Abstract
Species distribution models (SDMs) have been used to predict potential distributions of habitats and to model the effects of environmental changes. Despite their usefulness, currently there is no standardized sampling strategy that provides suitable and sufficiently representative predictive models for littoral marine benthic habitats. Here we aim to establish the best performing and most cost-effective sample design to predict the distribution of littoral habitats in unexplored areas. We also study how environmental variability, sample size, and habitat prevalence may influence the accuracy and performance of spatial predictions. For first time, a large database of littoral habitats (16,098 points over 562,895 km of coastline) is used to build up, evaluate, and validate logistic predictive models according to a variety of sampling strategies. A regularly interspaced strategy with a sample of 20% of the coastline provided the best compromise between usefulness (in terms of sampling cost and effort) and accuracy. However, model performance was strongly depen upon habitat characteristics. The proposed sampling strategy may help to predict the presence or absence of target species or habitats thus improving extensive cartographies, detect high biodiversity areas, and, lastly, develop (the best) environmental management plans, especially in littoral environments.
Highlights
The prediction of species and habitat distributions through numerical models still represents one of the most challenging areas of work in ecology [1], especially in light of the current scenario of a rapidly changing environment
We have modeled the spatial distribution of six littoral habitats (Table 1), which were strongly and significantly influenced by environmental variables [34]
Model accuracy and performance in predicting the distribution of the six benthic littoral habitats were tested for sampling design, sample size, and habitat prevalence
Summary
The prediction of species and habitat distributions through numerical models still represents one of the most challenging areas of work in ecology [1], especially in light of the current scenario of a rapidly changing environment. Species distribution models (SDMs) and habitat distribution models (HDMs) find many applications in ecology [2, 3], including conservation and management [4], and, in conjunction with rich, high coverage data sets and simulation experiments, may help in designing efficient sampling strategies for habitat suitability modelling [5] for both terrestrial and marine areas. Littoral habitats modelling funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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