# Metacommunity models and empirical data reveal asymmetric network-constrained fish dispersal

Paul Savary

<figure><img src="image_1.png" /></figure>

## To cite this version:

Paul Savary. Metacommunity models and empirical data reveal asymmetric network-constrained fish dispersal. Journal of Animal Ecology, 2025, 10.1111/1365-2656.70050. hal-05079836

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Submitted on 22 May 2025

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# Metacommunity models and empirical data reveal asymmetric network-constrained fish dispersal

Savary, Paul*¹

¹Université Marie et Louis Pasteur, CNRS, ThéMA, 32 rue Mégevand F-25000 Besançon, France

May 19, 2025

**Abstract**

Borthagaray, A. I., Teixeira de Mello, F., & Arim, M. (2025). Inferring river-scape dispersal processes from fish biodiversity patterns. *Journal of Animal Ecology*. https://doi.org/10.1111/1365-2656.70033. Dispersal is one of the main determinants of biodiversity. Previous studies have pointed out that the diversity and composition of communities depend not only on dispersal rates, but also on the complex spatial networks formed by dispersal paths. However, going beyond this observation to infer a spatially-explicit dispersal network for a particular system remains a challenge. Borthagaray et al. (2025) combined metacommunity models and empirical data from 58 fish communities to identify the most likely dispersal network in the large Negro River basin (Uruguay). They assessed the empirical support for alternative combinations of dispersal parameters (sources, (a)symmetry, distance decay, barriers). The best-supported combinations show that fish disperse asymmetrically along river branches; that is, dispersal is stronger downstream than upstream. Yet, the outlet remains an important source of upstream dispersers, even at large distances. Though they could not find evidence of any barrier effects of dams, this might be due to lagged responses to the induced fragmentation. By making the most of metacommunity models and empirical data, this study showcases an elegant way to infer complex dispersal patterns from empirical data, which could be advantageously replicated in other systems.

**Keywords:** dispersal; river networks, metacommunity ecology; fish; simulations; connectivity

<footnote>*Corresponding author: psavary@protonmail.com</footnote>
Ecologists seek explanations for the biodiversity patterns they observe. Over the past decades, metacommunity approaches have advanced our understanding by stressing the complementarity of conceptual models that were previously considered as mutually exclusive (Leibold & Chase, 2018; Thompson et al., 2020). However, reconciling niche definitions, coexistence mechanisms, dispersal, and stochasticity in a comprehensive framework leaves us with a difficult task: how to identify the right combination of processes generating a given empirical pattern? The objective is no longer to test whether a given conceptual model holds or not when confronted with data, but is instead to estimate the relative influence of ecological processes and, when possible, to infer their parameters. The main difficulty thus resides in estimating and understanding the nearly infinite number of parameters and processes combinations. In some systems, however, demographic processes alone (i.e., dispersal and stochastic ecological drift) can be the prevailing drivers of communities' diversity and composition. Riverine ecosystems constitute such systems due to the dendritic (tree-like) structure of river networks constraining dispersal (Altermatt & Fronhofer, 2018; Henriques-Silva et al., 2019; Tonkin et al., 2018; White & Rashleigh, 2012). The strong imprint left by dispersal on diversity patterns is an opportunity for ecologists to infer specific parameters of the dispersal of organisms along river networks. Do organisms disperse equally upstream and downstream? Does the outlet act as a significant source of dispersers? Are there barriers to dispersal along the way? Combining theoretical and empirical approaches to answer these apparently simple questions remains an active area of research (Grainger & Gilbert, 2016; Logue et al., 2011; McIntire & Fajardo, 2009; Savary et al., 2024).

Borthagaray et al. (2025) addressed this challenge in an elegant way, unravelling the complex spatial pattern of fish dispersal in the Negro River basin (Uruguay). Their method relied on metacommunity simulation models on the one hand, and empirical data, on the other. To simulate fish dispersal, they represented the Negro River basin as a network with 2,193 nodes, each corresponding to the confluence of two river segments and to the spatial entity occupied by a fish community. In the simulations, community dynamics were driven by spatial patterns of dispersal and by ecological drift (determined by population sizes). Dispersal patterns varied among simulations according to combinations of a few dispersal parameters: (i) the presence of "sources" of dispersers ("external" species pool or outlet), (ii) the spatial constraints of the river network structure, (iii) the (a)symmetric nature and distance decay of upstream/downstream dispersal, and (iv) the potential barrier effect of the three main dams of this river basin (Figure 1). Population sizes were either constant or reflected the empirically observed upstream decline. Borthagaray et al. (2025) then used the species richness of the communities simulated under these dispersal scenarios to model the empirical species richness of 58 sampled communities, using Poisson GLM. The AIC-based model comparisons revealed a much stronger support for a scenario including network constraints on dispersal, stronger downstream than upstream dispersal, and a significant influence of dispersers' inflow from the outlet, up to large distances. Scenarios considering a barrier effect of the three dams of the river basin were not more supported than others. Finally, including a gradient in the stochastic effect of ecological drift, driven by the observed upstream decline in population sizes, was needed to match the observed patterns of $\alpha$ as well as $\beta$ diversities.

The work of Borthagaray et al. (2025) follows a tradition of network-based research about dispersal in the metacommunity context (Campbell Grant et al., 2007; Economo & Keitt, 2008; Gilarranz et al., 2015; Layeghifard et al., 2015; Suzuki & Economo, 2021). It showcases an effective way to combine theory-grounded hypotheses, simulation models, and empirical data to advance our understanding of dispersal. Indeed, most previous studies on the role of dispersal networks were based on either simulations (e.g., Zhang et al. (2021)), experimental microcosms (e.g., Carrara et al. (2012)), or observations from aquatic ecosystems (e.g., Sarremejane et al. (2017)), but rarely combined these approaches. By doing so, the authors faced two common limitations. First, their empirical data included species lists from only 58 communities of this large basin. Second, the infinite ways in which fish can disperse had to be summarized into a limited set of realistic scenarios. In other words, for their inferential approach to remain powerful, they had to strike the right balance between realism, complexity, and number of alternative dispersal scenarios. Although they tested 55,296 combinations of dispersal parameters, the
risk of overfitting, and consequently overinterpreting the results, was limited by the use of systematic combinations of parameters and of a weight of evidence framework. In the end, the best model alone had a 40 % probability of representing the most likely dispersal pattern among all scenarios, which was reinforced by the close match between the observed and simulated values of α and β diversities. Making inference uncertainty explicit is crucial for future uses of this approach, especially because there are cases in which scenarios will be much less contrasted. Analysing the relative variability of results among simulation runs and among scenarios can also indicate whether the respective support for candidate scenarios should be confidently interpreted in ecological terms, as was the case here. When this condition is not met, limiting the number of scenarios or increasing the sampling effort are potential solutions.

The adoption by Borthagaray et al. (2025) of a neutral metacommunity framework was probably essential to narrow down the complexity of the processes driving metacommunity dynamics and focus on dispersal and stochastic ecological drift. Dispersal scenarios were converted into pairwise proportions of dispersers originating from neighbour communities, the outlet, or an external species pool. These parameters were seamlessly integrated in time-efficient coalescent and lottery simulation models. One could wonder whether more complex and non-neutral models (e.g., including inter-specific differences in environmental optima and dispersal, or biotic interactions) would still have made this inference possible. Indeed, this study exemplifies once again the heuristic value of neutral models for revealing the strong influence of demographic processes, but also demonstrates how their assumptions can reduce the complexity of metacommunity dynamics to enable precise inferences. Using neutral simulations, the authors also stressed the substantial influence of stochastic effects due to variations in population sizes. This is a good reminder that although the precise outcome of stochastic variations cannot be predicted, the drivers of their magnitude (here population sizes) can often be accounted for in models.

In sum, adopting a neutral and rather parsimonious approach to break down the complex combination of processes explaining a single empirical pattern can be very efficient, as shown here by Borthagaray et al. (2025). Not only does this study provide crucial information regarding fish dispersal in the Negro River basin, but it also paves the way to several avenues of research. First, the surprising lack of evidence for a barrier effect of dams raises questions. Would this effect be detected in the future, after the biotic relaxation period suggested by the authors? Are flooding and passive fish dispersal events sufficient to reverse the fragmentation effects of dams? Second, although neutral approaches are based on the conservative assumption of no inter-specific differences, the parameter uncertainty of corresponding models paradoxically stress aspects that would be better understood considering these differences. For instance, the similar support for models including barrier effects or not could stem from different responses to these barriers across species. We can also wonder whether the support for models in which the outlet is a source of dispersers would remain stable if we consider the fact that some species are recent invaders of these ecosystems, and might originate from the outlet. Finally, this study provides an example to be replicated in other systems where dispersal strongly shapes biodiversity patterns and where empirical data are readily available. This could for instance reveal how non-dendritic but often very heterogeneous networks structure dispersal across terrestrial ecosystems. Interestingly, this approach is compatible with the use of other response variables such as β or γ diversities, which could help to unravel dispersal patterns when they shape community composition at broad scale.
<figure><img src="image_2.png" /><figcaption>Figure 1: Schematic representation of the weight of evidence framework used by Borthagaray et al. (2025) to identify the most likely dispersal pattern of fish species in the Negro River basin (Uruguay). Empirical data (right panel) were collected at 58 sampling sites in the basin, and the observed species richness ($\alpha_{obs.}$) and community composition dissimilarities ($\beta_{obs.}$) were measured. In parallel, metacommunities were simulated on a network (2,193 nodes) through coalescent (initial steps to fill communities) and lottery (simulating individuals’ replacements in further iterations) approaches. The alternative simulated dispersal scenarios differed chiefly in their network constraints, their main dispersal sources (external species pool, outlet), and the presence of barriers due to dams. By comparing $\alpha_{obs.}$ to $\alpha_{mod.}$, and assessing the support of each model by the data, the authors identified the best supported dispersal pattern (bottom panel), whose characteristics include features from several scenarios as per the weight of evidence approach.</figcaption></figure>

## Conflict of interest

The author declares no conflict of interest.

## Data availability statement

Data have not been archived because this article does not use data.

## Acknowledgements

I would like to thank Katherine Hébert for critical comments on earlier versions of this manuscript. This work was supported by Agence Nationale de la Recherche (France) [grant number: ANR-24-CPJ1-0035-01].

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