Biology subjects

Guimaraes, P. R.

Publications and source records attributed to Guimaraes, P. R..

9 recordsLinked to original sources

bioRxiv2026

Biodiversity dynamics with complex genotype-to-phenotype architecture in multilayer networks

2The genotype-to-phenotype architecture (GPA), defined by complex interactions such as pleiotropy, epistasis, and regulatory control, is a fundamental yet often overlooked driver of biodiversity dynamics. While empirical evidence suggests that traits mediating species interactions (biotic) and environmental responses (abiotic) are frequently correlated, most eco-evolutionary theories treat these traits as independent, leaving a gap in our understanding of how genomic architecture influences community-level outcomes. In this study, we contrast two distinct GPAs, modular (independent trait evolution) and correlated (integrated trait evolution), within a spatially explicit multilayer network framework. We evaluate their impact on biodiversity across varying regimes of selection, migration, and biotic and environmental filtering. Our results reveal a hierarchy of drivers: selection strength dictates the absolute magnitude of the architectural effect, while migration and context-dependent biotic and abiotic effects determine which architecture yields a diversity advantage. Correlated GPAs enhance species coexistence and diversity in low-migration landscapes characterized by strong selection and moderate, balanced biotic and abiotic pressures. In these contexts, trait integration serves as a buffer against selective noise. Conversely, modular GPAs support higher diversity under high migration and strong biotic interactions, where the decoupling of trait modules provides the adaptive flexibility necessary to navigate spatially conflicting selective pressures. Our findings demonstrate that genomic architecture acts as a critical filter for environmental perturbations. Integrating complex GPAs into multispecies models is essential for quantifying the co-evolutionary feedbacks among traits, population adaptation, and species persistence. Our framework provides a path for predicting how biodiversity emerges and persists across biological scales, from genomics to communities and food webs, under the accelerating pressures of global change. 1 ConclusionsO_LIWe integrate trait architecture to spatial biodiversity to show biodiversity patterns are not merely products of ecological interactions, but are fundamentally constrained by Genotype-to-Phenotype Architecture (GPA). By linking GPA to biodiversity we show the interplay between the complexity of an organism and community structure in determining diversity patterns. C_LIO_LIThe hierarchy of Eco-Evolutionary Drivers: We establish a new conceptual hierarchy where selection strength acts as the fundamental governor of architectural impact, while the specific architecture predicting higher diversity (Correlational vs. Modular) is dictated by the interplay of migration scales and context-dependent biotic and abiotic dynamics. C_LIO_LISelection-Migration contingency for coexistence: We provide a new hypothesis for species coexistence: Correlational selection serves as a stabilizing force under dispersal limitation, whereas Modular trait architecture provides the adaptive flexibility to maintain diversity in high-migration, spatially heterogeneous landscapes. C_LIO_LIAdaptive decoupling as a diversity engine: We propose that trait modularity functions as a "buffer" against extinction by decoupling phenotypic responses. This allows populations to navigate conflicting selective pressures, effectively facilitating evolutionary rescue in complex biotic environments. C_LIO_LIMethodological framework for empirical inference: To bridge the gap between theory and data, we provide a novel likelihood-based framework. This enables researchers to infer latent trait architectures from population genomic samplings, turning GPA from a theoretical construct into a measurable sampling variable in natural populations. C_LIO_LIWe define a new roadmap for the next generation of eco-evolutionary modeling. By identifying the gaps between existing simulation engines, we provide a conceptual "blueprint" for a digital ecosystem that fully integrates complex genetic architecture with global bio-diversity dynamics. C_LI

evolutionary biology↗

bioRxiv2025

Tracking community change via network coherence

Understanding how ecological communities respond to environmental change remains a key challenge for biodiversity monitoring. To characterize such responses, we need tools that capture how coherently species respond across a community, and to predict their consequences, we must account for ecological interactions. We first introduce the Ecological Coherence (EC) framework, which describes how species co-responses are structured within a community. Building on this foundation, we extend it to Ecological Network Coherence (ENC), which embeds co-responses within the network of interactions by restricting them to interacting species. Both are expressed through two complementary representations: a response correlation matrix and the distribution of its values. The first can reveal aspects such as coherent or incoherent modules and the roles species play in shaping coherence, whereas the second provides a profile whose shape may serve as an early-warning indicator of instability. These can be applied to both intrinsic responses (environmental performance) and realized responses (abundance dynamics), derived from currently available monitoring data. We illustrate this approach in two empirical systems: a tropical pollination network, where interacting mutualists were more coherent in their temperature responses than the broader community, and a marine food web, where coherence in abundance trends shifted during collapse. Using a Lotka-Volterra model, we further show that ENC distributions with higher variance--reflecting stronger positive and negative co-responses--increase the risk of instability or amplification in dynamics. We also find that species influential in both the correlation matrix and the interaction matrix are key drivers of major dynamic shifts. These results point to the importance of further exploring ENC distributions as potential early-warning indicators of ecological disruption.

ecology↗

bioRxiv2025

Phylogenetic signal dynamics during niche filling in food webs

Understanding how phylogenetic signal in ecological networks--the tendency for closely related species to resemble one another in ecological roles--emerges and persists remains a central challenge in community ecology. Here, we simulate food web evolution to track how the correspondence between phylogeny and trophic structure changes as communities assemble and niche space fills. By simulating trait evolution coupled with trait-matching for ecological interactions, we quantify how phylogenetic signal in trophic structure changes through time and examine how species network positions relate to their phylogenetic distinctiveness and diversification dynamics. We find that the signal declines over time, driven by emergent feedbacks between node extinction, link reorganization, and trait divergence. Species with high phylogenetic distinctiveness tend to be more specialized and occupy peripheral network positions, particularly in late-stage communities. Centrality consistently constrains diversification in intermediate consumers, emerges as a limiting factor for top predators after niche saturation, and shows nonlinear effects in basal species diversification. Applying our framework to empirical food webs from the Galapagos Islands, we find partial support for these predictions: phylogenetic signal in foraging and vulnerability roles declines with island age, but shows contrasting trends with island area and elevation. We also detect discrepancies between distance-based and clustering-based measures of phylogenetic signal, highlighting the need for robust methods to compare phylogenetic and network structures. Together, our results reveal how trophic interactions mediate the erosion of phylogenetic structure during community assembly and offer testable predictions for systems at different stages of diversification.

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↗

bioRxiv2024

Urbanization as an environmental filter for megacolorful birds

The colorfulness of bird plumage plays a crucial role in intraspecific (e.g. sexual display) and interspecific ecological interactions (e.g. camouflage and predation). Consequently, bird plumage colorfulness can affect the success of individuals in novel environments, such as urban settings. However, our understanding of the impact of urbanization on the plumage colorfulness of birds, especially in tropical regions, is limited. To address this gap, we analyzed whether urban environments serve as environmental filters for plumage colorfulness in passerine (Passeriformes) bird assemblages across the biomes of Brazil, the worlds largest tropical country. Using generalized linear models that incorporate bird checklists, functional traits, and a continuous urbanization metric, we show that urbanization increases specific traits that are associated with plumage colorfulness in bird assemblages (i.e. proportion of omnivores, proportion of larger species, and average sexual dichromatism). While the average colorfulness of bird assemblages did not change with increasing urbanization, a negative correlation between the presence of megacolorful birds (i.e. the 5% most colorful species) and urbanization was detected, particularly in biomes with high urban concentrations, such as the Atlantic Forest and the Caatinga. This suggests that urban environments can be unsuitable for the most colorful tropical bird species. Our study additionally shows that factors like body size, diet, and sexual dichromatism play a mediating role in the urban filtering process. Our analyses provide insights into how urban environments act as environmental filters and can help to better understand the consequences of urbanization for tropical biodiversity.

ecology↗

bioRxiv2020

Macroevolutionary imprints on the assembly of frugivory networks

Assessing deep-time mechanisms affecting the assembly of ecological networks is key to understanding biodiversity changes on broader time scales. We combined analyses of diversification rates with interaction network descriptors from 468 bird species belonging to 29 seed-dispersal networks to show that bird species that contribute most to the network structure of plant-frugivorous interactions belong to lineages that show higher macroevolutionary stability. This association is stronger in warmer, wetter, less seasonal environments. We infer that the macroevolutionary sorting mechanism acts through the regional pool of species by sorting species based on the available relative differences in diversification rates, rather than absolute rates. Our results illustrate how the interplay between interaction patterns and diversification dynamics may shape the organization and long-term dynamics of ecological networks.

evolutionary biology↗

bioRxiv2018

Revealing biases in the sampling of ecological interaction networks

The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These issues may affect the accuracy of empirically constructed ecological networks. Yet statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying ecological networks structure. To explore properties of large-scale modular networks, we developed EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different sampling designs that may be employed in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties accurately depends both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, the modules with nested structure were the easiest to detect, regardless of sampling design. Sampling according to species degree (number of interactions) was consistently found to be the most accurate strategy to estimate network structure. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. We recommend that these findings be incorporated into field sampling design of projects aiming to characterize large species interactions networks to reduce sampling biases.\n\nAuthor SummaryEcological interactions are commonly modeled as interaction networks. Analyses of such networks may be sensitive to sampling biases and detection issues in both the interactors and interactions (nodes and links). Yet, statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying networks structure. For insight into ecological networks, we developed software EcoNetGen (available in R and Python). These allow the generation and sampling of several types of large-scale modular networks with predetermined topologies, representing a wide variety of communities and types of ecological interactions. Networks can be sampled according to designs employed in field observations. We demonstrate, through first uses of this software, that underlying network topology interacts strongly with empirical sampling design, and that constructing empirical networks by starting with highly connected species may be the give the best representation of the underlying network.

ecology↗

bioRxiv2018

Interaction pathways promote spliceosome module integration and network-level robustness to cascading effects

The functionality of distinct types of protein networks depends on the patterns of protein-protein interactions. A problem to solve is understanding the fragility of protein networks to predict system malfunctioning due to mutations and other errors. Spectral graph theory provides tools to understand the structural and dynamical properties of a system based on the mathematical properties of matrices associated with the networks. We combined two of such tools to explore the fragility to cascading effects of the network describing protein interactions within a key macromolecular complex, the spliceosome. Using S. cerevisiae as a model system we show that the spliceosome network has more indirect paths connecting proteins than random networks. Such multiplicity of paths may promote routes to cascading effects to propagate across the network. However, the modular network structure concentrates paths within modules, thus constraining the propagation of such cascading effects, as indicated by analytical results from the spectral graph theory and by numerical simulations of a minimal mathematical model parameterized with the spliceosome network. We hypothesize that the concentration of paths within modules favors robustness of the spliceosome against failure, but may lead to a higher vulnerability of functional subunits which may affect the temporal assembly of the spliceosome. Our results illustrate the utility of spectral graph theory for identifying fragile spots in biological systems and predicting their implications.

molecular biology↗

bioRxiv2017

Loss of generalist plant species and functional diversity decreases the robustness of a seed dispersal network

Understanding cascading effects of species loss has become a major challenge for ecologists. Traditionally, the robustness of ecological networks has been evaluated based on simulation studies where primary extinctions occur at random or as a function of species specialization, ignoring other important biological factors. Here, we estimate the robustness of a seed dispersal network from a grassland-forest mosaic in southern Brazil, simulating distinct scenarios of woody plant species extinction, including scenarios where species are eliminated based on their evolutionary and functional distinctiveness. Our results suggest that the network is more robust when species are eliminated based on their evolutionary uniqueness, followed by random extinctions, the extinction of the most specialist species, functional distinctiveness and, at last, when the most generalist species are sequentially eliminated. Our results provide important information for grassland-forest mosaic management, as they indicate that loss of generalist species and functional diversity makes the system more likely to collapse.

ecology↗