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opencc-zeroJan 2024View details →

Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach

Freshwater ecosystems are experiencing accelerating global biodiversity loss. Thus, knowing where these unique ecosystems' species richness reaches a peak can facilitate their conservation planning. By hosting more than 290 freshwater fishes, Iran is a major freshwater fish hotspot in the Middle East. Considering the accelerating rate of biodiversity loss, there is an urgent need to identify species rich areas and understanding of the mechanisms driving biodiversity distribution. In this study, we gathered distribution records of all endemic freshwater fishes of Iran (85 species) to develop their richness map and determine the most critical drivers of their richness patterns from an ecoregion approach. We performed a generalized linear model (GLM) with quasi-Poisson distribution to identify contemporary and historical determinants of endemic freshwater fish richness. We also quantified endemic fish similarity among the 15 freshwater ecoregions of Iran. Results showed that endemic freshwater fish richness is highest in the Zagros Mountains while moderate level of richness was observed between Zagros and Alborz Mountains. High, moderate and low richness of endemic freshwater fish match with Upper Tigris & Euphrates, Namak, and Kavir & Lut Deserts ecoregions respectively. Kura - South Caspian Drainages and Caspian Highlands were the most similar ecoregions and Orumiyeh was the most unique ecoregion according to endemic fish presence. Precipitation and precipitation change velocity since the Last Glacial Maximum were the most important predictors of endemic freshwater fish richness. Areas identified to have the highest species richness have high priority for the conservation of freshwater fish in Iran, therefore, should be considered in future protected areas development.

opencc-zeroNov 2021View details →

Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes

The lack of high-quality information on data-poor species can hinder efforts to inform conservation actions via spatial distribution modeling. This is particularly true for tropical birds of conservation concern, for which ecological studies and assessments of their conservation status have received limited funding. Here we use a cost- and time-efficient protocol for assessing the distribution of range-restricted taxa and to identify priority areas for their conservation based on a sequential application of Environmental Niche Models (ENMs) and Occupancy-Detection Models. This approach first uses available geographical information and niche-theory to prioritize potential study sites, which can later be surveyed to obtain high-quality presence-absence data to accurately model distributional ranges with limited resources. We apply this protocol to identify priority areas for two Neotropical birds of conservation concern endemic to the Colombian Andes: Yellow-headed Brush-finch (Atlapetes flaviceps) and Tolima Dove (Leptotila conoveri). We first fitted ENMs using spatially-filtered datasets containing all available records up to 2018. We then conducted field surveys across climatically suitable areas identified for both species, carrying out a total of 1750 counts to generate input data for the occupancy models. Overall, our results suggested more extended and more continuous distribution ranges for both species than previously reported, but also identified population strongholds that are not currently represented within the national protected areas system. Both species occupied a narrow elevational belt (~1300–2600) of the Central Andes of Colombia primarily on the slopes of the Magdalena River valley, with isolated populations in the Western and Eastern Andes; these areas have undergone some of the most marked landscape transformations in Colombia. This straightforward protocol maximizes available information and minimizes costs, while allowing for estimation of occurrence probabilities for range-restricted, data-poor taxa.

publicJan 2024View details →

Data from: mapping endemic freshwater fish richness to identify high priority areas for conservation: an ecoregion approach

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publicNov 2021View details →

Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes

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publicDec 2024View details →

Estimating the global population size and highlighting conservation priority areas for the endangered Titicaca grebe

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dryad32/100

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-zeroJan 2022View details →

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 + 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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)

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.

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.

Figure 5 in Bats (Mammalia: Chiroptera) from two priority areas for biodiversity conservation in the Brazilian Amazon and range extension for Carollia benkeithi (Phyllostomidae)

Figure 5: Skull of Micronycteris sp. (MPEG 37789, female). (A) Lateral view of the skull shows the developed maxillary premolars; (B) dorsal skull view; (C) maxillary external incisors visible to the naked eye (arrow); and (D) lateral views of mandible. Scale bar: 5 mm.

Figure 3 in Bats (Mammalia: Chiroptera) from two priority areas for biodiversity conservation in the Brazilian Amazon and range extension for Carollia benkeithi (Phyllostomidae)

Figure 3: Skull of Carollia benkeithi (MPEG 37664, female). Skull shown in (A) dorsal and (B) ventral views; (C) left lateral view of the skull, the arrow indicates the cingulum of the last maxillary premolar; and (D) left lateral mandible, the arrow indicates the pm2 greater than the m1. Scale bar: 5 mm.

Figure 2 in Bats (Mammalia: Chiroptera) from two priority areas for biodiversity conservation in the Brazilian Amazon and range extension for Carollia benkeithi (Phyllostomidae)

Figure 2: Geographic distribution of Carollia benkeithi (sensu McLellan and Koopman 2007) and new records (yellow stars), extending it ca. 590 km northwestward. Black dots are records recently published for eastern Pará state, Brazil (Lemos et al. 2020).