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.

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.

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.

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.

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.

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.

Environmental variability influences the evolution of phenotypic plasticity and the ecological interactions among species in communities, but it is unclear how these processes interact to affect ecological systems across scales. We investigate models describing how plasticity evolves in competitive communities exposed to seasonal variation in abiotic conditions. We conducted eco-evolutionary analyses to identify communities of coexisting species with evolutionarily stable plasticity traits. These analyses revealed that the presence of interspecific competitors constrained the evolution of plasticity in some species, which instead evolved to be specialists. Plasticity often impaired species coexistence by allowing individual species to control more niche space. Communities containing plastic species were usually less species-rich but more productive and less variable. Similar results were obtained when plasticity rate was evolutionarily labile, but plasticity rate altered the patterning of temporal niche partitioning. This work shows how plasticity and competition combine to shape ecological and trait responses to environmental variability.

Fishery-dependent indices of abundance are often used to inform stock assessments, but fishing effort is generally not randomly distributed. Fishers concentrate sampling in locations expected to be profitable, leading to a form of preferential sampling that can bias model-based indices of abundance. We show that when the strength of preferential sampling is constant over time, it biases only the magnitude of an index, which is absorbed by estimated catchability parameters in most stock assessments. A more interesting case arises when the strength of preferential sampling changes over time; in these cases the index trend can be biased in a way that conventional catchability estimation cannot correct. Using simulations structured on long running trawl survey data from the west coast of the USA for shortspine thornyhead (Sebastolobus alascanus), we evaluate two structurally different methods for correcting time-varying trend bias: a joint spatiotemporal model of the density and sampling processes, and a discrete-choice model of fisher location choice. Our simulations include two mechanisms generating preferential biases: effort tracking local density, and effort responding to a spatially structured, but unobserved anomaly that also inflates catch. We show that the ability of the estimation methods to correct bias depends on the mechanism responsible for concentrating effort; while the joint model is robust to both data-generating mechanisms, the discrete choice model is only able to correct biases when the mechanism is anomaly-driven. As a real-world case study, we apply these corrections to simulated preferentially sampled data and include indices in the shortspine thornyhead stock assessment model. We found that uncorrected time-varying preferential sampling positively biased estimates of a major recruitment event and, to a lesser extent, unfished recruitment. This bias propagated through to bias in overfishing limits that grew as the population was projected forward. While bias-corrected indices still produced some positive biases in overfishing limits, these were substantially reduced compared to the naive approach. As the mechanism of preferential sampling is rarely known in advance, our results suggest that the joint model represents a robust default for correcting fishery-dependent indices of abundance.

Shifts in predation and human harvest often coincide with changes in life history trait distributions in prey populations. These changes may arise via evolution or plasticity, and extrinsic environmental effects may affect such traits. This complexity prompts questions about how trait distributions are shaped by multiple factors' interactions. We hypothesized that food availability and climate affect both the magnitude, and mechanism (evolution vs. plasticity), by which traits change in response to harvest's intensity and size selectivity. We tested these hypotheses using individual-based bioenergetic population models allowing both plasticity and evolution. We exposed genetically diverse fish populations to gradients of productivity and stochasticity in food availability, crossed with gradients of selective or nonselective fishing mortality. Food availability shaped traits both plastically and evolutionarily, and modulated how harvesting influenced those responses. Food effects can suppress harvest impacts altogether, or obscure evolution's effects on phenotypes. However, when climatic factors shape food-driven changes by affecting both fish and their prey, either synergistic or antagonistic interactions can emerge between food variation and harvest in determining phenotype distributions. Our research suggests that understanding consumer-resource dynamics, and climatic processes shaping them, is essential to interpreting how harvest can shape traits relevant to population viability and fisheries sustainability.

Forecasting infectious disease dynamics is important for ecological management and emerging disease preparedness, yet many systems lack the long-term datasets required to develop reliable predictions. Spatial replication may provide an alternative by allowing information to be shared among populations, but the extent to which this improves forecasting remains unclear. Here, I used epidemic and temperature data from 20 replicated semi-natural Daphnia-parasite pond populations monitored across four seasons (80 epidemics) to test whether information from multiple populations improves forecasts and whether environmental similarity identifies informative populations. I forecasted disease prevalence, infected host density, and healthy host density using a benchmark model, autoregressive integrated moving average (ARIMA), and time-series regression models. Models were trained using focal-population data alone, mean dynamics across other populations, or temperature-weighted mean dynamics from environmentally similar populations. Epidemics showed strong seasonal structure, with prevalence and infected host density generally peaking during the warmest period of the season. However, no forecasting approach consistently outperformed others across all variables. Temperature improved forecasts of prevalence and infected host density but not healthy host density. Multi-population training improved forecasts in some cases, particularly for ARIMA models, whereas temperature-weighted averaging provided no additional benefit over simple averaging. Regression models consistently produced the most accurate forecasts of healthy host density. These results show that borrowing information among populations can improve forecasts under specific conditions but is not universally beneficial, highlighting the importance of matching forecasting approaches and data sources to the ecological processes being predicted.

Historical plague mortality records provide unusually detailed temporal records of epidemic dynamics. However, the ecological processes underlying those dynamics are rarely observed. This creates a fundamental challenge for inference into ecological mechanisms, as multiple combinations of unobserved ecological parameters and initial conditions may produce similar mortality trajectories. We investigated this problem by examining the geometry of parameter compensation for models of the Barcelona plague outbreak of 1490. We fit a mechanistic rat-flea-human transmission model to daily human mortality data under three values of flea search efficiency, then characterized the resulting joint posterior distributions. We then used a phenomenological model in which human infection was driven by a time-varying external force of infection, allowing us to separate parameter dependencies arising from deterministic structure. The mechanistic model reproduced the broad temporal structure of the observed epidemic, but exhibited strong linear and nonlinear dependencies among transmission parameters and initial rat and flea conditions. Similar dependencies persisted in the phenomenological model, despite its substantially reduced complexity. However, fixing the timing of the external hazard substantially reduced these dependencies. These results demonstrate that detailed mortality records can constrain the temporal pattern of an epidemic without uniquely identifying the mechanisms that generated it. Rather than simply adding additional mortality records, improving mechanistic inference from historical epidemics may require recovering independent observations of upstream ecological processes, such as timing of rodent or flea dynamics as well as environmental drivers. Mechanistic models can therefore serve not only to reconstruct historical epidemics but also to identify which missing observations would most improve inference.

Environmental change alters species growth rates and, through species interactions, can produce complex effects on community functioning and composition. Because functional traits mediate both environmental responses and competition, rapid trait adaptation may modify these effects, but its consequences in multispecies communities remain unclear. We used a trait-based model of competitive communities to test how adaptation affects the mean and diversity of species growth responses to environmental change, and how this influences functional and compositional resistance (how unaffected the community is by the environmental change) and resilience (how well it recovers after it has acclimated to the altered environment). Adaptation altered response variance more strongly than the mean by changing the balance between environmental optimisation and competition-driven trait divergence. As a result, adaptation strongly increased compositional stability, but had less clear effects on abundance. Adaptation also generated a trade-off between resistance and resilience: faster adaptation improved resistance but reduced resilience as communities were displaced less by the environmental change and thus recovered less. Overall, adaptation can be stabilising, but higher adaptation rates do not necessarily produce greater stability across all stability components.

Allergic conjunctivitis is an inflammation of the conjunctiva caused by allergen; it is common disorder affecting up to 40% of the population. In this work, we study seasonal allergic conjunctivitis (SAC), also called "hay fever eyes", which is caused by exposure to airborne pollens. We develop a mathematical model quantifying the ocular immune system response to the allergens. First, we present a simplified qualitative description of the immunopathogenesis of SAC. Then, we express each chosen immunopathological mechanism mathematically to construct a system of thirty-one ordinary differential equations. We compare summary statistics of the predicted observable immune signals to experimental measurements and find our model captures key qualitative features of SAC progression. We then compare our predicted time series of histamine concentration to symptom scores and find a strong correlation suggesting the model predicts relevant clinically trends. Next, we calibrate the model through multi-step process. We find the most influential parameters are the production and depletion rates of IL-4, and the production rates of IL-5 and IL-8. These cytokines are targeted in treatments for asthma, atopic dermatitis, and severe eosinophilic associated disorder and suggest potential therapeutic targets for SAC. Our calibrated model mimics most of the summary statistics of the experimentally observable immune signals with discrepancies for IL-5 and IL-13 indicating that additional immunopathological mechanisms could be important.

Phytoplankton-virus interactions are pivotal in understanding the intertwined dynamics of these key players of the global carbon cycle. Although antiviral resistance is the main hypothesis to solve the apparent paradox of their coexistence, identifying its molecular determinisms and mechanisms, as well as their dynamical consequences, remains challenging. By combining hypotheses about such determinisms and mechanisms, we designed 36 dynamical models and fitted them to experimental infection kinetics in a pioneering selection model approach. Through such a multiple hypotheses testing, we identified that resistance in the model species Ostreococcus tauri is most likely to be predominantly intracellular, to control lysis through budding, and to be acquired by phenotypic plasticity. We further showed that viral kinetics are strongly impacted by both the antiviral resistance's molecular determinants and virions' inactivation through attachment to lysis detritus, as the latter generates a key negative feedback loop during the 'bust' stage of the interaction dynamics.

Buruli ulcer (BU) is a neglected tropical skin disease caused by infection with Mycobacterium ulcerans. The mode of its transmission into the skin has not been fully clarified, but it is believed to involve mechanical transfer, perhaps by insect bites. M. ulcerans secretes a toxin called mycolactone which causes necrosis of the skin and formation of large skin lesions. Understanding the development of these skin lesions over time is crucial to understand the pathogenesis of BU. We formulate a mathematical model to describe the space-time dynamics of M. ulcerans bacteria, mycolactone and skin cell densities as well as fibrin deposition. We investigate the relative contributions to ulcer formation of direct cytotoxicity of mycolactone and ischaemia following the loss of coagulation control. Our results are consistent with existing biological evidence that higher mycolactone concentrations accelerate cell death. Furthermore, the model indicates that the rate of wound expansion is primarily controlled by the diffusion and net birth rate of bacteria, which sets the overall scaling for the speed of wound expansion, along with two dimensionless constants that define the shape of the trajectory of wound edge position against time, namely the ratio of the mycolactone diffusion coefficient to the bacterial diffusion coefficient, and the ratio of the mycolactone degradation rate to the bacterial net birth rate. When mycolactone diffuses significantly faster than bacteria spread or when mycolactone degrades more slowly than bacteria reproduce, the initial development of the wound is faster. In contrast, the long-term wound expansion speed appears to be relatively unchanged across a wide range of the dimensionless parameters.

Game theory in biology started as an attempt to model animal contests by making assumptions about the fitness costs individuals pay in aggressive interactions. Among the different approaches, a notable one is to construct mechanistic models, using assumptions about behaviours and cognitive processes. Reinforcement learning is an important cognitive process, and we use it here to model pairwise and coalitional contests. We study situations where territorial neighbours become so-called dear enemies. A defending individual can get help from a neighbour through a defender-neighbour coalition against a challenger attempting territory takeover. In our model, coalition members have an advantage in contests in terms of costs of aggression, with the challenger being exposed to aggression from both members and each member only receiving part of the aggression from the challenger. We find that notable costs of border conflicts between neighbours, together with substantial advantages for coalitions, favour intervention. Neighbours intervene when they can help weaker defenders, in this way avoiding costs of border renegotiation with a new and potentially stronger territory owner. We also introduce different forms of perceived costs into the model, which we show correspond to the range from pure mutual assessment to partial self-assessment.

Trigger waves are positive feedback-mediated traveling waves that propagate without slowing down or diminishing in amplitude, enabling biochemical coordination over distances where diffusion alone would be insufficient. Apoptosis can spread as trigger waves of caspase activity, as demonstrated in Xenopus eggs and egg extracts. We measured apoptotic wave speeds in extracts at two cell cycle states and found that waves propagated more slowly in M-phase arrested cytostatic factor (CSF) extracts than in interphase extracts (21 versus 27 m min-1). Luther's equation, v=2{surd}D{kappa}, predicts wave speed from the diffusion coefficient D and the positive-feedback doubling rate {kappa}. However, D and {kappa} were comparable between the two extracts and could not explain the speed difference. This discrepancy highlighted a key limitation of the classical equation: it does not account for an activation threshold in trigger wave initiation, and in fact the threshold required to trigger apoptosis was found to differ between cell cycle states. We therefore extended Luther's equation to include such a threshold, obtaining v{propto}{surd}(AD/Uc ), where A is the reaction rate and Uc is the activation threshold. Perturbing apoptotic sensitivity confirmed the predicted threshold-speed relationship. These findings establish the activation threshold as a key determinant of trigger wave speed and provide a broadly applicable scaling law for predicting the spread of switch-like biochemical signals.

Rubber vines (Cryptostegia madagascariensis and C. grandiflora) are highly invasive weeds in the semi-arid landscapes of many tropical regions. The rust fungus Maravalia cryptostegiae has provided a spetatular and sustainable control of invasive C. grandiflora populations in Australia, after its scientific and quarantine-authorized release in the context of a classical biological control program. The Caatinga biome of Northeastern Brazil is under a devastating invasion by C. madagascariensis. This species is also susceptible to M. cryptostegia but its epidemic potential towards C. madagascariensis in Brazil remains unknown. We developed a weather-driven epidemiological model integrating experimentally derived relationships for infection as a function of temperature and wetness duration and for temperature-dependent latent development. Three infection-response models were fitted to 20 experimental temperature x wetness combinations from the literature and compared using information criteria, grouped cross-validation and a parametric bootstrap. The selected model, in which thermal breadth increases with wetness duration, was best supported (R2 = 0.931; {Delta}AICc [≥] 3.3 relative to the alternatives), and prolonged wetness significantly broadened the thermal range favorable for infection ({gamma} = 0.30 oC h-1; bootstrap P = 0.002). The infection and latent-development models were coupled with 25 years (2001-2025) of hourly NASA POWER data for 65 grid cells representing rubber-vine-infested locations in Northeastern Brazil, with wetness defined as hours with relative humidity equal or greater than 90%. Infection potential was concentrated in March-June (73% of the annual total, peaking in April), coinciding with the regional rainy season, and was highest in the interior of northern and central Ceara. Predicted potential varied more than a hundredfold among grid cells and was lowest along parts of the Ceara coast and the lower Jaguaribe valley, which include some of the largest infestations; the magnitude of these contrasts depended on the humidity threshold used to define wetness. Applied with identical rules to 13 historical release sites in Queensland, Australia, the model reproduced the documented wet-season timing of rust activity, and predicted potential was on average higher at sites with reported high rust impact, although the difference was not statistically significant. These results indicate strong seasonal and geographic structuring of weather-driven epidemic potential and provide testable hypotheses for selecting release periods and locations for M. cryptostegiae in Brazil.

Forecasting and causal inference in complex, non-linear dynamical systems are fundamentally challenging due to noisy and high-dimensional time-series data. While Empirical Dynamic Modeling addresses this via state-space reconstruction in Euclidean space, it suffers from the "curse of dimensionality," distorting distance measurements as dimensions increase. This study proposes Forest-Weighted S-map (FORWS) and Forest-Weighted Causal Inference (FORWC), utilizing adaptive "forest weights" derived from random forest ensembles instead of Euclidean metrics. Using simulated and empirical ecological datasets, I demonstrate that these methods exhibit comparable or improved forecasting skill relative to conventional tools, while offering substantial resilience against dynamic process noise and mitigating the curse of dimensionality. Notably, FORWC enables multimodal causal inference, successfully extracting directed interactions between high-dimensional acoustic vectors and scalar temperature data monitored in a honeybee hive. By bridging chaotic physics with machine learning, this framework accurately extracts intrinsic manifold topology, providing a robust paradigm for uncovering causal networks in complex real-world environments.

Complex biological behaviours often emerge when a system is faced with mutually incompatible priorities. In such situations, the field of multi-objective optimisation can provide an informative and predictive theoretical foundation for biology. Here we adopt this paradigm in exploring the rich dynamic behaviour of mitochondria inside cells. Using plant cells as a model system, we use physical modelling to characterise the "morphospace" of possible mitochondrial behaviours, and single-cell microscopy with video analysis and network modelling to characterise collective mitochondrial dynamics. We show that wildtype Arabidopsis mitochondrial dynamics near-optimally resolve a tradeoff between maintaining physical spacing and supporting biomolecular exchange. With existing and new experimental data, we show that these dynamics adapt under mutational and chemical challenges to support a rebalanced, but still near-optimal, resolution to this tradeoff under different densities of the mitochondrial population. We also show how an assumption of multi-objective optimisation supports inference of biological mechanisms before any data are observed, and discuss the potential of this multi-objective optimisation paradigm to form a broader theoretical framework of spatial cell biology.