Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias

Abstract

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.

Differential effects of climate and species interactions on range limits at a hybrid zone: potential direct and indirect impacts of climate change

“…2011; Fourcade et al. 2014). Even after taking these measures, completely removing all sources of bias from citizen science databases is difficult.…”

Niche overlap of mountain hare subspecies and the vulnerability of their ranges to invasion by the European hare; the (bad) luck of the Irish

“…Furthermore, data exhibited considerable sample bias (Yackulic et al 2013), with large numbers of records occurring around urban centres, particularly in the UK and Sweden. There are a number of methods available for accounting for sample bias (see Fourcade et al 2014), including the utilisation of target background points (Phillips et al 2009) or bias grids (Elith et al 2010). Targetbackgrounds are defined as background points drawn from occurrences of a focal class (e.g.…”

“…10,000 background data points (i.e. pseudo-absences) were generated randomly within the range of each individual (sub-)species, analogous to the Restricted Background approach detailed in Fourcade et al (2014).…”

Modeling the distribution of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann, 1824) (Diptera, Tephritidae) in Turkey and its range expansion in Black Sea Region

“…In order to correct this sampling bias, a Gaussian kernel density of presence records was created by a sampling bias distance of two decimal degree (Elith et al, 2010;Fourcade et al, 2014). (Moss et al, 2008).…”