EEMtoolbox: A user-friendly R package for flexible ensemble ecosystem modeling DOI Creative Commons
Luz Valerie Pascal, Sarah A. Vollert, Malyon D. Bimler

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

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

Language: Английский

EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling DOI Creative Commons
Luz Valerie Pascal, Sarah A. Vollert, Malyon D. Bimler

et al.

Methods in Ecology and Evolution, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

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

Language: Английский

Citations

0
EEMtoolbox: A user-friendly R package for flexible ensemble ecosystem modeling DOI Creative Commons
Luz Valerie Pascal, Sarah A. Vollert, Malyon D. Bimler

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 6, 2024

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

Language: Английский

Citations

2