Biology subjects

Bimler, M. D.

Publications and source records attributed to Bimler, M. D..

4 recordsLinked to original sources

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

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

Ecological network inference is not consistent across sales or approaches

Identifying the most suitable method of ecological network inference in line with individual research considerations is a non-trivial task, which significantly hinders adoption of network approaches to forest management applications. To advance the study of ecological networks and better guide their use in managing forest ecosystems, we propose a framework that aligns pairwise species-association inference methods with specific research questions, biological interaction types, data availability, and spatial scales of study. We motivate the adoption of this framework through an empirical comparison of multiple inference methods, highlighting substantial inconsistencies that arise across scales and methodologies. Using data on species distributions and attributes at local, regional, and continental scales for temperate conifer forests in North America, we show that network inference varies significantly depending on whether occurrence, abundance, or performance data are used and the degree to which confounding factors are accounted for. Across four widely used and/or cutting-edge inference methods (COOCCUR, NETASSOC, HMSC, NDD-RIM), we find notable disparities in both whole-network metrics and pairwise species associations, particularly at continental scales. These findings underscore that no single method is likely to universally outperforms others across scales, emphasizing the importance of choosing an inference approach that aligns with specific ecological and spatial contexts. Our framework aids in interpreting network topologies and interactions in light of these method- and datatype-driven variances, providing a structured approach to more reliably infer ecological associations and address complex network dynamics in forest management practices.

bioinformatics↗

bioRxiv2022

Estimating interaction matrices from performance data for diverse systems

O_LINetwork theory allows us to understand complex systems by evaluating how their constituent elements interact with one another. Such networks are built from matrices which describe the effect of each element on all others. Quantifying the strength of these interactions from empirical data can be difficult, however, because the number of potential interactions increases non-linearly as more elements are included in the system, and not all interactions may be empirically observable when some elements are rare. C_LIO_LIWe present a novel modelling framework which estimates the strength of pairwise interactions in diverse horizontal systems, using measures of species performance in the presence of varying densities of their potential interaction partners. C_LIO_LIOur method allows us to directly estimate pairwise effects when they are statistically identifiable and approximate pairwise effects when they would otherwise be statistically unidentifiable. The resulting interaction matrices can include positive and negative effects, the effect of a species on itself, and are non-symmetrical. C_LIO_LIThe advantages of these features are illustrated with a case study on an annual wildflower community of 22 focal and 52 neighbouring species, and a discussion of potential applications of this framework extending well beyond plant community ecology. C_LI

ecology↗