Scale-dependent influence of environmental variables on species distribution: a case study on five coastal benthic species in the Baltic Sea
2013 (English)In: Ecography, ISSN 0906-7590, E-ISSN 1600-0587, Vol. 36, no 3, 354-363 p.Article in journal (Refereed) Published
Statistical modelling can be used to relate biological survey data to environmental factors, thereby providing a basis for predictive mapping of species or communities. Although environmental variables vary and influence biota at different scales, models are often fitted without discussion of how scale dependency influences the results. In this study, we analysed the relative importance of environmental factors for the distribution of aquatic species as a function of extent, using data on the cover of five common benthic species (four macrophytes and one animal), from 1731 sites along the Swedish Baltic Sea coast. We modelled the cover and distribution of the five species in relation to salinity, depth, slope, wave exposure and substrate in scale steps from 25 to 1500 km, and analysed the relative contribution of the environmental variables to each species model. The average total deviance explained by the models was generally quite high, and decreased with increasing scale for all macrophyte species, while it increased for the animal, the Baltic Sea blue mussel Mytilus edulis. The relative contribution of different environmental variables changed with scale, and responses also differed between species. The average contribution of salinity increased for all species when moving from local to Baltic Sea scale, and for the Baltic Sea blue mussel it was the single most important factor at the Baltic Sea scale. The average contribution of depth decreased with increasing scale for all species. However, regardless of scale, depth was the most important environmental factor to explain the distribution of all but one of the investigated macrophyte species. This shows that fine scale predictor variables can be of major importance also for species distribution models covering large areas.
Place, publisher, year, edition, pages
2013. Vol. 36, no 3, 354-363 p.
IdentifiersURN: urn:nbn:se:su:diva-89737DOI: 10.1111/j.1600-0587.2012.07053.xISI: 000316468800011OAI: oai:DiVA.org:su-89737DiVA: diva2:620029