Biology subjects

Angulo, M. T.

Publications and source records attributed to Angulo, M. T..

14 recordsLinked to original sources

bioRxiv2026

From species-area relationships to biodiversity risk assessment

Biodiversity is commonly summarized by macroecological mean patterns, most prominently the species-area relationship (SAR) linking habitat area to expected species richness. Yet conservation, policy, and economic decisions increasingly require risk metrics: probabilities of rare but consequential biodiversity shortfalls, including local collapse. Such tail risks are central in finance and insurance but remain difficult to quantify in ecology because the data needed to estimate full richness distributions are rarely available at decision scales. Here we provide a mechanistic route from species-area relationships to biodiversity risk metrics. We show that when regional species abundances are well approximated by Fishers log-series, a minimal immigration-extinction mechanism yields a closed-form stationary distribution for local richness whose structure tightly couples the mean SAR to richness variability and lower-tail probabilities. This coupling implies exact fluctuation-response identities and an explicit integral transform that reconstructs collapse probabilities and other tail risk measures directly from the mean SAR. These results define ecological analogues of financial risk metrics--such as collapse probability and lower-tail quantiles--without requiring direct estimation of the full richness distribution. Using high-resolution ForestGEO tree censuses spanning tropical, subtropical, and temperate forests, we find empirical support for these predictions across spatial scales. Together, our results show how widely measurable species-area relationships can be elevated from descriptive averages to operational tools for biodiversity risk assessment and reliability-based conservation planning.

ecology↗

bioRxiv2026

Energetic constraints shape the diversity of feasible ecological networks

The relationship between energy supply and biodiversity is a longstanding question in ecology. Although a monotonic increase in diversity with energy availability is often assumed, unimodal species-energy relationships have been widely documented across ecosystems, and their origin from first principles remains unclear. Here, we develop a geometric framework that recasts ecological feasibility in explicitly energetic terms. By treating total energy supply as a system-level constraint on an energy-based network model, we define nested feasibility domains in the space of energy capture rates and quantify feasibility probabilities as their volume ratios. We show that the probability of initializing a feasible network increases monotonically and saturates with energy supply, whereas the probability of sustaining steady-state biomass follows a unimodal relationship--revealing a bounded energetic window within which network maturation is most likely. Extending this analysis to all candidate subcommunities via feasibility partitions, we find that different community sizes are most feasible at different energy levels, and that average diversity itself peaks at intermediate supply. Together, these results suggest that energetic constraints determine the diversity of ecological networks not through energy scarcity alone, but through the geometric interplay between external energy supply and internal energy exchange. Author SummaryWhy do many ecosystems show the highest biodiversity not where energy is most abundant, but at intermediate levels? This unimodal species-energy relationship has been documented across grasslands, wetlands, and rainforests, yet its origin from first principles has remained unclear. We approached this question by developing a simplified model that treats ecological networks as energy-processing systems. In this model, each species captures energy from the environment and exchanges it with others, and the total energy available to the network is explicitly limited. By measuring how the likelihood of species coexistence changes with energy supply within this framework, we found that while a minimum energy threshold is needed for any community to persist, too much energy can paradoxically reduce the chance of long-term coexistence. This creates a bounded energy window most favorable for community persistence. When we extended the analysis to all possible subsets of species, we found that different-sized communities are most likely to persist at different energy levels, and that overall expected diversity peaks at intermediate supply. These results suggest a possible geometric origin for why more energy does not always support more species, providing a theoretical baseline for connecting the structure of energy flow within networks to observed biodiversity patterns.

ecology↗

bioRxiv2025

On the energetic boundaries of trophic systems

Energy is the fundamental currency of life. Every organism requires a minimum intake to meet maintenance needs, and every trophic transfer dissipates part of the energy flow as heat. Classical theory has emphasized lower bounds on supply--the minimum resource level required for consumers to persist--yet the possible upper bounds and their role in limiting food-chain length remain unclear. We develop a thermodynamically grounded framework that reveals a triad of boundaries: (i) consumer thresholds, (ii) a basal protection limit that reframes the paradox of enrichment as a thermodynamic constraint, and (iii) environmental or physiological supply caps. We show that the intersection of these three boundaries determines the exact range of conditions under which a given ecosystem structure can occur, and that within this range trophic configurations arise as the unique solution that maximizes energy flow. As chain length increases, consumer thresholds rise while the basal protection limit falls, causing this range to contract and eventually disappear. Whether this limit is reached depends on the relative position of these two boundaries and the external supply cap. Their crossing defines an energetic ceiling: a thermodynamic bound on food-chain length beyond which no further level can be sustained. Our results show that feasibility conditions are directly shaped by thermodynamic laws, providing a mechanistic explanation for the emergence and limits of trophic systems.

ecology↗

bioRxiv2025

Linking power, efficiency, and bifurcations in ecological systems

Three hypotheses help organize energetic thinking about living systems: Lotkas Maximum Power Principle, Odum-Pinkertons Intermediate Efficiency Principle, and Morowitzs Biological Cycling Principle. Here we show how these hypotheses fit together in consumer-resource systems, moving from qualitative principles to formal, testable statements. Using the Rosen-zweig-MacArthur model, we prove that the consumers maximum output power lies exactly on the Hopf boundary that separates stable points from cycles; at that point, the resulting power efficiency is intermediate. In the Rosenzweig-MacArthur model the Hopf is supercritical, so a stable limit cycle appears smoothly as the equilibrium loses stability. We treat the Hopf onset of time-periodic population oscillations as a population-level analogue of sustained cycling under energy flux. We then embed these energetic statements in adaptive dynamics: with convex trait costs, evolutionary singular strategies exist and are locally stable, but they coincide with the maximum-power state only under explicit marginal-cost conditions. Together, these results unify classic ideas in the concrete setting of consumer-resource systems and suggest measurements to evaluate when bifurcations, energetics, and evolution can converge.

ecology↗

bioRxiv2025

A general allometric rule predicts sustainable growth across societies

Balancing population growth against finite resources remains a foundational challenge in sustainability science. Although qualitative sustainability principles have matured over decades, translating these principles into universal quantitative rules has been challenging due to the diverse environmental and cultural contexts under which human societies develop. Here, we demonstrate that a general sustainability rule emerges naturally when population-resource feedbacks follow empirically observed allometric scaling laws, condensing ecological, technological, and social complexities into four fundamental parameters. This rule predicts "sustainability corridors" within which societies must remain to ensure long-term persistence. Testing our theoretically derived rule against ethnographic data from 299 hunter-gatherer societies across varied ecological and social environments, we find consistent alignment: these societies tend to occupy the predicted corridors despite substantial contextual differences. By grounding sustainability in biophysical scaling rather than context-specific variables, our approach bridges ecology and sustainability science, suggesting that despite cultural and technological differences, all societies face fundamental constraints for sustainable growth.

ecology↗

bioRxiv2025

Quantifying ecosystem degradation via energetic deficits

Ecosystem degradation is accelerating, threatening biodiversity and human economies. Quantifying this degradation relative to a reference baseline remains a key challenge. Yet, this quantification is essential to provide the necessary resources to regenerate ecosystems. We address this challenge by studying the energetics of population dynamics, where energy inputs such as sunlight and rainfall are transformed into other forms of energy embodied as biomass. This approach provides a unifying framework to quantify degradation across diverse ecosystems via the energetic ecodeficit: the extra energy supply the degraded ecosystem would require to regenerate to a reference state. This energetic deficit can then be translated into a monetary ecodeficit, representing the economic cost of regeneration, considering the local cost of embodied energy. Our analysis reveals nonlinear relationships between biomass loss and energetic ecodeficit, including points of no return beyond which restoration becomes economically infeasible. We showcase our methodology using the current mammal deficits across the African savanna, finding that the monetary ecodefict is about 100 times larger than current estimates. Our work demonstrates how integrating energetic principles into conservation planning can transform ecosystem valuation globally, linking ecosystems and economies sustainably.

ecology↗

bioRxiv2023

Assembly archetypes in ecological communities

An instrumental discovery in comparative and developmental biology is the existence of assembly archetypes that synthesize the vast diversity of organisms body plans--from legs and wings to human arms--into simple, interpretable, and general design principles. Here we combine a novel mathematical formalism based on Category Theory with experimental data to show that similar "assembly archetypes" exist at the larger organization scale of ecological communities when assembling a species pool across diverse environmental contexts, particularly when species interactions are highly structured. We applied our formalism to clinical data discovering two assembly archetypes that differentiate between healthy and unhealthy human gut microbiota. The concept of assembly archetypes and the methods to synthesize them can pave the way to discovering the general assembly principles of the ecological communities we observe in nature.

ecology↗

bioRxiv2023

Identifying keystone species in microbial communities using deep learning

Previous studies suggested that microbial communities harbor keystone species whose removal can cause a dramatic shift in microbiome structure and functioning. Yet, an efficient method to systematically identify keystone species in microbial communities is still lacking. This is mainly due to our limited knowledge of microbial dynamics and the experimental and ethical difficulties of manipulating microbial communities. Here, we propose a Data-driven Keystone species Identification (DKI) framework based on deep learning to resolve this challenge. Our key idea is to implicitly learn the assembly rules of microbial communities from a particular habitat by training a deep learning model using microbiome samples collected from this habitat. The well-trained deep learning model enables us to quantify the community-specific keystoneness of each species in any microbiome sample from this habitat by conducting a thought experiment on species removal. We systematically validated this DKI framework using synthetic data generated from a classical population dynamics model in community ecology. We then applied DKI to analyze human gut, oral microbiome, soil, and coral microbiome data. We found that those taxa with high median keystoneness across different communities display strong community specificity, and many of them have been reported as keystone taxa in literature. The presented DKI framework demonstrates the power of machine learning in tackling a fundamental problem in community ecology, paving the way for the data-driven management of complex microbial communities.

microbiology↗

bioRxiv2021

Predicting microbiome compositions through deep learning.

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, here we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowledge of any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply it to experimental data of both in vitro and in vivo communities, including ocean and soil microbial communities, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. In particular, we show how our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

microbiology↗

bioRxiv2020

Coexistence holes characterize the assembly and disassembly of multispecies systems

A central goal of life science has been to understand the limits of species coexistence. However, we know surprisingly little about the structure of species coexistence below such limits, and how it affects the assembly and disassembly of ecological systems. Here we introduce a novel hypergraph-based formalism that fully captures the structure of coexistence in multispecies systems. Our formalism uncovers that, below its limits, coexistence in ecological systems has ubiquitous discontinuities that we call "coexistence holes." These coexistence holes do not occur arbitrarily but tend to obey patterns that make them predictable. We provide direct evidence showing that the biotic and abiotic constraints of empirical systems produce an over-representation of coexistence holes. By highlighting discontinuities in the form of coexistence holes, our work provides a new platform to uncover the order and structure of the assembly and disassembly of ecological systems.

ecology↗

bioRxiv2018

Identifying sensor species to predict critical transitions in complex ecosystems

Ecosystems provide key services needed for human well-being, such as purifying air and regulating our health, many of which are disrupted when these ecosystems undergo a critical transition to undesired states. Detecting early-warning signals of these critical transitions remains challenging for complex ecosystems with a large number of species. Here we built a mathematical formalism to identify minimal sets of \"sensor species\" from which we can determine the state of a whole ecosystem, allowing us to predict a critical transition in an ecosystem by monitoring a minimal subset of its species. We rigorously prove that minimal sets of sensor species can be generically identified knowing only the structure of the ecological network underlying the ecosystem, regardless of its population dynamics. We numerically validated our formalism to predict critical transitions in large complex ecosystems, and then we applied it to experimental data of a critical transition in a lake food-web. Our results contribute to better monitoring complex ecosystems, especially those with poorly known population dynamics such as host-associated microbial communities.

ecology↗

bioRxiv2017

Mapping the ecological networks of microbial communities from steady-state data

Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

ecology↗

bioRxiv2017

Controlling microbial communities: a theoretical framework

Microbes comprise nearly half of all biomass on Earth. Almost every habitat on Earth is teeming with microbes, from hydrothermal vents to the human gastrointestinal tract. Those microbes form complex communities and play critical roles in maintaining the integrity of their environment or the well-being of their hosts. Controlling microbial communities can help us restore natural ecosystems and maintain healthy human microbiota. Yet, our ability to precisely manipulate microbial communities has been fundamentally impeded by the lack of a systematic framework to control them. Here we fill this gap by developing a control framework based on the new notion of structural accessibility. This framework allows identifying minimal sets of \"driver species\" through which we can achieve feasible control of the entire microbial community. We numerically validate our control framework on large microbial communities, and then we demonstrate its application for controlling the gut microbiota of gnotobiotic mice infected with Clostridium difficile and the core microbiota of the sea sponge Ircinia oros.

microbiology↗

bioRxiv2017

Inferring a qualitative contact rate index of uncertain epidemics

We will inevitably face new epidemic outbreaks where the mechanisms of transmission are still uncertain, making it difficult to obtain quantitative predictions. Thus we present a novel algorithm that qualitatively predicts the start, relative magnitude and decline of uncertain epidemic outbreaks, requiring to know only a few of its \\macroscopic\" parameters. The algorithm is based on estimating exactly the time-varying contact rate of a canonical but time-varying Susceptible-Infected-Recovered epidemic model parametrized to the particular outbreak. The algorithm can also be extended to other canonical epidemic models. Even if dynamics of the outbreak deviates significantly from the underlying epidemic model, we show the predictions of the algorithm remain robust. We validated our algorithm using real time-series data of measles, dengue and the current zika outbreak, comparing its performance to existing algorithms that also use a few macroscopic parameters (e.g., those estimating reproductive numbers) and to those using a thorough understanding of the mechanisms of the epidemic outbreak. We show our algorithm can outperform existing algorithms using a few macroscopic parameters, providing an informative qualitative evaluation of the outbreak.

epidemiology↗