Abstract
Understanding the response of biotic systems to multiple environmental drivers is one of the major concerns in ecology. The most common approach in multiple driver research includes the classification of interactive responses into categories (antagonistic, synergistic). However, there are situations where the use of classification schemes limits our understanding or cannot be applied. Here, we introduce and explore an approach that allows us to better appreciate variability in responses to multiple drivers. We then apply it to a case, comparing effects of heatwaves on performance of a cold-adapted species and a warm-adapted competitor. The heatwaves had a negative effect on the native (but not on the exotic) species and the approach highlighted that the exotic species was less responsive to multivariate environmental variation than the native species. Overall, we show how the proposed approach can enhance our understanding of variation in responses due to different driver intensities, species, genotypes, ontogeny, life-phases or among spatial scales at any level of biological organization.
Highlights
Understanding the response of biotic systems to multiple environmental drivers is one of the major concerns in ecology
As a consequence of anthropogenic change, biotic systems must cope with multivariate alterations in natural habitats; for instance, organisms are being exposed to increased temperature combined with habitat loss[1], food limitation[2], pollutants[3], ocean acidification[4] and deoxygenation[5]
Over the past 20 years, a series of reviews have shown that the combined action of several environmental variables cannot be predicted from the additive effects of each driver acting in isolation[6,7,8]
Summary
Understanding the response of biotic systems to multiple environmental drivers is one of the major concerns in ecology. An approach to study multiple-driver responses consists of quantifying the magnitude of responses and assign them to the above categories (‘classification of responses’: thereafter called CAR) This is important to better understand how biological systems respond to both anthropogenic influence and environmental change[6,9,10,11]. There are logistical difficulties in assessing and understanding responses to multiple environmental drivers[16], and there is a need to offer mechanistic explanations for the development of predictive models[17] Another challenge in multiple-drivers research concerns evaluation of the generality of responses to climate change[18,19], i.e. the range of spatial, or temporal scale, or the biological level at which a particular response occurs[20]. We present the statespace approach and apply it to our study system
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