Outcomes of biotic interactions are dependent on multiple environmental variables

Abstract

AbstractQuestionCan variation in the outcome of biotic interactions in relation to environmental severity be more accurately predicted when considering multiple stress and/or disturbance variables?LocationArctic‐alpine tundra in Kilpisjärvi, North Finland.MethodsTo test the impact of including multiple environmental variables in analyses of the outcomes of biotic interactions, we modelled reproductive effort and cover of 17 arctic‐alpine species as a function of Empetrum nigrum subsp. hermaphroditum cover, geomorphological disturbance and soil moisture with statistical interactions of the explanatory variables included. We implemented a best‐subset approach using generalized linear models (GLM) and selected the best model for each species based on Akaike's information criterion (AIC).ResultsFor the majority of species, models including multiple environmental variables were selected as best. Reproductive effort depended on one or both environmental variables for all species, and 14 species were additionally influenced by Empetrum, with the impact of Empetrum varying with abiotic conditions in all but one of those species. Moreover, the three‐way interaction of three explanatory variables was included in the best‐fit models for six species. The impact of Empetrum on species cover showed a similar pattern, with 11 species affected by Empetrum and its statistical interactions with one or both abiotic variables.ConclusionsBiotic interactions have an important role in arctic‐alpine vegetation, but to fully understand variation in their effects multiple environmental factors should be explicitly considered. In this study, the outcome of biotic interactions was frequently dependent on two abiotic variables (and occasionally additionally on their statistical interaction). Therefore, we demonstrate that studies based on only one environmental factor may cause misleading interpretations of the nature of biotic interactions in plant communities where there are multiple independent variables underlying the habitat severity gradient.