Are drivers of microbial diatom distributions context dependent in human-impacted and pristine environments?

Abstract

Species occurrences are influenced by numerous factors whose effects may be context dependent. Thus, the magnitude of the effects and their relative importance to species distributions may vary among ecosystems due to anthropogenic stressors. To investigate context dependency in factors governing microbial bioindicators, we developed species distribution models (SDMs) for epilithic stream diatom species in human-impacted and pristine sites separately. We performed SDMs using boosted regression trees for 110 stream diatom species, which were common to both data sets, in 164 human-impacted and 164 pristine sites in Finland (covering ~1,000 km, 60° to 68° N). For each species and site group, two sets of models were conducted: climate model, comprising three climatic variables, and full model, comprising the climatic and six local environmental variables. No significant difference in model performance was found between the site groups. However, climatic variables had greater importance compared with local environmental variables in pristine sites, whereas local environmental variables had greater importance in human-impacted sites as hypothesized. Water balance and conductivity were the key variables in human-impacted sites. The relative importance of climatic and local environmental variables varied among individual species, but also between the site groups. We found a clear context dependency among the variables influencing stream diatom distributions as the most important factors varied both among species and between the site groups. In human-impacted streams, species distributions were mainly governed by water chemistry, whereas in pristine streams by climate. We suggest that climatic models may be suitable in pristine ecosystems, whereas the full models comprising both climatic and local environmental variables should be used in human-impacted ecosystems.

Keywords: boosted regression trees; climate; land use gradient; local environment; species distribution modeling; stream diatoms.