Comparing species distribution models: a case study of four deep sea urchin species

• Published in: Hydrobiologia Vol. 745; no. 1; pp. 43 - 57
• Main Authors: González-Irusta, José M, González-Porto, Marcos, Sarralde, Roberto, Arrese, Beatriz, Almón, Bruno, Martín-Sosa, Pablo
• Format: Journal Article
• Language: English
• Published: Cham Springer-Verlag 01-02-2015; Springer International Publishing; Springer Nature B.V
• Subjects: algorithms; Biodiversity; biogeography; Biomedical and Life Sciences; Case studies; Centrostephanus longispinus; Cidaris cidaris; Coelopleurus floridanus; Comparative studies; Deep sea; Echinoidea; Ecology; entropy; Factor analysis; Fish; Freshwater & Marine Ecology; Life Sciences; Marine; Marine ecosystems; Marine environment; Primary Research Paper; Stylocidaris affinis; Zoology; Species distribution models; Niche; Presence-only; Sea urchins
• ISSN: 0018-8158; 1573-5117
• DOI: 10.1007/s10750-014-2090-3

There is an increasing demand for biodiversity mapping to address new challenges in the management of marine ecosystems. Species distribution models are a key tool in supplying part of this information. However, the use of these models in the marine environment is still developing and the reasons for the underlying use of different methodological approaches are not always clear. In this work, we compared four different statistical techniques: the ecological niche factor analysis (ENFA), the MAXimun ENTropy algorithm (MAXENT), general additive Models (GAMs), and Random Forest. ENFA and MAXENT were applied using presence-only data whereas GAM and Random Forest used presence–absence data. As a case study, we used four deep sea urchin species: Centrostephanus longispinus, Coelopleurus floridanus, Stylocidaris affinis, and Cidaris cidaris. The distribution of the studied sea urchins showed strong bathymetric segregation. Depth was the most important variable, followed by reflectivity and slope. The correlations between the predictive outputs of the models were similar between GAM, Random Forest and MAXENT, and lower for ENFA. Models using presence/absence data showed the highest scores in the four species, significantly outperforming ENFA in most of the cases, although differences with MAXENT were significant in only one species.