Integrating ecological theories and traits in process‐based modeling of macroinvertebrate community dynamics in streams

Abstract

Predicting the composition and dynamics of communities is a challenging but useful task to efficiently support ecosystem management. Community ecology has developed a number of promising theories, including food webs, metabolic theory, ecological stoichiometry, and environmental filtering. Their joint implementation in a mechanistic modeling framework should help us to bring community ecology to a new level by improving its predictive abilities. One of the challenges lies in the proper consideration of model uncertainty. In this paper, we contribute to this challenging task by modeling the temporal dynamics of macroinvertebrate communities in a stream subjected to hydropeaking in Switzerland. To this end, we extended the mechanistic model Streambugs regarding flood-induced drift processes and the use of trait information to define performance filters. Model predictions without any calibration were in the right order of magnitude but did not reflect the dynamics of most of the invertebrate taxa well. Bayesian inference drastically improved the model fit. It revealed that a large share of total model output uncertainty can be attributed to observation errors, which exceeded model parameter uncertainty. Observed and simulated community-aggregated traits helped to identify and understand model deficits. The combination of different ecological theories and trait information in a single mechanistic modeling framework combined with Bayesian inference can thus help to predict responses of communities to environmental changes, which can support ecosystem management.

Chemical Mixtures and Multiple Stressors: Same but Different?

“…A few ecotoxicological models for communities and food webs have been developed, as reviewed by Larras et al (2022). Promising approaches that consider chemicals are the Streambugs model simulating populations of freshwater invertebrates in streams (Kattwinkel et al, 2016; Mondy & Schuwirth, 2017; Schuwirth & Reichert, 2013), the AQUATOX model simulating lake food webs (Park et al, 2008), and the ALMaSS modeling framework that has been used to simulate several terrestrial populations, including birds, invertebrates, and vertebrates (Sibly et al, 2009; Topping & Lagisz, 2012; Topping & Weyman, 2018). These models currently do not consider chemical mixtures, but their incorporation should require a minor effort compared with the general model development.…”

Three perspectives on the prediction of chemical effects in ecosystems

“…However, this remains a common challenge in community and ecosystem models (with some exceptions, e.g. Streambugs) (Mondy & Schuwirth, 2017; Schuwirth et al, 2015), which are required to extend chemical effect prediction to the community or ecosystem level.…”

“…Most of these models derive from classical ecological models and consider individuals as the smallest unit, thereby ignoring suborganismal processes related to the MoA (Accolla et al, 2020; Rohr et al, 2016). Notwithstanding, process‐based models have successfully been used to quantitatively predict the responses of populations, communities and food webs to chemicals (Kattwinkel et al, 2016; Lei et al, 2008; Mondy & Schuwirth, 2017; Topping et al, 2003). The main challenge for process‐based modelling remains model validation and the spatially explicit modelling of communities and food webs, which still faces conceptual, practical and partly computational constraints.…”

When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species?

“…In particular, macroinvertebrates have evolved distinct adaptations to flow conditions and hence are affected by their changes (Domisch et al, ). These dependencies on flow have been used frequently to assess the occurrence (Pyne & Poff, ) and diversity (Poff & Zimmerman, ) of riverine species, such as fish (O'Keeffe et al, ), benthic invertebrates (Armanini, Horrigan, Monk, Peters, & Baird, ), or phytoplankton (Qu, Wu, Guse, & Fohrer, ), using microcosm experiments (Ceola et al, ), statistical models (Kakouei et al, ), or process‐based models (Mondy & Schuwirth, ). In the absence of direct measurement data or for scenario assessments, modelled streamflow is often used as a data basis for such analysis.…”

“…, using microcosm experiments (Ceola et al, 2013), statistical models (Kakouei et al, 2018), or process-based models (Mondy & Schuwirth, 2017). In the absence of direct measurement data or for scenario assessments, modelled streamflow is often used as a data basis for such analysis.…”