Habitat suitability modelling as a mapping tool for macrobenthic communities: An example from the Belgian part of the North Sea

Abstract

Being ecologically important and well-known, the spatial distribution pattern of the macrobenthos is often used to support an ecologically sustainable marine management. Though in many cases the macrobenthic spatial distribution is relatively well-known, this information is merely restricted to point observations at the sampling stations: although being increasingly demanded, full coverage spatial distribution maps are generally lacking. This study therefore aimed at demonstrating the usefulness of habitat suitability modelling as a full coverage mapping tool with high relevance for marine management through (1) the construction of a habitat suitability model for the soft sediment macrobenthic communities in the Belgian part of the North Sea (BPNS) and (2) predicting the full coverage spatial distribution of macrobenthic communities within the BPNS. The BPNS was selected as a case study area because of the high data availability on both macrobenthos and environmental characteristics. Discriminant function analysis (DFA) objectively selected median grain size and sediment mud content and omitted bathymetry, slope and distance to the coast to represent the most important environmental variables determining the macrobenthic community distribution. The consequent crossvalidated, empirical habitat suitability model, using both median grain size and mud content, showed an a posteriori average correctly classified instances (CCI) of 79% (community-dependent CCI ranging from 72% to 86%) and a Cohen's kappa of 0.71, pointing towards a very good agreement between model predictions and observations. The application of the habitat suitability model on the full coverage maps of median grain size and sediment mud content, taken from literature, allowed to reliably assess the distribution of the macrobenthic communities within 96.3% of the 53,297 BPNS grid cells with a resolution of 250 m. Next to its applicability to the BPNS, the model is further anticipated to potentially perform well in the full Southern Bight of the North Sea: testing is advised here. Since the habitat suitability is considered far more stable through time compared to the permanently fluctuating macrobenthic communities, information on the habitat suitability of an area is considered highly important for a scientifically sound marine management.

Prediction of benthic community structure from environmental variables in a soft-sediment tidal basin (North Sea)

“…According to Zimmermann and Kienast (1999), this comparison should be made using a model which also used benthic communities as a response variable, as statistical model fitting is easier for communities than for individual species due to the communities' more uniform response to environmental gradients. From threefold cross-validation tests, Degraer et al (2008) obtained a j TEST -value of about 0.7 (4 macrobenthic communities, Belgian continental shelf, 364 samples). Compared to this model performance, the present study's j TEST of 0.49 for the combination ''A1/H ?…”

“…The benthos data sampled in the years 2001, 2002 and 2003 were combined into a single dataset. The justification for this was shown by a threefold cross-validation test as presented in Degraer et al (2008).…”

“…From 1985 onwards (Ferrier and Guisan 2006), studies focused more on the prediction of species or biotic communities. Three examples for the application of predictive habitat modeling are: (a) to generate full coverage spatial distributions of species or communities from point data (e.g., Degraer et al 2008). The project ''Mapping European Seabed Habitats (MESH)'' provided a methodological framework for marine habitat mapping (http://www.searchmesh.net).…”

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

“…Boosted regression trees Supervised Costa et al, 2014;Hewitt et al, 2015 Classification rule with unbiased interaction selection and estimation Supervised Ierodiaconou et al, 2011 Discriminant function analysis Supervised Degraer et al, 2008 Ecological niche factor analysis Supervised Tong et al, 2012;Sánchez-Carnero et al, 2016 Fuzzy k-means Unsupervised Falace et al, 2015 Generalized additive model Supervised Schmiing et al, 2013;Touria et al, 2015 Generalized Quick, unbiased, efficient tree Supervised Ierodiaconou et al, 2011;Hasan et al, 2012 Random forest Both Hasan et al, 2012;Diesing et al, 2014;Piechaud et al, 2015 Support vector machine Supervised Hasan et al, 2012 Frontiers in Marine Science | www.frontiersin.orgFIGURE 2 | Example of how different methods can produce different outcomes. The input data were bathymetric data, backscatter data, and topographic data (i.e., slope, easterness, northerness, and relative deviation from mean value) (see Lecours et al, 2016b).…”

On the parameterization of biological influences on offshore sand wave dynamics

“…In this way, we are able to couple the biological and morphological system. By combining the habitat suitability studies of Degraer et al (2008) and Baptist et al (2006) with the parameterization proposed in this study, a first insight can be given in the feedback from the change in sedimentary processes to the change in biological activity.…”