Mapping Species Distributions With MAXENT Using A Geographically Biased Sample Of Presence Data: A Performance Assessment Of Methods For Correcting Sampling Bias. - Info and Reading Options

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28PLoS%20ONE%29" rel="nofollow">PLoS ONE</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28PLoS%20ONE%29%20AND%20volume%3A%289%29" rel="nofollow">volume 9</a>.<h2>Abstract</h2>MAXENT is now a common species distribution modeling (SDM) tool used by conservation practitioners for predicting the distribution of a species from a set of records and environmental predictors. However, datasets of species occurrence used to train the model are often biased in the geographical space because of unequal sampling effort across the study area. This bias may be a source of strong inaccuracy in the resulting model and could lead to incorrect predictions. Although a number of sampling bias correction methods have been proposed, there is no consensual guideline to account for it. We compared here the performance of five methods of bias correction on three datasets of species occurrence: one “virtual” derived from a land cover map, and two actual datasets for a turtle (Chrysemys picta) and a salamander (Plethodon cylindraceus). We subjected these datasets to four types of sampling biases corresponding to potential types of empirical biases. We applied five correction methods to the biased samples and compared the outputs of distribution models to unbiased datasets to assess the overall correction performance of each method. The results revealed that the ability of methods to correct the initial sampling bias varied greatly depending on bias type, bias intensity and species. However, the simple systematic sampling of records consistently ranked among the best performing across the range of conditions tested, whereas other methods performed more poorly in most cases. The strong effect of initial conditions on correction performance highlights the need for further research to develop a step-by-step guideline to account for sampling bias. However, this method seems to be the most efficient in correcting sampling bias and should be advised in most cases.

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