Andreazzi, C. S. | Search bioRxiv

bioRxiv2025

Quantifying the geographic mosaic of coevolutionary temperature: from coldspots to hotspots

The Geographic Mosaic Theory of Coevolution (GMTC) predicts that reciprocal evolutionary effects vary across landscapes, generating hotspots and coldspots. Traditionally, these states are treated as discrete categories, even though the intensity of coevolutionary selection can vary continuously. To capture this variation, we introduce a concept of coevolutionary temperature, ranging from coldspot to hotspot. We propose two complementary metrics to quantify it: reciprocity and strength of pairwise evolutionary effects. We also extend the GMTC framework beyond its traditional focus on pairwise systems to species-rich communities. Applying this approach to empirical plant-pollinator networks in a fragmented landscape, we find pronounced geographic mosaics in coevolutionary temperature. Smaller habitat patches support small, highly connected, and weakly nested communities with high reciprocity and strength, suggesting that they act as coevolutionary hotspots. In contrast, larger patches host species-rich, poorly connected, and highly nested communities with low reciprocity and strength, consistent with coldspots. At the interaction scale, reciprocity depends on degree similarity, with interactions between species that have similar numbers of partners exhibiting higher reciprocity. Together, these results highlight the strong dependence of coevolutionary effects on spatial variation in community structure and show how extending the geographic mosaic framework to species-rich communities can deepen our understanding of coevolution in complex systems.

ecology↗

bioRxiv2025

Trapped in the web: network architectures spread coevolution and shape adaptation

Adaptation is critical for biodiversity to persist under global change. Within ecological communities, species often face trade-offs between adapting to shifting abiotic conditions and navigating the complex selective pressures imposed by interaction networks. We hypothesize that network architectures characterized by high interaction diversity and overlap constrain coevolutionary dynamics, with asymmetric outcomes for exploiters and victims. Specifically, we predict that exploiters, subject to spread and conflicting selection imposed by their victims, will evolve more slowly and show reduced capacity to track victims evolutionary responses, with these constraints strongest for generalist exploiters. In contrast, victims will show more variable dynamics depending on the coherence of selection (i.e., whether pressures from different exploiters push the victims trait in the same vs. different directions). To test this, we simulated trait evolution in coevolving communities of exploiters and victims across 91 empirical networks, and in artificial networks designed to isolate specific structural effects. Our results show that higher connectance, species richness, nestedness, and centrality homogenize biotic effects and increase fluctuations in trait matching, ultimately weakening coevolutionary coupling. Under these conditions, exploiters face conflicting selection that slows evolution, whereas victims either benefit from aligned selection that accelerates evolution or are constrained by multiple pressures. Together, our findings suggest that network architecture plays a fundamental role in shaping coevolution and adaptation, and raises broader questions about its influence on eco-evolutionary processes in more complex and environmentally variable systems.

ecology↗