Dataset associated with the paper "Analysis of Progress Towards a Comprehensive System of Marine Protected Areas in Brazil”. Data was collected using data compilation within Arc GIS 10.2. The habitat maps were derived from three sources: a subtidal benthic 20 map, built using high-resolution bathymetry data (Becker et al. 2009), and maps of 21 coral reef habitat (Brasil 2006) and mangrove ecosystems (Magris & Barreto 2011). 22 Depth zones, mangrove, coral reef habitat and ecoregions were used to define benthic 23 habitats. Study region spans all Brazilian coast. Data was collected to evaluate the performance of the Marine Protected Areas regarding conservation objectives for representation and persistence.
Spatial management, including setting aside conservation areas, is central to curbing the global decline of biodiversity, but many threats originate from beyond the boundaries of conservation areas. This is a particular problem in marine systems, which are influenced by many activities on land. In particular, human-induced changes to river loads of nutrients and sediments pose a significant threat to coastal marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine species and habitats. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. Integrated land-sea conservation planning incorporates ecological connections between land and sea and seeks to limit land-based threats to coastal-marine ecosystems, while achieving conservation objectives on the land.
This dataset comprises four sub-datasets with spatial and non-spatial information required to develop and integrate models of catchments, land-use change, and river plumes with conservation planning software to inform prioritisation of catchment management in selected catchments draining into the Gulf of California, Mexico.
This dataset contains raw spatial prioritisation outputs from the decision-support software tool Marxan. These include the two main outputs from prioritisations: 1) individual solutions created for each conservation scenario run (100 solutions each), and 2) selection frequency data of each of the 20 scenarios, detailing the relative importance of each planning unit in the spatial prioritisation.
The allssolnfreq.csv file consists of selection frequency data for each planning unit for each of the 20 conservation prioritisation scenarios run. Selection frequency values reflect the number of times each planning unit was selected as part of a good solution (100 total per scenario).
The allsols.csv files consists of each individual solution produced for each scenario (100 solutions per scenario, 20 scenarios total). Binary data, 0 representing unselected planning units; 1 representing selected planning units.
Scenario code IDs reflect those used in publication to which these datasets relate.
This dataset contains a database extracted from the literature related to marine conservation planning.
The tables include details and a systematic classification of reviewed studies that incorporated or recommended connectivity (Table A1) or climate change (Table A2) for systematic conservation planning.
This dataset contains the dissimilarity matrices used to determine differences in spatial similarity between all reserve solutions produced with changes in levels of each investigated factor (planning-unit size, thematic resolution of habitats, socioeconomic cost). For individual reserve solutions, there are pair-wise comparisons between each of the 2000 solutions produced (100 for each of 20 total scenarios). For selection frequencies of each scenario, there are pair-wise comparisons between each of the 20 selection frequency outputs. These data matrices were Hellinger-transformed to allow meaningful use of parametric ordination methods.
This dataset contains information on the degree to which conservation objectives for high thematic resolution would be achieved by priority areas identified by the ten coarse scenarios tested with large planning units.
The incidental_rep_results.csv files consists of Csv table containing information on each of the high thematic resolution (code L5) habitats assessed for incidental representation. Codes correspond to habitat types categorised in the original spatial dataset. Natural log-transformed rarity values for each L5 habitat are also shown for each L5 habitat, calculated as the extent of each feature relative to that of the whole study area, expressed as a percentage. Formula used: [1 - (total habitat extent / total planning extent)] x 100. Values of incidental representation for each of the L5 habitats are shown for each of the unique prioritisation scenarios (20 total).
Please refer to accompanying publication for further details on methods used to calculate incidental representation of L5 habitats.
Data collection was through anonymous questionnaires and key informant interviews, from February to April 2014. The survey was designed to collect information to study the levels and drivers of illegal fishing. Questionnaires were mostly quantitative, and respondents were artisanal fishermen and tourism operators, including those involved in sport fishing. These two stakeholder groups were selected because they spend considerable time on the water, giving them a good idea of the reality of each location. The questionnaires were conducted in communities adjacent to marine protected areas in the Pacific and Caribbean coasts. We selected questionnaire respondents using snowball sampling, and convenience sampling at beaches, fish landing sites, marinas, and tourism companies. Key informant interviews were semistructured and were used to validate the information received from the questionnaires. Key informants included government staff, managers of tour companies, community leaders, leaders of fishing associations, and researchers. Most key informants were contacted by telephone or email to arrange meetings. All interviews were conducted in person and in Spanish by AA, a Costa Rican. For more information see: Arias, A., J. E. Cinner, R. E. Jones, and R. L. Pressey. 2015. Levels and drivers of fishers' compliance with marine protected areas. Ecology and Society 20(4):19. http://dx.doi.org/10.5751/ES-07999-200419
This dataset contains information on the degree to which fine-resolution priorities (determined with small planning units) were spatially nested within all coarse-resolution priorities (determined with large planning units). All coarse-resolution priorities were evaluated against two test scenarios, of the highest resolutions possible.
Coarse-scenario and test ("best") scenario codes reflect those used in publication to which these datasets relate. SF is an abbreviation for selection frequency; high-priority areas were defined at two levels. Percentage values represent the percentage small high-priority planning units from each of the test scenarios that overlapped with high-priority large planning units from the ten coarse scenarios that were tested.
Dataset associated with the paper "Conservation Planning for Coral Reefs Accounting for Climate Warming Disturbances”. Data was collected using satellite imagery and includes spatially- and temporally-varying sea-surface temperature (SST) data, integrating both observed (1985–2009) and projected (2010–2099) time-series. For historical analysis, data was acquired on sea-surface temperature (SST) from the National Oceanic and Atmospheric Administration (NOAA) Pathfinder Project (http://pathfinder.nodc.noaa.gov). For analysis of future projections, data was used from the global monthly SST output (2010–2099) by the Parallel Climate Model PCM1, which is a General Circulation Model (GCM) developed by the National Center for Atmospheric Research (NCAR) for the Intergovernmental Panel on Climate Change, Fourth Assessment (IPCC AR4). All Brazilian reefs were used in the study. Data was collected to derive indices of acute (time under reduced ecosystem function following short-term events) and chronic thermal stress (rate of warming), which were combined to delineate thermal-stress regimes. I then evaluated if/how these regimes are contained within Brazilian Marine Protected Areas and identified priority areas where additional protection would reinforce resilience.
This dataset contains information on the total reserve extents and costs (calculated as a proportion of the maximum possible cost) on each of the individual solutions generated (100 each) for each conservation prioritisation scenario created (20 each)
The allcosts.csv files consists of data on total reserve costs for each individual solution (100 total) produced for each scenario (20 total). Total costs are calculated as proportions of the maximum possible cost, to allow comparison between different cost data. Rows indicate the individual solutions; columns indicate the unique prioritisation scenarios.
The allsizes.csv files consists of data on total reserve extents for each individual solution (100 total) produced for each scenario (20 total). Rows indicate the individual solutions; columns indicate the unique prioritisation scenarios. Reserve extent values are in squared kilometers.
Scenario code IDs reflect those used in publication to which these datasets relate.
Spatial management, including setting aside conservation areas, is central to curbing the global decline of biodiversity, but many threats originate from beyond the boundaries of conservation areas. This is a particular problem in marine systems, which are influenced by many activities on land. In addition, connections between land and sea support many species and ecological processes valued for conservation. Integrated land-sea conservation planning incorporates ecological connections between land and sea and seeks to limit land-based threats to coastal-marine ecosystems, while achieving conservation objectives on the land. We reviewed 25 case studies that followed a land-sea planning approach, including considerations of land-sea processes (natural flows occurring between realms), cross-system threats (threats originating in one realm and affecting another), and socioeconomic interactions associated with management decisions to maintain or restore land-sea processes and to prevent or mitigate cross-system threats.
This dataset summarizes selected elements of land-sea planning case studies.
Relevant dataset associated with the PhD chapter "Integrating multiple species connectivity and habitat quality into conservation planning for coral reefs”. Data was compiled from previous chapters and additional data on biodiversity. Data on biodiversity includes: (i) spatial data representing coral reef ecosystems derived from satellite imagery; (ii) species distribution data for 405 species of reef fish from a geographic range data set compiled by a previous study (Vila-Nova et al. 2014; see thesis for the full reference). Data on connectivity and climate warming were collected as previously described in other chapters. All Brazilian reefs were used for this study. Data was collected to demonstrate how functional demographic connectivity for four candidate reef-associated species with varying dispersal abilities and a suite of connectivity metrics weighted by habitat quality can be used to set conservation objectives and inform marine protected area placement. Data was collected to explore interactions between different sets of objectives (i.e. biodiversity, connectivity, and climate warming) and evaluate the consequences of pursuing single objectives in marine planning.
Socioeconomic data collected in five villages in Bali and seven villages in North Sulawesi. Data was collected during September-December 2012. Data consists of 589 household surveys, and includes demographic, perception and experimental economic game data.
Dataset associated with the paper "Integrating multiple species connectivity and habitat quality into conservation planning for coral reefs”. The data was collected using biophysical modelling (for connectivity) and remote sensing techniques (for stressors). For connectivity, data on coral reef locations and extents were obtained from the Brazilian Ministry of the Environment database. Data on daily ocean current velocity from 2008 to 2012 was obtained from the Atlantic Operational Real Time Ocean Forecasting System (Atlantic RTOFS) and used to represent ocean dynamics in the dispersal model. Data on stressors were: fishing intensity (Google Earth Pro and government reports); thermal stress (NOAA AVHRR); sedimentation (MODIS Aqua); coastal development (DMSP/NOAA/NGDC). Brazilian reefs were used in the study. Data was collected to demonstrate how functional demographic connectivity for four candidate reef-associated species with varying dispersal abilities and a suite of connectivity metrics weighted by habitat quality can be used to set conservation objectives and inform marine protected area placement.
Overfishing, pollution, coastal development and climate change threaten marine biodiversity globally and compromise the services that marine ecosystems provide. Systematic conservation planning (SCP) provides a framework to identify areas where actions can be effective in addressing these threats, while minimizing the costs of interventions. This study investigated the application of SCP in the Gulf of California, Mexico, a marine hotspot where at least seven prioritization exercises have been undertaken. We reviewed the seven marine conservation planning exercises and undertook spatial analyses for six of them. The existence of multiple marine conservation plans in the Gulf of California highlights some of the complexities and benefits of having multiple sets of priorities.
This dataset summarizes selected elements of the seven marine planning exercises, including information about the overall prioritization approach and methods followed by planners.