Abstract
Predicting species range shifts in response to climatic change is a central aspect of global change studies. An ever growing number of species have been modeled using a variety of species distribution models (SDMs). However, quantitative studies of the characteristics of range shifts are rare, predictions of range changes are hard to interpret, analyze and summarize, and comparisons between the various models are difficult to make when the number of species modeled is large. Maxent was used to model the distribution of 12 Abies spp. in China under current and possible future climate conditions. Two fuzzy set defined indices, range increment index (I) and range overlapping index (O), were used to quantify range shifts of the chosen species. Correlation analyses were used to test the relationships between these indices and species distribution characteristics. Our results show that Abies spp. range increments (I) were highly correlated with longitude, latitude, and mean roughness of their current distributions. Species overlapping (O) was moderately, or not, correlated with these parameters. Neither range increments nor overlapping showed any correlation with species prevalence. These fuzzy sets defined indices provide ideal measures of species range shifts because they are stable and threshold-free. They are reliable indices that allow large numbers of species to be described, modeled, and compared on a variety of taxonomic levels.
Highlights
IntroductionThe increasing availability of species distribution models (SDMs) [1,2,3,4,5], open-access high resolution climate data sets [3,6,7,8,9], digital species distribution maps, and digital voucher specimen data sets [10,11,12,13,14] (BGIF http://www.gbif.org; TROPICOS http://www.tropicos.org), make it possible to model species distribution and to predict shifts in species’ ranges in response to possible future changes in climate
Performances in model fitting The area under the curve (AUC) of the receiver operating characteristic, maximum kappa, and maximum true skill statistic are the three most widely used indices to indicate the performance of a model for current climate [44]
Why does the threshold problem matter? As mentioned in the introduction, it is imperative to apply standard indices so that: 1) large numbers of data-rich predicted maps can be adequately summarized; 2) inter-species comparisons can be used to evaluate the overall influence of climate change on biodiversity; and 3) the methodological differences in evaluating model performances, from goodness-of-fit to direct measurement methods, can be examined
Summary
The increasing availability of species distribution models (SDMs) [1,2,3,4,5], open-access high resolution climate data sets [3,6,7,8,9], digital species distribution maps, and digital voucher specimen data sets [10,11,12,13,14] (BGIF http://www.gbif.org; TROPICOS http://www.tropicos.org), make it possible to model species distribution and to predict shifts in species’ ranges in response to possible future changes in climate. Many such studies have been carried out [15,16,17,18,19,20] and many more can be expected. Studies of changes in biodiversity (either of whole biota or within a certain taxonomic group) will clearly benefit from these methodological advances [21,22,23]. Interpretation of the large quantities of data and predictions likely to be produced will certainly present a challenge. Standardized methods will be required to quantify and compare predicted changes in species distribution. Conventional mapping and visual inspection methods will be inadequate to cope with the amount of data generated, and quantitative indices will be required
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