Confronting expert-based and modelled distributions for species with uncertain conservation status: A case study from the corncrake (Crex crex)

Abstract

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.

Keywords

IUCN red list; Species distribution modelling; SDM; Maxent; Expert knowledge; Grassland birds

Published in

Biological Conservation
2013, Volume: 167, pages: 161-171
Publisher: ELSEVIER SCI LTD

UKÄ Subject classification

Ecology


Publication identifier

DOI: https://doi.org/10.1016/j.biocon.2013.08.009

Permanent link to this page (URI)

https://res.slu.se/id/publ/85326