EcoMem: An R package for quantifying ecological memory

Abstract

Ecological processes may exhibit memory to past disturbances affecting the resilience of ecosystems to future disturbance. Understanding the role of ecological memory in shaping ecosystem responses to disturbance under global change is a critical step toward developing effective adaptive management strategies to maintain ecosystem function and biodiversity. We developed EcoMem, an R package for quantifying ecological memory functions using common environmental time series data (continuous, count, proportional) applying a Bayesian hierarchical framework. The package estimates memory functions for continuous and binary (e.g., disturbance chronology) variables making no a priori assumption on the form of the functions. EcoMem allows users to quantify ecological memory for a wide range of ecosystem processes and responses. The utility of the package to advance understanding of the memory of ecosystems to environmental drivers is demonstrated using a simulated dataset and a case study assessing the memory of boreal tree growth to insect defoliation.

Marine epibenthic functional diversity on Flemish Cap (north‐west Atlantic)—Identifying trait responses to the environment and mapping ecosystem functions

“…Accordingly, during the last decade, ecologists have increasingly studied species’ biological traits, and their subset of functional traits, as a way to connect niche‐based mechanisms to community patterns, because it is recognized that species can be grouped according to common effects on ecosystem processes mediated by their functional traits, in addition to common responses to the environment (Cadotte, Arnillas, Livingstone, & Yasui, ; Lavorel & Garnier, ; McGill, Enquist, Weiher, & Westoby, ). Therefore, the assembly processes influencing ecological communities are a result of the combined effects of environmental filters restricting distributions, past and present biotic interactions and stochastic processes (Itter, Vanhatalo, & Finley, ) where the response of the species to these factors will depend on their traits, which in turn are to varying degrees constrained by phylogenetic relationships (Ovaskainen et al, ).…”

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“…Recent literature suggests that it might indeed be possible to empirically detect the presence of memory based on the broad properties of a time series. It has been shown that longitudinal time series of microbial communities may carry detectable signatures of underlying ecological processes [4,58]; and recently, Bayesian hierarchical models [10,14], Random Forests [11], neural networks [59], and unsupervised Hebbian learning [60] have been proposed to detect signatures of memory in other contexts.…”

“…It has been shown that longitudinal time series of microbial communities may carry detectable signatures of underlying ecological processes [7, 70]. Recently, Bayesian hierarchical models [11, 19], random forests [12], neural networks [71], and unsupervised Hebbian learning [24] have been proposed to detect signatures of memory in other contexts. Furthermore, specifically designed longitudinal experiments could be used to characterize memory in real communities.…”

“…Diverse approaches have been proposed to explore ecological memory, including time delays [10,14,15],…”

Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data

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