Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.

Conservationists are increasingly relying on distribution models to predict where species are likely to occur, especially in poorly-surveyed but biodiverse areas. Modeling is challenging in these cases because locality data necessary for model formation are often scarce and spatially imprecise. To identify methods best suited to modeling in these conditions, we compared the success of three algorithms (Maxent, Mahalanobis Typicalities and Random Forests) at predicting distributions of eight bird and eight mammal species endemic to the eastern slopes of the central Andes. We selected study species to have a range of locality sample sizes representative of the data available for endemic species of this region and also that vary in their distribution characteristics. We found that for species that are known from moderate numbers (N = 38-94) of localities, the three methods performed similarly for species with restricted distributions but Maxent and Electronic supplementary material The online version of this article (Random Forests yielded better results for species with wider distributions. For species with small numbers of sample localities (N = 5-21), Maxent produced the most consistently successful results, followed by Random Forests and then Mahalanobis Typicalities. Because evaluation statistics for models derived from few localities can be suspect due to the poor spatial representation of the evaluation data, we corroborated these results with review by scientists familiar with the species in the field. Overall, Maxent appears to be the most capable method for modeling distributions of Andean bird and mammal species because of the consistency of results in varying conditions, although the other methods have strengths in certain situations.

BackgroundThe Andes-Amazon basin of Peru and Bolivia is one of the most data-poor, biologically rich, and rapidly changing areas of the world. Conservation scientists agree that this area hosts extremely high endemism, perhaps the highest in the world, yet we know little about the geographic distributions of these species and ecosystems within country boundaries. To address this need, we have developed conservation data on endemic biodiversity (~800 species of birds, mammals, amphibians, and plants) and terrestrial ecological systems (~90; groups of vegetation communities resulting from the action of ecological processes, substrates, and/or environmental gradients) with which we conduct a fine scale conservation prioritization across the Amazon watershed of Peru and Bolivia. We modelled the geographic distributions of 435 endemic plants and all 347 endemic vertebrate species, from existing museum and herbaria specimens at a regional conservation practitioner's scale (1:250,000-1:1,000,000), based on the best available tools and geographic data. We mapped ecological systems, endemic species concentrations, and irreplaceable areas with respect to national level protected areas.ResultsWe found that sizes of endemic species distributions ranged widely (< 20 km2 to > 200,000 km2) across the study area. Bird and mammal endemic species richness was greatest within a narrow 2500-3000 m elevation band along the length of the Andes Mountains. Endemic amphibian richness was highest at 1000-1500 m elevation and concentrated in the southern half of the study area. Geographical distribution of plant endemism was highly taxon-dependent. Irreplaceable areas, defined as locations with the highest number of species with narrow ranges, overlapped slightly with areas of high endemism, yet generally exhibited unique patterns across the study area by species group. We found that many endemic species and ecological systems are lacking national-level protection; a third of endemic species have distributions completely outside of national protected areas. Protected areas cover only 20% of areas of high endemism and 20% of irreplaceable areas. Almost 40% of the 91 ecological systems are in serious need of protection (= < 2% of their ranges protected).ConclusionsWe identify for the first time, areas of high endemic species concentrations and high irreplaceability that have only been roughly indicated in the past at the continental scale. We conclude that new complementary protected areas are needed to safeguard these endemics and ecosystems. An expansion in protected areas will be challenged by geographically isolated micro-endemics, varied endemic patterns among taxa, increasing deforestation, resource extraction, and changes in climate. Relying on pre-existing collections, publically accessible datasets and tools, this working framework is exportable to other regions plagued by incomplete conservation data.

No abstract