Browsing by Author "Secondi, Jean"

The Red List classification of IUCN has become one of the most important evaluations of threats that affect biodiversity at the species level. However, many estimations of species range, one essential factor in the Red List classification, are derived from expert-based assessments that sometimes lack empirical evidence. Our study focused on the corncrake (Crex crex), a grassland Palaearctic bird whose conservation status has been revised recently following some new assessments of range and population size. However, the amount of data that form the basis of this reclassification appears weak compared to the large area involved. We used a method of species distribution modelling (MAXENT) to predict the corncrake range and confronted it to the expert-based map. We resolved the huge geographic bias in the distribution of presence points by using a relevant method of sampling bias correction. We found a rather similar distribution with the IUCN estimated range, although less widespread. We also highlighted a relationship between habitat suitability computed by the model and population estimates per country when the effect of agriculture intensity is taken into account. This result supports the current expert-based estimates of corncrake distribution and emphasizes that a relevant modelling strategy should be able to predict the distribution of a species even from a biased dataset. IUCN estimates of species' ranges would certainly benefit from a model-based approach in addition to expert and field controls. (C) 2013 Elsevier Ltd. All rights reserved.

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.