Theoretical Ecology

Blooms of the toxic dinoflagellate Ostreopsis cf. ovata can cause respiratory irritation in beach users, yet their environmental drivers and temporal dynamics remain poorly understood. Here, we use a dynamical-systems approach to investigate how environmental conditions shape bloom dynamics from long-term time series of algal abundance and abiotic variables. Using the global modelling framework GPoM, we reconstruct nonlinear ordinary differential equation systems and identify candidate models capable of reproducing key features of the observed dynamics, including chaotic behaviour. Topological and statistical analyses reduce the candidate models to two systems that best capture the observed distributions and dynamical patterns. These models reveal distinct combinations of environmental conditions associated with bloom occurrence and amplitude, providing a framework for interpreting bloom trajectories and a potential basis for short-term prediction.

Ecological stoichiometry allows us to investigate how food quality affects food-web population dynamics. We consider periphyton, phytoplankton, and a shared Daphnia consumer in a closed system with separate benthic and pelagic phosphate pools. The producers have variable phosphorus-to-carbon ratios, while the consumer maintains a constant ratio. We construct the model, establish boundedness under stated conditions, characterize its grazer-free equilibrium families and their local spectra, derive conditions for positive consumer growth, and examine its numerical bifurcation structure as light-dependent carrying capacity varies. The bifurcation diagrams contain stable and unstable equilibrium branches together with a stable periodic family, revealing a more complex response to light enrichment than a single sequence of steady states and oscillations. A descending consumer equilibrium branch illustrates that greater light availability need not increase consumer biomass. We then introduce periodically varying carrying capacities and compare weak and strong seasonal forcing. Stronger forcing produces brief consumer rebounds in the high-light example, although the peaks diminish over the displayed interval. Together, the growth conditions and branch diagrams provide a framework for interpreting population dynamics in terms of food quantity and food quality. Independent numerical continuation identifies a Hopf crossing and a fold; the termination mechanism of the periodic branch remains unresolved.

Many angiosperm species possess self-incompatibility (SI) systems that prevent self-fertilization. Because empirical studies often report higher selfing rates in tetraploids than in diploids, we investigate whether sporophytic self-incompatibility (SSI) is more likely to break down after the introduction of a self-compatible (SC) allele in tetraploid populations. To address this question, we use analytical models and individual-based simulations to compare diploid and tetraploid populations under two main scenarios: (1) all SI alleles are codominant, and (2) SI alleles are structured into dominance classes. Overall, our results indicate that SSI is more difficult to maintain in tetraploids than in diploids, with dominance relationships playing a key role in the invasion success of an SC allele. When SI alleles are organized into dominance classes, increasing the dominance of the SC allele generally favors SSI breakdown in tetraploids, while diploids show weaker sensitivity to dominance, with SSI maintained across all dominance scenarios for the SC allele under sufficiently high inbreeding depression. However, when the SC allele is dominant over all SI alleles, SSI is more readily maintained in both the codominant and dominance-class models.

Species at the highest trophic levels are generally the least resilient to perturbations, yet the factors that determine their persistence remain poorly understood. We ask how the intrinsic self-regulation of a basal host governs the persistence of the trophic levels above it. To address this question, we use a minimal discrete-time model of a host-parasitoid-hyperparasitoid system motivated by the biology of trigonalid wasps, obligate hyperparasitoids that complete development only when their caterpillar host is also attacked by a primary parasitoid. We compare two forms of host self-regulation in the absence of natural enemies: scramble (Ricker) fecundity, appropriate when the parasitoid is the host's principal enemy and which recovers the classical model of Beddington and Hammond as a special case; and contest (Beverton-Holt) fecundity, appropriate when the host is held in check by many additional enemies. Using numerical continuation, Lyapunov exponents, and direct computation of the model's trajectories, we find that the form of host regulation is decisive for persistence at the top trophic level. Under scramble host dynamics, coexistence is extremely fragile with respect to both parameter values and initial conditions. Under contest host dynamics, coexistence is robust across broad parameter ranges and loses stability only gradually, through a supercritical Neimark--Sacker bifurcation and a subsequent torus-doubling route to chaos. Therefore, in at least some trophic networks, the resilience of the highest levels can be determined at the base of the network.

Squid abundance has been reported to increase globally between 1970 and 2010. This increase has been hypothesized to result from two primary factors: the loss of top predators due to overfishing and rising ocean temperatures. The decline in apex predators may lead to the expansion of squid populations either through reduced predation pressure or diminished competition with juvenile predators. Concurrently, increased temperatures could enhance the somatic growth rates of squid, thereby accelerating their population growth. However, empirically disentangling the impacts of predator loss and temperature on squid biomass remains challenging, especially in a food-web context. In this study, we used a size- and trait-based model of upper trophic levels that resolves the ecosystem structure -- biomass and trophic interactions of fish and squid -- for varying depth, temperature, and secondary production, to investigate two hypotheses of the historical expansion of squid, i.e., the effects of predator depletion from fishing and rising temperatures on squid biomass. Our model reveals that intensified fishing of squid predators -- specifically large demersal fish in shelf systems and large pelagic fish in open oceans -- leads to a slight increase in squid biomass. Conversely, elevated temperatures are associated with a decline in squid biomass. This temperature-driven reduction in biomass is attributed to an increased metabolism of squids beyond the available food supply. If historic overfishing on large marine predators continues to be curtailed, we expect a corresponding reduction in global squid biomass and fisheries potential, which could be further exacerbated by rising temperatures.

Spatial population structure and seasonality are both central to the spread of many infectious diseases of plants, animals and humans. While seasonal forcing in transmission often plays an important role in epidemiological models of a wide range of infectious disease, and we now have some theoretical understanding of the dynamical impacts of spatial structure, the combined effects of these two ubiquitous processes has not been examined in detail. Here, we develop a novel model to explore the combined influence of spatial structure and temporal variability on disease dynamics. Spatial structure is represented using a lattice-based approach with near-neighbour interactions, while temporal variability is included through regular, seasonal, variation of the transmission rate. We use bifurcation analysis of a pair approximation of the full spatial model to identify the parameter regimes associated with qualitatively distinct dynamical behaviours. The model exhibits a remarkably wide range of complex dynamics, including limit cycles, quasi-periodic cycles, multi-year cycles, chaotic dynamics and bistability between these different states. In particular, complex dynamics occur when reproduction is predominantly local, with the dynamics depending critically on the amplitude of the seasonal transmission rate. We show how high transmission rates, high birth rates and in particular low recovery rates are requirements for complex dynamics. We predict that SI-type disease interactions in plant pathogen systems will show complex dynamics even with relatively global transmission dynamics.

A high proportion of the world's population relies on marine fisheries as a source of food and employment, highlighting the need for sustainable exploitation strategies. However, fisheries management commonly relies on single-species models that overlook ecological interactions and economic trade-offs, which may lead to stocks being exploited above sustainable levels. To address this gap, we developed a novel size-structured, multispecies bioeconomic module integrated within the spatially explicit, individual-based OSMOSE ecosystem model. OSMOSE represents exploited fish communities in which individuals interact through opportunistic, size-dependent predator-prey interactions which are explicitly incorporated into the bioeconomic framework. The bioeconomic model accounts for multiple species and size classes, allowing for size-dependent market prices. Fishing costs and profits are represented using an extension of the Gordon-Schaefer model to multiple interacting species, while consumer demand is modelled using a nested Dixit-Stiglitz utility function with three levels of constant elasticity of substitution for fish commodity, species and size classes. We present a method for estimating parameters for the bioeconomic module when empirical estimates are unavailable, using the North Sea OSMOSE configuration, which comprises 15 species. Our method successfully estimated the six bioeconomic parameters required to operationalise the model. Results indicate that differences in cost parameters were primarily associated with variability in species biomass rather than fishing gear, while fish prices were more strongly influenced by consumer's demand than by availability. This framework provides a basis for assessing the economic consequences of alternative climate change scenarios and supporting sustainable fisheries management.

Despite the impacts of within-host disease dynamics on disease outcomes in individual hosts and disease spread among-hosts, generic models of within-host population dynamics have received far less attention than their among-host counterparts. While a number of models have been proposed to explore theoretical eco-evolutionary dynamics, they have yet to be evaluated for estimability, raising questions about their ability to provide reliable inference when confronted with data. We evaluated the estimability of two generic within-host population dynamics models by assessing: (1) parameter estimation, our ability to recover correct values of model parameters from data, (2) the consequences of mis-assigning the underlying mechanistic model on parameter estimation, and (3) the reproduction of qualitative dynamics, or, our ability to use parameter estimates to reproduce observed dynamical behaviours. In some cases, fitting a mis-matched mechanistic model to time series data produced reasonable parameter estimates that were able to reproduce system dynamics, and that when provided the data-generating model, parameter uncertainty can produce substantial behavioural uncertainty. Our findings highlight the impacts of structural, parametric, and behavioural uncertainty on inference, and demonstrate the value of improving system-specific knowledge to prevent the use of incorrect functional forms and of measuring consequential parameters to improve estimability.

Investigating the factors that stabilize biological communities is a central topic in ecology. Response diversity, defined as variation in species responses to environmental change, has been proposed as a key mechanism underlying the biodiversity-ecosystem functional stability (BEFS) relationship, whereby greater species diversity enhances ecological stability. Previous studies have shown that response diversity promotes ecological stability by generating asynchronous population fluctuations and the resulting compensatory dynamics. Although several metrics have been proposed to quantify response diversity, they do not explicitly consider the presence of insensitive species whose performance is unaffected by current environmental conditions. To examine how insensitive species influence response diversity, species persistence, and ecological stability, we conducted numerical simulations of a generalized Lotka-Volterra model under environmental forcing. We first confirmed that increasing variation among sensitive species increased the response diversity index and stabilized community dynamics. We then examined a scenario in which response diversity depended solely on the proportion of sensitive and insensitive species, assuming that all sensitive species responded identically to environmental change. Under this assumption, the response diversity index was maximized when sensitive and insensitive species occurred in equal proportions, whereas increasing the number of sensitive species monotonically destabilized community dynamics. Consequently, the relationship between response diversity and community stability depended on how response diversity was generated, such that higher response diversity could even be associated with lower community stability. These findings demonstrate that overlooking environmentally insensitive species can obscure the mechanisms linking response diversity and ecological stability. More broadly, our results reveal that response diversity comprises at least two distinct biological components--species sensitivity and response variation among sensitive species--that can have contrasting consequences for community stability. We therefore highlight the need to quantify sensitive species empirically and to develop response diversity metrics that distinguish these components. Author SummaryUnderstanding why some communities remain stable despite environmental change is a longstanding goal in ecology. Response diversity, which refers to differences in how species respond to environmental change, has been proposed as a key mechanism explaining why greater biodiversity (species richness) can promote ecological stability. Because species respond differently to changing environments, declines in some species can be compensated by increases in others, helping to stabilize community dynamics. However, previous studies have rarely considered species that are insensitive to current environmental changes. Using a mathematical model, we show that response diversity can arise from two distinct biological components--the number of sensitive species and variation in their responses--and that these components can have contrasting effects on ecological stability. When response diversity reflects variation among sensitive species, greater response diversity stabilizes community dynamics, as expected. In contrast, when response diversity changes only because of the proportions of sensitive and insensitive species, higher response diversity can be associated with lower community stability. Our findings highlight the importance of quantifying the number of sensitive species and developing response diversity metrics that distinguish species sensitivity from variation in responses among sensitive species.

Ecological stability and function emerge from the many and diverse types of interactions among species. Ecological networks represent the structure and strength of species interactions, but their study is often restricted to single interaction-types, likely due to an absence of models for plausibly (i.e., nonrandomly) structured 'multiplex networks' of multiple interaction types. Here, we present a general but mechanistic model that simulates multiplex networks using the different, but correlated, traits that determine different species interactions. We show that ecological stability varies systematically with these trait correlations by controlling the emergent complexity and positioning of strong interactions in the community. Our model provides a flexible but power tool to study multiplex networks under alternative trait-based hypotheses, offering new insights into the structure and stability of complex natural systems.

Seasonal changes in climate, resources, and trophic interactions jointly shape prey population dynamics in ecological systems. In monsoon-driven tropical and subtropical landscapes, prey populations cycle between cool, wet, favourable periods during the rainy seasons and hot/cold, dry and sub-optimal conditions outside the rainy seasons, with resource availability and predation risk changing across seasons of the year. Defensive strategies and species interactions such as aposematism and Batesian mimicry are expected to interact with seasonal changes in resource availability and predation risk in determining population dynamics of prey species. Here we study population dynamics of mimetic butterfly community members using a 12-year long-term dataset from a subtropical urban forest in peninsular India. Our results show that climate and species interactions differentially influence population dynamics of different functional categories in mimetic butterfly communities, i.e., of aposematic species, mimetic and non-mimetic forms of mimetic species, and close relatives treated as ecological and phylogenetic contrasts. Population dynamics of non-mimetic species and forms were predominantly influenced by climate parameters such as temperature and precipitation, whereas population dynamics of mimics were more deeply impacted by mimetic interactions. Population dynamics of non-mimetic and mimetic forms of the same species showed distinct decoupling, with population dynamics of non-mimetic forms being similar to their non-mimetic relatives (phylogenetic contrasts). On the other hand, population dynamics of aposematic species and mimetic forms/species followed the predictions of negative frequency dependence and phase-shifting in mimicry theory: (a) mimetic forms/species were less abundant than their Batesian models, (b) the harmonic mean of populations of Batesian models influenced the upper limit of relative frequency of mimetic forms/species to a greater degree in these continuously breeding, seasonally fluctuating populations, and (c) populations of Batesian mimics peaked after population peaks of their Batesian models. These results reveal that climate and species interactions differentially determine population dynamics of prey species by functional categories at the community level, rather than by species identity and individual species attributes and resource demands.

When ecological communities experience environmental change, continued coexistence may require rapid adaptation to avoid extinction, i.e., evolutionary rescue. This rescue can be inhibited by competition. Combining Lotka-Volterra and population genetic models, we investigate how both within- and between-species competition impact the probability of rescue. With a single species we derive closed-form approximations that quantify the negative impact of competition. We then use a two-species model to examine how the sweep of a rescue mutant in one species inhibits establishment of rescue mutants in another, a process we term survival interference. This interference is strongest when the stronger competitor begins recovering first, creating eco-evolutionary priority effects despite conditions permitting deterministic coexistence. Finally, considering larger competitive communities, we document how survival interference reduces the number of rescued species but weakens with species richness and unevenness. These results move us closer to predicting evolutionary rescue in ecological communities.

After introduction, many non-native marine species are dispersed planktonically. Secondary spread within the non-native range has been shown to prevent the establishment of the introduced species if the advection of larvae prevents sufficient return of larvae to maintain the population in the face of competition with native species. However, those studies have largely neglected the effects of spatial variation in alongshore larval transport. We examine the introduction of a novel species with planktonic dispersal into a more realistic coastal environment which includes spatial variation in larval transport estimated from the Mercator Ocean 1/12th degree global circulation model. The introduction may either be from a distant habitat, or through range expansion. We find that there are locations in the global coastal ocean where introduced species are more likely to persist because of spatial variation of coastal currents. These include regions where alongshore larval transport diverges, such as estuaries. The location where a non-native species is introduced may not be where it flourishes - it cannot be assumed that the region where invading species are first noticed to be abundant is the region where it was introduced. We extend closed-population theory to open coastal systems to estimate persistence as a function of local circulation, habitat extent, and the competitive advantage of the introduced species. Software is provided which allows the estimations of regions where introduced species are more likely to persist and flourish as a function of larval depth behavior, planktonic duration and release timing.

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.

For organisms with biparental care, successful reproduction hinges on coordination between partners. Seabirds face an extreme coordination challenge because parents must schedule nest attendance on land with long-distance foraging trips at sea. We present a computational model of incubation schedules for a vulnerable seabird, the Leachs Storm-Petrel (Hydrobates leucorhous). Using only simple energetic rules and parameters, the model recapitulates natural incubation rhythms, exposes a tradeoff between parent energy and egg attendance, and predicts severe reproductive failure in harsh environments. Incubation primarily fails through "schedule breakdown" -- a single point in the season when both parents spend too long foraging and the egg dies from cold. The resilience of the developing offspring to neglect is thus a fundamental adaptation to the uncertainties of biparental care. These results raise new alarms about the indirect causes of reproductive failure in sensitive marine species and provide theoretical foundations for the evolutionary ecology of scheduling behaviors.

In natural ecosystems, community dynamics are driven by diverse interactions operating through both ecological and reproductive processes. Although theory predicts that these processes jointly generate complex and contingent dynamics through the interplay of stabilizing (negative frequency dependence arising from niche differences) and destabilizing effects (positive frequency dependence caused by reproductive interference), empirical tests of this prediction remain scarce. Here, we combined experimental and theoretical approaches to investigate the dynamics of two congeneric annual plant species (Commelina) experiencing both ecological competition and reproductive interference. Our empirically parameterized model revealed a large niche difference and mutual reproductive interference. However, the reproductive interference was not strong enough to generate pronounced contingency of community dynamics on initial conditions. Instead, coexistence was the most likely outcome regardless of initial species composition. Parameter exploration indicated that the weak contingency arose because reproductive assurance strategies of Commelina species buffered the minority disadvantage caused by reproductive interference. These findings provide empirical evidence that reproductive interference does not necessarily destabilize coexistence and that it may be outweighed by ecological niche and fitness differences. Our study highlights the importance of integrating ecological and reproductive processes towards better prediction of community dynamics.

Host-parasite relationships are defined by transmission. Transmission takes diverse forms, with horizontal transmission modes generally characterized as density- or frequency-dependent. However, many observed parasites display mixed-mode transmission using both density- and frequency-dependent routes. To investigate how mixed-mode transmission impacts epidemics, we assessed infection prevalence under single- and mixed-mode transmission when there was a linear trade-off between the probability of infection by the two modes. Our results show that the prevalence of a parasite with mixed-mode transmission can be greater than one with a single-mode only when density- and frequency-dependent routes are associated with different effects of the parasite on host fitness. This work shows that mixed transmission modes per se may not necessarily increase disease prevalence, and that substituting two transmission modes for a single one may result in higher prevalence under limited conditions.

Understanding how species interactions, ecological drift, and immigration jointly shape community composition is a central goal of community ecology. These processes determine not only average community composition, but also how often different community states occur. Here, we ask how competition reshapes the neutral distribution of community composition generated by drift and immigration. We analyze a stochastic two-species competition model with immigration and derive the stationary distribution of relative species composition near neutrality. The stationary distribution shows when communities are usually balanced and when they are instead dominated by one species. Species dominance can become common even when the corresponding deterministic model predicts stable coexistence, particularly in smaller communities. Balanced and dominance states can also occur together, producing a trimodal distribution. Competitive asymmetry further biases the distribution toward the competitively favored species. Thus, the interplay between competition, ecological drift, and immigration determines not only how much community composition varies, but also which community states are most likely to be observed. Our results provide an analytical bridge between neutral and competition theory and a framework for interpreting variation in community composition.

Genome scale metabolic models provide detailed mechanistic descriptions of cellular metabolism, whereas black box growth models capture physiological behaviors using a small number of effective parameters. However, the quantitative relationship between these two modeling scales remains unclear. In this study, we develop a thermodynamic framework that connects black box growth models with thermodynamically constrained genome scale metabolic models. By linking black box model parameters with Gibbs energy dissipation rates derived from genome scale metabolic models, we demonstrate that coarse grained physiological descriptions can be obtained from detailed metabolic networks while preserving their thermodynamic foundation. We validate this framework in both Escherichia coli and yeast, showing that the resulting black box models reproduce key physiological behaviors, including growth dynamics, biomass yield, and overflow metabolism observed experimentally. Our results indicate that black box growth models and genome scale metabolic models are connected through shared thermodynamic constraints, revealing a consistent thermodynamic basis across different levels of metabolic description. This framework provides a general approach for integrating detailed metabolic networks with simple black box growth models and enables efficient multiscale modeling of cellular metabolism.

O_LISeasonal forcing is a dominant factor shaping host-pathogen interactions and disease dynamics across many wildlife systems, including species impacted by environmentally transmitted parasites. How seasonality in parasite dynamics translates to the host when the infection and disease impacts operate at different timescales, however, remains poorly understood. C_LIO_LIWe investigate how seasonality shapes sarcoptic mange in bare-nosed wombats, Vombatus ursinus, a disease caused by the environmentally transmitted parasitic mite Sarcoptes scabiei, causing a protracted clinical time-course in the host. Using an empirically informed state-based deterministic model we explore how wombat population trajectories are influenced by (i) seasonal constraints to mite survival and (ii) in context of host-pathogen encounter rates, as measured by the ratio of burrows to wombats. C_LIO_LIWe demonstrate three long-term outcomes of wombat-mange: host and parasite extinction, endemic disease, and disease-free. We find seasonal environments narrow the range of host-pathogen encounter rates that support S. scabiei persistence relative to stable environments, and prevalence and population sizes vary more in seasonal compared to stable environments except under moderate host-pathogen encounter rates when seasonal effects are less apparent. We also find that a protracted infectious period is essential for host-parasite coexistence in the wombat-mange system. C_LIO_LIOur seasonal model results are consistent with field observations, such that mange prevalence in natural populations increases during seasons of longer off-host mite survival. Application of these findings suggest management efforts could reduce host population impacts through disease management in seasons with longer off-host parasite survival or reduce the environmental reservoir through disease management in seasons with shorter off-host survival. C_LIO_LIWe provide novel, mechanistic explanations for distinctive population trajectories that arise from a seasonally forced wildlife disease, including climate factors that operate independently on parasites, host demography, and disparate timescales over which seasonality affects parasites and hosts. Broadly, linking seasonality to long-term population dynamics can improve the predictability and management of wildlife diseases, but requires an understanding of how local intrinsic factors interact with seasonal pressures over time. C_LI