Biology subjects

Savage, V.

Publications and source records attributed to Savage, V..

7 recordsLinked to original sources

bioRxiv2026

When does heterogeneity average out in coarse-grained ecological communities?

Ecological coarse-graining of individuals or species into aggregate variables, such as total biomass or regional abundance, is common practice, yet differences among species can still influence aggregate dynamics. We propose a metric for quantifying this irreducible dynamical heterogeneity. For small fluctuations near a stable equilibrium, we compare the aggregate fluctuation spectrum of the full dynamical system with that of the best Ornstein-Uhlenbeck model having the same dimension as the aggregate. The remaining spectral mismatch quantifies the dynamical effects of hidden differences that the fitted aggregate model cannot capture. For weakly interacting random community models at fixed collective interaction strength, we show that increasing species richness can either reduce or increase this heterogeneity, depending on how species interact. Our framework helps understand when ecological and evolutionary differences average out versus when their dynamical effects persist.

ecology↗

bioRxiv2025

A General Framework for Predicting the Temperature-Dependence of Microbial Interactions

Despite its global importance, our ability to predict the impacts of temperature change on the community dynamics of heterotrophic microbes remains limited. Here, we develop a metabolic trait-based mathematical framework to predict the temperature dependence of pairwise interactions among heterotrophic microbial consumers, accounting for their resource environment and community composition. Applying this framework leads to two general predictions. First, microbial species interactions are typically more thermally sensitive than the underlying metabolic traits. Second, temperature systematically reshapes intra- and interspecific interactions: their variance peaks at intermediate temperatures, while mean interaction strengths increase with warming more rapidly than interspecific interaction strengths. We show that these features of temperature-dependent interactions have far-reaching implications for the temperature responses of community coexistence, diversity, and stability. Our framework provides a mechanistic foundation for predicting how temperature affects the dynamics of heterotrophic microbial communities across diverse biological and environmental contexts. SignificanceCommunities of heterotrophic microbes, including bacteria, archaea, protists, and fungi, play a fundamental role in human health, bioprocessing, and global biogeochemical cycles. Predicting their responses to environmental change is a major challenge, with a key missing link being the effects of temperature on species interactions that ultimately shape community dynamics. By integrating metabolic constraints into consumer-resource theory, we derive general predictions about how interactions among heterotrophic microbial species respond to temperature changes. We show that interactions are more sensitive to warming than individual metabolic traits and undergo systematic temperature-dependent changes that alter community-level coexistence, diversity, and stability. These results hold across diverse environmental contexts and heterotrophic systems, providing a foundation for predicting how microbial community dynamics respond to environmental temperature, from biotechnological applications to natural ecosystems.

ecology↗

bioRxiv2024

Improved tests for the origin of allometric scaling across tree architectures

The scaling of organismal metabolic rates with body size is one of the most prominent empirical patterns in biology. For over a century, the nature and causes of metabolic scaling have been the subject of much focus and debate. West, Brown, and Enquist (WBE) proposed a general model for the origin of metabolic scaling from branching vascular networks. However, recent empirical tests of WBE vascular scaling predictions in plants and animals have reported deviations caused by variability in network geometry. After clarifying the core assumptions of the WBE model, we revisit the methods and conclusions of recent tests conducted in trees, finding support for key WBE predictions in woody plant architecture. To do this, we apply an approach that better captures: i) network branching self-similarity and ii) leaf area as a proxy of plant metabolic capacity. The WBE model also predicts curvature in metabolic scaling in smaller organisms, and we introduce a novel method that accounts for curvature in plant branching geometry. Together, these advances allow more direct measurements of metabolic scaling than previous work, and we apply them to a dataset of diverse laser-scanned tree architectures. Analyses reveal the predicted interspecific [3/4] metabolic scaling across tree crowns, with intraspecific variation within individual tree crowns. Scaling variability is consistent with WBE predictions for curvature from asymptotic growth and underlying variation in branching geometry. We conclude that linking fine-scale branching variation to metabolic scaling allometries remains a challenge, while our results support the foundational hypotheses of the WBE model. Author summaryTrees survive in a variety of habitats and lifestyles across Earth. They are also characterized by a stunning array of sizes and shapes that make trees objects of vast cultural, economic, and ecological importance. At the same time, the need to link vascular plant function with traits and environment is more pressing than ever. Size (body mass) is fundamentally linked to plant functioning within ecosystems through allometric relationships. Allometric relationships emerge from the geometry of branch networks in trees, which are increasingly well-characterized with remote-sensing data. We use a dataset of laser-scanned tree crowns to test allometric predictions that link size to key traits, particularly metabolic capacity, understood as total leaf area. Our results indicate that i) scanning technology can provide accurate assessments of branch allometry with proper data preparation, and ii) studying branch allometries provides an organizing framework for interpreting natural variation in tree architecture.

plant biology↗

bioRxiv2022

Temperature impacts on dengue incidence are nonlinear and mediated by climatic and socioeconomic factors

Temperature can influence mosquito-borne diseases like dengue. These effects are expected to vary geographically and over time in both magnitude and direction and may interact with other environmental variables, making it difficult to anticipate changes in response to climate change. Here, we investigate global variation in temperature-dengue relationship by analyzing published correlations between temperature and dengue and matching them with remotely sensed climatic and socioeconomic data. We found that the correlation between temperature and dengue was most positive at intermediate (near 24{degrees}C) temperatures, as predicted from the thermal biology of the mosquito and virus. Positive temperature-dengue associations were strongest when temperature variation and population density were high and decreased with infection burden and rainfall mean and variation, suggesting alternative limiting factors on transmission. Our results show that while climate effects on diseases are context-dependent they are also predictable from the thermal biology of transmission and its environmental and social mediators.

ecology↗

bioRxiv2021

How Axon and Dendrite Branching Are Governed by Time, Energy, and Spatial Constraints

Neurons are connected by complex branching processes - axons and dendrites - that collectively process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental part of neuroscience. Here, by constructing new biophysical theory and testing against our empirical measures of branching structure, we establish a correspondence between neuron structure and function as mediated by principles such as time or power minimization for information processing as well as spatial constraints for forming connections. Specifically, based on these principles, we use undetermined Lagrange multipliers to predict scaling ratios for axon and dendrite sizes across branching levels. We test our predictions for radius and length scale factors against those extracted from neuronal images, measured for cell types and species that range from insects to whales. Notably, our findings reveal that the branching of axons and peripheral nervous system neurons is mainly determined by time minimization, while dendritic branching is mainly determined by power minimization. Further comparison of different dendritic cell types reveals that Purkinje cell dendrite branching is constrained by material costs while motoneuron dendrite branching is constrained by conduction time delay over a range of species. Our model also predicts a quarter-power scaling relationship between conduction time delay and species body size, which is supported by experimental data and may help explain the emergence of hemispheric specialization in larger animals as a means to offset longer time delays. Author summaryNeurons are the basic building blocks of the nervous system, responsible for information processing and communication in animals. They consist of a centralized cell body and two types of processes - axons and dendrites - that connect to one another. Previous studies of the differences among neuron cell types have focused on comparisons of either structure or function separately, without considering combined effects. Based on theory for structure of and flow through biological resource distribution networks, we develop a new model that relates neuron structure to function. We find that differences in structure between axons and dendrites as well as between dendrites of different cell types can be related to differences in function and associated evolutionary pressures. Moreover, using our mathematical model, we find that the conduction time delay of electrical signals systematically varies with species body size - neurons in larger species have longer delays - providing a possible explanation for hemispheric specialization in larger animals.

neuroscience↗

bioRxiv2021

Scaling of cellular proteome with ploidy

Ploidy changes are frequent in nature and contribute to evolution, functional specialization and tumorigenesis (1,2). Analysis of model organisms of different ploidies revealed that increased ploidy leads to an increase in cell and nuclear volume, reduced proliferation (2-4), metabolic changes (5), lower fitness (6,7), and increased genomic instability (8,9), but the underlying mechanisms remain poorly understood. To investigate how the gene expression changes with cellular ploidy, we analyzed isogenic series of budding yeasts from 1N to 4N. We show that mRNA and protein abundance scales allometrically with ploidy, with tetraploid cells showing only threefold increase in proteins compared to haploids. This ploidy-specific scaling occurs via decreased rRNA and ribosomal protein abundance and reduced translation. We demonstrate that the Tor1 activity is reduced with increasing ploidy, which leads to rRNA gene repression via a novel Tor1-Sch9-Tup1 signaling pathway. mTORC1 and S6K activity are also reduced in human tetraploid cells and the concomitant increase of the Tup1 homolog Tle1 downregulates the rDNA transcription. Our results revealed a novel conserved mTORC1-S6K-Tup1/Tle1 pathway that ensures proteome remodeling in response to increased ploidy.

cell biology↗

bioRxiv2019

Predicting Collapse of Complex Ecological Systems: Quantifying the Stability-Complexity Continuum

Dynamical shifts between the extremes of stability and collapse are hallmarks of ecological systems. These shifts are limited by and change with biodiversity, complexity, and the topology and hierarchy of interactions. Most ecological research has focused on identifying conditions for a system to shift from stability to any degree of instability--species abundances do not return to exact same values after perturbation. Real ecosystems likely have a continuum of shifting between stability and collapse that depends on the specifics of how the interactions are structured, as well as the type and degree of disturbance due to environmental change. Here we map boundaries for the extremes of strict stability and collapse. In between these boundaries, we find an intermediate regime that consists of single-species extinctions, which we call the Extinction Continuum. We also develop a metric that locates the position of the system within the Extinction Continuum--thus quantifying proximity to stability or collapse--in terms of ecologically measurable quantities such as growth rates and interaction strengths. Furthermore, we provide analytical and numerical techniques for estimating our new metric. We show that our metric does an excellent job of capturing the system behaviour in comparison with other existing methods--such as Mays stability criteria or critical slowdown. Our metric should thus enable deeper insights about how to classify real systems in terms of their overall dynamics and their limits of stability and collapse.

ecology↗