Assessing the accuracy of species distribution models to predict amphibian species richness patterns

Abstract

Summary 1.Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66 113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km 2 , which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.

Potential distribution of the endangered endemic lizard Liolaemus lutzae Mertens, 1938 (Liolaemidae): are there other suitable areas for a geographically restricted species?

“…On the other hand, range maps generated by distribution modelling are expected to overpredict the distributional limits of species, predicting presence where it is known to be truly absent (errors of commission). This occurs because many modelling methods are unable to evaluate absences generated by evolutionary history of species, dispersal limitations, and biotic interactions with other species Pineda and Lobo, 2009).…”

Can Species Richness Patterns Be Interpolated From a Limited Number of Well-Known Areas? Mapping Diversity Using GLM and Kriging

“…We have chosen species richness because is considered the basic parameter of biodiversity, and although its value for conservation planning is relatively limited (see e.g . Ferrier 2002), it provides a first approximation to the geographic variations of species diversity in the absence of good quality data on species distributions or composition, which is more prone to error (see Hortal et al 2007;Pineda & Lobo 2009;Aranda & Lobo 2011 for comparisons of both kinds of data). Being based in a simpler variable, it also provides a simpler way of analyzing the effects of data quality on model reliability; we assume that the results of this study can be extrapolated to ALM applications based on other aspects of biodiversity (see Hortal & Lobo 2006;Guisan & Rahbek 2011).…”

Climate and land-use changes effects on the distribution of a regional endemism: Melanophryniscus sanmartini (Amphibia, Bufonidae)

“…Aside from the use of different AOGCM and sets of climatic variables (see Zank et al, 2014), there is a key factor determining such a difference between both modelling studies. The validation procedure applied here contributes to avoid commission errors, and thus allows us to get a distribution more similar to the 'realized distribution' of the species (Pineda & Lobo, 2009). Likewise, the applied threshold (i.e.…”

“…To obtain an 'adjusted distribution', it is useful to incorporate information on the locations where the species is absent, avoiding overestimation of its geographical extent (Pineda & Lobo, 2009). Here we applied the method proposed by Pineda & Lobo (2009) to check for omission or commission errors based on wellknown cells of amphibians in Uruguay, which comprises the greater portion of M. sanmartini known distribution. We started by pre-selecting those cells with 100 or more amphibian records.…”