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opencc-zeroDec 2015View details →

Data from: Landscape genetics informs mesohabitat preference and conservation priorities for a surrogate indicator species in a highly fragmented river system

Poor dispersal species represent conservative benchmarks for biodiversity management because they provide insights into ecological processes influenced by habitat fragmentation that are less evident in more dispersive organisms. Here we used the poorly dispersive and threatened river blackfish (Gadopsis marmoratus) as a surrogate indicator system for assessing the effects of fragmentation in highly modified river basins and for prioritizing basin-wide management strategies. We combined individual, population and landscape-based approaches to analyze genetic variation in samples spanning the distribution of the species in Australia's Murray–Darling Basin, one of the world's most degraded freshwater systems. Our results indicate that G. marmoratus displays the hallmark of severe habitat fragmentation with notably scattered, small and demographically isolated populations with very low genetic diversity—a pattern found not only between regions and catchments but also between streams within catchments. By using hierarchically nested population sampling and assessing relationships between genetic uniqueness and genetic diversity across populations, we developed a spatial management framework that includes the selection of populations in need of genetic rescue. Landscape genetics provided an environmental criterion to identify associations between landscape features and ecological processes. Our results further our understanding of the impact that habitat quality and quantity has on habitat specialists with similarly low dispersal. They should also have practical applications for prioritizing both large- and small-scale conservation management actions for organisms inhabiting highly fragmented ecosystems.

opencc-zeroDec 2013View details →

Data from: Conservation genetics of the Philippine tarsier: cryptic genetic variation restructures conservation priorities for an island archipelago primate

Establishment of conservation priorities for primates is a particular concern in the island archipelagos of Southeast Asia, where rates of habitat destruction are among the highest in the world. Conservation programs require knowledge of taxonomic diversity to ensure success. The Philippine tarsier is a flagship species that promotes environmental awareness and a thriving ecotourism economy in the Philippines. However, assessment of its conservation status has been impeded by taxonomic uncertainty, a paucity of field studies, and a lack of vouchered specimens and genetic samples available for study in biodiversity repositories. Consequently, conservation priorities are unclear. In this study we use mitochondrial and nuclear DNA to empirically infer geographic partitioning of genetic variation and to identify evolutionarily distinct lineages for conservation action. The distribution of Philippine tarsier genetic diversity is neither congruent with expectations based on biogeographical patterns documented in other Philippine vertebrates, nor does it agree with the most recent Philippine tarsier taxonomic arrangement. We identify three principal evolutionary lineages that do not correspond to the currently recognized subspecies, highlight the discovery of a novel cryptic and range-restricted subcenter of genetic variation in an unanticipated part of the archipelago, and identify additional geographically structured genetic variation that should be the focus of future studies and conservation action. Conservation of this flagship species necessitates establishment of protected areas and targeted conservation programs within the range of each genetically distinct variant of the Philippine tarsier.

opencc-zeroDec 2015View details →

Data from: Remotely sensed data informs red list evaluations and conservation priorities in southeast Asia

The IUCN Red List has assessed the global distributions of the majority of the world's amphibians, birds and mammals. Yet these assessments lack explicit reference to widely available, remotely-sensed data that can sensibly inform a species' risk of extinction. Our first goal is to add additional quantitative data to the existing standardised process that IUCN employs. Secondly, we ask: do our results suggest species of concern—those at considerably greater risk than hitherto appreciated? Thirdly, these assessments are not only important on a species-by-species basis. By combining distributions of species of concern, we map conservation priorities. We ask to what degree these areas are currently protected and how might knowledge from remote sensing modify the priorities? Finally, we develop a quick and simple method to identify and modify the priority setting in a landscape where natural habitats are disappearing rapidly and so where conventional species' assessments might be too slow to respond. Tropical, mainland Southeast Asia is under exceptional threat, yet relatively poorly known. Here, additional quantitative measures may be particularly helpful. This region contains over 122, 183, and 214 endemic mammals, birds, and amphibians, respectively, of which the IUCN considers 37, 21, and 37 threatened. When corrected for the amount of remaining natural habitats within the known elevation preferences of species, the average sizes of species ranges shrink to <40% of their published ranges. Some 79 mammal, 49 bird, and 184 amphibian ranges are <20,000km2—an area at which IUCN considers most other species to be threatened. Moreover, these species are not better protected by the existing network of protected areas than are species that IUCN accepts as threatened. Simply, there appear to be considerably more species at risk than hitherto appreciated. Furthermore, incorporating remote sensing data showing where habitat loss is prevalent changes the locations of conservation priorities.

opencc-zeroJan 2022View details →

Identifying hotspots and priority areas for xenarthran research and conservation

Aim: Limited funds for conservation and research require the development of prioritization schemes. Traditionally, biodiversity metrics were used to delineate priority areas; however, a growing realization emphasizes that logistic factors should be also considered. Here, we combine species richness, past collection efforts, degree of habitat loss and accessibility to define priority areas and spatially orient fieldwork in a cost-effective manner for xenarthrans, one of the four main mammalian radiation. Location: Neotropics. Methods: We assessed spatial patterns of species richness in Xenarthra and identify diversity hotspots based on species distribution models. Spatial patterns and biases in the Xenarthra past collection efforts were analyzed using a comprehensive database including 33,464 individual records. Finally, we produced priority area indices relating species richness and collection efforts with levels of habitat loss and accessibility (roads and rivers network) to highlight important but neglected areas. Results: Collection efforts were concentrated to a small portion of the Neotropics (central-southern Brazil, eastern Bolivia and north-western Argentina) and were biased toward access routes. Major xenarthran diversity hotspots include the Amazonian lowlands of Bolivia and the dry Chaco of Paraguay and Argentina. Priority areas for research varied depending on the metric analyzed. Amazon holds a high diversity that remains poorly explored. Central Argentina and northeastern Brazil are priority areas for research and conservation given the low sampling efforts, high diversity and endemic species, high levels of habitat loss and a dense road network. Main conclusions: Most areas of the Neotropics lack a proper assessment of the xenarthran assemblage, reflecting extensive knowledge shortfalls. Overall, sites close to roads tend to be better sampled, but many areas with a dense road network are under-sampled, being good candidates for low-cost studies. There is an alarming spatial overlap between Xenarthra diversity hotspots and areas facing intense human modification. Some of the priority areas for research should also be viewed as priority areas for conservation.

opencc-zeroMay 2022View details →

Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis

<div> <div> <div> <div> Gorgonian forests are among the most complex of subtidal habitats in the Mediterranean Sea, supporting high biodiversity and providing diverse ecosystem services. Despite their iconic status, the geographical distribution and condition of gorgonian species is poorly known. Using multiple online data sources, our primary aims were to compile, map and analyse observations of gorgonian forests in Italian coastal waters to assess the biological complexity of gorgonian forests; evaluate impacts and vulnerable species, and identify areas of special interest inside and outside of existing MPAs to help prioritise conservation strategies and actions. </div> </div> </div> </div>

opencc-zeroAug 2022View details →

Data from: A comprehensive evaluation of flowering plant diversity and conservation priority for national park planning in China

Establishment of a national park protection system in China, including the latest target proposed to protect at least 30% of the land area, calls for a comprehensive exploration of conservation priorities incorporating multiple diversity facets. We herein evaluate the spatial distribution of Chinese flowering plants from the perspectives of richness, uniqueness, vulnerability, and evolutionary history, by integrating three mega-phylogenies and comprehensive distribution data. We detect significantly high consistency among hotspots of different diversity measures for Chinese flowering plants, suggesting that multiple facets of evolutionary diversity are concentrically distributed in China. Affording legal protection to these areas is expected to maximize positive conservation outcomes. We propose two integrative diversity indices by incorporating three richness-based and three phylogeny-based measures, respectively. Both methods identify areas with high species richness, but the integrative phylogeny-based index also locates key areas with ancient and unique evolutionary histories (e.g., Ailao-Wuliang Mts, Dabie Mts, Hainan rainforest, Karst area of Yunnan-Guizhou-Guangxi, Nanling Mts, and southeast coastal regions). Of all the diversity indices explored, phylogenetic endemism maximizes the incidental protection of other indices in most cases, emphasizing its significance for conservation planning. Finally, 42 priority areas are identified by combining the 5%-criterion hotspots of two integrative indices and the minimum area to protect all threatened species analyzed. These priorities cover only 13.3% of China's land area but host 97.1% of species richness (23,394/24,095), 96.5% of endemic species (11,841/12,274), 100% of threatened species (2,613/2,613), and 99.3% of phylogenetic diversity for flowering plants involved in this study. These frameworks provide a solid scientific basis for national park planning in China.

opencc-by-4.0Jun 2024View details →

Mining threat maps and conservation priorities for freshwater biodiversity

Open the record for dataset details and reuse information.

FIGURE 4 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe

FIGURE 4. Two models of species endemism distribution among mammals of the Italian Peninsula. 3a) Occurrence in Central-Southern Peninsula and Sicily with often geographic substructuring; Lepus corsicanus. 3b) Occurrence limited to the Calabria bioprovince; Sciurus meridionalis. Question mark denotes uncertain outcomes in the case northern lineages meet the endemics.

FIGURE 1 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe

FIGURE 1. Some endemic taxa of the Italian Peninsula; 1. Apennine chamois in the Maiella National Park (photo Alessia Costa); 2. Apennine hare from North Latium (Central Italy) (photo Paola di Luzio); 3. Maremma wild boar from the Castelporziano Reserve (photo Roberto Isotti).

FIGURE 3 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe

FIGURE 3. Camerano's model of Capra pyrenaica and Capra hispanica dispersion in the Iberian Peninsula (redrawn from García-González, 2011).

FIGURE 2 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe

FIGURE 2. Cabrera's model of Capra pyrenaica dispersion in the Iberian Peninsula (redrawn from García-González, 2011).

FIGURE 6 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe

FIGURE 6. Discriminant analysis of skulls and horns in population samples of Rupicapra. Sample sizes are as follows: (1) groups on which the discrimination was based: Chartreuse, 12; Alps, 16; Pyrenees, 14; (2) groups entered as "unknowns": Balkans, 2; Caucasus, 4; Carpathians, 3; Abruzzi, 2 (data from Groves and Grubb, 2011).

FIGURE 5 in Overlooked mammal diversity and conservation priorities in Italy: Impacts of taxonomic neglect on a Biodiversity Hotspot in Europe

FIGURE 5. Horn length (Hornl; in mm) in population samples of Rupicapra males (data from Groves and Grubb, 2011).

opencc-zeroOct 2018View details →

Supplementary material 1 from: Miu IV, Chisamera GB, Popescu VD, Iosif R, Nita A, Manolache S, Gavril VD, Cobzaru I, Rozylowicz L (2018) Conservation priorities for terrestrial mammals in Dobrogea Region, Romania. ZooKeys 792: 133-158. https://doi.org/10.3897/zookeys.792.25314

Publications used to compile distribution of terrestrial mammal species from Dobrogea Region, Romania (field reports and data from museum collections) :

opencc-zeroOct 2018View details →

Supplementary material 2 from: Miu IV, Chisamera GB, Popescu VD, Iosif R, Nita A, Manolache S, Gavril VD, Cobzaru I, Rozylowicz L (2018) Conservation priorities for terrestrial mammals in Dobrogea Region, Romania. ZooKeys 792: 133-158. https://doi.org/10.3897/zookeys.792.25314

Occurrences maps for 59 mammal species : Explanation note: The mammal species old (before 1990) and new (after 1990) occurrence records at a 5 × 5 km grid resolution within Dobrogea, Romania.

opencc-by-4.0Nov 2018View details →

Priority areas for boreal songbird conservation in Canada: Results for 128 scenarios based on Zonation conservation planning software

Priority areas for boreal songbird conservation in Canada: Results for 128 scenarios based on Zonation conservation planning software Published in: Stralberg, D., A. Camfield, M. Carlson, C. Lauzon, N. K. S. Barker, A. Westwood, and F. K. A. Schmiegelow. in press. Strategies for identifying priority areas for songbird conservation in Canada's boreal forest. Avian Conservation and Ecology. Scenarios:  ----------------- #    Name 1    Representation 2    Representation + Disturbance 3    Representation + BCR Strata 4    Representation + BCR Strata + Disturbance 5    Representation + Forest Birds 6    Representation + Disturbance + Forest Birds 7    Representation + BCR Strata + Forest Birds  8    Representation + BCR Strata + Disturbance + Forest Birds 9    Representation + Conservation Status 10    Representation + Disturbance + Conservation Status 11    Representation + BCR Strata + Conservation Status 12    Representation + BCR Strata + Disturbance + Conservation Status 13    Representation + Forest Birds + Conservation Status 14    Representation + Disturbance + Forest Birds + Conservation Status 15    Representation + BCR Strata + Forest Birds  + Conservation Status 16    Representation + BCR Strata + Disturbance + Forest Birds + Conservation Status 17    Representation + Current Uncertainty 18    Representation + Current Uncertainty + Disturbance 19    Representation + Current Uncertainty + BCR Strata 20    Representation + Current Uncertainty + BCR Strata + Disturbance 21    Representation + Current Uncertainty + Forest Birds 22    Representation + Current Uncertainty + Disturbance + Forest Birds 23    Representation + Current Uncertainty + BCR Strata + Forest Birds 24    Representation + Current Uncertainty + BCR Strata + Disturbance + Forest Birds 25    Representation + Current Uncertainty + Conservation Status 26    Representation + Current Uncertainty + Disturbance + Conservation Status 27    Representation + Current Uncertainty + BCR Strata + Conservation Status 28    Representation + Current Uncertainty + BCR Strata + Disturbance + Conservation Status 29    Representation + Current Uncertainty + Forest Birds + Conservation Status 30    Representation + Current Uncertainty + Disturbance + Forest Birds + Conservation Status 31    Representation + Current Uncertainty + BCR Strata + Forest Birds + Conservation Status 32    Representation + Current Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status 33    Representation + Current/Future Uncertainty 34    Representation + Current/Future Uncertainty + Disturbance 35    Representation + Current/Future Uncertainty + BCR Strata 36    Representation + Current/Future Uncertainty + BCR Strata + Disturbance 37    Representation + Current/Future Uncertainty + Forest Birds 38    Representation + Current/Future Uncertainty + Disturbance + Forest Birds 39    Representation + Current/Future Uncertainty + BCR Strata + Forest Birds 40    Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds 41    Representation + Current/Future Uncertainty + Conservation Status 42    Representation + Current/Future Uncertainty + Disturbance + Conservation Status 43    Representation + Current/Future Uncertainty + BCR Strata + Conservation Status 44    Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Conservation Status 45    Representation + Current/Future Uncertainty + Forest Birds + Conservation Status 46    Representation + Current/Future Uncertainty + Disturbance + Forest Birds + Conservation Status 47    Representation + Current/Future Uncertainty + BCR Strata + Forest Birds + Conservation Status 48    Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status 49    Representation + Future Uncertainty 50    Representation + Future Uncertainty + Disturbance 51    Representation + Future Uncertainty + BCR Strata 52    Representation + Future Uncertainty + BCR Strata + Disturbance 53    Representation + Future Uncertainty + Forest Birds 54    Representation + Future Uncertainty + Disturbance + Forest Birds 55    Representation + Future Uncertainty + BCR Strata + Forest Birds 56    Representation + Future Uncertainty + BCR Strata + Disturbance + Forest Birds 57    Representation + Future Uncertainty + Conservation Status 58    Representation + Future Uncertainty + Disturbance + Conservation Status 59    Representation + Future Uncertainty + BCR Strata + Conservation Status 60    Representation + Future Uncertainty + BCR Strata + Disturbance + Conservation Status 61    Representation + Future Uncertainty + Forest Birds + Conservation Status 62    Representation + Future Uncertainty + Disturbance + Forest Birds + Conservation Status 63    Representation + Future Uncertainty + BCR Strata + Forest Birds + Conservation Status 64    Representation + Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status 65    Diversity 66    Diversity + Disturbance 67    Diversity + BCR Strata 68    Diversity + BCR Strata + Disturbance 69    Diversity + Forest Birds 70    Diversity + Disturbance + Forest Birds 71    Diversity + BCR Strata + Forest Birds  72    Diversity + BCR Strata + Disturbance + Forest Birds 73    Diversity + Conservation Status 74    Diversity + Disturbance + Conservation Status 75    Diversity + BCR Strata + Conservation Status 76    Diversity + BCR Strata + Disturbance + Conservation Status 77    Forest Birds + Conservation Status 78    Disturbance + Forest Birds + Conservation Status 79    BCR Strata + Forest Birds  + Conservation Status 80    BCR Strata + Disturbance + Forest Birds + Conservation Status 81    Diversity + Current Uncertainty 82    Diversity + Current Uncertainty + Disturbance 83    Diversity + Current Uncertainty + BCR Strata 84    Diversity + Current Uncertainty + BCR Strata + Disturbance 85    Diversity + Current Uncertainty + Forest Birds 86    Diversity + Current Uncertainty + Disturbance + Forest Birds 87    Diversity + Current Uncertainty + BCR Strata + Forest Birds 88    Diversity + Current Uncertainty + BCR Strata + Disturbance + Forest Birds 89    Diversity + Current Uncertainty + Conservation Status 90    Diversity + Current Uncertainty + Disturbance + Conservation Status 91    Diversity + Current Uncertainty + BCR Strata + Conservation Status 92    Diversity + Current Uncertainty + BCR Strata + Disturbance + Conservation Status 93    Diversity + Current Uncertainty + Forest Birds + Conservation Status 94    Diversity + Current Uncertainty + Disturbance + Forest Birds + Conservation Status 95    Diversity + Current Uncertainty + BCR Strata + Forest Birds + Conservation Status 96    Diversity + Current Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status 97    Diversity + Current/Future Uncertainty 98    Diversity + Current/Future Uncertainty + Disturbance 99    Diversity + Current/Future Uncertainty + BCR Strata 100    Diversity + Current/Future Uncertainty + BCR Strata + Disturbance 101    Diversity + Current/Future Uncertainty + Forest Birds 102    Diversity + Current/Future Uncertainty + Disturbance + Forest Birds 103    Diversity + Current/Future Uncertainty + BCR Strata + Forest Birds 104    Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds 105    Diversity + Current/Future Uncertainty + Conservation Status 106    Diversity + Current/Future Uncertainty + Disturbance + Conservation Status 107    Diversity + Current/Future Uncertainty + BCR Strata + Conservation Status 108    Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Conservation Status 109    Diversity + Current/Future Uncertainty + Forest Birds + Conservation Status 110    Diversity + Current/Future Uncertainty + Disturbance + Forest Birds + Conservation Status 111    Diversity + Current/Future Uncertainty + BCR Strata + Forest Birds + Conservation Status 112    Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status 113    Diversity + Future Uncertainty 114    Diversity + Future Uncertainty + Disturbance 115    Diversity + Future Uncertainty + BCR Strata 116    Diversity + Future Uncertainty + BCR Strata + Disturbance 117    Diversity + Future Uncertainty + Forest Birds 118    Diversity + Future Uncertainty + Disturbance + Forest Birds 119    Diversity + Future Uncertainty + BCR Strata + Forest Birds 120    Diversity + Future Uncertainty + BCR Strata + Disturbance + Forest Birds 121    Diversity + Future Uncertainty + Conservation Status 122    Diversity + Future Uncertainty + Disturbance + Conservation Status 123    Diversity + Future Uncertainty + BCR Strata + Conservation Status 124    Diversity + Future Uncertainty + BCR Strata + Disturbance + Conservation Status 125    Diversity + Future Uncertainty + Forest Birds + Conservation Status 126    Diversity + Future Uncertainty + Disturbance + Forest Birds + Conservation Status 127    Diversity + Future Uncertainty + BCR Strata + Forest Birds + Conservation Status 128    Diversity + Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status Projection information ------------------- "+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs" ------------------- Projection    LAMBERT Spheroid      GRS80 Units         METERS Zunits        NO Xshift        0.0 Yshift        0.0 Parameters     49  0  0.0 /* 1st standard parallel 77  0  0.0 /* 2nd standard parallel -95  0  0.0 /* central meridian 0  0  0.0 /* latitude of projection's origin 0.0 /* false easting (meters) 0.0 /* false northing (meters)

opencc-by-4.0Sep 2017View details →

Aggregated Spatial Data by province GPKG Format | Biodiversity Publication Bias Compromises Setting Conservation Priorities

Result of running https://github.com/raffael-hickisch/provincer

opennotspecifiedMay 2024View details →

Figure 4 in Bats (Mammalia: Chiroptera) from two priority areas for biodiversity conservation in the Brazilian Amazon and range extension for Carollia benkeithi (Phyllostomidae)

Figure 4: Female specimen of Micronycteris sp. (MPEG 37789) registered in the Mamirauá SDR. (A) Dorsal (left) and ventral (right) fur views; (B) two upper dorsal views of the head showing the separation of the ear band (arrows). Scale bars: 10 mm.

opennotspecifiedMay 2024View details →

Figure 1 in Bats (Mammalia: Chiroptera) from two priority areas for biodiversity conservation in the Brazilian Amazon and range extension for Carollia benkeithi (Phyllostomidae)

Figure 1: Map of South America showing the Mamirauá and Amanã Sustainable Development Reserves in Amazonas state, Brazil.