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23 results for “conservation prioritization”
Criteria for prioritizing selection of Mexican maize landrace accessions for conservation in situ or ex situ based on phylogenetic analysis
<p>Data for processed SSR markers in maize accessions. A database in Structured Query Language (SQL) is provided. Please see the text file "READMEmaizeSSR.pdf".</p>
Integrating dynamic processes into waterfowl conservation prioritization tools
<p><b>Aim: </b> Traditional approaches for including species' distributions in conservation planning have presented them as long-term averages of variation. Like these approaches, the main waterfowl conservation targeting tool in the United States Prairie Pothole Region (US PPR) is based primarily on long-term averaged distributions of breeding pairs. While this tool has supported valuable conservation, it does not explicitly consider spatiotemporal changes in spring wetland availability and does not assess wetland availability during the brood rearing period. We sought to develop a modeling approach and targeting tool that incorporated these types of dynamics for breeding waterfowl pairs and broods. This goal also presented an opportunity for us to compare predictions from a traditional targeting tool based on long-term averages to predictions from spatiotemporal models. Such a comparison facilitated tests of the underlying assumption that this traditional targeting tool could provide an effective surrogate measure for conservation objectives such as brood abundance and climate refugia.</p> <p><b>Location: </b>US PPR</p> <p><b>Methods:</b> We developed spatiotemporal models of waterfowl pair and brood abundance within the PPR of the US. We compared the distributions predicted by these models and assessed similarity with the averaged pair data that is used to develop the current waterfowl targeting tool.</p> <p><b>Results:</b> Results demonstrated low similarity and correlation between the averaged pair data and spatiotemporal brood and pair models. The spatiotemporal pair model distributions served as better surrogates for brood abundance than the averaged pair data.</p> <p><b>Main conclusions:</b> Our study underscored the contributions that the current targeting tool has made to waterfowl conservation but also suggested that conservation plans in the region would benefit from the consideration of inter- and intra-annual dynamics. We suggested that using only the averaged pair data and derived products might result in the omission of 46-98% of important pair and brood habitat, respectively, from conservation plans.</p>
Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
<p><strong>Aim</strong>: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analysing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts.</p> <p><strong>Location</strong>: Global.</p> <p><strong>Methods</strong>: We collated telemetry data from 1,235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.</p> <p><strong>Results</strong>: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.</p> <p><strong>Main conclusions</strong>: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness, but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed towards areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.</p>
Data from: Choice of prioritization method impacts recommendations for climate-informed bird conservation in the United States
<p class="MsoNormal">Climate-informed spatial planning is urgently needed to guide initiatives aimed at both conserving biodiversity as a whole (e.g., protection of 30% of lands and waters by 2030) and recovering North American avifauna in particular. Various methods for prioritizing conservation areas exist, yet alternative methods may direct managers to different lands for protection and thus varying recommendations for meeting area-based targets. Here, we used bird species distribution models and landcover projections to systematically evaluate two widely-used methods for prioritizing areas most likely to facilitate the persistence of multiple species under climate change: (1) <em>in situ </em>macrorefugia, identified as areas of high predicted species retention; and (2) complementarity-based optimizations, identified using the Zonation conservation planning software. For 17 biogeographical groups in the continental United States, we compared priority areas for bird conservation derived from these two alternatives with respect to their spatial distributions and consensus (i.e., overlap), expected conservation outcomes (e.g., species and functional diversity), predicted climate change exposure, habitat characteristics, landscape configurations, and degree of formal protection. Spatial distributions of priority areas differed by biogeographical group and method, with 40.5% consensus on average across groups. Consensus was extensive within mountainous and coastal regions and limited at high latitudes (e.g., Alaska) and in flat, interior regions (e.g., grasslands). As expected, complementarity-based optimizations more efficiently represented species than retention-based <em>in situ </em>macrorefugia, especially for forest groups, and had greater overall biodiversity value and better habitat condition. Conversely, <em>in situ </em>macrorefugia encompassed higher elevations and larger contiguous patches and were expected to experience less winter-season warming. Formal protection averaged <50% across biogeographical groups, regardless of prioritization method. Our findings illustrate the value of complementarity-based optimizations for bird conservation under climate change. More broadly, comparing approaches for prioritizing areas for long-term species persistence can reveal critical tradeoffs in recommendations for climate-informed protected area planning.</p>
Data for: Planning for a future of changes: prioritizing areas for conservation of small mammals in the Caatinga, Brazil
<p>Human land use and climate change are two of the main threats affecting biodiversity, especially in arid/semiarid regions. The most effective way to protect the species in these ecosystems against these threats is through the delimitation of Protected Areas (PAs). However, such PAs need to be targeted cost-efficiently and consider future climate change. We identify priority areas to preserve small mammal species in the Caatinga in the present and in the future of climate change. We also evaluate how well these priority areas are protected by current PAs and identify ways forward to improve their protection. We use ecological niche models and Zonation spatial prioritisation software to identify the top 30% priority areas to preserve small mammal species under current climate and land use scenarios, besides considering optimistic and pessimistic scenarios of future climate change. We also evaluate how much these priority areas are covered by current PAs, identify ways to further improve their protection using hierarchical mask analysis, and evaluate species mean distribution coverage. The consequences of climate change will not hugely impact the distribution of priority areas for species conservation in the Caatinga. Around 13% of the identified priority areas overlap with current PAs, and planning the expansion of PAs considering integral protection areas increases the coverage of priority areas to more than 18% and captures more than 72% of species-suitable areas. Our prioritisations take into account climate change and provide low risk if conducted as a "no-regrets" conservation action. These priority areas are poorly supported by the Brazilian PA system, and need of further protection. One cost-effective option could be to upgrade some Sustainable Use PAs into more restrictive ones. Securing these priority areas helps preserve the long-term ecosystem functioning and to prevent biodiversity loss in a changing world.</p>
Data from: Choice of prioritization method impacts recommendations for climate-informed bird conservation in the United States
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Data for: Planning for a future of changes: prioritizing areas for conservation of small mammals in the Caatinga, Brazil
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Integrating dynamic processes into waterfowl conservation prioritization tools
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A gap analysis modeling framework to prioritize collecting for ex situ conservation of crop landraces
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Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
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Data from: Combining spatial, genetic, and environmental risk data to define and prioritize in situ conservation units
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A novel index to aid in prioritizing habitats for site-based conservation
<p><span>Funding biodiversity conservation strategies are usually minimal, thus prioritizing habitats at high risk should be conducted. We developed and tested a conservation priority index (CPI) that ranks habitats to aid in prioritizing them for conservation. We tested the index using 1897 fish species from 273 African inland lakes and 34 countries. In the index, lake surface area, rarity, and their International Union for Conservation of Nature (IUCN) Red List status were incorporated. We retrieved data from the Global Biodiversity Information Facility (GBIF), and IUCN data repositories. Lake Nyasa had the highest species richness (424), Tanganyika (391), Nokoué (246), Victoria (216), and Ahémé (216). However, lakes Otjikoto and Giunas had the highest CPI of 137.2 and 52.1, respectively. Lakes were grouped into high priority (CPI > 0.5; n=56) and low priority (CPI <0.5; n=217). The median surface area between priority classes was significantly different (W = 11768, p<0.05, effect size = 0.65). Prediction accuracy of Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) for priority classes were 0.912 and 0.954 respectively. Both models exhibited lake surface area as the variable with the highest importance. CPI generally increased with a decrease in lake surface area. This was attributed to less ecological substitutability and higher exposure levels of anthropogenic stressors such as pollution to a species in smaller lakes. Also, the highest species richness per unit area was recorded for high-priority lakes. Thus, smaller habitats or lakes may be prioritized for conservation although larger water bodies or habitats should not be ignored. The index can be customized to local, regional, and international scales as well as marine and terrestrial habitats.</span></p>
Input data and scripts for "Spatial conservation prioritization for the East Asian islands: a balanced representation of multi-taxon biogeography in a protected area network"
<p>This release contains the input files 'input_data.zip' for the spatial conservation prioritization analysis by Zonation software, which are conducted in Lehtomäki et al. Input data includes biodiversity features (species distribution maps from vascular plants, mammals, birds, reptiles, amphibians, and freshwater fishes), habitat condition map (human influence index), priority mask information (the categorized protected area distribution) and the Japanese prefecture polygons in GeoTiff format, and the list of species attributes for conservation weighting in CSV format. Note that endangered rare species have been excluded from this dataset, though they were reflected in the output files. The Zonation setting files and R scripts for pre- and post analyses are included in 'japan-zsetup-1.0.zip' and also placed at GitHub : https://github.com/cbig/japan-zsetup</p> <p>The output files (priority score maps and removal curves) from the original Zonation analyses are summarized in 'output_from_original.data.zip'</p>
Data from: Prioritizing tiger conservation through landscape genetics and habitat linkages
Even with global support for tiger (Panthera tigris) conservation their survival is threatened by poaching, habitat loss and isolation. Currently about 3,000 wild tigers persist in small fragmented populations within seven percent of their historic range. Identifying and securing habitat linkages that connect source populations for maintaining landscape-level gene flow is an important long-term conservation strategy for endangered carnivores. However, habitat corridors that link regional tiger populations are often lost to development projects due to lack of objective evidence on their importance. Here, we use individual based genetic analysis in combination with landscape permeability models to identify and prioritize movement corridors across seven tiger populations within the Central Indian Landscape. By using a panel of 11 microsatellites we identified 169 individual tigers from 587 scat and 17 tissue samples. We detected four genetic clusters within Central India with limited gene flow among three of them. Bayesian and likelihood analyses identified 17 tigers as having recent immigrant ancestry. Spatially explicit tiger occupancy obtained from extensive landscape-scale surveys across 76,913 km2 of forest habitat was found to be only 21,290 km2. After accounting for detection bias, the covariates that best explained tiger occupancy were large, remote, dense forest patches; large ungulate abundance, and low human footprint. We used tiger occupancy probability to parameterize habitat permeability for modeling habitat linkages using least-cost and circuit theory pathway analyses. Pairwise genetic differences (FST) between populations were better explained by modeled linkage costs (r>0.5, p<0.05) compared to Euclidean distances, which was in consonance with observed habitat fragmentation. The results of our study highlight that many corridors may still be functional as there is evidence of contemporary migration. Conservation efforts should provide legal status to corridors, use smart green infrastructure to mitigate development impacts, and restore habitats where connectivity has been lost.
Spatial conservation prioritization for the Amur tiger in Northeast China
<p class="Paragraph">Amur tiger (<i>Panthera tigris altaica</i>) is critically endangered and also the subspecies of the tiger with the most restoration potential in China. It is challenging to protect large-ranging carnivores like tigers under increasing pressure of human development. To provide a more technically robust foundation for tiger habitat conservation prioritization, we conducted a comprehensively empirical analysis based on a broadly collected occurrence dataset of tigers and their prey. We modeled tiger distribution by running an ensemble model integrating nine different algorithms. We found that the ensemble model performed well and outperformed any individual model regarding the discrimination ability. We used cumulative resistant kernel analysis to identify core habitats as with high predicted movement density and used factorial least-cost paths to model corridors among tiger occurrence locations. We found core habitats for Amur tigers are distributed in three mountain areas, namely eastern Wanda Mountain, southern Zhangguangcailing, and Laoyeling-Dalongling. We found significant protection gaps as existing protected areas only cover less than 1/4 of predicted core habitats, but this proportion will rise significantly with the establishment of the Northeast China Tiger and Leopard National Park. Furthermore, we ranked spatial priorities for the expansion of the protected area network, simultaneously considering biological and socioeconomic dimensions under the Zonation framework. Our study presented the most up-to-date and detailed maps of the predicted potential distribution and area of the most important habitats for the Amur tigers in China, which can provide quantitative guidance in the effort to maximize the efficiency of conservation initiatives at a regional scale.</p>
Spatial conservation prioritization for the Amur tiger in Northeast China
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A novel index to aid in prioritizing habitats for site-based conservation
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Data from: Combining genetic and demographic information to prioritize conservation efforts for anadromous alewife and blueback herring
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Data from: Prioritizing tiger conservation through landscape genetics and habitat linkages
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Ecological traps and species distribution models: a challenge for prioritizing areas of conservation importance
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