Species distribution modelling of marine benthos: a North Sea case study

Abstract

Species distribution models (SDMs) were applied to predict the distribution of benthic species in the North Sea. An understanding of species distribution patterns is essential to gain insight into ecological processes in marine ecosystems and to guide ecosystem management strategies. Therefore, we compared 9 different SDM methods, including GLM, GBM, FDA, SVM, RF, MAXENT, BIOCLIM, GARP and MARS, by using 10 environmental variables to model the distribution of 20 marine benthic species. Most of the models showed good or very good performance in terms of predictive power and accuracy, with highest mean area under the curve (AUC) values of 0.845 and 0.840, obtained for the MAXENT and GBM models, respectively. The poorest performance was shown by the BIOCLIM model, which had a mean AUC of 0.708. Nevertheless, the mapped distribution patterns varied remarkably depending on the model used, with up to 32.5% differences in predictions between models. For species with a narrow distribution range, the models showed a better performance based on the AUC than for species with a broad distribution range, which can most likely be attributed to the restricted spatial scale and the model evaluation procedure. Of the environmental variables, bottom water temperature and depth had the greatest effect on the distribution of 14 benthic species, based on MAXENT results. We examine the potential utility of this strategy for predicting future distribution of benthic species in response to climate change.

Predicting the Composition of Polychaete Assemblages in the Aegean Coast of Turkey

“…A number of environmental parameters with well established relationships with the benthos, namely hydrodynamic variables, such as current speed and wave orbital velocity (Jenness and Duineveld, 1985;Wright et al, 1987Wright et al, , 1997Hall et al, 1994) and sediment characteristics, particularly granulometric composition and organic carbon content (Snelgrove and Butman, 1994;Degraer et al, 1999;Ellingsen, 2002;Van Hoey et al, 2004;Çinar et al, 2012, 2015 were not addressed in this study due to the lack of available data layers of sufficient resolution and information. Even though, at the scale studied here, the sedimentary environment may be of lesser importance for predicting distributions due to its small scale variability (Reiss et al, 2011), missing explanatory variables and the inherent uncertainty in the existing predictors have undoubtedly affected the uncertainty in predictions and subsequent model performance.…”

Modeling the coastal ecosystem complex: present situation and challenges

“…These are generally statistical models that are constructed using observed habitat-organism relationships. Various statistical algorithms, including generalized linear models, generalized additive models, and maximum entropy models, are used to predict the distribution of marine organisms (Jones et al 2012;Murase et al 2009;Pittman and Brown 2011;Reiss et al 2011). Although SD models are powerful tools for evaluating the spatial distribution of organisms in diverse habitats, they are usually empirical and most of them cannot provide mechanistic predictions.…”

Looking at the soft-bottom around a coastal coral reef: the impact of terrigenous input on Polychaeta (Annelida) community

“…The focus on sediment characteristics was chosen because Abrolhos Bank is a unique reef area where sedimentation rates may be higher than the maximum of 10 mg cm -2 day -1 CASTRO, 2011;CASTRO et al, 2012), a limiting rate usually used to consider the reef healthy (ROGERS, 1990). Studies of the statistical modelling of marine communities are mainly undertaken for fishery resources (REISS et al, 2011;ROBINSON et al, 2011), but spatial and temporal patterns in benthic communities are well suited to statistical modelling too. According to ROBINSON et al (2011), there are many data on the distribution of invertebrates that, in general, disperse less than fishes do.…”