Guillaumot, C.

Martin, A.

Saucède, T.

Open Marine Archive

153 
 
S04: Modelling approaches to understanding species distributions 
Modelling species distribution: influences of temporal, spatial, and 
sampling heterogeneities in data-poor areas. An example from the 
Kerguelen Plateau 
Guillaumot Charlène1, Alexis Martin2, Marc Eléaume2 and Thomas Saucède3 
1  Laboratoire de Biologie Marine CP160/15, Université Libre de Bruxelles, F. D. Roosevelt, 50, 1050 
Brussels, Belgium 
E-mail: charleneguillaumot21@gmail.com  
2  Muséum national d’Histoire naturelle, Département Systématique et Évolution, UMR ISYEB 7205, 
57 rue Cuvier, F-75231 Paris Cedex 05, France 
3  UMR 6282 Biogéosciences, Univ. Bourgogne Franche-Comté, CNRS, 6 bd Gabriel F-21000 Dijon, 
France 
 
The field of species and community distribution modelling has undergone a remarkable increase of 
interest from ecologists during the last decades. Species Distribution Models (SDM) can constitute 
seminal conservation tools and usually have three main objectives: (i) estimating species realized 
ecological niches, (ii) interpolating occurrence point data over continuous maps based on 
correlative connections with environmental descriptors, and (iii) estimating potential shifts in 
species distribution associated to past and future environmental changes. 
 
Interpolating species distribution over wide expanses of water like the Southern Ocean can 
constitute a real input to our understanding of marine life in remote and little-investigated areas. 
However, there are methodological issues caused by data heterogeneities because our knowledge of 
species distribution relies on diverse sampling campaigns and strategies. Spatial and temporal 
heterogeneities can impact the performance of modelling procedures, a critical point when 
considering the growing interest for the approach. 
 
In the present work, we tested the reliability of different modelling procedures using true 
occurrence data of four common echinoid species of the Kerguelen Plateau with contrasted 
ecological niches. We used presence-only data compiled from various sampling campaigns led 
between 1872 and 2015. We tested the impact of spatial bias, record addition, and temporal 
variation on model performances, assessed the respective prediction performances and proposed a 
procedure to correct for the different bias. In addition, potential shifts in species distributions were 
modelled according to IPCC A2 scenario for 2100; they were compared to the magnitude of present 
changes (2005-2012). 
 
This work provides a synthetic overview of the potential consequences of data heterogeneities for 
distribution modelling. It proposes a protocol to test and correct for the bias and gives some clues 
for improvement when using such procedures in conservation issues. 
  


Modelling species distribution: influences of temporal, spatial, and sampling heterogeneities in data-poor areas. An example from the Kerguelen Plateau

Modelling species distribution: influences of temporal, spatial, and sampling heterogeneities in data-poor areas. An example from the Kerguelen Plateau

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