensure that the full size of the oocytes was recorded. Gonads were grouped in different size classes relative to the final size of the oocytes. For A. millepora and L. pauciflorum the size groups were determined as following: Stage I oocytes (< 50 µm), Stage II oocytes (50 – 99 µm), Stage III oocytes (100 – 199 µm) and Stage 4 oocytes (> 200 µm). F. concinna oocytes were significantly smaller than Acropora and Lobophytum oocytes (p < 0.05). The size groups were therefore adjusted for this species: Stage I oocytes (< 25 µm), Stage II oocytes (25 – 49 µm), Stage III oocytes (50 – 74 µm) and Stage 4 oocytes (> 75 µm). In order to test for possible correlation of oocyte development with water temperature, data for daily average water temperatures at 6.1 m depth for the sampling period at Orpheus Island were downloaded from the AIMS data centre for relay pole 2 (http://data.aims.gov.au/aimsrtds/datatool.xhtml?site=9).</p><p>Data files included in this dataset are as follows:</p><ul><li>Fungia_concinna_sex_weight_height_categories.xlsx (summaries of sex allocation, weight and height of tagged corals over the 4 year study period</li><li>Fungia_concinna_sex_weight_height_categories.pzf (statistical analyses of Fungia concinna sex allocation, weight and height)</li><li>OIRS monthly sampling.xlsx (oocyte measurements from all 3 coral species investigated for onset of gametogenesis over 13 months</li><li>Oocyte Overview Combined.pzf (analysed oocyte measurements from all 3 coral species investigated)</li><li>Lobophytum Oocytes.pzfx (analysed oocyte counts per size group in Lobophytum pauciflorum)</li><li>Acropora Oocytes.pzfx (analysed oocyte counts per size group in Acropra millepora)</li><li>Gametes_SexDetermination.png (histological overview of gamete production and size and time point of first observation in all 3 coral species studied)</li></ul>", "full", "<p>This dataset contains 9 files in MS Excel (.xlsx), Prism (.pzfx and .pzf) format and Portable Network Graphics (.png) formats. MS Excel files are also saved in Open Document Format (.ods). Prism files can be viewed using the free Prism viewer, see <a href="http://graphpad.com/support/faqid/788/">http://graphpad.com/support/faqid/788/</a> for details.</p>", "<p>This dataset contains 9 files in MS Excel (.xlsx), Prism (.pzfx and .pzf) format and Portable Network Graphics (.png) formats. MS Excel files are also saved in Open Document Format (.ods). Prism files can be viewed using the free Prism viewer, see <a href="http://graphpad.com/support/faqid/788/">http://graphpad.com/support/faqid/788/</a> for details.</p>", "note", ""] A comparison of closed system and flow-through respirometry techniques using adult Acanthochromis polyacanthus fascinator 791727d1bab6f1b5a78a7e5d57979ea3 2018-03-20T23:06:23Z ["<p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Experimental setup </em></p><p class="MsoNormal" style="text-align: justify;">Six adult spiny chromis damselfish (<em style="mso-bidi-font-style: normal;">Acanthochromis polyacanthus</em>) were used to compare closed system and flow-through respirometry techniques. Fish utilised were from stock originally collected from the central Great Barrier Reef and maintained at the James Cook University Marine Aquarium Research Facility, Queensland, Australia. All fish were moved into the experimental room one month prior to commencement of the project to allow for acclimation to the indoor aquarium environment.<span style="mso-spacerun: yes;">  </span>Throughout the acclimation and experimental period fish were maintained in individual tanks and were continuously supplied with flow-through filtered seawater at a temperature of 30 ± 0.4<span style="mso-bidi-font-family: Vrinda;">°</span>C. Feeding was once daily with NRD Aquaculture Nutrition commercial pellet.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Measuring oxygen consumption </em></p><p class="MsoNormal" style="text-align: justify;">Oxygen consumption for each fish was tested using both closed system and flow-through respirometry techniques. Each <em style="mso-bidi-font-style: normal;">A. polyacanthus </em>was tested in a 3.33 - 3.43 L chamber. Chamber volume depended on closed system or flow-through respirometry as additional hosing was required for the flow-through setup. Treatment order was randomised for each fish and a mix treatments were tested on each experimental day.</p><p class="MsoNormal" style="text-align: justify;">Food was withheld 24 hours prior to the commencement of each measurement to remove any effects of digestion on oxygen consumption. Fish were then introduced into their assigned respirometer by gently corralling the fish, allowing them to swim into the chamber and avoiding additional stress caused by handling or netting. The chamber was then sealed and submerged in a temperature controlled aquarium. Oxygen consumption measurements began before 12:00 and were recorded for approximately 7 hours using the Firesting contactless oxygen monitoring system (Firesting O<sub>2</sub>, PyroScience; www.pyro-science.com). During this time conditions alternated every 12 minutes between experimental conditions where the chamber was completely sealed, and a recovery period where new oxygenated water was flushed through the chamber. After the fish had been tested in all chamber sizes and in both the closed and flow-through system the wet weight of that fish was recorded. On each day a blank chamber was included for the calculation of microbial (background) oxygen consumption. Flow-through respirometry was used to calculate background oxygen consumption as there was no fish to mix the water in these chambers.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">File analysis</em></p><p class="MsoNormal" style="text-align: justify;">Text files from the Firesting O<sub>2</sub> system were formatted for analysis in Excel and the slopes of the fall in oxygen concentration over time was used to calculate the oxygen consumption of each fish in mg of O<sub>2</sub> consumed kg<sup>-1 h-1</sup>. Microbial oxygen consumption was generally very low for both the closed system and flow-through methods. Microbial oxygen consumption recorded in blank chambers was subtracted from the fish oxygen consumption values for the day that the blank was run. For one day, data from the blank chamber was not recorded and so the average of all backgrounds records was used (one individual was tested in the closed system and one in the flow-through system on this day).</p>", "full", "<p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Experimental setup </em></p><p class="MsoNormal" style="text-align: justify;">Six adult spiny chromis damselfish (<em style="mso-bidi-font-style: normal;">Acanthochromis polyacanthus</em>) were used to compare closed system and flow-through respirometry techniques. Fish utilised were from stock originally collected from the central Great Barrier Reef and maintained at the James Cook University Marine Aquarium Research Facility, Queensland, Australia. All fish were moved into the experimental room one month prior to commencement of the project to allow for acclimation to the indoor aquarium environment.<span style="mso-spacerun: yes;">  </span>Throughout the acclimation and experimental period fish were maintained in individual tanks and were continuously supplied with flow-through filtered seawater at a temperature of 30 ± 0.4<span style="mso-bidi-font-family: Vrinda;">°</span>C. Feeding was once daily with NRD Aquaculture Nutrition commercial pellet.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Measuring oxygen consumption </em></p><p class="MsoNormal" style="text-align: justify;">Oxygen consumption for each fish was tested using both closed system and flow-through respirometry techniques. Each <em style="mso-bidi-font-style: normal;">A. polyacanthus </em>was tested in a 3.33 - 3.43 L chamber. Chamber volume depended on closed system or flow-through respirometry as additional hosing was required for the flow-through setup. Treatment order was randomised for each fish and a mix treatments were tested on each experimental day.</p><p class="MsoNormal" style="text-align: justify;">Food was withheld 24 hours prior to the commencement of each measurement to remove any effects of digestion on oxygen consumption. Fish were then introduced into their assigned respirometer by gently corralling the fish, allowing them to swim into the chamber and avoiding additional stress caused by handling or netting. The chamber was then sealed and submerged in a temperature controlled aquarium. Oxygen consumption measurements began before 12:00 and were recorded for approximately 7 hours using the Firesting contactless oxygen monitoring system (Firesting O<sub>2</sub>, PyroScience; www.pyro-science.com). During this time conditions alternated every 12 minutes between experimental conditions where the chamber was completely sealed, and a recovery period where new oxygenated water was flushed through the chamber. After the fish had been tested in all chamber sizes and in both the closed and flow-through system the wet weight of that fish was recorded. On each day a blank chamber was included for the calculation of microbial (background) oxygen consumption. Flow-through respirometry was used to calculate background oxygen consumption as there was no fish to mix the water in these chambers.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">File analysis</em></p><p class="MsoNormal" style="text-align: justify;">Text files from the Firesting O<sub>2</sub> system were formatted for analysis in Excel and the slopes of the fall in oxygen concentration over time was used to calculate the oxygen consumption of each fish in mg of O<sub>2</sub> consumed kg<sup>-1 h-1</sup>. Microbial oxygen consumption was generally very low for both the closed system and flow-through methods. Microbial oxygen consumption recorded in blank chambers was subtracted from the fish oxygen consumption values for the day that the blank was run. For one day, data from the blank chamber was not recorded and so the average of all backgrounds records was used (one individual was tested in the closed system and one in the flow-through system on this day).</p>", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "full", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", ""] ["<p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Experimental setup </em></p><p class="MsoNormal" style="text-align: justify;">Six adult spiny chromis damselfish (<em style="mso-bidi-font-style: normal;">Acanthochromis polyacanthus</em>) were used to compare closed system and flow-through respirometry techniques. Fish utilised were from stock originally collected from the central Great Barrier Reef and maintained at the James Cook University Marine Aquarium Research Facility, Queensland, Australia. All fish were moved into the experimental room one month prior to commencement of the project to allow for acclimation to the indoor aquarium environment.<span style="mso-spacerun: yes;">  </span>Throughout the acclimation and experimental period fish were maintained in individual tanks and were continuously supplied with flow-through filtered seawater at a temperature of 30 ± 0.4<span style="mso-bidi-font-family: Vrinda;">°</span>C. Feeding was once daily with NRD Aquaculture Nutrition commercial pellet.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Measuring oxygen consumption </em></p><p class="MsoNormal" style="text-align: justify;">Oxygen consumption for each fish was tested using both closed system and flow-through respirometry techniques. Each <em style="mso-bidi-font-style: normal;">A. polyacanthus </em>was tested in a 3.33 - 3.43 L chamber. Chamber volume depended on closed system or flow-through respirometry as additional hosing was required for the flow-through setup. Treatment order was randomised for each fish and a mix treatments were tested on each experimental day.</p><p class="MsoNormal" style="text-align: justify;">Food was withheld 24 hours prior to the commencement of each measurement to remove any effects of digestion on oxygen consumption. Fish were then introduced into their assigned respirometer by gently corralling the fish, allowing them to swim into the chamber and avoiding additional stress caused by handling or netting. The chamber was then sealed and submerged in a temperature controlled aquarium. Oxygen consumption measurements began before 12:00 and were recorded for approximately 7 hours using the Firesting contactless oxygen monitoring system (Firesting O<sub>2</sub>, PyroScience; www.pyro-science.com). During this time conditions alternated every 12 minutes between experimental conditions where the chamber was completely sealed, and a recovery period where new oxygenated water was flushed through the chamber. After the fish had been tested in all chamber sizes and in both the closed and flow-through system the wet weight of that fish was recorded. On each day a blank chamber was included for the calculation of microbial (background) oxygen consumption. Flow-through respirometry was used to calculate background oxygen consumption as there was no fish to mix the water in these chambers.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">File analysis</em></p><p class="MsoNormal" style="text-align: justify;">Text files from the Firesting O<sub>2</sub> system were formatted for analysis in Excel and the slopes of the fall in oxygen concentration over time was used to calculate the oxygen consumption of each fish in mg of O<sub>2</sub> consumed kg<sup>-1 h-1</sup>. Microbial oxygen consumption was generally very low for both the closed system and flow-through methods. Microbial oxygen consumption recorded in blank chambers was subtracted from the fish oxygen consumption values for the day that the blank was run. For one day, data from the blank chamber was not recorded and so the average of all backgrounds records was used (one individual was tested in the closed system and one in the flow-through system on this day).</p>", "full", "<p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Experimental setup </em></p><p class="MsoNormal" style="text-align: justify;">Six adult spiny chromis damselfish (<em style="mso-bidi-font-style: normal;">Acanthochromis polyacanthus</em>) were used to compare closed system and flow-through respirometry techniques. Fish utilised were from stock originally collected from the central Great Barrier Reef and maintained at the James Cook University Marine Aquarium Research Facility, Queensland, Australia. All fish were moved into the experimental room one month prior to commencement of the project to allow for acclimation to the indoor aquarium environment.<span style="mso-spacerun: yes;">  </span>Throughout the acclimation and experimental period fish were maintained in individual tanks and were continuously supplied with flow-through filtered seawater at a temperature of 30 ± 0.4<span style="mso-bidi-font-family: Vrinda;">°</span>C. Feeding was once daily with NRD Aquaculture Nutrition commercial pellet.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">Measuring oxygen consumption </em></p><p class="MsoNormal" style="text-align: justify;">Oxygen consumption for each fish was tested using both closed system and flow-through respirometry techniques. Each <em style="mso-bidi-font-style: normal;">A. polyacanthus </em>was tested in a 3.33 - 3.43 L chamber. Chamber volume depended on closed system or flow-through respirometry as additional hosing was required for the flow-through setup. Treatment order was randomised for each fish and a mix treatments were tested on each experimental day.</p><p class="MsoNormal" style="text-align: justify;">Food was withheld 24 hours prior to the commencement of each measurement to remove any effects of digestion on oxygen consumption. Fish were then introduced into their assigned respirometer by gently corralling the fish, allowing them to swim into the chamber and avoiding additional stress caused by handling or netting. The chamber was then sealed and submerged in a temperature controlled aquarium. Oxygen consumption measurements began before 12:00 and were recorded for approximately 7 hours using the Firesting contactless oxygen monitoring system (Firesting O<sub>2</sub>, PyroScience; www.pyro-science.com). During this time conditions alternated every 12 minutes between experimental conditions where the chamber was completely sealed, and a recovery period where new oxygenated water was flushed through the chamber. After the fish had been tested in all chamber sizes and in both the closed and flow-through system the wet weight of that fish was recorded. On each day a blank chamber was included for the calculation of microbial (background) oxygen consumption. Flow-through respirometry was used to calculate background oxygen consumption as there was no fish to mix the water in these chambers.</p><p class="MsoNormal" style="text-align: justify;"><em style="mso-bidi-font-style: normal;">File analysis</em></p><p class="MsoNormal" style="text-align: justify;">Text files from the Firesting O<sub>2</sub> system were formatted for analysis in Excel and the slopes of the fall in oxygen concentration over time was used to calculate the oxygen consumption of each fish in mg of O<sub>2</sub> consumed kg<sup>-1 h-1</sup>. Microbial oxygen consumption was generally very low for both the closed system and flow-through methods. Microbial oxygen consumption recorded in blank chambers was subtracted from the fish oxygen consumption values for the day that the blank was run. For one day, data from the blank chamber was not recorded and so the average of all backgrounds records was used (one individual was tested in the closed system and one in the flow-through system on this day).</p>", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "full", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", ""] AIMS river nutrient data collected from downstream sites in major Queensland rivers draining to the Great Barrier Reef lagoon, 1987-2000 fascinator 16df4effe0ca5feb7bfb8c24791af4c5 2017-12-11T09:32:39Z ["brief", "The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy.", "note", "There are 3 datasets (xls, ods and pdf formats) including 1. A report. 2. Analysis for N and P. 3. %DIN and %DIP.", "logo", "http://www.jcu.edu.au/tdh/resolveuid/5c43f53a-32b3-4bfc-afea-9c0e79555732/@@images/image", "Coinvestigators: Alan Mitchell: ACTFR (Australian Centre for Tropical Freshwater Research), James Cook University, Townsville. Miles Furnas: AIMS (Australian Institute of Marine Science), Townsville. Related JCU Research Themes: Tropical Ecosystems, Conservation and Climate Change Industries and Economies in the Tropics", "<p>The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy.</p>", "<p>The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy.</p>", "<p>There are 3 datasets (xls, ods and pdf formats) including 1. A report. 2. Analysis for N and P. 3. %DIN and %DIP.</p>", "<p>There are 3 datasets (xls, ods and pdf formats) including 1. A report. 2. Analysis for N and P. 3. %DIN and %DIP.</p>", "<p>http://www.jcu.edu.au/tdh/resolveuid/5c43f53a-32b3-4bfc-afea-9c0e79555732/@@images/image</p>", "<p>http://www.jcu.edu.au/tdh/resolveuid/5c43f53a-32b3-4bfc-afea-9c0e79555732/@@images/image</p>", "The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy."] ["brief", "The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy.", "note", "There are 3 datasets (xls, ods and pdf formats) including 1. A report. 2. Analysis for N and P. 3. %DIN and %DIP.", "logo", "http://www.jcu.edu.au/tdh/resolveuid/5c43f53a-32b3-4bfc-afea-9c0e79555732/@@images/image", "Coinvestigators: Alan Mitchell: ACTFR (Australian Centre for Tropical Freshwater Research), James Cook University, Townsville. Miles Furnas: AIMS (Australian Institute of Marine Science), Townsville. Related JCU Research Themes: Tropical Ecosystems, Conservation and Climate Change Industries and Economies in the Tropics", "<p>The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy.</p>", "<p>The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy.</p>", "<p>There are 3 datasets (xls, ods and pdf formats) including 1. A report. 2. Analysis for N and P. 3. %DIN and %DIP.</p>", "<p>There are 3 datasets (xls, ods and pdf formats) including 1. A report. 2. Analysis for N and P. 3. %DIN and %DIP.</p>", "<p>http://www.jcu.edu.au/tdh/resolveuid/5c43f53a-32b3-4bfc-afea-9c0e79555732/@@images/image</p>", "<p>http://www.jcu.edu.au/tdh/resolveuid/5c43f53a-32b3-4bfc-afea-9c0e79555732/@@images/image</p>", "The principal objective of the sampling program was to determine nutrient loads carried by the major rivers adjacent to the Central-Southern sections of the Great Barrier Reef (GBR), for use in the compilation of a nutrient budget of the GBR shelf. Six major rivers were sampled extensively, the Barron, South Johnstone, Tully, Herbert, Burdekin and Fitzroy."] Aboriginal and Torres Strait Islander student experiences in social work field education fascinator 6b0a6ca70630b9a1024b7a43adfad869 2018-11-07T06:11:24Z ["<p>Attracting more Aboriginal and Torres Strait Islander people to the social work profession is an important strategy in responding to Indigenous disadvantage. The literature suggests that the inclusion of Aboriginal and Torres Strait Islander people, knowledge, and skills in social work is impeded by racism and white privilege. This  research project  aimed to explore the field education experiences of Aboriginal and Torres Strait Islander social work students. Interviews were conducted with 11 Aboriginal and Torres Strait Islander students and graduates and their narratives were analysed through a collaborative process. Findings reveal experiences of subtle and overt racism as every day features of their placements. The findings highlight the need to address racism, the value of cultural mentors, and the necessity to increase the employment of Aboriginal and Torres Strait Islander academic staff in social work education.</p><p>The dataset deposited includes the transcripts of the qualitative interviews with the participants and the themes from the focus group interview.</p><p> </p>", "full", "<p>Attracting more Aboriginal and Torres Strait Islander people to the social work profession is an important strategy in responding to Indigenous disadvantage. The literature suggests that the inclusion of Aboriginal and Torres Strait Islander people, knowledge, and skills in social work is impeded by racism and white privilege. This  research project  aimed to explore the field education experiences of Aboriginal and Torres Strait Islander social work students. Interviews were conducted with 11 Aboriginal and Torres Strait Islander students and graduates and their narratives were analysed through a collaborative process. Findings reveal experiences of subtle and overt racism as every day features of their placements. The findings highlight the need to address racism, the value of cultural mentors, and the necessity to increase the employment of Aboriginal and Torres Strait Islander academic staff in social work education.</p><p>The dataset deposited includes the transcripts of the qualitative interviews with the participants and the themes from the focus group interview.</p><p> </p>", "<p>Data consists of 16 files saved in both MS Word (.doc and .docx) and PDF formats and stored in 2 zip files in the secure section of the Tropical Data Hub (TDH) archive.</p>", "note", "<p>Data consists of 16 files saved in both MS Word (.doc and .docx) and PDF formats and stored in 2 zip files in the secure section of the Tropical Data Hub (TDH) archive.</p>", ""] ["<p>Attracting more Aboriginal and Torres Strait Islander people to the social work profession is an important strategy in responding to Indigenous disadvantage. The literature suggests that the inclusion of Aboriginal and Torres Strait Islander people, knowledge, and skills in social work is impeded by racism and white privilege. This  research project  aimed to explore the field education experiences of Aboriginal and Torres Strait Islander social work students. Interviews were conducted with 11 Aboriginal and Torres Strait Islander students and graduates and their narratives were analysed through a collaborative process. Findings reveal experiences of subtle and overt racism as every day features of their placements. The findings highlight the need to address racism, the value of cultural mentors, and the necessity to increase the employment of Aboriginal and Torres Strait Islander academic staff in social work education.</p><p>The dataset deposited includes the transcripts of the qualitative interviews with the participants and the themes from the focus group interview.</p><p> </p>", "full", "<p>Attracting more Aboriginal and Torres Strait Islander people to the social work profession is an important strategy in responding to Indigenous disadvantage. The literature suggests that the inclusion of Aboriginal and Torres Strait Islander people, knowledge, and skills in social work is impeded by racism and white privilege. This  research project  aimed to explore the field education experiences of Aboriginal and Torres Strait Islander social work students. Interviews were conducted with 11 Aboriginal and Torres Strait Islander students and graduates and their narratives were analysed through a collaborative process. Findings reveal experiences of subtle and overt racism as every day features of their placements. The findings highlight the need to address racism, the value of cultural mentors, and the necessity to increase the employment of Aboriginal and Torres Strait Islander academic staff in social work education.</p><p>The dataset deposited includes the transcripts of the qualitative interviews with the participants and the themes from the focus group interview.</p><p> </p>", "<p>Data consists of 16 files saved in both MS Word (.doc and .docx) and PDF formats and stored in 2 zip files in the secure section of the Tropical Data Hub (TDH) archive.</p>", "note", "<p>Data consists of 16 files saved in both MS Word (.doc and .docx) and PDF formats and stored in 2 zip files in the secure section of the Tropical Data Hub (TDH) archive.</p>", ""] Acanthochromis polyacanthus genome sequencing and assembly fascinator 8af60bb9a15e79258237292ba0e45b24 2018-01-05T04:52:04Z ["<p>Genome sequencing and assembly data for Acanthochromis polyacanthus (study on the effects of ocean acidifcation) from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA328211. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: The impact of ocean acidification on marine ecosystems will depend on species capacity to adapt<sup><a id="ref-link-abstract-1" title="Sunday, J. M. et al. Evolution in an acidifying ocean. Trends Ecol. Evol. 29, 117–125 (2014)." href="https://www.nature.com/articles/nclimate3087#ref1">1</a>,<a id="ref-link-abstract-2" title="Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013)." href="https://www.nature.com/articles/nclimate3087#ref2">2</a></sup>. Recent studies show that the behaviour of reef fishes is impaired at projected CO<sub> 2</sub> levels<sup><a id="ref-link-abstract-3" title="Munday, P. L., Cheal, A. J., Dixson, D. L., Rummer, J. L. & Fabricius, K. E. Behavioural impairment in reef fishes caused by ocean acidification at CO2 seeps. Nature Clim. Change 4, 487–492 (2014)." href="https://www.nature.com/articles/nclimate3087#ref3">3</a>,<a id="ref-link-abstract-4" title="Munday, P. L. et al. Replenishment of fish populations is threatened by ocean acidification. Proc. Natl Acad. Sci. USA 107, 12930–12934 (2010)." href="https://www.nature.com/articles/nclimate3087#ref4">4</a></sup>; however, individual variation exists that might promote adaptation. Here, we show a clear signature of parental sensitivity to high CO<sub> 2</sub> in the brain molecular phenotype of juvenile spiny damselfish, <em>Acanthochromis polyacanthus</em>, primarily driven by circadian rhythm genes. Offspring of CO<sub> 2</sub>-tolerant and CO<sub> 2</sub>-sensitive parents were reared at near-future CO<sub> 2</sub> (754 μatm) or present-day control levels (414 μatm). By integrating 33 brain transcriptomes and proteomes with a <em>de novo</em> assembled genome we investigate the molecular responses of the fish brain to increased CO<sub> 2</sub> and the expression of parental tolerance to high CO<sub> 2</sub> in the offspring molecular phenotype. Exposure to high CO<sub> 2</sub> resulted in differential regulation of 173 and 62 genes and 109 and 68 proteins in the tolerant and sensitive groups, respectively. Importantly, the majority of differences between offspring of tolerant and sensitive parents occurred in high CO<sub> 2</sub> conditions. This transgenerational molecular signature suggests that individual variation in CO<sub> 2</sub> sensitivity could facilitate adaptation of fish populations to ocean acidification.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p>", "full", "<p>Genome sequencing and assembly data for Acanthochromis polyacanthus (study on the effects of ocean acidifcation) from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA328211. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: The impact of ocean acidification on marine ecosystems will depend on species capacity to adapt<sup><a id="ref-link-abstract-1" title="Sunday, J. M. et al. Evolution in an acidifying ocean. Trends Ecol. Evol. 29, 117–125 (2014)." href="https://www.nature.com/articles/nclimate3087#ref1">1</a>,<a id="ref-link-abstract-2" title="Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013)." href="https://www.nature.com/articles/nclimate3087#ref2">2</a></sup>. Recent studies show that the behaviour of reef fishes is impaired at projected CO<sub> 2</sub> levels<sup><a id="ref-link-abstract-3" title="Munday, P. L., Cheal, A. J., Dixson, D. L., Rummer, J. L. & Fabricius, K. E. Behavioural impairment in reef fishes caused by ocean acidification at CO2 seeps. Nature Clim. Change 4, 487–492 (2014)." href="https://www.nature.com/articles/nclimate3087#ref3">3</a>,<a id="ref-link-abstract-4" title="Munday, P. L. et al. Replenishment of fish populations is threatened by ocean acidification. Proc. Natl Acad. Sci. USA 107, 12930–12934 (2010)." href="https://www.nature.com/articles/nclimate3087#ref4">4</a></sup>; however, individual variation exists that might promote adaptation. Here, we show a clear signature of parental sensitivity to high CO<sub> 2</sub> in the brain molecular phenotype of juvenile spiny damselfish, <em>Acanthochromis polyacanthus</em>, primarily driven by circadian rhythm genes. Offspring of CO<sub> 2</sub>-tolerant and CO<sub> 2</sub>-sensitive parents were reared at near-future CO<sub> 2</sub> (754 μatm) or present-day control levels (414 μatm). By integrating 33 brain transcriptomes and proteomes with a <em>de novo</em> assembled genome we investigate the molecular responses of the fish brain to increased CO<sub> 2</sub> and the expression of parental tolerance to high CO<sub> 2</sub> in the offspring molecular phenotype. Exposure to high CO<sub> 2</sub> resulted in differential regulation of 173 and 62 genes and 109 and 68 proteins in the tolerant and sensitive groups, respectively. Importantly, the majority of differences between offspring of tolerant and sensitive parents occurred in high CO<sub> 2</sub> conditions. This transgenerational molecular signature suggests that individual variation in CO<sub> 2</sub> sensitivity could facilitate adaptation of fish populations to ocean acidification.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p>", ""] ["<p>Genome sequencing and assembly data for Acanthochromis polyacanthus (study on the effects of ocean acidifcation) from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA328211. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: The impact of ocean acidification on marine ecosystems will depend on species capacity to adapt<sup><a id="ref-link-abstract-1" title="Sunday, J. M. et al. Evolution in an acidifying ocean. Trends Ecol. Evol. 29, 117–125 (2014)." href="https://www.nature.com/articles/nclimate3087#ref1">1</a>,<a id="ref-link-abstract-2" title="Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013)." href="https://www.nature.com/articles/nclimate3087#ref2">2</a></sup>. Recent studies show that the behaviour of reef fishes is impaired at projected CO<sub> 2</sub> levels<sup><a id="ref-link-abstract-3" title="Munday, P. L., Cheal, A. J., Dixson, D. L., Rummer, J. L. & Fabricius, K. E. Behavioural impairment in reef fishes caused by ocean acidification at CO2 seeps. Nature Clim. Change 4, 487–492 (2014)." href="https://www.nature.com/articles/nclimate3087#ref3">3</a>,<a id="ref-link-abstract-4" title="Munday, P. L. et al. Replenishment of fish populations is threatened by ocean acidification. Proc. Natl Acad. Sci. USA 107, 12930–12934 (2010)." href="https://www.nature.com/articles/nclimate3087#ref4">4</a></sup>; however, individual variation exists that might promote adaptation. Here, we show a clear signature of parental sensitivity to high CO<sub> 2</sub> in the brain molecular phenotype of juvenile spiny damselfish, <em>Acanthochromis polyacanthus</em>, primarily driven by circadian rhythm genes. Offspring of CO<sub> 2</sub>-tolerant and CO<sub> 2</sub>-sensitive parents were reared at near-future CO<sub> 2</sub> (754 μatm) or present-day control levels (414 μatm). By integrating 33 brain transcriptomes and proteomes with a <em>de novo</em> assembled genome we investigate the molecular responses of the fish brain to increased CO<sub> 2</sub> and the expression of parental tolerance to high CO<sub> 2</sub> in the offspring molecular phenotype. Exposure to high CO<sub> 2</sub> resulted in differential regulation of 173 and 62 genes and 109 and 68 proteins in the tolerant and sensitive groups, respectively. Importantly, the majority of differences between offspring of tolerant and sensitive parents occurred in high CO<sub> 2</sub> conditions. This transgenerational molecular signature suggests that individual variation in CO<sub> 2</sub> sensitivity could facilitate adaptation of fish populations to ocean acidification.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p>", "full", "<p>Genome sequencing and assembly data for Acanthochromis polyacanthus (study on the effects of ocean acidifcation) from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA328211. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: The impact of ocean acidification on marine ecosystems will depend on species capacity to adapt<sup><a id="ref-link-abstract-1" title="Sunday, J. M. et al. Evolution in an acidifying ocean. Trends Ecol. Evol. 29, 117–125 (2014)." href="https://www.nature.com/articles/nclimate3087#ref1">1</a>,<a id="ref-link-abstract-2" title="Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013)." href="https://www.nature.com/articles/nclimate3087#ref2">2</a></sup>. Recent studies show that the behaviour of reef fishes is impaired at projected CO<sub> 2</sub> levels<sup><a id="ref-link-abstract-3" title="Munday, P. L., Cheal, A. J., Dixson, D. L., Rummer, J. L. & Fabricius, K. E. Behavioural impairment in reef fishes caused by ocean acidification at CO2 seeps. Nature Clim. Change 4, 487–492 (2014)." href="https://www.nature.com/articles/nclimate3087#ref3">3</a>,<a id="ref-link-abstract-4" title="Munday, P. L. et al. Replenishment of fish populations is threatened by ocean acidification. Proc. Natl Acad. Sci. USA 107, 12930–12934 (2010)." href="https://www.nature.com/articles/nclimate3087#ref4">4</a></sup>; however, individual variation exists that might promote adaptation. Here, we show a clear signature of parental sensitivity to high CO<sub> 2</sub> in the brain molecular phenotype of juvenile spiny damselfish, <em>Acanthochromis polyacanthus</em>, primarily driven by circadian rhythm genes. Offspring of CO<sub> 2</sub>-tolerant and CO<sub> 2</sub>-sensitive parents were reared at near-future CO<sub> 2</sub> (754 μatm) or present-day control levels (414 μatm). By integrating 33 brain transcriptomes and proteomes with a <em>de novo</em> assembled genome we investigate the molecular responses of the fish brain to increased CO<sub> 2</sub> and the expression of parental tolerance to high CO<sub> 2</sub> in the offspring molecular phenotype. Exposure to high CO<sub> 2</sub> resulted in differential regulation of 173 and 62 genes and 109 and 68 proteins in the tolerant and sensitive groups, respectively. Importantly, the majority of differences between offspring of tolerant and sensitive parents occurred in high CO<sub> 2</sub> conditions. This transgenerational molecular signature suggests that individual variation in CO<sub> 2</sub> sensitivity could facilitate adaptation of fish populations to ocean acidification.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p>", ""] Acanthochromis polyacanthus transcriptome fascinator ec693e5c5b2d51d5354a0d20beacf204 2018-01-05T04:49:58Z ["<p>Transcriptome of the tropical fish Acanthochromis polyacanthus from two populations (Heron and Palm Island regions, Great Barrier Reef) exposed to current day and predicted elevated temperatures from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA255544. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: Some animals have the remarkable capacity to acclimate across generations to projected future climate change 1, 2, 3, 4; however, the underlying molecular processes are unknown. We sequenced and assembled de novo transcriptomes of adult tropical reef fish exposed developmentally or transgenerationally to projected future ocean temperatures and correlated the resulting expression profiles with acclimated metabolic traits from the same fish. We identified 69 contigs representing 53 key genes involved in thermal acclimation of aerobic capacity. Metabolic genes were among the most upregulated transgenerationally, suggesting shifts in energy production for maintaining performance at elevated temperatures. Furthermore, immune- and stress-responsive genes were upregulated transgenerationally, indicating a new complement of genes allowing the second generation of fish to better cope with elevated temperatures. Other differentially expressed genes were involved with tissue development and transcriptional regulation. Overall, we found a similar suite of differentially expressed genes among developmental and transgenerational treatments. Heat-shock protein genes were surprisingly unresponsive, indicating that short-term heat-stress responses may not be a good indicator of long-term acclimation capacity. Our results are the first to reveal the molecular processes that may enable marine fishes to adjust to a future warmer environment over multiple generations.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p><p><span style="color: #000000; font-family: 'Arial Unicode MS'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> </span></p>", "full", "<p>Transcriptome of the tropical fish Acanthochromis polyacanthus from two populations (Heron and Palm Island regions, Great Barrier Reef) exposed to current day and predicted elevated temperatures from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA255544. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: Some animals have the remarkable capacity to acclimate across generations to projected future climate change 1, 2, 3, 4; however, the underlying molecular processes are unknown. We sequenced and assembled de novo transcriptomes of adult tropical reef fish exposed developmentally or transgenerationally to projected future ocean temperatures and correlated the resulting expression profiles with acclimated metabolic traits from the same fish. We identified 69 contigs representing 53 key genes involved in thermal acclimation of aerobic capacity. Metabolic genes were among the most upregulated transgenerationally, suggesting shifts in energy production for maintaining performance at elevated temperatures. Furthermore, immune- and stress-responsive genes were upregulated transgenerationally, indicating a new complement of genes allowing the second generation of fish to better cope with elevated temperatures. Other differentially expressed genes were involved with tissue development and transcriptional regulation. Overall, we found a similar suite of differentially expressed genes among developmental and transgenerational treatments. Heat-shock protein genes were surprisingly unresponsive, indicating that short-term heat-stress responses may not be a good indicator of long-term acclimation capacity. Our results are the first to reveal the molecular processes that may enable marine fishes to adjust to a future warmer environment over multiple generations.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p><p><span style="color: #000000; font-family: 'Arial Unicode MS'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> </span></p>", ""] ["<p>Transcriptome of the tropical fish Acanthochromis polyacanthus from two populations (Heron and Palm Island regions, Great Barrier Reef) exposed to current day and predicted elevated temperatures from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA255544. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: Some animals have the remarkable capacity to acclimate across generations to projected future climate change 1, 2, 3, 4; however, the underlying molecular processes are unknown. We sequenced and assembled de novo transcriptomes of adult tropical reef fish exposed developmentally or transgenerationally to projected future ocean temperatures and correlated the resulting expression profiles with acclimated metabolic traits from the same fish. We identified 69 contigs representing 53 key genes involved in thermal acclimation of aerobic capacity. Metabolic genes were among the most upregulated transgenerationally, suggesting shifts in energy production for maintaining performance at elevated temperatures. Furthermore, immune- and stress-responsive genes were upregulated transgenerationally, indicating a new complement of genes allowing the second generation of fish to better cope with elevated temperatures. Other differentially expressed genes were involved with tissue development and transcriptional regulation. Overall, we found a similar suite of differentially expressed genes among developmental and transgenerational treatments. Heat-shock protein genes were surprisingly unresponsive, indicating that short-term heat-stress responses may not be a good indicator of long-term acclimation capacity. Our results are the first to reveal the molecular processes that may enable marine fishes to adjust to a future warmer environment over multiple generations.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p><p><span style="color: #000000; font-family: 'Arial Unicode MS'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> </span></p>", "full", "<p>Transcriptome of the tropical fish Acanthochromis polyacanthus from two populations (Heron and Palm Island regions, Great Barrier Reef) exposed to current day and predicted elevated temperatures from the National Center for Biotechnology Information (NCBI) BioProject number PRJNA255544. Submitted by King Abdullah University of Science and Technology.</p><p>Abstract [Related Publication]: Some animals have the remarkable capacity to acclimate across generations to projected future climate change 1, 2, 3, 4; however, the underlying molecular processes are unknown. We sequenced and assembled de novo transcriptomes of adult tropical reef fish exposed developmentally or transgenerationally to projected future ocean temperatures and correlated the resulting expression profiles with acclimated metabolic traits from the same fish. We identified 69 contigs representing 53 key genes involved in thermal acclimation of aerobic capacity. Metabolic genes were among the most upregulated transgenerationally, suggesting shifts in energy production for maintaining performance at elevated temperatures. Furthermore, immune- and stress-responsive genes were upregulated transgenerationally, indicating a new complement of genes allowing the second generation of fish to better cope with elevated temperatures. Other differentially expressed genes were involved with tissue development and transcriptional regulation. Overall, we found a similar suite of differentially expressed genes among developmental and transgenerational treatments. Heat-shock protein genes were surprisingly unresponsive, indicating that short-term heat-stress responses may not be a good indicator of long-term acclimation capacity. Our results are the first to reveal the molecular processes that may enable marine fishes to adjust to a future warmer environment over multiple generations.</p><p>The full methodology for genome sequencing and assembly is included in the Supplementary Information (PDF) file with the Related Publication.</p><p><span style="color: #000000; font-family: 'Arial Unicode MS'; font-size: medium; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;"> </span></p>", ""] Acclimation of cold tolerance in Carlia longipes fascinator 529ccd7466bfbb3367e411f709c5ab00 2017-12-11T09:33:39Z ["<p>This data set contains the cold tolerance of the tropical skink Carlia longipes during its entire acclimation process to low temperatures, data on the temperatures where this study took place recorded by the bureau of meteorology and experimentally recorded habitat temperatures of the study species.</p>", "<p>This data set contains the cold tolerance of the tropical skink Carlia longipes during its entire acclimation process to low temperatures, data on the temperatures where this study took place recorded by the bureau of meteorology and experimentally recorded habitat temperatures of the study species.</p>", "brief", "<p>This data set contains the critical thermal minimum temperatures (CTmin) of individuals of the restricted tropical ectotherm Carlia longipes over an extensive acclimation process (more than 16 weeks) to a cold temperature regime and supplementary material on the lag of body temperature behind air temperature in the experiment used to measure CTmin. It also includes data obtained from the bureau of meteorology (http://www.bom.gov.au/climate/data/ accessed 10.6.2014 on minimum temperatures recorded during the study as well as experimentally collected habitat temperatures of the study species at the coldest time of day throughout the year. The data was collected in Cairns QLD, Australia, as part of a PhD project from 2012 to 2013. Detailed methodologies can be found in the related PhD thesis and publication.</p>", "<p>This data set contains the critical thermal minimum temperatures (CTmin) of individuals of the restricted tropical ectotherm Carlia longipes over an extensive acclimation process (more than 16 weeks) to a cold temperature regime and supplementary material on the lag of body temperature behind air temperature in the experiment used to measure CTmin. It also includes data obtained from the bureau of meteorology (http://www.bom.gov.au/climate/data/ accessed 10.6.2014 on minimum temperatures recorded during the study as well as experimentally collected habitat temperatures of the study species at the coldest time of day throughout the year. The data was collected in Cairns QLD, Australia, as part of a PhD project from 2012 to 2013. Detailed methodologies can be found in the related PhD thesis and publication.</p>", "full", "This data set contains the cold tolerance of the tropical skink Carlia longipes during its entire acclimation process to low temperatures, data on the temperatures where this study took place recorded by the bureau of meteorology and experimentally recorded habitat temperatures of the study species."] ["<p>This data set contains the cold tolerance of the tropical skink Carlia longipes during its entire acclimation process to low temperatures, data on the temperatures where this study took place recorded by the bureau of meteorology and experimentally recorded habitat temperatures of the study species.</p>", "<p>This data set contains the cold tolerance of the tropical skink Carlia longipes during its entire acclimation process to low temperatures, data on the temperatures where this study took place recorded by the bureau of meteorology and experimentally recorded habitat temperatures of the study species.</p>", "brief", "<p>This data set contains the critical thermal minimum temperatures (CTmin) of individuals of the restricted tropical ectotherm Carlia longipes over an extensive acclimation process (more than 16 weeks) to a cold temperature regime and supplementary material on the lag of body temperature behind air temperature in the experiment used to measure CTmin. It also includes data obtained from the bureau of meteorology (http://www.bom.gov.au/climate/data/ accessed 10.6.2014 on minimum temperatures recorded during the study as well as experimentally collected habitat temperatures of the study species at the coldest time of day throughout the year. The data was collected in Cairns QLD, Australia, as part of a PhD project from 2012 to 2013. Detailed methodologies can be found in the related PhD thesis and publication.</p>", "<p>This data set contains the critical thermal minimum temperatures (CTmin) of individuals of the restricted tropical ectotherm Carlia longipes over an extensive acclimation process (more than 16 weeks) to a cold temperature regime and supplementary material on the lag of body temperature behind air temperature in the experiment used to measure CTmin. It also includes data obtained from the bureau of meteorology (http://www.bom.gov.au/climate/data/ accessed 10.6.2014 on minimum temperatures recorded during the study as well as experimentally collected habitat temperatures of the study species at the coldest time of day throughout the year. The data was collected in Cairns QLD, Australia, as part of a PhD project from 2012 to 2013. Detailed methodologies can be found in the related PhD thesis and publication.</p>", "full", "This data set contains the cold tolerance of the tropical skink Carlia longipes during its entire acclimation process to low temperatures, data on the temperatures where this study took place recorded by the bureau of meteorology and experimentally recorded habitat temperatures of the study species."] Acropora Reproduction and Biogeography fascinator 41f9168af3177dece1f51882c932e8d1 2018-01-03T04:27:38Z ["<p>Species level abundance, distribution and reproductive condition of Acropora.</p>", "<p>Species level abundance, distribution and reproductive condition of Acropora.</p>", "brief", "<p>Data format is an MS Access database.</p>", "<p>Data format is an MS Access database.</p>", "note", ""] ["<p>Species level abundance, distribution and reproductive condition of Acropora.</p>", "<p>Species level abundance, distribution and reproductive condition of Acropora.</p>", "brief", "<p>Data format is an MS Access database.</p>", "<p>Data format is an MS Access database.</p>", "note", ""] Acropora bleaching data, Lizard Island 2016 fascinator 6dcf3de6a7809e62bb0f46e7a8a27238 2018-02-19T01:13:12Z ["<p>This data set contains physical locations and measured variables of Acropora colonies affected by bleaching. The data set also contains the images used to assess the extent of the bleaching for each colony.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", "<p>This data set contains physical locations and measured variables of Acropora colonies affected by bleaching. The data set also contains the images used to assess the extent of the bleaching for each colony.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", "brief", "<p>This data set is available as a spreadsheet saved in Microsoft Excel (.xlsx) and Open Document (.ods) formats, and as a KMZ file which can be opened in Google Earth.</p>", "<p>This data set is available as a spreadsheet saved in Microsoft Excel (.xlsx) and Open Document (.ods) formats, and as a KMZ file which can be opened in Google Earth.</p>", "note", ""] ["<p>This data set contains physical locations and measured variables of Acropora colonies affected by bleaching. The data set also contains the images used to assess the extent of the bleaching for each colony.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", "<p>This data set contains physical locations and measured variables of Acropora colonies affected by bleaching. The data set also contains the images used to assess the extent of the bleaching for each colony.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", "brief", "<p>This data set is available as a spreadsheet saved in Microsoft Excel (.xlsx) and Open Document (.ods) formats, and as a KMZ file which can be opened in Google Earth.</p>", "<p>This data set is available as a spreadsheet saved in Microsoft Excel (.xlsx) and Open Document (.ods) formats, and as a KMZ file which can be opened in Google Earth.</p>", "note", ""] Acropora digitifera: deciphering the nature of the coral-Chromera association fascinator 1b595d251e523b5260931fc09cd49db5 2018-09-24T04:28:19Z ["<p>Since the discovery of Chromera velia as a novel coral-associated microalga, this organism has attracted interest because of its unique evolutionary position between the photosynthetic dinoflagellates and the parasitic apicomplexans. The nature of the relationship between Chromera and its coral host is controversial. Is it a mutualism, from which both participants benefit, or is Chromera a parasite, harming its host? To better understand the interaction, larvae of the common Indo-Pacific reef-building coral Acropora digitifera were experimentally infected with Chromera and the impact on the host transcriptome assessed at 4, 12, and 48 h post-infection using Illumina RNA-Seq technology. The transcriptomic response of the coral to Chromera was complex and implies that host immunity is strongly suppressed, and both phagosome maturation and the apoptotic machinery modified. These responses differ markedly from those described for infection with a competent strain of the coral symbiont Symbiodinium, instead resembling those of vertebrate hosts to parasites and/or pathogens such as Mycobacterium tuberculosis. Consistent with ecological studies suggesting that the association may be accidental, the transcriptional response of A. digitifera larvae leads us to conclude that Chromera is more likely to be a coral parasite, commensal, or accidental bystander, but certainly not a beneficial mutualist.</p><p>Overall design: The goal of the study is to determine the nature of the coral-Chromera association based on host transcriptional response post-Chromera infection.There were 2 conditions (Chromera-infected and control). Samples were taken at 3 time points post-Chromera infection; 4h, 12h and 48h. There were 3 biological replicates per condition. RNA was isolated and 17 RNA-Seq libraries were sequenced on Illumina Hi-Seq 2000 platform. Samples were analysed to infer differential gene expression, comparing the Chromera-infected samples to the control ones at each time point.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", "full", "<p>Since the discovery of Chromera velia as a novel coral-associated microalga, this organism has attracted interest because of its unique evolutionary position between the photosynthetic dinoflagellates and the parasitic apicomplexans. The nature of the relationship between Chromera and its coral host is controversial. Is it a mutualism, from which both participants benefit, or is Chromera a parasite, harming its host? To better understand the interaction, larvae of the common Indo-Pacific reef-building coral Acropora digitifera were experimentally infected with Chromera and the impact on the host transcriptome assessed at 4, 12, and 48 h post-infection using Illumina RNA-Seq technology. The transcriptomic response of the coral to Chromera was complex and implies that host immunity is strongly suppressed, and both phagosome maturation and the apoptotic machinery modified. These responses differ markedly from those described for infection with a competent strain of the coral symbiont Symbiodinium, instead resembling those of vertebrate hosts to parasites and/or pathogens such as Mycobacterium tuberculosis. Consistent with ecological studies suggesting that the association may be accidental, the transcriptional response of A. digitifera larvae leads us to conclude that Chromera is more likely to be a coral parasite, commensal, or accidental bystander, but certainly not a beneficial mutualist.</p><p>Overall design: The goal of the study is to determine the nature of the coral-Chromera association based on host transcriptional response post-Chromera infection.There were 2 conditions (Chromera-infected and control). Samples were taken at 3 time points post-Chromera infection; 4h, 12h and 48h. There were 3 biological replicates per condition. RNA was isolated and 17 RNA-Seq libraries were sequenced on Illumina Hi-Seq 2000 platform. Samples were analysed to infer differential gene expression, comparing the Chromera-infected samples to the control ones at each time point.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", ""] ["<p>Since the discovery of Chromera velia as a novel coral-associated microalga, this organism has attracted interest because of its unique evolutionary position between the photosynthetic dinoflagellates and the parasitic apicomplexans. The nature of the relationship between Chromera and its coral host is controversial. Is it a mutualism, from which both participants benefit, or is Chromera a parasite, harming its host? To better understand the interaction, larvae of the common Indo-Pacific reef-building coral Acropora digitifera were experimentally infected with Chromera and the impact on the host transcriptome assessed at 4, 12, and 48 h post-infection using Illumina RNA-Seq technology. The transcriptomic response of the coral to Chromera was complex and implies that host immunity is strongly suppressed, and both phagosome maturation and the apoptotic machinery modified. These responses differ markedly from those described for infection with a competent strain of the coral symbiont Symbiodinium, instead resembling those of vertebrate hosts to parasites and/or pathogens such as Mycobacterium tuberculosis. Consistent with ecological studies suggesting that the association may be accidental, the transcriptional response of A. digitifera larvae leads us to conclude that Chromera is more likely to be a coral parasite, commensal, or accidental bystander, but certainly not a beneficial mutualist.</p><p>Overall design: The goal of the study is to determine the nature of the coral-Chromera association based on host transcriptional response post-Chromera infection.There were 2 conditions (Chromera-infected and control). Samples were taken at 3 time points post-Chromera infection; 4h, 12h and 48h. There were 3 biological replicates per condition. RNA was isolated and 17 RNA-Seq libraries were sequenced on Illumina Hi-Seq 2000 platform. Samples were analysed to infer differential gene expression, comparing the Chromera-infected samples to the control ones at each time point.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", "full", "<p>Since the discovery of Chromera velia as a novel coral-associated microalga, this organism has attracted interest because of its unique evolutionary position between the photosynthetic dinoflagellates and the parasitic apicomplexans. The nature of the relationship between Chromera and its coral host is controversial. Is it a mutualism, from which both participants benefit, or is Chromera a parasite, harming its host? To better understand the interaction, larvae of the common Indo-Pacific reef-building coral Acropora digitifera were experimentally infected with Chromera and the impact on the host transcriptome assessed at 4, 12, and 48 h post-infection using Illumina RNA-Seq technology. The transcriptomic response of the coral to Chromera was complex and implies that host immunity is strongly suppressed, and both phagosome maturation and the apoptotic machinery modified. These responses differ markedly from those described for infection with a competent strain of the coral symbiont Symbiodinium, instead resembling those of vertebrate hosts to parasites and/or pathogens such as Mycobacterium tuberculosis. Consistent with ecological studies suggesting that the association may be accidental, the transcriptional response of A. digitifera larvae leads us to conclude that Chromera is more likely to be a coral parasite, commensal, or accidental bystander, but certainly not a beneficial mutualist.</p><p>Overall design: The goal of the study is to determine the nature of the coral-Chromera association based on host transcriptional response post-Chromera infection.There were 2 conditions (Chromera-infected and control). Samples were taken at 3 time points post-Chromera infection; 4h, 12h and 48h. There were 3 biological replicates per condition. RNA was isolated and 17 RNA-Seq libraries were sequenced on Illumina Hi-Seq 2000 platform. Samples were analysed to infer differential gene expression, comparing the Chromera-infected samples to the control ones at each time point.</p><p>The full methodology is available in the Open Access publication from the Related Publications link below.</p>", ""] Acropora millepora transcriptome fascinator ea31a0b6b94e1f178cacca1e9a7fcb02 2018-01-05T04:19:50Z ["<p>Acropora millepora transcriptome (mixed stages) from the National Center for Biotechnology Information, BioProject number PRJNA74409. Submitted by James Cook University.</p><p>Abstract [Related Publication]: The impact of ocean acidification (OA) on coral calcification, a subject of intense current interest, is poorly understood in part because of the presence of symbionts in adult corals. Early life history stages of Acropora spp. provide an opportunity to study the effects of elevated CO2 on coral calcification without the complication of symbiont metabolism. Therefore, we used the Illumina RNAseq approach to study the effects of acute exposure to elevated CO2 on gene expression in primary polyps of Acropora millepora, using as reference a novel comprehensive transcriptome assembly developed for this study. Gene ontology analysis of this whole transcriptome data set indicated that CO2-driven acidification strongly suppressed metabolism but enhanced extracellular organic matrix synthesis, whereas targeted analyses revealed complex effects on genes implicated in calcification. Unexpectedly, expression of most ion transport proteins was unaffected, while many membrane-associated or secreted carbonic anhydrases were expressed at lower levels. The most dramatic effect of CO2-driven acidification, however, was on genes encoding candidate and known components of the skeletal organic matrix that controls CaCO3 deposition. The skeletal organic matrix effects included elevated expression of adult-type galaxins and some secreted acidic proteins, but down-regulation of other galaxins, secreted acidic proteins, SCRiPs and other coral-specific genes, suggesting specialized roles for the members of these protein families and complex impacts of OA on mineral deposition. This study is the first exhaustive exploration of the transcriptomic response of a scleractinian coral to acidification and provides an unbiased perspective on its effects during the early stages of calcification.</p><p>The full methodology is available in the publication shown in the Related Publications link below.</p>", "full", "<p>Acropora millepora transcriptome (mixed stages) from the National Center for Biotechnology Information, BioProject number PRJNA74409. Submitted by James Cook University.</p><p>Abstract [Related Publication]: The impact of ocean acidification (OA) on coral calcification, a subject of intense current interest, is poorly understood in part because of the presence of symbionts in adult corals. Early life history stages of Acropora spp. provide an opportunity to study the effects of elevated CO2 on coral calcification without the complication of symbiont metabolism. Therefore, we used the Illumina RNAseq approach to study the effects of acute exposure to elevated CO2 on gene expression in primary polyps of Acropora millepora, using as reference a novel comprehensive transcriptome assembly developed for this study. Gene ontology analysis of this whole transcriptome data set indicated that CO2-driven acidification strongly suppressed metabolism but enhanced extracellular organic matrix synthesis, whereas targeted analyses revealed complex effects on genes implicated in calcification. Unexpectedly, expression of most ion transport proteins was unaffected, while many membrane-associated or secreted carbonic anhydrases were expressed at lower levels. The most dramatic effect of CO2-driven acidification, however, was on genes encoding candidate and known components of the skeletal organic matrix that controls CaCO3 deposition. The skeletal organic matrix effects included elevated expression of adult-type galaxins and some secreted acidic proteins, but down-regulation of other galaxins, secreted acidic proteins, SCRiPs and other coral-specific genes, suggesting specialized roles for the members of these protein families and complex impacts of OA on mineral deposition. This study is the first exhaustive exploration of the transcriptomic response of a scleractinian coral to acidification and provides an unbiased perspective on its effects during the early stages of calcification.</p><p>The full methodology is available in the publication shown in the Related Publications link below.</p>", ""] ["<p>Acropora millepora transcriptome (mixed stages) from the National Center for Biotechnology Information, BioProject number PRJNA74409. Submitted by James Cook University.</p><p>Abstract [Related Publication]: The impact of ocean acidification (OA) on coral calcification, a subject of intense current interest, is poorly understood in part because of the presence of symbionts in adult corals. Early life history stages of Acropora spp. provide an opportunity to study the effects of elevated CO2 on coral calcification without the complication of symbiont metabolism. Therefore, we used the Illumina RNAseq approach to study the effects of acute exposure to elevated CO2 on gene expression in primary polyps of Acropora millepora, using as reference a novel comprehensive transcriptome assembly developed for this study. Gene ontology analysis of this whole transcriptome data set indicated that CO2-driven acidification strongly suppressed metabolism but enhanced extracellular organic matrix synthesis, whereas targeted analyses revealed complex effects on genes implicated in calcification. Unexpectedly, expression of most ion transport proteins was unaffected, while many membrane-associated or secreted carbonic anhydrases were expressed at lower levels. The most dramatic effect of CO2-driven acidification, however, was on genes encoding candidate and known components of the skeletal organic matrix that controls CaCO3 deposition. The skeletal organic matrix effects included elevated expression of adult-type galaxins and some secreted acidic proteins, but down-regulation of other galaxins, secreted acidic proteins, SCRiPs and other coral-specific genes, suggesting specialized roles for the members of these protein families and complex impacts of OA on mineral deposition. This study is the first exhaustive exploration of the transcriptomic response of a scleractinian coral to acidification and provides an unbiased perspective on its effects during the early stages of calcification.</p><p>The full methodology is available in the publication shown in the Related Publications link below.</p>", "full", "<p>Acropora millepora transcriptome (mixed stages) from the National Center for Biotechnology Information, BioProject number PRJNA74409. Submitted by James Cook University.</p><p>Abstract [Related Publication]: The impact of ocean acidification (OA) on coral calcification, a subject of intense current interest, is poorly understood in part because of the presence of symbionts in adult corals. Early life history stages of Acropora spp. provide an opportunity to study the effects of elevated CO2 on coral calcification without the complication of symbiont metabolism. Therefore, we used the Illumina RNAseq approach to study the effects of acute exposure to elevated CO2 on gene expression in primary polyps of Acropora millepora, using as reference a novel comprehensive transcriptome assembly developed for this study. Gene ontology analysis of this whole transcriptome data set indicated that CO2-driven acidification strongly suppressed metabolism but enhanced extracellular organic matrix synthesis, whereas targeted analyses revealed complex effects on genes implicated in calcification. Unexpectedly, expression of most ion transport proteins was unaffected, while many membrane-associated or secreted carbonic anhydrases were expressed at lower levels. The most dramatic effect of CO2-driven acidification, however, was on genes encoding candidate and known components of the skeletal organic matrix that controls CaCO3 deposition. The skeletal organic matrix effects included elevated expression of adult-type galaxins and some secreted acidic proteins, but down-regulation of other galaxins, secreted acidic proteins, SCRiPs and other coral-specific genes, suggesting specialized roles for the members of these protein families and complex impacts of OA on mineral deposition. This study is the first exhaustive exploration of the transcriptomic response of a scleractinian coral to acidification and provides an unbiased perspective on its effects during the early stages of calcification.</p><p>The full methodology is available in the publication shown in the Related Publications link below.</p>", ""] Adenosine, Lidocaine and Mg2+ (ALM) fluid therapy for treatment of non-compressible uncontrolled haemorrhage fascinator 422e80b51a5d06a7c5a6d1ea6201c4d0 2017-12-11T09:33:53Z ["<p>Systemic inflammation and coagulopathy are major drivers of injury progression following haemorrhagic trauma. Our aim was to examine the effect of small-volume 3% NaCl adenosine, lidocaine and Mg2+ (ALM) bolus and 0.9% NaCl/ALM 'drip' on inflammation and coagulation in a rat model of haemorrhagic shock. Male Sprague-Dawley rats were randomly assisned to: 1) shams, 2) no-treatment, 3) saline controls, 4) ALM therapy, and 5) Hextend. Haemorrhage was induced in anaesthetized and ventilated animals by liver resection (60% left lateral lobe and 50% medial lobe). After 15 min, a bolus of 3% NaCl +/- ALM (0.7 ml/kg) was administered intravenously (Phase 1) followed 60 min later by 4 hours infusion of 0.9% NaCl +/- ALM (0.5 ml/kg/hr) with 60 min monitoring (Phase 2). Plasma cytokines were measured on Magpix and coagulation using Stago/Rotational Thromboelastometry.</p>", "full", "<p>Systemic inflammation and coagulopathy are major drivers of injury progression following haemorrhagic trauma. Our aim was to examine the effect of small-volume 3% NaCl adenosine, lidocaine and Mg2+ (ALM) bolus and 0.9% NaCl/ALM 'drip' on inflammation and coagulation in a rat model of haemorrhagic shock. Male Sprague-Dawley rats were randomly assisned to: 1) shams, 2) no-treatment, 3) saline controls, 4) ALM therapy, and 5) Hextend. Haemorrhage was induced in anaesthetized and ventilated animals by liver resection (60% left lateral lobe and 50% medial lobe). After 15 min, a bolus of 3% NaCl +/- ALM (0.7 ml/kg) was administered intravenously (Phase 1) followed 60 min later by 4 hours infusion of 0.9% NaCl +/- ALM (0.5 ml/kg/hr) with 60 min monitoring (Phase 2). Plasma cytokines were measured on Magpix and coagulation using Stago/Rotational Thromboelastometry.</p>", "<p>This dataset is available as 7 files saved in .sav (SPSS) and comma-separated values (CSV) formats.</p>", "note", "<p>This dataset is available as 7 files saved in .sav (SPSS) and comma-separated values (CSV) formats.</p>", ""] ["<p>Systemic inflammation and coagulopathy are major drivers of injury progression following haemorrhagic trauma. Our aim was to examine the effect of small-volume 3% NaCl adenosine, lidocaine and Mg2+ (ALM) bolus and 0.9% NaCl/ALM 'drip' on inflammation and coagulation in a rat model of haemorrhagic shock. Male Sprague-Dawley rats were randomly assisned to: 1) shams, 2) no-treatment, 3) saline controls, 4) ALM therapy, and 5) Hextend. Haemorrhage was induced in anaesthetized and ventilated animals by liver resection (60% left lateral lobe and 50% medial lobe). After 15 min, a bolus of 3% NaCl +/- ALM (0.7 ml/kg) was administered intravenously (Phase 1) followed 60 min later by 4 hours infusion of 0.9% NaCl +/- ALM (0.5 ml/kg/hr) with 60 min monitoring (Phase 2). Plasma cytokines were measured on Magpix and coagulation using Stago/Rotational Thromboelastometry.</p>", "full", "<p>Systemic inflammation and coagulopathy are major drivers of injury progression following haemorrhagic trauma. Our aim was to examine the effect of small-volume 3% NaCl adenosine, lidocaine and Mg2+ (ALM) bolus and 0.9% NaCl/ALM 'drip' on inflammation and coagulation in a rat model of haemorrhagic shock. Male Sprague-Dawley rats were randomly assisned to: 1) shams, 2) no-treatment, 3) saline controls, 4) ALM therapy, and 5) Hextend. Haemorrhage was induced in anaesthetized and ventilated animals by liver resection (60% left lateral lobe and 50% medial lobe). After 15 min, a bolus of 3% NaCl +/- ALM (0.7 ml/kg) was administered intravenously (Phase 1) followed 60 min later by 4 hours infusion of 0.9% NaCl +/- ALM (0.5 ml/kg/hr) with 60 min monitoring (Phase 2). Plasma cytokines were measured on Magpix and coagulation using Stago/Rotational Thromboelastometry.</p>", "<p>This dataset is available as 7 files saved in .sav (SPSS) and comma-separated values (CSV) formats.</p>", "note", "<p>This dataset is available as 7 files saved in .sav (SPSS) and comma-separated values (CSV) formats.</p>", ""] Adult attachment theory and attachment to place: exploring relationships between people and places fascinator 9feb4c669caf8c263e4e074d96242f83 2017-12-11T09:31:05Z ["<p>This study investigates the relationships between place attachment and interpersonal attachment especially in relation to the home environment.</p>", "<p>This study investigates the relationships between place attachment and interpersonal attachment especially in relation to the home environment.</p>", "brief", "<p>The primary goal of this thesis was to apply an interpersonal attachment model to place attachment. Four broad research questions were addressed, the first of which concerned links between place and interpersonal attachment. The second was to identify attachment style differences in the experience of childhood places and the current home. The third research question examined whether the bonds that we form with place can in fact be classified as attachment bonds, with characteristics similar to those that we form with people. The final research question focused on the composition and structure of the network of places in which people live, and how they relate to those places.The research was conducted across two studies, using a questionnaire battery which contained a combination of new and published, qualitative and quantitative measures. The first study, using a sample of 99 undergraduate students (age 17- 55), investigated the relationship between interpersonal and place attachment and examined attachment style differences in the experience of place using favourite childhood places, the present home, and personal possessions as the primary objects of attachment. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association between place and possession attachment. The study also illustrated both place and interpersonal attachment style differences in the experience of childhood places and current homes. Secure place and interpersonal attachment were associated with time spent with others and higher levels of positive affect, whereas insecure place and interpersonal attachment were associated with higher levels of negative affect, and the recall of negative memories of childhood places. The second study, with a sample of 105 adults (age 18-79), examined the structure of the network of places in which people live and how they relate to those places and the network of people that they interact with. It also investigated place and interpersonal attachment, and personality style differences in the composition of those attachment networks and examined whether or not relationships with place can be classified as 'attachment bonds'. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association with the Big Five personality traits. Relationships with several types of place were confirmed as attachment bonds based on the use of these places for a range of attachment functions (e.g. using the place as a safe haven and secure base; evidence of hypothetical sense of loss). Attachment style differences in the interaction between people and the places listed in their attachment network were also illustrated. Those who were securely attached to place valourised their current home whereas those who were insecurely attached valourised previous homes, leisure environments and holiday destinations. Overall the current research suggests empirical support for the proposed theoretical links between interpersonal and place attachment. It also supports the proposition that our relationships with place are attachment bonds with similar characteristics to those identified for interpersonal attachment. Theoretical implications as well as future directions for research are outlined in relation to the findings.</p>", "<p>The primary goal of this thesis was to apply an interpersonal attachment model to place attachment. Four broad research questions were addressed, the first of which concerned links between place and interpersonal attachment. The second was to identify attachment style differences in the experience of childhood places and the current home. The third research question examined whether the bonds that we form with place can in fact be classified as attachment bonds, with characteristics similar to those that we form with people. The final research question focused on the composition and structure of the network of places in which people live, and how they relate to those places.The research was conducted across two studies, using a questionnaire battery which contained a combination of new and published, qualitative and quantitative measures. The first study, using a sample of 99 undergraduate students (age 17- 55), investigated the relationship between interpersonal and place attachment and examined attachment style differences in the experience of place using favourite childhood places, the present home, and personal possessions as the primary objects of attachment. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association between place and possession attachment. The study also illustrated both place and interpersonal attachment style differences in the experience of childhood places and current homes. Secure place and interpersonal attachment were associated with time spent with others and higher levels of positive affect, whereas insecure place and interpersonal attachment were associated with higher levels of negative affect, and the recall of negative memories of childhood places. The second study, with a sample of 105 adults (age 18-79), examined the structure of the network of places in which people live and how they relate to those places and the network of people that they interact with. It also investigated place and interpersonal attachment, and personality style differences in the composition of those attachment networks and examined whether or not relationships with place can be classified as 'attachment bonds'. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association with the Big Five personality traits. Relationships with several types of place were confirmed as attachment bonds based on the use of these places for a range of attachment functions (e.g. using the place as a safe haven and secure base; evidence of hypothetical sense of loss). Attachment style differences in the interaction between people and the places listed in their attachment network were also illustrated. Those who were securely attached to place valourised their current home whereas those who were insecurely attached valourised previous homes, leisure environments and holiday destinations. Overall the current research suggests empirical support for the proposed theoretical links between interpersonal and place attachment. It also supports the proposition that our relationships with place are attachment bonds with similar characteristics to those identified for interpersonal attachment. Theoretical implications as well as future directions for research are outlined in relation to the findings.</p>", "full", "This study investigates the relationships between place attachment and interpersonal attachment especially in relation to the home environment."] ["<p>This study investigates the relationships between place attachment and interpersonal attachment especially in relation to the home environment.</p>", "<p>This study investigates the relationships between place attachment and interpersonal attachment especially in relation to the home environment.</p>", "brief", "<p>The primary goal of this thesis was to apply an interpersonal attachment model to place attachment. Four broad research questions were addressed, the first of which concerned links between place and interpersonal attachment. The second was to identify attachment style differences in the experience of childhood places and the current home. The third research question examined whether the bonds that we form with place can in fact be classified as attachment bonds, with characteristics similar to those that we form with people. The final research question focused on the composition and structure of the network of places in which people live, and how they relate to those places.The research was conducted across two studies, using a questionnaire battery which contained a combination of new and published, qualitative and quantitative measures. The first study, using a sample of 99 undergraduate students (age 17- 55), investigated the relationship between interpersonal and place attachment and examined attachment style differences in the experience of place using favourite childhood places, the present home, and personal possessions as the primary objects of attachment. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association between place and possession attachment. The study also illustrated both place and interpersonal attachment style differences in the experience of childhood places and current homes. Secure place and interpersonal attachment were associated with time spent with others and higher levels of positive affect, whereas insecure place and interpersonal attachment were associated with higher levels of negative affect, and the recall of negative memories of childhood places. The second study, with a sample of 105 adults (age 18-79), examined the structure of the network of places in which people live and how they relate to those places and the network of people that they interact with. It also investigated place and interpersonal attachment, and personality style differences in the composition of those attachment networks and examined whether or not relationships with place can be classified as 'attachment bonds'. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association with the Big Five personality traits. Relationships with several types of place were confirmed as attachment bonds based on the use of these places for a range of attachment functions (e.g. using the place as a safe haven and secure base; evidence of hypothetical sense of loss). Attachment style differences in the interaction between people and the places listed in their attachment network were also illustrated. Those who were securely attached to place valourised their current home whereas those who were insecurely attached valourised previous homes, leisure environments and holiday destinations. Overall the current research suggests empirical support for the proposed theoretical links between interpersonal and place attachment. It also supports the proposition that our relationships with place are attachment bonds with similar characteristics to those identified for interpersonal attachment. Theoretical implications as well as future directions for research are outlined in relation to the findings.</p>", "<p>The primary goal of this thesis was to apply an interpersonal attachment model to place attachment. Four broad research questions were addressed, the first of which concerned links between place and interpersonal attachment. The second was to identify attachment style differences in the experience of childhood places and the current home. The third research question examined whether the bonds that we form with place can in fact be classified as attachment bonds, with characteristics similar to those that we form with people. The final research question focused on the composition and structure of the network of places in which people live, and how they relate to those places.The research was conducted across two studies, using a questionnaire battery which contained a combination of new and published, qualitative and quantitative measures. The first study, using a sample of 99 undergraduate students (age 17- 55), investigated the relationship between interpersonal and place attachment and examined attachment style differences in the experience of place using favourite childhood places, the present home, and personal possessions as the primary objects of attachment. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association between place and possession attachment. The study also illustrated both place and interpersonal attachment style differences in the experience of childhood places and current homes. Secure place and interpersonal attachment were associated with time spent with others and higher levels of positive affect, whereas insecure place and interpersonal attachment were associated with higher levels of negative affect, and the recall of negative memories of childhood places. The second study, with a sample of 105 adults (age 18-79), examined the structure of the network of places in which people live and how they relate to those places and the network of people that they interact with. It also investigated place and interpersonal attachment, and personality style differences in the composition of those attachment networks and examined whether or not relationships with place can be classified as 'attachment bonds'. The results provided evidence of the predicted associations between interpersonal and place attachment styles, but failed to support an association with the Big Five personality traits. Relationships with several types of place were confirmed as attachment bonds based on the use of these places for a range of attachment functions (e.g. using the place as a safe haven and secure base; evidence of hypothetical sense of loss). Attachment style differences in the interaction between people and the places listed in their attachment network were also illustrated. Those who were securely attached to place valourised their current home whereas those who were insecurely attached valourised previous homes, leisure environments and holiday destinations. Overall the current research suggests empirical support for the proposed theoretical links between interpersonal and place attachment. It also supports the proposition that our relationships with place are attachment bonds with similar characteristics to those identified for interpersonal attachment. Theoretical implications as well as future directions for research are outlined in relation to the findings.</p>", "full", "This study investigates the relationships between place attachment and interpersonal attachment especially in relation to the home environment."] African Darter (Anhinga melanogaster) - current and future species distribution models fascinator 2557660458ef46d5ce557864f72805f4 2017-12-11T09:33:08Z ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for African Darter (Anhinga melanogaster).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for African Darter (Anhinga melanogaster).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for African Darter (Anhinga melanogaster).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for African Darter (Anhinga melanogaster).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] African Darter (Anhinga melanogaster) - occurrence records filtered for species distribution modelling fascinator 155760c51408f66cddc608c5fd012abd 2017-12-11T09:34:30Z ["<p>African Darter (Anhinga melanogaster) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>African Darter (Anhinga melanogaster) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of African Darter (Anhinga melanogaster) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of African Darter (Anhinga melanogaster) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] ["<p>African Darter (Anhinga melanogaster) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>African Darter (Anhinga melanogaster) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of African Darter (Anhinga melanogaster) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of African Darter (Anhinga melanogaster) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] Albert's Lyrebird (Menura (Harriwhitea) alberti) - current and future species distribution models fascinator 1f72298f7f7ce65f00b3efa2115ec952 2017-12-11T09:31:50Z ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Albert's Lyrebird (Menura (Harriwhitea) alberti).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Albert's Lyrebird (Menura (Harriwhitea) alberti).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] ["<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Albert's Lyrebird (Menura (Harriwhitea) alberti).</p>", "<p>This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Albert's Lyrebird (Menura (Harriwhitea) alberti).</p>", "brief", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "<p>Observation records were filtered from the Atlas of Living Australia's (ALA) database based on ALA's 'assertions', expert-derived range polygons and expert opinion, and those observations inappropriate for modelling were excluded. Only species with >20 unique spatiotemporal records were used for modelling.</p><p>Current climate was sourced as monthly precipitation and temperature minima and maxima from 1975 until 2005 at a 0.05° grid scale from the Australian Water Availability Project (AWAP - <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au/jsp/awap/</a>) (Jones et al 2007, Grant et al 2008).</p> <p>Future climate projections were sourced through a collaboration with Drs Rachel Warren and Jeff Price, Tyndall Centre, University of East Anglia, UK. This data is available on <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a>. Although new GCM runs for RCPs have not been fully completed, several research groups have implemented methods to utilize knowledge gained from SRES predictions to recreate predictions for the new RCPs using AR4 GCMs (e.g., Meinshausen, Smith et al. 2011; Rogelj, Meinshausen et al. 2012). The methods used to generate the GCM predictions for the RCP emission scenarios are defined at <a href="http://climascope.tyndall.ac.uk">http://climascope.tyndall.ac.uk</a> and in associated publications (Mitchell and Jones 2005; Warren, de la Nava Santos et al. 2008; Meinshausen, Raper et al. 2011). This data was downscaled to 0.05 degrees (~5km resolution) using a cubic spline of the anomalies; these anomalies were applied to a current climate baseline of 1976 to 2005 – climate of 1990 – generated from aggregating monthly data from Australia Water Availability Project (AWAP; <a href="http://www.bom.gov.au/jsp/awap/">http://www.bom.gov.au</a>). These monthly temperature and precipitation values user used to create 19 standard bioclimatic variables. These bioclimatic variables are listed at <a href="http://www.worldclim.org/bioclim">http://www.worldclim.org/bioclim</a>. All downscaling and bioclimatic variable creation was done using the climates package (VanDerWal, Beaumont et al. 2011) in R (<a href="http://www.r-project.org/">http://www.r-project.org/</a>).</p><p>Used in the modelling were annual mean temperature, temperature seasonality, max and min monthly temperature, annual precipitation, precipitation seasonality, and precipitation of the wettest and driest quarters for current and all RCP scenarios (RCP3PD, RCP45, RCP6, RCP85) at 8 time steps between 2015 and 2085.</p><p>Species distribution models were run using the presence-only modelling program Maxent (Phillips et al 2006). Maxent uses species presence records to statistically relate species occurrence to environmental variables on the principle of maximum entropy. All default settings were used except for background point allocation. We used a target group background (Phillips & Dudik 2008) to remove any spatial or temporal sampling bias in the modelling exercise.</p>", "full", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "<p>These species distribution models are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>.</p><p>The dataset is a tarred, zipped file (.tar.gz), approximately 5GB in size and contains 609 ASCII grid files:<ul><li>1 current distribution map</li><li>32 median maps - 8 time step median maps (averaged across all 18 GCMs) for each RCP</li><li>576 maps - 8 time step maps for each GCM for each RCP</li></ul>", "note"] Albert's Lyrebird (Menura (Harriwhitea) alberti) - occurrence records filtered for species distribution modelling fascinator 48d1fecd1f47e3146a904913728d3e22 2017-12-11T09:32:03Z ["<p>Albert's Lyrebird (Menura (Harriwhitea) alberti) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>Albert's Lyrebird (Menura (Harriwhitea) alberti) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of Albert's Lyrebird (Menura (Harriwhitea) alberti) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of Albert's Lyrebird (Menura (Harriwhitea) alberti) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] ["<p>Albert's Lyrebird (Menura (Harriwhitea) alberti) occurrence records from continental Australia suitable for species distribution modelling.</p>", "<p>Albert's Lyrebird (Menura (Harriwhitea) alberti) occurrence records from continental Australia suitable for species distribution modelling.</p>", "brief", "<p>This dataset includes observations of Albert's Lyrebird (Menura (Harriwhitea) alberti) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "<p>This dataset includes observations of Albert's Lyrebird (Menura (Harriwhitea) alberti) that are sourced from the Atlas of Living Australia (ALA) database. Rather than raw observations, these have been filtered such that they are assumed to be suitable for species distribution modelling exercises. The cleaning process included:<ol> <li>automatic vetting based on the ALA's 'assertions' whereby observations were assessed as inappropriate for modelling (ie. 'ZERO_COORDINATES', 'INVALID SCIENTIFIC NAME'); </li><li>determining if the observations fell within expert-derived range polygons. These polygons were supplied by BirdLife Australia to represent, for each species, its core breeding habitat, non-breeding, historic, irruptive, or invasive ranges. Records that fall outside these ranges were marked as inappropriate for modelling; and </li><li> human-derived classification of records after previous two assessments. Through the Edgar project (<a href="http://tropicaldatahub.org/goto/edgar">http://tropicaldatahub.org/goto/edgar</a>), users were able to map all species observations and comment on the suitability of records for distribution modelling. This included records deemed inappropriate by other means. </p><p>Every 6 months the occurrence record download file is updated to reflect recent vetting by experts. In the data download, sensitive records have been obfuscated by truncating the lat/long to two decimal places. Obfuscated records will be indicated in the data file. Access to the accurate data will need to be arranged with the original data owners - contact the ALA for more information. </p><p>The resulting downloadable file of occurrence records reflects which records are suitable for species distribution modelling.</p>", "full", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "<p>Data is downloadable as a zipped CSV file.</p> <p>These occurrence records are displayed on Edgar: <a href="http://tropicaldatahub.org/goto/Edgar">http://tropicaldatahub.org/goto/Edgar</a>.", "note"] Algae associated with coral degradation affects risk assessment in coral reef fishes fascinator 8d5cde4be0db2a2e8ca71f503be228d2 2018-01-08T22:57:30Z ["<p>Habitat degradation alters the chemical landscape through which information about community dynamics is transmitted. Olfactory information is crucial for risk assessment in aquatic organisms as predators release odours when they capture prey that lead to an alarm response in conspecific prey. Recent studies show some coral reef fishes are not unable to use alarm odours when surrounded by dead-degraded coral. Our study examines the spatial and temporal dynamics of this alarm odour-nullifying effect, and which substratum types may be responsible. Field experiments showed that settlement-stage damselfish were not able to detect alarm odours within 2 m downcurrent of degraded coral, and that the antipredator response was re-established 20 - 40 min after transferral to live coral. Laboratory experiments indicate that the chemicals from common components of the degraded habitats, the cyanobacteria, Okeania sp., and diatom, Pseudo-nitzschia sp., prevented an alarm odour response. The same nullifying effect was found for the common red algae, Galaxauria robusta, suggesting that the problem is of a broader nature than previously realised. Those fish species best able to compensate for a lack of olfactory risk information at key times will be those potentially most resilient to the effects of coral degradation that operate through this mechanism.</p><p> </p>", "<p>Habitat degradation alters the chemical landscape through which information about community dynamics is transmitted. Olfactory information is crucial for risk assessment in aquatic organisms as predators release odours when they capture prey that lead to an alarm response in conspecific prey. Recent studies show some coral reef fishes are not unable to use alarm odours when surrounded by dead-degraded coral. Our study examines the spatial and temporal dynamics of this alarm odour-nullifying effect, and which substratum types may be responsible. Field experiments showed that settlement-stage damselfish were not able to detect alarm odours within 2 m downcurrent of degraded coral, and that the antipredator response was re-established 20 - 40 min after transferral to live coral. Laboratory experiments indicate that the chemicals from common components of the degraded habitats, the cyanobacteria, Okeania sp., and diatom, Pseudo-nitzschia sp., prevented an alarm odour response. The same nullifying effect was found for the common red algae, Galaxauria robusta, suggesting that the problem is of a broader nature than previously realised. Those fish species best able to compensate for a lack of olfactory risk information at key times will be those potentially most resilient to the effects of coral degradation that operate through this mechanism.</p><p> </p>", "full", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "note", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", ""] ["<p>Habitat degradation alters the chemical landscape through which information about community dynamics is transmitted. Olfactory information is crucial for risk assessment in aquatic organisms as predators release odours when they capture prey that lead to an alarm response in conspecific prey. Recent studies show some coral reef fishes are not unable to use alarm odours when surrounded by dead-degraded coral. Our study examines the spatial and temporal dynamics of this alarm odour-nullifying effect, and which substratum types may be responsible. Field experiments showed that settlement-stage damselfish were not able to detect alarm odours within 2 m downcurrent of degraded coral, and that the antipredator response was re-established 20 - 40 min after transferral to live coral. Laboratory experiments indicate that the chemicals from common components of the degraded habitats, the cyanobacteria, Okeania sp., and diatom, Pseudo-nitzschia sp., prevented an alarm odour response. The same nullifying effect was found for the common red algae, Galaxauria robusta, suggesting that the problem is of a broader nature than previously realised. Those fish species best able to compensate for a lack of olfactory risk information at key times will be those potentially most resilient to the effects of coral degradation that operate through this mechanism.</p><p> </p>", "<p>Habitat degradation alters the chemical landscape through which information about community dynamics is transmitted. Olfactory information is crucial for risk assessment in aquatic organisms as predators release odours when they capture prey that lead to an alarm response in conspecific prey. Recent studies show some coral reef fishes are not unable to use alarm odours when surrounded by dead-degraded coral. Our study examines the spatial and temporal dynamics of this alarm odour-nullifying effect, and which substratum types may be responsible. Field experiments showed that settlement-stage damselfish were not able to detect alarm odours within 2 m downcurrent of degraded coral, and that the antipredator response was re-established 20 - 40 min after transferral to live coral. Laboratory experiments indicate that the chemicals from common components of the degraded habitats, the cyanobacteria, Okeania sp., and diatom, Pseudo-nitzschia sp., prevented an alarm odour response. The same nullifying effect was found for the common red algae, Galaxauria robusta, suggesting that the problem is of a broader nature than previously realised. Those fish species best able to compensate for a lack of olfactory risk information at key times will be those potentially most resilient to the effects of coral degradation that operate through this mechanism.</p><p> </p>", "full", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", "note", "<p>This dataset is available as a spreadsheet in MS Excel (.xlsx) and Open Document formats (.ods)</p>", ""] Algal biochar: effects and applications data fascinator 958d5c40baff53e893ed35238fe27e34 2017-12-11T09:34:31Z ["<p>In this study biochar was produced from one freshwater (FW) alga and one saltwater (SW) alga using commercial pyrolysis equipment, and the impact of biochar amendment on plant biomass production was assessed.</p>", "<p>In this study biochar was produced from one freshwater (FW) alga and one saltwater (SW) alga using commercial pyrolysis equipment, and the impact of biochar amendment on plant biomass production was assessed.</p>", "brief", "<p>Algae represent a promising target for the generation of bioenergy through slow pyrolysis, leading to the production of biochar. This study reports experiments conducted on the production of freshwater and saltwater macroalgal biochar in pilotscale quantities, the physical and chemical characteristics of the biochars, and their impact on plant growth. The biochars are low in carbon (C) content, surface area and cation exchange capacity, while being high in ash and nutrients. Trace element analysis demonstrates that macroalgal biochar produced from unpolluted water does not contain toxic trace elements in excess of levels mandated for unrestricted use as a biosolids amendment to soils. Pot trials conducted using a C and nutrient-poor soil, without and with additional fertilizer, demonstrate dramatic increases between 15 and 32 times, respectively, in plant growth rate for biochar treatments compared with the no biochar controls, with additional smaller increases when fertilizer was added. Pot trials conducted using a relatively fertile agricultural soil showed smaller but significant impacts of biochar amendment over the controls.</p>", "<p>Algae represent a promising target for the generation of bioenergy through slow pyrolysis, leading to the production of biochar. This study reports experiments conducted on the production of freshwater and saltwater macroalgal biochar in pilotscale quantities, the physical and chemical characteristics of the biochars, and their impact on plant growth. The biochars are low in carbon (C) content, surface area and cation exchange capacity, while being high in ash and nutrients. Trace element analysis demonstrates that macroalgal biochar produced from unpolluted water does not contain toxic trace elements in excess of levels mandated for unrestricted use as a biosolids amendment to soils. Pot trials conducted using a C and nutrient-poor soil, without and with additional fertilizer, demonstrate dramatic increases between 15 and 32 times, respectively, in plant growth rate for biochar treatments compared with the no biochar controls, with additional smaller increases when fertilizer was added. Pot trials conducted using a relatively fertile agricultural soil showed smaller but significant impacts of biochar amendment over the controls.</p>", "full", "<p>The dataset is in open document spreadsheet (.ods) format.</p>", "<p>The dataset is in open document spreadsheet (.ods) format.</p>", "note", "In this study biochar was produced from one freshwater (FW) alga and one saltwater (SW) alga using commercial pyrolysis equipment, and the impact of biochar amendment on plant biomass production was assessed."] ["<p>In this study biochar was produced from one freshwater (FW) alga and one saltwater (SW) alga using commercial pyrolysis equipment, and the impact of biochar amendment on plant biomass production was assessed.</p>", "<p>In this study biochar was produced from one freshwater (FW) alga and one saltwater (SW) alga using commercial pyrolysis equipment, and the impact of biochar amendment on plant biomass production was assessed.</p>", "brief", "<p>Algae represent a promising target for the generation of bioenergy through slow pyrolysis, leading to the production of biochar. This study reports experiments conducted on the production of freshwater and saltwater macroalgal biochar in pilotscale quantities, the physical and chemical characteristics of the biochars, and their impact on plant growth. The biochars are low in carbon (C) content, surface area and cation exchange capacity, while being high in ash and nutrients. Trace element analysis demonstrates that macroalgal biochar produced from unpolluted water does not contain toxic trace elements in excess of levels mandated for unrestricted use as a biosolids amendment to soils. Pot trials conducted using a C and nutrient-poor soil, without and with additional fertilizer, demonstrate dramatic increases between 15 and 32 times, respectively, in plant growth rate for biochar treatments compared with the no biochar controls, with additional smaller increases when fertilizer was added. Pot trials conducted using a relatively fertile agricultural soil showed smaller but significant impacts of biochar amendment over the controls.</p>", "<p>Algae represent a promising target for the generation of bioenergy through slow pyrolysis, leading to the production of biochar. This study reports experiments conducted on the production of freshwater and saltwater macroalgal biochar in pilotscale quantities, the physical and chemical characteristics of the biochars, and their impact on plant growth. The biochars are low in carbon (C) content, surface area and cation exchange capacity, while being high in ash and nutrients. Trace element analysis demonstrates that macroalgal biochar produced from unpolluted water does not contain toxic trace elements in excess of levels mandated for unrestricted use as a biosolids amendment to soils. Pot trials conducted using a C and nutrient-poor soil, without and with additional fertilizer, demonstrate dramatic increases between 15 and 32 times, respectively, in plant growth rate for biochar treatments compared with the no biochar controls, with additional smaller increases when fertilizer was added. Pot trials conducted using a relatively fertile agricultural soil showed smaller but significant impacts of biochar amendment over the controls.</p>", "full", "<p>The dataset is in open document spreadsheet (.ods) format.</p>", "<p>The dataset is in open document spreadsheet (.ods) format.</p>", "note", "In this study biochar was produced from one freshwater (FW) alga and one saltwater (SW) alga using commercial pyrolysis equipment, and the impact of biochar amendment on plant biomass production was assessed."]