%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Moua, Y.
%A Roux, Emmanuel
%A Seyler, Frédérique
%A Briolant, S.
%T Correcting the effect of sampling bias in species distribution modeling : a new method in the case of a low number of presence data
%D 2020
%L fdi:010079029
%G ENG
%J Ecological Informatics
%@ 1574-9541
%K Sampling bias ; Data scarcity ; Species distribution models ; Maxent
%K GUYANE FRANCAISE
%M ISI:000528216500015
%P art. 101086 [10 ]
%R 10.1016/j.ecoinf.2020.101086
%U https://www.documentation.ird.fr/hor/fdi:010079029
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers20-05/010079029.pdf
%V 57
%W Horizon (IRD)
%X Species distribution models that only require presence data provide potentially inaccurate results due to sampling bias and presence data scarcity. Methods have been proposed in the literature to minimize the effects of sampling bias, but without explicitly considering the issue of sample size. A new method developed to better take into account environmental biases in a context of data scarcity is proposed here. It is compared to other sampling bias correction methods primarily used in the literature by analyzing their absolute and relative impacts on model performances. Results showed that the number of presence sites is critical for selecting the applicable method. The method proposed was regularly placed in the first or second rank and tends to be more proficient than other methods in the context of presence site scarcity (<100). It tends to improve results regarding environment-based performance indexes. Eventually, its parametrization, requiring background knowledge on species bio-ecology, appears to be more robust and convenient to perform than those based on geographical criteria.
%$ 052 ; 021 ; 122 ; 020