EcoMem: An R package for quantifying ecological memory

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
JournalEnvironmental Modelling & Software
Volume119
Pages (from-to)305-308
Number of pages4
ISSN1364-8152
DOIs
Publication statusPublished - Sep 2019
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 113 Computer and information sciences
  • 1172 Environmental sciences

Cite this

@article{09c0c8a4f7a04ed4b3a3c6f035917709,
title = "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.",
keywords = "Bayesian hierarchical model, Disturbance, EcoMem, Ecosystem resilience, R package, Time series, 113 Computer and information sciences, 1172 Environmental sciences",
author = "Itter, {Malcolm S.} and Jarno Vanhatalo and Finley, {Andrew O.}",
year = "2019",
month = "9",
doi = "10.1016/j.envsoft.2019.06.004",
language = "English",
volume = "119",
pages = "305--308",
journal = "Environmental Modelling & Software",
issn = "1364-8152",
publisher = "ELSEVIER SCI IRELAND LTD",

}

EcoMem: An R package for quantifying ecological memory. / Itter, Malcolm S.; Vanhatalo, Jarno; Finley, Andrew O.

In: Environmental Modelling & Software, Vol. 119, 09.2019, p. 305-308.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - EcoMem: An R package for quantifying ecological memory

AU - Itter, Malcolm S.

AU - Vanhatalo, Jarno

AU - Finley, Andrew O.

PY - 2019/9

Y1 - 2019/9

N2 - 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.

AB - 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.

KW - Bayesian hierarchical model

KW - Disturbance

KW - EcoMem

KW - Ecosystem resilience

KW - R package

KW - Time series

KW - 113 Computer and information sciences

KW - 1172 Environmental sciences

U2 - 10.1016/j.envsoft.2019.06.004

DO - 10.1016/j.envsoft.2019.06.004

M3 - Article

VL - 119

SP - 305

EP - 308

JO - Environmental Modelling & Software

JF - Environmental Modelling & Software

SN - 1364-8152

ER -