Šiaulys, A., Bučas, M.
Species distribution modelling of benthic invertebrates in
the south-eastern Baltic Sea
The distribution of benthic invertebrates is one of the key
parameters for the marine spatial planning and management, however
traditionally the data on benthic invertebrates are based on point sampling.
Recently statistical methods of predictive modelling are used to create maps of
species distribution, nevertheless, no comparative analysis of different
modelling methods has been yet performed in the Baltic Sea region. In this
study the occurrence and biomass distribution of 23 benthic species in the
southeastern Baltic Sea were modelled. A comparison of the following predictive
modelling methods was performed: random forests (RF), generalized additive
models (GAM), multivariate adaptive regression splines (MARS) and maximum
entropy (MaxEnt). In order to assess the consistency of the methods, 100
iterations with different train/test datasets were made for each of them.
Random forests achieved the highest predictive performance for both species
occurrence and biomass distribution models; also it was the most consistent for
different iterations. Predictive performance of GAMs and MARS followed RF,
whereas MaxEnt accurately predicted occurrence only for the species with a
relatively low distribution range.