Tracking changes in birds' interaction milieu
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Abstract
As biodiversity is declining, the dynamics of species interactions is a growing conservation concern. However, estimating and monitoring explicit species interactions across large spatial and temporal scales remain challenging. An alternative and yet under-explored approach is to track whether and how the interaction milieu, defined as the background of all realised interactions, is changing in space and time. Here, we assess changes in the interaction milieu of common bird communities in France. We estimate associated species pairs using spatial and temporal information for 109 species monitored across 1,969 sites during 17 years. We validate the ecological significance of associated species pairs by testing the relationship between the propensity to be associated and species functional proximity or shared habitat preference. We reconstruct association networks for these intra-guild bird communities and track temporal changes in network layout in terms of size, density of links, modularity and degree distribution. We show that, beyond changes usually documented based on species numbers and abundances, the interaction milieu is also changing non-randomly. Communities become smaller with a similar relative number of associations that becomes unevenly distributed through time. These structural changes vary among bird communities according to their habitat and may impact community functioning and how communities can cope with global change.
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Documenting species interactions is quite a difficult task (Jordano, 2016), especially for rare or endangered species as interactions are often identified using invasive methods and because sampling methods affect interaction network properties (Gibson et al., 2011, Brimacombe et al., 2023). By contrast, species associations or co-occurrences are much easier to assess and can inform about the interaction milieu of species and communities (McGill et al., 2006). Changes in associations can be due to the loss of one of the associated species (e.g. through land-use changes and a decrease in habitat suitability), external environmental variables differently affecting species abundances (e.g. disruption of association through phenological desynchronization induced by climate change), or the introduction or removal of some other species (e.g. …
Documenting species interactions is quite a difficult task (Jordano, 2016), especially for rare or endangered species as interactions are often identified using invasive methods and because sampling methods affect interaction network properties (Gibson et al., 2011, Brimacombe et al., 2023). By contrast, species associations or co-occurrences are much easier to assess and can inform about the interaction milieu of species and communities (McGill et al., 2006). Changes in associations can be due to the loss of one of the associated species (e.g. through land-use changes and a decrease in habitat suitability), external environmental variables differently affecting species abundances (e.g. disruption of association through phenological desynchronization induced by climate change), or the introduction or removal of some other species (e.g. invasion of the community by a dominant competitor). Therefore, monitoring changes in species associations can serve as a valuable indicator of the impacts of global changes. Evaluating shifts in these associations is a first important step toward understanding changes of functional interactions within ecological communities.
Rigal et al. (2025) elegantly address the analysis of association networks among common bird species by combining convergent cross-mapping to evidence significant associations (Sugihara et al., 2012) with generalized linear models to assess both the functional relevance of such associations and the temporal trends affecting the association network. This study reports a decline in species richness together with an increase in association connectance and a decrease of the evenness of the number of associations per species. Such findings suggest that, despite a simplification of bird communities in farmland and forest habitats, associations could be more resilient than expected to further changes as they might involve a higher proportion of association generalists. Reassuringly, Rigal et al. (2025) did not find changes in association modularity, which might play out as a barrier against further disruptions of these association networks. Taken together, these results raise an alarm – common bird species are declining – but stress some potential properties of association networks that can increase their resilience against further changes.
This study is remarkable for its ability to identify certain patterns in species associations that raise questions about the resilience of common bird communities to future anthropogenic disturbances in their habitats. While classic community studies can assess trends in diversity, they are blind to species associations and thus tend to consider communities as lists of species names, not interacting entities contributing to some ecosystem functions (McGill et al., 2006). The results of this study thus highlight the importance of taking into account pairwise species associations when investigating the effects of community trends. As interaction milieu explicitly accounts for habitats, one can only hope that such a study will pave the way for the exploration of the empirical “keystoneness” of some habitats for the maintenance of species associations within complex landscape mosaics. And as new, less invasive methods become available to identify species and interactions (Høye et al., 2021), hopefully such analyses will eventually be applied to interactions rather than associations.
Literature cited
Brimacombe, C., Bodner, K., Michalska-Smith, M., Poisot, T. & Fortin, M.-J. (2023) Shortcomings of reusing species interaction networks created by different sets of researchers. PLoS Biology, 21, e3002068. https://doi.org/10.1371/journal.pbio.3002068
Gibson, R. H., Knott, B., Eberlein, T. & Memmott, J. (2011) Sampling method influences the structure of plant–pollinator networks. Oikos, 120, 822-831. https://doi.org/10.1111/j.1600-0706.2010.18927.xDigital Object Identifier
Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C. & Raitoharju, J. (2021) Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences, 118, e2002545117. https://doi.org/10.1073/pnas.2002545117
Jordano, P. (2016) Sampling networks of ecological interactions. Functional Ecology, 30, 1883-1893. https://doi.org/10.1111/1365-2435.12763
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. (2006) Rebuilding community ecology from functional traits. Trends in Ecology & Evolution, 21, 178-185. https://doi.org/10.1016/j.tree.2006.02.002
Rigal, S., Devictor, V. & Dakos, V. (2025) Tracking changes in birds' interaction milieu. bioRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.1101/2025.02.14.638220
Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, M. & Munch, S. (2012) Detecting causality in complex ecosystems. Science, 338, 496-500. 10.1126/science.1227079
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