Biology subjects

Adams, M. P.

Publications and source records attributed to Adams, M. P..

6 recordsLinked to original sources

bioRxiv2026

Parameter estimation and identifiability analysis of stability and tipping points in potentially bistable ecosystems

Ecological regime shifts are potentially a common property of ecosystems, describing transitions between alternative stable states that can represent healthy or unhealthy conditions under the same environmental drivers. Once a tipping point, defined as a critical threshold separating alternative stable states, is crossed, the system may degrade and recovery can be difficult, making early detection essential for effective ecosystem management. Predicting these tipping points requires models that exhibit bistability, representing systems that can exist in two alternative stable states under identical environmental conditions. A key question is whether standard ecological monitoring data can be used to identify bistability and accurately estimate tipping points. Using the Carpenter model of lake eutrophication, which expresses bistability between clear and polluted water states, we generate synthetic data under known stability regimes. Profile likelihood analysis is then applied to assess parameter identifiability and detect system stability and tipping points. Our results show that standard monitoring data do not always provide sufficient information to distinguish bistable from stable regimes. Importantly, bistability and tipping points become practically identifiable only when data are collected very close to the tipping point.

ecology↗

bioRxiv2026

When should we use non-stationary adaptive management? A value of information analysis

O_LIMaking informed conservation decisions under climate change is a challenging task for practitioners, since decisions depend on changing environmental conditions and uncertain ecosystem responses to climate change. Given such uncertainties, the best practice to manage natural systems is adaptive management, where decisions dynamically adapt to the response of the ecosystem to previous conservation actions. Although adaptive approaches are optimal, they are also difficult to implement, have high computational costs, and recommend strategies that can be complex to interpret. These factors can hinder their on-ground application. On the other hand, simpler but suboptimal decision models can result in more interpretable recommendations, and might still yield good outcomes for ecosystems. Exploring trade-offs between complex optimal solutions and simpler sub-optimal solutions is essential for maximising conservation impact. C_LIO_LIIn this manuscript, we use value of information theory to help managers simplify their decision-models, while balancing optimality of strategies. Our approach provides modelling recommendations by determining the benefits of modelling non-stationary ecosystem dynamics and the uncertain ecosystem response to climate change. We illustrate our approach on four scenarios inspired from the management of the Great Barrier Reef, Australia, under different climate change trajectories. C_LIO_LIWe find that the two main drivers of the recommended reduction in model complexity are the strength of non-stationarity (e.g. climate change trajectory) and the degree of uncertainty in ecosystem responses to climate change (e.g. uncertainty in the thermal resistance of a coral reef). When non-stationarity is weak, the decision problem can be reduced from a non-stationary to a stationary formulation. Similarly, when uncertainty in the response to climate change is low, this uncertainty can be safely ignored in the decision-making process. Conversely, when non-stationarity is strong and/or uncertainty is high, our approach justifies the need to account for these complexities when making decisions, as simpler approaches would yield poor outcomes. C_LIO_LIThis manuscript guides managers in simplifying a modelling approach to manage ecosystems in the face of climate change. Our protocol can help simplify complex decision problems, allowing to reduce computational costs and enhance interpretability. By finding the balance between simplicity and optimality of models, this work contributes to bridging the gap between complex modelling and on-ground applications. C_LI

ecology↗

bioRxiv2024

EEMtoolbox: A user-friendly R package for flexible ensemble ecosystem modeling

O_LIForecasting ecosystem changes due to disturbances or conservation interventions is essential to improve ecosystem management and anticipate unintended consequences of conservation decisions. Mathematical models allow practitioners to understand the potential effects and unintended consequences via simulation. However, calibrating these models is often challenging due to a paucity of appropriate ecological data. C_LIO_LIEnsemble ecosystem modelling (EEM) is a quantitative method used to parameterize models from theoretical ecosystem features rather than data. Two approaches have been considered to find parameter values satisfying those features: a standard accept-reject algorithm, appropriate for small ecosystem networks; and a sequential Monte Carlo (SMC) algorithm, that is more computationally efficient for larger networks. In practice, using SMC for EEM generation requires advanced statistical and mathematical knowledge, as well as strong programming skills, which might limit its uptake. In addition, current EEM approaches have been developed for only one model structure (generalized Lotka-Volterra). C_LIO_LITo facilitate the usage of EEM methods we introduce EEMtoolbox, an R package for calibrating quantitative ecosystem models. Our package allows the generation of parameter sets satisfying ecosystem features, by using either the standard accept-reject algorithm or the novel SMC procedure. Our package extends the existing EEM methodology, originally developed for the generalized Lotka-Volterra model, to two additional model structures (the multi-species Gompertz, and the Bimler-Baker model), and additionally allows users to define their own model structures. C_LIO_LIWe demonstrate the usage of EEMtoolbox by modelling the introduction of sihek (extinct-in-the-wild) on Palmyra Atoll in the Pacific Ocean. With its simple interface, our package facilitates straightforward generation of EEM parameter sets, thus unlocks advanced statistical methods supporting conservation decisions using ecosystem network models. C_LI

ecology↗

bioRxiv2024

Developing new technologies to protect ecosystems: planning with adaptive management

Technology development is an essential investment for policymakers to address contemporary global crises, including climate change, biodiversity loss, the energy transition, and emergent infectious diseases. However, investing limited resources in the development of new technologies is risky. The research and development process is unpredictable, with unknown timelines and outcomes. In addition, even after successful development, the effects of deploying a new technology remain uncertain. When confronted with these uncertainties, policymakers must determine how long they should allocate resources to developing new technologies. Informed decisions require anticipating possible successes and failures of both technology development and deployment, which is a challenging optimisation task when managing dynamic systems, such as threatened ecological systems. Using an adaptive management approach from Artificial Intelligence, we discover a time limit new technologies should be developed for, which balances costs, benefits, and uncertainties during development and deployment. We extract clear and transparent general rules for investing in new technologies, building on an analytical approximation. Using Australias Great Barrier Reef as a case study, we demonstrate how characteristics of the managed system influence the optimal investment strategy. Our approach can inform the development of new technologies in multiple domains including biodiversity conservation, public health, energy production, and the technology industry more broadly. SignificanceTechnology development is essential to address the crises our world faces, such as ecosystem collapse. With limited resources, policymakers must decide whether to invest in developing new technologies and, if ever, when to stop. Informed decisions require anticipating possible failures of both technology development and deployment, a challenging task when dealing with changing systems. Using an Artificial Intelligence approach, we find a time limit for technology development that depends on characteristics of the managed ecosystem. This work can guide technology investments in many domains such as biodiversity conservation, epidemiology, energy production and the technology industry more broadly.

ecology↗

bioRxiv2024

A simple model of population dynamics with beneficial and harmful interaction networks for empirical applications.

O_LIPopulation dynamic models can forecast changes in the abundances of multiple interconnected species, which makes them potentially powerful tools for managing ecological communities, yet they remain largely under-utilised in applied settings. High data requirements and the ability to only model a narrow range of ecological interactions and/or trophic levels together limits their usefulness when faced with complex and data-poor systems, where beneficial (e.g. mutualism) and harmful (e.g. competition) interactions may operate simultaneously within and between species. C_LIO_LIWe present a model of population dynamics that can describe a wide range of ecological interaction outcomes with a simple, unified structure. Species growth rates are constrained by a maximum growth rate parameter which prevents the risk of population explosions even in the case of mutualism. Species interactions are defined by two, not mutually-exclusive interactions matrices that describe the effects of beneficial and harmful interactions respectively, together providing the potential for the net effect of interactions between one species and another to switch from beneficial to harmful as population density increases. C_LIO_LIThis model recreates classic dynamics in two-species mutualistic, competitive, and predator-prey scenarios, allowing us to model a wide range of trophic levels and interaction types together within the same equation. The maximum growth rate parameter, theoretically based in intrinsic constraints on reproduction, can be parameterised from a wide range of sources including natural history, historical data, and breeding programs. We illustrate the potential of this model with a data-poor case study of a threatened species and two interacting predators. C_LIO_LIThis new model is generaliseable to a wide range of natural ecological communities. Its model structure lowers data requirements whilst remaining intuitive and biologically realistic, making it an accessible option for predicting community-wide population changes in applied contexts where data is sparse and/or uncertain. C_LI

ecology↗

bioRxiv2023

Trade-off between light deprivation and desiccation in intertidal seagrasses due to periodic tidal inundation and exposure: insights from a data-calibrated model

Many seagrass species thrive in shallow intertidal zones globally, adapting to periodic tidal inundation and exposure with distinctive physiological traits and offering crucial ecosystem services. However, predicting the responses of intertidal seagrasses to external stressors is hampered by the complexity of the dynamic and harsh environments they occupy. Consequently, intertidal seagrass growth models, especially those incorporating dynamic physiological responses, are scarce in the literature. Our study comprehensively collated relevant data from the literature to parameterize the relationship between air exposure, seagrass leaf water content and photosynthetic efficiency to inform new growth rate functions for generalisable intertidal seagrass growth models. We tested the applicability of these model formulations for scenarios with varying physiological process assumptions, seagrass species, tidal conditions, meadow elevations and water turbidity. We found that neglecting air-exposed physiological responses (i.e., leaf water content loss and reduced photosynthetic efficiency) can substantially overestimate seagrass growth rates. We also observed a trade-off between light deprivation and desiccation on intertidal seagrass growth under specific tidal ranges and turbidity conditions. This can yield an "optimal" elevation where combined stressors of desiccation (increasing with meadow elevation) and light deprivation (decreasing with meadow elevation) are minimized. The predicted optimal elevation, i.e., the most suitable habitat for intertidal seagrass, moves upward as water turbidity increases. Our study provides both conceptual and quantitative guidance for ecological modellers to include air exposure responses of intertidal seagrasses in coastal ecosystem models. The model also helps to evaluate the viability of intertidal seagrass habitats and inform site selection for seagrass restoration.

ecology↗