Explore the vast range of titles available.

Forecasting 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. Ensemble 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 ecosystem 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 (generalised Lotka–Volterra). To 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 generalised Lotka–Volterra model, to two additional model structures (the multispecies Gompertz and the Bimler–Baker model) and additionally allows users to define their own model structures. We demonstrate the usage of EEMtoolbox by simulating changes in species abundance immediately after the release of the sihek (Todiramphus cinnamominus, extinct‐in‐the‐wild species) on Palmyra Atoll in the Pacific Ocean. With its simple interface, our package facilitates straightforward generation of EEM parameter sets, thus unlocking advanced statistical methods supporting conservation decisions using ecosystem network models.

Conservation translocations are widely used to recover threatened species, but can pose risks to recipient ecosystems, particularly in the case of conservation introductions. Because of limited data and uncertainty, risk assessments for such projects often rely on extrapolated evidence and expert opinion, further complicating decision making. The Environmental Impact Classification for Alien Taxa (EICAT) serves to classify the realised impacts of invasive species. We developed a protocol combining EICAT and formal expert elicitation to predict these impacts. We applied our protocol to the extinct-in-the-wild sihek (Guam kingfisher; Todiramphus cinnamominus ), for which introduction outside the known historical range is being considered. We elicited from multiple experts probability estimates of impact levels across four impact mechanisms and five candidate release sites. We aggregated estimates using simulation-based and Bayesian approaches, with and without accounting for expert confidence. Experts generally agreed that sihek introduction might impact the recipient ecosystem through predation, competition, and disease, although they disagreed about the likely impact levels. Releases to Palmyra Atoll were considered to pose the lowest risk across candidate sites, so this site was selected for further detailed ecological assessments and risk mitigation efforts. EICAT, with its standardized impact mechanisms and definitions, helped reduce the linguistic uncertainty and subjectivity common to expert-based assessments. Expressing judgments as probabilities allowed us to evaluate uncertainty transparently and to assess the weight of expert confidence on the overall risk estimation. Formal quantitative elicitation and aggregation then allowed a transparent evaluation of results, facilitating communication with stakeholders and decision-makers.

Forecasting 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. Ensemble 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). To 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. We 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.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/luzvpascal/EEMtoolbox* https://anonymous.4open.science/r/EEMtoolbox-submission