Improved understanding and prediction of freshwater fish communities through the use of joint species distribution models

Abstract

Two primary goals in fisheries research are to (1) understand how habitat and environmental conditions influence the distribution of fishes across the landscape, and (2) make predictions about how fish communities will respond to environmental and anthropogenic change. In inland, freshwater ecosystems, quantitative approaches traditionally used to accomplish these goals largely ignore the effects of species interactions (competition, predation, mutualism) on shaping community structure, potentially leading to erroneous conclusions regarding habitat associations and unrealistic predictions about species distributions. Using two contrasting case studies, we highlight how joint species distribution models (JSDMs) can address the aforementioned deficiencies by simultaneously quantifying the effects of abiotic habitat variables and species dependencies. In particular, we show that conditional predictions of species occurrence from JSDMs can better predict species presence/absence compared to predictions that ignore species dependencies. JSDMs also allow for the estimation of site-specific probabilities of species co-occurrence, which can be informative for generating hypotheses about species interactions. JSDMs provide a flexible framework that can be used to address a variety of questions in fisheries science and management.

Uncovering patterns of freshwater positive interactions using meta‐analysis: Identifying the roles of common participants, invasive species and environmental context

“…Perhaps, invasive facilitators occur in high densities, have traits that lead to disproportionately large positive effects, or play a strong role in already‐degraded, stressed ecosystems where beneficiaries have more opportunity to respond positively (MacDougall and Turkington, 2005; Ramus et al ., 2017). A more explicit identification of invasive facilitator traits may inform species distribution models that account for positive biotic interactions (Maestre et al ., 2009; Kissling et al ., 2012; Tikhonov et al ., 2017; Wagner et al ., 2020). These traits may be particularly tied to habitat modification, the mechanism showing the strongest positive response in our data set (Crooks, 2002).…”

It’s Complicated and It Depends: A Review of the Effects of Ecosystem Changes on Walleye and Yellow Perch Populations in North America

“…Multispecies models that account for species dependencies are effective at quantifying responses of multiple species to environmental change simultaneously (Clark et al. 2014; Tikhonov et al 2017; Wagner et al 2020). Nonlinear models, such as generalized additive models, are useful for modeling the frequently nonlinear relationships we observe in fish responses to environmental change (Jacobson and Anderson 2007; Jacobson et al 2017; Pedersen et al.…”

“…Hierarchical Bayesian techniques are particularly well suited for ecological questions because they account for multiple sources of uncertainty and can identify cross-scale interactions (Clark 2005;Wagner et al 2007;Soranno et al 2014). Multispecies models that account for species dependencies are effective at quantifying responses of multiple species to environmental change simultaneously (Clark et al 2014;Tikhonov et al 2017;Wagner et al 2020). Nonlinear models, such as generalized additive models, are useful for modeling the frequently nonlinear relationships we observe in fish responses to environmental change (Jacobson and Anderson 2007;Jacobson et al 2017;Pedersen et al 2017).…”

The shadow model: how and why small choices in spatially explicit species distribution models affect predictions

“…Furthermore, as it is becoming clear that fisheries portfolios (diversification of sampling gear, species targeted, seasons fished) are a key path to maintaining the profitability of fishers in response to environmental variation ( e.g ., Anderson et al, 2017 ), additional analyses are needed to assess effects of shifting local availability of fishes in space and time ( e.g ., Selden et al, 2020 ). Multi-species distribution models: Species distribution models typically do not explicitly model species interactions, but these relationships greatly shape species distributions ( Pendleton et al, 2012 ; Peoples, Blanc & Frimpong, 2015 ; Wagner et al, 2020 ). While joint species distribution models ( Thorson & Barnett, 2017 ; Ovaskainen & Abrego, 2020 ; Tikhonov et al, 2020 ; Wagner et al, 2020 ) can help address this critical methods gap, many current implementations provide only phenomenological representation of associations among species.…”

“… Multi-species distribution models: Species distribution models typically do not explicitly model species interactions, but these relationships greatly shape species distributions ( Pendleton et al, 2012 ; Peoples, Blanc & Frimpong, 2015 ; Wagner et al, 2020 ). While joint species distribution models ( Thorson & Barnett, 2017 ; Ovaskainen & Abrego, 2020 ; Tikhonov et al, 2020 ; Wagner et al, 2020 ) can help address this critical methods gap, many current implementations provide only phenomenological representation of associations among species. Thus, there is a particular need for development of mechanistic multi-species SDMs.…”