Large-scale distribution analysis of Antarctic echinoids using ecological niche modelling

Abstract

Understanding the factors that determine the distribution of taxa at various spatial scales is a crucial challenge in the context of global climate change. This holds particularly true for polar marine biota that are composed of both highly adapted and vulnerable faunas. We analysed the distribution of 2 Antarctic echinoid species, Sterechinus antarcticus and S. neumayeri, at the scale of the entire Southern Ocean using 2 niche modelling procedures. The performance of distribution models was tested with regard to the known ecology of the species. The respective contributions of environmental parameters are discussed along with the putative roles played by biotic interactions and biogeographic processes. Depth was the parameter that contributed most to both distribution models, whereas sea ice coverage and sea surface temperature had significant contributions for S. neumayeri only. Suitability maps of the 2 species were mostly similar, with a few notable differences. The Campbell Plateau and Tasmania were predicted as suitable areas for S. antarcticus only, while S. neumayeri was restricted to the south of the Ant arctic Polar Front. However, numerous sampling data attest that S. antarcticus is absent from the Campbell Plateau and from Tasmania. Different hypotheses are formulated to explain the mismatch between observed and modelled distribution data. They stress the putative roles played by both oceanographic barriers to dispersal (Antarctic Polar Front), biotic factors (species exclusion patterns) and biogeographic processes (ongoing dispersal).KEY WORDS: Habitat suitability map · Sterechinus · Echinoidea · GARP · Maxent · Southern OceanResale or republication not permitted without written consent of the publisher

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

“…6 Partial dependence plots for selected predictor variables for Random Forest prediction of the probability of the presence. The partial dependence is the dependence of the probability of the presence on one predictor variable after averaging out the effects of the other predictor variables in the model has studied the species distribution of marine benthic fauna (Gogina et al, 2010;Reiss et al, 2011) and with studies examining the ecological niche of sea urchins (Jacob et al, 2003;González-Irusta et al, 2012;Pierrat et al, 2012). Depth serves more as a proxy for several other variables-temperature, pressure, light intensity, near-bottom oxygen, salinity, food availability or competitor/predator occurrence -than a real environmental variable (Harris & Whiteway, 2009).…”

“…2). These variables were selected for their possible relevance to the habitat suitability of echinoids species and because they have been frequently used in similar studies (Galparsoro et al, 2009;Monk et al, 2010;Reiss et al, 2011;Pierrat et al, 2012). Unfortunately, the inclusion of oceanographic variables was not possible given the relatively high cell resolution used (50 m 2 ) and the relatively small area studied (2,723 km 2 ).…”

“…Comparing both approaches in marine ecosystems is relevant since the use of presence-only models in the marine environment has increased markedly during the last few years (e.g., Bryan & Metaxas, 2007;Galparsoro et al 2009;Monk et al, 2010;Hermosilla et al, 2011;Reiss et al, 2011;Pierrat et al, 2012;Ross & Howell, 2013;Martín-García et al, 2013;García-Alegre et al, in press) although the reasons behind choosing this type of method and the consequences of not including the absences are not always clear (Dorazio, 2012;Monk, 2013;Yackulic et al, 2013).…”

Echinoids of the Kerguelen Plateau – occurrence data and environmental setting for past, present, and future species distribution modelling

“…The dataset was modified after Pierrat et al (2012) and Saucède et al (2015a). Specimens from recent cruises (POKER II and PROTEKER) were identified at species level and added to the dataset.…”

“…Identifications and taxonomic accuracies are based on Anderson (2009), Anderson (2012), David et al (2005), Kroh and Smith (2010), Pierrat et al (2012), and Saucède et al (2015a).…”