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At least 19 recordsLinked to original sources

bioRxiv2026

Ecology of metagenomes: incorporating genotype-to-phenotype maps into ecological models

A major theoretical problem in community ecology is to understand how genes, organisms, and environments combine to shape the structure and diversity of ecological communities. However, most classic ecological models work entirely with phenotypic parameters, neglecting the central role played by genes. This limitation is particularly acute in microbial ecology, where the widespread use of sequencing technologies allows researchers to directly measure the genomic and metagenomic properties of communities. Here, we bridge this gap by incorporating genotype-to-phenotype maps into classical ecological models, including the generalized Lotka-Volterra model (GLV) and consumer resource models (CRMs). We focus on the case where genotype-to-phenotype maps are linear, which provides a tractable yet powerful framework for analyzing complex traits. Even in this simple setting, the resulting ecological dynamics give rise to novel gene-level ecological dynamics that can be recast entirely in terms of genes, allowing us to develop an ecology of metagenomes. We find that ecological interactions between genes lead to pervasive "metagenomic hitchhiking" -- low-fitness genes can survive in the ecosystem because they are integrated into genomes of high-fitness species. We also show that phylogenetic relationships between species mold the ability of closely related strains to stably coexist in complex communities. This highlights how lineage structure and competitive interactions jointly shape community composition. Our framework provides a principled foundation for interpreting metagenomic data through the lens of ecological theory. Author summaryRecent advances in sequencing technologies have transformed our ability to characterize microbial communities at the genomic level. However, most classic ecological models work entirely with phenotypic parameters, neglecting the central role played by genes. Here, we address this gap by extending classical ecological models to explicitly include genotype-to-phenotype maps. We focus on complex traits where the genotype-to-phenotype map is approximately linear. We show that the resulting ecological dynamics that can be recast entirely in terms of genes, allowing us to develop an ecology of metagenomes. Our framework provides a novel perspective for interpreting metagenomic data through the lens of ecological theory.

ecology↗

bioRxiv2026

Identifying Ecological Restoration Priority Areas through a Function-Structure-Dynamic Framework: Integrating Ecological Security Patterns and Future Land-Use Simulation in the Anhui Section of the Yangtze River Basin

Rapid urbanization has intensified ecological degradation and threatened ecosystem sustainability in the Yangtze River Basin. This study proposes a Function-Structure-Dynamic (FSD) framework integrating ecological protection importance assessment, ecological security pattern (ESP) construction, and future land-use simulation to identify ecological restoration priorities in the Anhui section of the Yangtze River Basin. Ecologically important areas were identified by coupling ecosystem services and ecological vulnerability. MSPA, landscape connectivity analysis, resistance surface modeling, and circuit theory were applied to construct the ESP, while the PLUS model simulated land-use change and ecological risks in 2032. Results identified 37 ecological sources covering 6,901.39 km{superscript 2} and 84 ecological corridors with a total length of 916.80 km. Ecological pinch points, barrier areas, and 17 ecological warning areas were further delineated. Based on these findings, four ecological restoration zones were proposed with differentiated management strategies. The FSD framework effectively integrates ecological functions, spatial structure, and future dynamics, providing scientific support for ecological restoration and sustainable territorial spatial planning in rapidly urbanizing regions.

ecology↗

bioRxiv2018

Convergence of socio-ecological dynamics in disparate ecological systems under strong coupling to human social systems

It is widely recognized that coupled socio-ecological dynamics can be qualitatively different from the dynamics of social or ecological systems in isolation from one another. The influence of the type of ecological dynamics on the dynamics of the larger socio-ecological system is less well studied, however. Here, we carry out such a comparison using a mathematical model of a common pool resource problem. A population must make decisions about harvesting a renewable resource. Individuals may either be cooperators, who harvest at a sustainable level, or defectors, who over-harvest. Cooperators punish defectors through social ostracism. Individuals can switch strategies according the costs and benefits of harvesting and the strength of social ostracism. These mechanisms are represented by a differential equation for social dynamics which is coupled to three different types of resource dynamics: logistic growth, constant inflow, and threshold growth. We find that when human influence is sufficiently weak, the form of natural dynamics leaves a strong imprint on the socio-ecological dynamics, and human social dynamics are qualitatively very different from natural dynamics. However, stronger human influence introduces a broad intermediate parameter regime where dynamical patterns converge to a common type: the three types of ecological systems exhibit similar dynamics, but also, social and ecological dynamics strongly mirror one another. This is a consequence of stronger coupling and is reminiscent of synchrony from other fields, such as the classic problem of coupled oscillators in physics. Socio-ecological convergence has implications for how we understand and manage complex socio-ecological systems. In an era of growing human influence on ecological systems, further empirical and theoretical work is required to determine whether socio-ecological convergence is present in real systems.

ecology↗

bioRxiv2026

Interpretable spatial ecological dynamics reveal ecological retention escape and amplification in pediatric leukemia

Spatial transcriptomics enables tumor ecosystems to be examined within their native tissue architecture, yet many computational approaches remain primarily descriptive and provide limited insight into how spatial organization may constrain or facilitate disease persistence. Here, we develop a therapy-aware spatial ecological framework that integrates ecological-context discovery, ecological opportunity landscapes, Ornstein-Uhlenbeck (OU)-like retention, Levy-like escape, and branching-like amplification to characterize pediatric leukemia tissues. Using normal bone marrow reference programs and pediatric leukemia spatial transcriptomic sections, we identify five recurrent candidate ecological contexts with distinct malignant, immune, stromal, vascular, inflammatory, and stress-associated features. These contexts form spatially heterogeneous ecological opportunity landscapes across bone marrow and extramedullary samples. OU-like summaries quantify context-specific attractor position and ecological retention, whereas Levy-like analyses identify rare cross-context displacements consistent with discontinuous ecological remodeling. Branching-like amplification integrates ecological abundance, latent displacement, opportunity, escape, and escaped-state fraction into an interpretable index of ecological expansion. An independent treatment-associated cohort shows that a primitive-state retention proxy, escape, and amplification generate coherent ecological profiles qualitatively consistent with residual persistence and relapse-associated expansion. Because the available spatial datasets are cross-sectional, these quantities are interpreted as operational summaries of tissue organization rather than direct estimates of temporal evolutionary processes. Together, the framework moves spatial transcriptomic analysis beyond static tissue mapping by providing an interpretable representation of ecological constraint, rare displacement, amplification, and therapy-associated persistence in pediatric leukemia.

cancer biology↗

bioRxiv2023

Impossible ecologies: Interaction networks and stability of coexistence in ecological communities

Does an ecological community allow stable coexistence? Identifying the general principles that determine the answer to this question is a central problem of theoretical ecology. Random matrix theory approaches have uncovered the general trends of the effect of competitive, mutualistic, and predator-prey interactions between species on stability of coexistence. However, an ecological community is determined not only by the counts of these different interaction types, but also by their network arrangement. This cannot be accounted for in a direct statistical description that would enable random matrix theory approaches. Here, we therefore develop a different approach, of exhaustive analysis of small ecological communities, to show that this arrangement of interactions can influence stability of coexistence more than these general trends. We analyse all interaction networks of N [<=] 5 species with Lotka-Volterra dynamics by combining exact results for N [<=] 3 species and numerical exploration. Surprisingly, we find that a very small subset of these networks are "impossible ecologies", in which stable coexistence is non-trivially impossible. We prove that the possibility of stable coexistence in general ecologies is determined by similarly rare "irreducible ecologies". By random sampling of interaction strengths, we then show that the probability of stable coexistence varies over many orders of magnitude even in ecologies that differ only in the network arrangement of identical ecological interactions. Finally, we demonstrate that our approach can reveal the effect of evolutionary or environmental perturbations of the interaction network. Overall, this work reveals the importance of the full structure of the network of interactions for stability of coexistence in ecological communities.

ecology↗

bioRxiv2024

Ecological and functional niches comparison reveals differentiated resource-use strategies and ecological thresholds in four key floating-leaved macrophytes

Niche theory has been widely used in ecology; however, few studies have attempted to combine information on functional and ecological niches (i.e. variation in traits and environmental requirements), especially for freshwater macrophytes. In this study we aim to describe the functional and ecological niches of four key nymphaeid species (Nuphar lutea, Nymphaea alba, Nelumbo nucifera and Nymphoides peltata) to investigate their environmental tolerance and functional adaptability. Twelve Italian populations per species were sampled. Functional and ecological niches were determined using hypervolumes based on eight functional traits and environmental variables, related to leaf structure and economics spectrum, and to water and sediment quality. Among the three Italian native species, N. lutea and N. alba showed intermediate niche size and position in the functional and ecological space, although N. alba appears to be less competitive due to a small functional niche and lower trait performance, which could explain its general tendency to decline. N. peltata appeared more specialized in its environmental requirements and characterized by highly acquisitive leaves, while the invasive alien N. nucifera exerted its competitive success by distinguishing its functional niche and expanding its ecological niche, through high investment of resources in leaves. Overall, all four target species share similar ecological niches, colonizing eutrophic ecosystems typical of intensive agricultural landscapes, but show different patterns in their functional niche. We demonstrate the applicability of an approach based on both functional and ecological niches to unravel species adaptation and strategies.

ecology↗

bioRxiv2024

Nonlinear thinking in ecology and evolution: The case for ecological scaling of the Threshold Elemental Ratio

Nonlinear dynamics govern ecological processes, thus understanding thresholds is important for measuring and forecasting effects of climate change and management of natural resources. However, identifying whether and how such thresholds scale across biological levels of organization remains challenging. Ecological stoichiometry, the study of the balance of multiple elements and energy in ecological systems, provides a framework for scaling thresholds. We broaden a key organismal concept from ecological stoichiometry theory, the Threshold Elemental Ratio (TER), to study how nonlinear dynamics operate in evolutionary and ecological processes across the organizational hierarchy. Traditionally, TERs are used to describe the elemental ratio at which the limitation of organismal growth shifts from one element to another. Following this definition, we make a case for broadening the ecological scale of the TER beyond organisms to include populations, clades, communities, and ecosystems. We show how TERs can be detected and translated across different scales of biological and evolutionary organization through simulation modeling, literature review, and synthesis of empirical examples from diverse systems and ecological scales including: cyanotoxin production in lakes, alder-salmon dynamics, and the Cambrian explosion. Collectively, we demonstrate that TERs are widespread and consequential across levels of biological organization and that such thresholds manifest from a diversity of mechanisms. Thus, scaling of the TER concept holds promise for advancing our understanding of nonlinear dynamics from the micro-evolutionary to macro-ecological.

ecology↗

bioRxiv2023

Machines learn ecological networks: automated discovery of ecological networks based on empirical data

Constructing ecological networks is known to be important and challenging in community ecology. In particular, to construct the holistic structure of ecological networks, identifying species interaction is essential but often costly and impalpable. Recent studies providing major challenges in assembling ecological networks have highlighted the need of new and more powerful approaches to reconstruct biological networks, including species interaction networks. In literature, there are no promising verifications in using machine leaning (ML) approaches to reconstruct ecological networks. In this work, we develop and employ a variety of ML methods, including penalized regression and graphical tools, to reconstruct ecological networks. For evaluation, we apply the methods to empirical time series data sets of 20 species abundances collected at Lake Constance in central Europe. We use resampled data to identify highly-ranked interactions among species and measure their consistency across 7 ML methods and 5,000 learning processes. We show that the best precision, recall, and F1 score were 0.48, 0.97, and 0.64, respectively, among all penalized regression methods under comparison. In summary, our study shows that machine learning methods offer promising data-driven and automated tools for reconstructing ecological networks and discovering underlying biological interactions among species.

ecology↗

bioRxiv2023

Ecological Network Resilience & Extinction Proxies - Updating Projections of Ecological Networks

Forecasting biodiversity and functioning changes to ecosystem composition and functioning under climate change requires using multi-species approaches that explicitly consider ecological interactions. Here, we propose a framework with which to incorporate considerations of (1) localised extinction risk proxies, (2) resilience mechanisms of ecological networks, and (3) extinction cascade directionality as a driving force of ecological change. These three aspects are seldomly considered when establishing ecosystems responses to climate change and biodiversity loss. Using this framework, we demonstrate that current practices may severely underpredict ecological change measured as loss of biodiversity and change in connectedness. Our novel framework which explicitly explores two-dimensional resilience landscapes defined by network resilience mechanisms (i.e., link loss sensitivity and realisation of rewiring potential) represents the most complete toolbox for assessment of vulnerability of ecological networks to extinction cascades. Ultimately, we propose that using localised extinction proxies, explicitly quantifying ecological network resilience through link-loss sensitivity and realisation of rewiring potential, as well as simulation of bidirectional extinction cascades will lead to improved capabilities of estimating ecosystem trajectories throughout the Anthropocene.

ecology↗

bioRxiv2026

Synchrony Genetics: Linking Ecological Mechanisms to Genetic Structure A framework for genetic inference in ecologically coupled systems

Spatial synchrony, correlated population dynamics across space, is a defining feature of ecological dynamics, shaping outbreaks, cycles, and waves across ecosystems. Yet its genetic consequences remain poorly resolved because classical population-genetic models assume demographic independence and equilibrium conditions that synchronised populations systematically violate. Here I introduce Synchrony Genetics, a general framework that treats ecological coupling as the causal process and spatial synchrony as only one observable manifestation of that coupling. The framework links the three canonical ecological coupling mechanisms, environmental (Moran-type) coupling, dispersal-driven coupling, interaction-mediated coupling, alone or in combination, to their characteristic genetic signatures. Under this view, genetic structure is not a static property of populations or a proxy for equilibrium connectivity, but an emergent indicator of how populations are ecologically coupled across space. These expectations are synthesised in a Prediction Matrix that maps coupling mechanisms to diagnostic contrasts across widely used genetic metrics, enabling mechanism attribution from genetic data alone or in combination with demographic information. By reframing genetic patterns as evidence of coupling mechanisms rather than equilibrium processes, Synchrony Genetics provides a mechanistic foundation for interpreting genetic data in spatially coherent systems where dispersal, demographic covariance, and ecological interactions jointly shape genetic signatures. More broadly, the framework establishes a new baseline for genetic inference in systems where ecological coupling violates demographic independence, repositioning genetic structure as mechanistic evidence of how populations are linked across space.

ecology↗

bioRxiv2026

The integrative ecology of the saddleback anemonefish, Amphiprion polymnus, reveals it is a host generalist across multiple ecological axes

Understanding how symbiosis generates biodiversity requires comprehensive insight into the underlying ecology to reveal broad evolutionary patterns, and the clownfish-anemone symbiosis serves as an excellent model. Recently, a new paradigm revealed sea anemone host associations of adult clownfish drove convergent evolution of host specialists and generalists. An alternative study downplayed host specificity in explaining clownfish evolution due partly to an incongruence between the behavior, biology, and host specificity of a single species--Amphiprion polymnus. Previously classified as a host specialist, we revisit the host specificity and ecology of A. polymnus by studying its host associations, behavioral, and dietary ecology compared to a co-occurring host generalist and specialist. By incorporating n = 358 observations, we find A. polymnus associates with five sea anemone species, including a newly documented host, and four at the adult life stage. We show that A. polymnus swim consistently far from their hosts and have broad dietary niches, an ecology that aligns with generalists. Cumulatively, our data indicate A. polymnus is a host generalist, and not a specialist as previously classified. Our findings serve to reconcile previously conflicting studies about A. polymnus, and reinforce the hypothesis that host is the key ecological variable for understanding clownfish evolution. Suitable subject areasbehaviour, ecology, evolution

ecology↗

bioRxiv2019

Is the addition of higher-order interactions in ecological models increasing the understanding of ecological dynamics?

Recent work has shown that higher-order interactions can increase the stability, promote the diversity, and better explain the dynamics of ecological communities. Yet, it remains unclear whether the perceived benefits of adding higher-order terms into population dynamics models come from fundamental principles or a simple mathematical advantage given by the nature of multivariate polynomials. Here, we develop a general method to quantify the mathematical advantage of adding higher-order interactions in ecological models based on the number of free-equilibrium points that can be engineered in a system (i.e., equilibria that can be feasible or unfeasible by tunning model parameters). We apply this method to calculate the number of free-equilibrium points in Lotka-Volterra dynamics. While it is known that Lotka-Volterra models without higher-order interactions only have one free-equilibrium point regardless of the number of parameters, we find that by adding higher-order terms this number increases exponentially with the dimension of the system. Our results suggest that while adding higher-order interactions in ecological models may be good for prediction purposes, they cannot provide additional explanatory power of ecological dynamics if model parameters are not ecologically restricted.

ecology↗

bioRxiv2017

Ecological, angler and spatial heterogeneity drive social and ecological outcomes in an integrated landscape model of freshwater recreational fisheries

Freshwater recreational fisheries constitute complex adaptive social-ecological systems (SES) where mobile anglers link spatially structured ecosystems. We present a general social-ecological model of a spatial recreational fishery for northern pike (Esox lucius) that included an empirically measured mechanistic utility model driving angler behaviors. We studied emergent properties at the macro-scale (e.g., region) as a result of local-scale fish-angler interactions, while systematically examining key heterogeneities (at the angler and ecosystem level) and sources of uncertainty. We offer three key insights. First, the angler population size and the resulting latent reginal angling effort exerts a much greater impact on the overall regional-level overfishing outcome than any residential pattern (urban or rural), while the residential patterns strongly affects the location of local overfishing pockets. Second, simplifying a heterogeneous angler population to a homogenous one representing the preference and behaviours of an average angler risks severely underestimating landscape-level effort and regional overfishing. Third, we did not find that ecologically more productive lakes were more systematically overexploited than lower-productive lakes. We conclude that understanding regional-level outcomes depends on considering four key ingredients: regional angler population size, the angler population composition, the specific residential pattern in place and spatial ecological variation. Simplification of any of these may obscure important dynamics and render the system prone to collapse.

ecology↗

bioRxiv2024

Ecological niche divergence or ecological niche partitioning in a widespread Neotropical bird lineage

Ecological niche divergence is generally considered to be a facet of evolution that may accompany geographic isolation and diversification in allopatry, contributing to species evolutionary distinctiveness through time. The null expectation for any two diverging species in geographic isolation is that of niche conservatism, wherein populations do not rapidly shift to or adapt to novel environments. Here, I test ecological niche divergence for a widespread, pan- American lineage, the avian genus of martins (Progne). Despite containing species with distributions that go from continent-spanning to locally endemic, I found limited evidence for niche divergence across the breeding distributions of Progne, and much stronger support for niche conservatism with patterns of niche partitioning. The ancestral Progne had a relatively broad ecological niche, similar to extant basal Progne lineages, and several geographically localized descendant species occupy only portions of the larger ancestral Progne niche. I recovered strong evidence of breeding niche divergence for four of 36 taxon pairs but only one of these divergent pairs involved two widespread, continental species (Southern Martin P. elegans vs. Gray-breasted Martin P. chalybea). Potential niche expansion from the ancestral species was observed in the most wide-ranging present-day species, namely the North American Purple Martin P. subis and P. chalybea. I analyzed populations of P. subis separately, as a microcosm of Progne evolution, and again found only limited evidence of niche divergence. This study adds to the mounting evidence for niche conservatism as a dominant feature of diversifying lineages. Even taxa that appear unique in terms of habitat or behavior may still not be diversifying with respect to their ecological niches, but merely partitioning ancestral niches among descendant taxa.

evolutionary biology↗

bioRxiv2018

Directional selection limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources

Microbial communities can evade competitive exclusion by diversifying into distinct ecological niches. This spontaneous diversification often occurs amid a backdrop of directional selection on other microbial traits, where competitive exclusion would normally apply. Yet despite their empirical relevance, little is known about how diversification and directional selection combine to determine the ecological and evolutionary dynamics within a community. To address this gap, we introduce a simple, empirically motivated model of eco-evolutionary feedback based on the competition for substitutable resources. Individuals acquire heritable mutations that alter resource uptake rates, either by shifting metabolic effort between resources or by increasing overall fitness. While these constitutively beneficial mutations are trivially favored to invade, we show that the accumulated fitness differences can dramatically influence the ecological structure and evolutionary dynamics that emerge within the community. Competition between ecological diversification and ongoing fitness evolution leads to a state of diversification-selection balance, in which the number of extant ecotypes can be pinned below the maximum capacity of the ecosystem, while the ecotype frequencies and genealogies are constantly in flux. Interestingly, we find that fitness differences generate emergent selection pressures to shift metabolic effort toward resources with lower effective competition, even in saturated ecosystems. We argue that similar dynamical features should emerge in a wide range of models with a mixture of directional and diversifying selection.

evolutionary biology↗

bioRxiv2021

Integrating ecological monitoring and local ecological knowledge to evaluate conservation outcomes

Successful conservation of long-lived species requires reliable understanding of long-term trends and historical baselines. Using a green turtle (Chelonia mydas) foraging aggregation in the northern Gulf of California, Mexico as case study, we integrated scientific monitoring data with historic catch rate reconstructions derived from Local Ecological Knowledge (LEK). Models fit to LEK and monitoring data indicate that turtle abundance is increasing, but only after ~40 years of safeguarding the species nesting and foraging habitats in Mexico. However, as population declines occurred 75% faster than increases, and current abundance is at ~60% of historical baseline levels, indicating the need for sustained, long-term conservation actions. This study demonstrates the potential of linking LEK and ecological science to provide critical information for conservation, by establishing reference baselines and gauging population status, while promoting equitable and sustainable futures for local communities.

ecology↗

bioRxiv2026

Feeding ecology and ecological risks of the invasive fish Coreoperca herzi revealed by gut content DNA and environmental DNA metabarcoding

Understanding the dietary patterns of introduced predators is essential for assessing their impacts on freshwater ecosystems. Here, we investigated the feeding ecology of the invasive Korean perch (Coreoperca herzi) introduced to the Oyodo River system, Japan, by integrating gut content DNA metabarcoding and environmental DNA (eDNA) metabarcoding. Fifty specimens were collected, and prey taxa were identified using metabarcoding targeting fish, aquatic insects, and crustaceans. In parallel, eDNA metabarcoding of habitat water samples was used to assess prey availability and selectivity. The results revealed that the Korean perch prey extensively on aquatic insects and fish. Aquatic insect prey were dominated by epilithic clinger taxa inhabiting stone surfaces, particularly mayflies, suggesting visual-mediated prey selection. Fish predation was frequently detected even in small individuals (<100 mm SL), in contrast to previous studies based on conventional methods, indicating that piscivory begins early and ontogenetic dietary shifts are not pronounced. Furthermore, quantitative fish eDNA analysis showed a positive relationship between eDNA concentrations of prey species and predation frequency, indicating opportunistic feeding on abundant, size-accessible prey. By applying two metabarcoding approaches, this study provides an integrated assessment of prey utilisation and environmental context, highlighting ecological risks posed by the Korean perch to freshwater communities in Japan.

ecology↗

bioRxiv2021

The non-random structure of multi-trophic ecological interactions maximizes species coexistence within ecologically realistic constraints

Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength, and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here we model community feasibility domains, a theoretically-informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators, and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.

ecology↗