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33 results for “protected areas network”
Navigating protected areas networks for improving diffusion of conservation practices
<p>The Natura 2000 protected area network is the cornerstone of European Union's biodiversity conservation strategy. These protected areas range across multiple biogeographic regions, and they include a diversity of species assemblages along with a diversity of managing organizations, altogether making difficult to pool relevant sites to facilitate the flow of knowledge significant to their management. Here we introduce an approach to navigating protected area networks that has the potential to foster systematic identification of key sites for facilitating the exchange of knowledge and diffusion of information within the network. To demonstrate our approach, we abstractly represented Romanian Natura 2000 network as a co-occurrence network, with individual sites as nodes and shared species as edges, further combining into our analysis network topology, community detection, and network reduction methods. We identified most representative Natura 2000 sites that may increase the transfer of information within the national network of protected areas, detected clusters of sites and key sites for maintaining network cohesiveness, and highlighted the subsample of sites that retain the characteristics of the entire network. Our analysis provides implications for protected area prioritization by proposing a network perspective approach to collaboration rooted in ecological principles.</p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Species occurrence and occupancy in protected areas of the Natura2000 network in Belgium
<p><strong>Context</strong></p> <p>Invasive alien species have been pointed out as an important driver of biodiversity loss. Many policy responses are being developed to address this threat. Protected areas often represent and preserve hotspots of biological diversity and ensure the maintenance of ecosystem services crucial to human livelihoods. The impact of biological invasions can be particularly severe in protected areas and their occurrence and impact in such areas is an important element of the risk they pose. To address this, there is a need for data on the occurrence and extent of alien species invasions in protected areas.</p> <p><strong>Description</strong></p> <p>This dataset contains species occurrence and occupancy in protected areas of the Natura2000 network in Belgium (Special Conservation Areas sensu Habitat Directive and Special Protection Areas sensu Bird Directive). The dataset was generated using the <a href="https://doi.org/10.5281/zenodo.3637911">Belgian occurrence cube at species level</a> and the <a href="https://doi.org/10.5281/zenodo.3635510">Belgian occurrence cube for non-native taxa</a> (both containing GBIF data aggregated using Oldoni et al. 2020), the 1x1km <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">EEA reference grid</a> and the <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-spatial-data/natura-2000-shapefile-1">Natura2000 protected areas shapefiles</a> from the European Environment Agency.</p> <p>Data are grouped by protected area (<code>SITECODE</code>), year (<code>year</code>) and (infra)species (<code>taxonKey</code>, <code>speciesKey</code>). For each group, it provides the number of occurrences found in GBIF (<code>n</code>), the area of occupancy (<code>aoo</code>: number of 1 km<sup>2</sup> squares), the coverage (<code>coverage</code>: % of 1 km<sup>2</sup> squares), the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (<code>min_coord_uncertainty</code>), and the alien status (<code>is_alien</code>) based on the <a href="https://doi.org/10.15468/xoidmd">Global Register of Introduced and Invasive Species - Belgium</a>. For infraspecific taxa in the latter, the <a href="https://github.com/trias-project/indicators/blob/00e1ae72df3fb98b2a215c3af8769e53fbcd0182/reference/species_of_infraspecific_alien_taxa.tsv">alien status of the species</a> is looked up and included.</p> <p>The dataset is built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on Zenodo, with the download DOIs listed in the related identifiers of this dataset package.</li> <li>The <a href="https://trias-project.github.io/indicators/10_species_observations_occupancy_in_protected_areas.html">code</a> to process the data is publicly available and documented on GitHub.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>protected_areas_species_occurrence.csv</strong>: number of occurrences (<code>n</code>), area of occupancy (<code>aoo</code>) and <code>coverage</code> of taxa (<code>taxonKey</code>) in Natura2000 areas of Belgium (<code>SITECODE</code>). Other columns included: <code>speciesKey</code> (for species is <code>speciesKey</code> = <code>taxonKey</code>), <code>SITETYPE</code> containing the site type of the Natura2000 area (one of <code>A</code>, <code>B</code> or <code>C</code>), <code>min_coord_uncertainty</code> with the lowest coordinate uncertainty in meters, <code>is_alien</code> containing the alien status (<code>TRUE</code> or <code>FALSE</code>) and <code>remarks</code> containing, if present, the infraspecific alien taxa whose occurrences contribute to the calculated <code>aoo</code> (only for species).</li> <li><strong>protected_areas_species_info.csv</strong>: taxonomic information of taxa in <code>protected_areas_species_occurrence.csv</code> as retrieved from <a href="https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c">GBIF Backbone Taxonomy</a>. Columns: <code>taxonKey</code>, <code>speciesKey</code>, <code>scientificName</code>, <code>kingdom</code>, <code>phylum</code>, <code>order</code>, <code>class</code>, <code>genus</code>, <code>family</code>, <code>species</code>, <code>rank</code> and <code>includes</code>. The latter contains the infraspecific taxa and synonyms whose occurrences contribute to the number of occurrences at species level.</li> <li><strong>protected_areas_metadata.csv</strong>: protected area information for areas included in <code>protected_areas_species_occurrence.csv</code>. Columns: <code>SITECODE</code> as in <code>protected_areas_species_occurrence.csv</code> (<code>BE*******</code>), <code>SITENAME</code> containing the name of the protected area, <code>SITETYPE</code> as in <code>protected_areas_species_occurrence.csv</code>, <code>flanders</code>, <code>wallonia</code> and <code>brussels</code> containing whether the area is situated respectively in Flanders, Wallonia or Brussels-Capital Region (<code>TRUE</code> or <code>FALSE</code>). Field codes are in line with <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-tabular-data-12-tables">EEA element definitions</a> for Natura 2000 sites.</li> </ul> <p><strong>Potential use of the dataset</strong></p> <p>Currently, there is no comprehensive reporting system for invasive alien species in Natura 2000 sites. This dataset provides a baseline as to which species occur in which protected area. We envisage this dataset can be an interesting starting point for various types of analyses on alien species in protected areas in Belgium, but that it can also be used in complement to other data on alien species in protected areas to study more general patterns. Some examples of research questions:</p> <ul> <li>Which protected areas are most invaded by alien species</li> <li>Which alien species are most distributed in protected areas and which traits do they have</li> <li>How does the proportion of alien species in protected areas change in time</li> <li>How does the occurrence/occupancy of alien species in protected areas match lists of regulated species (e.g. Union List, EPPO lists)</li> <li>To what extent can the network of protected areas contribute to providing safe refuge to native species from the impacts of invasive alien species</li> <li>How widespread are the impacts of certain alien species on protected areas</li> </ul> <h2>Acknowledgements</h2> <p>This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).</p>
Data and scripts for: Green turtles highlight connectivity across a regional marine protected area network in West Africa
<p>Data derivates and analysis scripts (in R) used for the paper on analyzing green turtle MPA coverage and connectivity in West Africa.</p>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
Gaps and weaknesses in the global protected area network for safeguarding at-risk species
<p>Dataset and example code for the 4946 species of terrestrial mammals, amphibians and birds analysed in Zeng et al.</p>
Expanding China's protected areas network to enhance resilience of climate connectivity
<p>The code used for analysis, mammalian species occurrence points, prediction accuracy(AUC), and prediction distribution.</p> <p>Specifically,</p> <p>The main nodes are named "network efficiency.R" and "network efficiency_random.R", which can be run in RStudio software (download at https://posit.co/), and the files "hfp_expanded.csv" and "EN_length_expanded.net" are example data.</p> <p>"Terrestrial mammalian species occurrence points.csv" is the latitude and longitude coordinates of occurrence records.</p> <p>"AUC_prediction accuracy of Maxent.xlsx" is the prediction accuracy of each species in the Maxent model.</p> <p>"all_species_maxent.zip" is the composite terrestrial mammalian species distribution of equal weight overlapping the spatial distribution of each species ("each_species_maxent.zip").</p> <p> </p> <p> </p>
Data from: Planning for climate change through additions to a national protected area network: implications for cost and configuration
<p>Expanding the network of protected areas is a core strategy for conserving biodiversity in the face of climate change. Here we explore the impacts on reserve network cost and configuration associated with planning for climate change in the United States using networks that prioritize areas projected to be climatically suitable for 1,460 species both today and into the future, climatic refugia, and areas likely to facilitate climate-driven species movements. For 14% of the species, networks of sites selected solely to protect areas currently climatically suitable failed to provide climatically suitable habitat in the future. Protecting sites climatically suitable for species today and in the future significantly changed the distribution of priority sites across the U.S.—increasing relative protection in the northeast, northwest, and central U.S. Protecting areas projected to retain their climatic suitability for species cost 59% more than solely protecting currently suitable areas. Including all climatic refugia and 20% of areas that facilitate climate-driven movements increased the cost by another 18%. Our results indicate that protecting some types of climatic refugia may be a relatively inexpensive adaptation strategy. Moreover, although addressing climate change in conservation plans will have significant implications for the configuration of networks, the increased cost of doing so may be relatively modest.</p> <p> </p>
Using dung densities to assess the ecological effectiveness of a protected area network
<p>Given recent global endeavors to increase protected area coverage, it is crucial to comprehensively evaluate the efficacy of various area-based conservation strategies in effectively reducing biodiversity loss. Here, we investigated responses of wildlife populations to different protection levels and environmental variables at the landscape scale in the Katavi-Rukwa Ecosystem, western Tanzania. To this end, we conducted line distance sampling surveys and counted dung of six target mammal species (elephant, giraffe, buffalo, zebra, topi, hartebeest) along foot transects within areas differing in protection levels (from strict to less-strictly protected: national park, game reserve, forest reserve, game-controlled area, and unprotected areas). Based on these dung counts, we modelled the spatial distribution of these six mammal species using a species-specific density surface modelling framework. We, found consistent effects of protection level and land-use variables on the spatial distribution of the target mammal species: dung densities were highest in the national park and game reserves, intermediate in less-strictly protected areas and lowest in un-protected areas. Beyond species-specific environmental predictors for dung densities, our results highlight consistent negative associations between dung densities of the target species and distance to cropland and avoidance of areas in proximity to houses. Our findings underpin differences in ecological effectiveness of protected areas within one ecosystem. Protection level and land use play crucial roles in moderating the spatial distribution of all considered mammal species. Our findings suggest that a landscape approach needs to guide effective conservation across the entire protection gradient of the Katavi-Rukwa Ecosystem.</p>
Figure 3 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 3. Comparison of the number of interactions between frugivorous birds and plants between native forest and eucalyptus plantation in the PEIT. (a): Fecal samples interactions (P-value = 0.83, W = 3.5); (b): Focal observation interactions (P-value = 0.99, W = 4.0).
Figure 2 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 2. Interaction networks between frugivorous birds and zoochoric plants, according to the focal observations of birds in both sampled habitats. The circles represent the plant species, and the species of birds are represented by triangles. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with between species from other clusters due to its modularity (Q). (a): Fragments of native forest; (b): Fragments of eucalyptus plantation.
Figure 1 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 1. Interaction networks between frugivorous birds and zoochoric plants, according to the fecal samples of birds in the understory of the two sampled habitats. The circles represent the plant species, and the triangles are representing the species of birds. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with species from other clusters due to its modularity (Q). (a): F fragments of native forest; (b): Fragments of eucalyptus plantation.
Supplementary material 1 from: Virkkala R, Rajasärkkä A (2012) Preserving species populations in the boreal zone in a changing climate: contrasting trends of bird species groups in a protected area network. Nature Conservation 3: 1-20. https://doi.org/10.3897/natureconservation.3.3635
Mean densities and number of observations of species in 1981–1999 and in 2000–2009
Using dung densities to assess the ecological effectiveness of a protected area network
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Data from: Climate change and pathways of vessel traffic create marine protected area networks of invasion
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Data from: Planning for climate change through additions to a national protected area network: implications for cost and configuration
Open the record for dataset details and reuse information.
Data from: Assessing the effectiveness of a national protected area network in maintaining carnivore populations
<p>Protected areas (PAs) are essential to prevent further biodiversity loss yet their effectiveness varies largely with governance and external threats. Although methodological advances have permitted assessments of PA effectiveness in mitigating deforestation, we still lack similar studies for the impact of PAs on wildlife populations. Here we demonstrate the application ofuse an innovative combination of matching methods and hurdle-mixed models with a large-scale and long-term dataset of unprecedented coverage for Finland's large carnivore species. We show that the national PA network , at the national level, PAs does not support higher densities than non-protected habitat for 3 of the 4 species investigated. For the brown bear, PAs appear to have lower densities than non-protected areas. For some species, PA effects interact with region or time, i.e. wolverine densities decreased inside PAs over the study period and lynx densities increased inside eastern PAs. Although we show that matching approaches could and should be applied to wildlife population data, Wwe support their application of matching methods in combination of additional analytical frameworks for deeper understanding of conservation impacts on wildlife populations. These methodological advances are crucial for improving PA targets and extremely timely for preparing ambitious PA targets a post-2020 global framework for biodiversity.</p>
Data from: Modelling the current and future biodiversity distribution in the Chilean Mediterranean Hotspot. The role of protected areas network in a warmer future
Aim: Mediterranean Chile is part of the five recognized Mediterranean-type climates in the world and harbors a very rich floral diversity. Climate change has been reported as a significant threat to its biodiversity. We used the flora of Mediterranean Chile to analyze how biodiversity patterns, as measured by Phylogenetic Diversity, genus and species richness will respond to climate change scenarios and identify the areas that will harbor the greatest evolutionary potential and biodiversity richness. We also evaluated how these spatial patterns are depicted within the current network of protected areas. Location: Chilean Mediterranean climate-type Region, South America. Methods: Biodiversity metrics were evaluated for current and future climatic scenarios. Species distribution models were done using Maxent for 1.727 species and 571 genera. Relationships between species/genera gain, loss and turnover were evaluated. For Mediterranean endemic species, loss and gain was also related to life form. Finally, variation in species gain, loss and turnover was evaluated in future climate change scenarios within and outside Mediterranean Chile state protected areas. Results: We found a general decrease in species richness in the entire Region toward future climate change scenarios. Phylogenetic Diversity is predicted to be higher than expected by richness in the north and south of the area, and lower than expected by richness in the Andes mountain. The highest average species and genus loss is predicted to occur outside the protected areas, meanwhile species and genus gain is higher within them. Main conclusions: Future biodiversity patterns are reported here for the first time in the Chilean Mediterranean Region. Our findings enhance the importance of the current protected areas to harbor this future variation, despite their reduced number and size along the region.
Integrating functional connectivity in designing networks of protected areas under climate change: a caribou case-study
<p>Land-use change and climate change are recognized as two main drivers of the current biodiversity decline. Protected areas help safeguard the landscape from additional anthropogenic disturbances and, when properly designed, can help species cope with climate change impacts. When designed to protect the regional biodiversity rather than to conserve focal species or landscape elements, protected areas need to cover a representative sample of the regional biodiversity and be functionally connected, facilitating individual movements among protected areas in a network to maximize their effectiveness. We developed a methodology to define effective protected areas to implement in a regional network using ecological representativeness and functional connectivity as criteria. We illustrated this methodology in the Gaspésie region of Québec, Canada. We simulated movements for the endangered Atlantic-Gaspésie caribou population (<i>Rangifer tarandus caribou</i>), using an individual-based model, to determine functional connectivity based on this large mammal. We created multiple protected areas network scenarios and evaluated their ecological representativeness and functional connectivity for the current and future conditions. We selected a subset of the most effective network scenarios and extracted the protected areas included in them. There was a tradeoff between ecological representativeness and functional connectivity for the created networks. Only a few protected areas among those available were repeatedly chosen in the most effective networks. Protected areas maximizing both ecological representativeness and functional connectivity represented suitable areas to implement in an effective protected areas network. These areas ensured that a representative sample of the regional biodiversity was covered by the network, as well as maximizing the movement over time between and inside the protected areas for the focal population.</p>
Data from: Benefits and challenges of scaling up expansion of marine protected area networks in the Verde Island Passage, Central Philippines
Locally-established marine protected areas (MPAs) have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective—ecologically, socially, and economically—than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was slightly more efficient than the coordinated scenarios. Overall, however, coordinated MPA networks have the potential to be more efficient than the uncoordinated ones, especially when coordinated planning uses systematic methods.
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OpenNeuro
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