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Riverine ecosystems provide various ecosystem services. One of these services is the biological control of eutrophication by grazing macroinvertebrates.
However, riverine ecosystems are subject to numerous stressors that affect community structure, functions, and stability properties. To manage rivers in response to these stressors, a better understanding of the ecological functions underlying services is needed.
This requires consideration of local and regional processes, which requires a metacommunity approach that links local food webs through drift and dispersal. This takes into account long-distance interactions that can compensate for local effects of stressors.
Our modular model MASTIFF (Multiple Aquatic STressors In Flowing Food webs) is stage-structured, spatially explicit, and includes coupled food webs consisting of benthic resource-consumer interactions between biofilm and three competing macroinvertebrate functional types. River segments are unidirectionally connected through organismal drift and bidirectionally connected through dispersal. Climate and land use stressors along the river can be accounted for. Biocontrol of biofilm eutrophication is used as an exemplary functional indicator.
We present the model and the underlying considerations, and show in an exemplary application that explicit consideration of drift and dispersal is essential for understanding the spatiotemporal biocontrol of eutrophication.
The combination of drift and dispersal reduced eutrophication events. While dispersal events were linked to specific periods in the species life cycles and therefore had limited potential to control, drift was ubiquitous and thus responded more readily to changing habitat conditions.
This indicates that drift is an important factor for coping with stress situations.
Finally, we outline and discuss the potential and possibilities of MASTIFF as a tool for mechanistic, cross-scale analyses of multiple stressors to advance knowledge of riverine ecosystem functioning.
Understanding how variance in environmental factors affects physiological performance, population growth, and persistence is central in ecology. Despite recent interest in the effects of variance in single biological drivers, such as temperature, we have lacked a comprehensive framework for predicting how the variances and covariances between multiple environmental factors will affect physiological rates. Here, we integrate current theory on variance effects with co-limitation theory into a single unified conceptual framework that has general applicability. We show how the framework can be applied (1) to generate mathematically tractable predictions of the physiological effects of multiple fluctuating co-limiting factors, (2) to understand how each co-limiting factor contributes to these effects, and (3) to detect mechanisms such as acclimation or physiological stress when they are at play. We show that the statistical covariance of co-limiting factors, which has not been considered before, can be a strong driver of physiological performance in various ecological contexts. Our framework can provide powerful insights on how the global change-induced shifts in multiple environmental factors affect the physiological performance of organisms.
BEEHAVE offers a valuable tool for researchers to design and focus field experiments, for regulators to explore the relative importance of stressors to devise management and policy advice and for beekeepers to understand and predict varroa dynamics and effects of management interventions. We expect that scientists and stakeholders will find a variety of applications for BEEHAVE, stimulating further model development and the possible inclusion of other stressors of potential importance to honeybee colony dynamics.