Wee Hao Ng, Christopher R Myers, Scott McArt, Stephen P Ellner
{"title":"A Time for Every Purpose: Using Time-Dependent Sensitivity Analysis to Help Understand and Manage Dynamic Ecological Systems.","authors":"Wee Hao Ng, Christopher R Myers, Scott McArt, Stephen P Ellner","doi":"10.1086/726143","DOIUrl":null,"url":null,"abstract":"<p><p>AbstractSensitivity analysis is often used to help understand and manage ecological systems by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act on. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life history events, and endogenously driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.</p>","PeriodicalId":50800,"journal":{"name":"American Naturalist","volume":"202 5","pages":"630-654"},"PeriodicalIF":2.4000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Naturalist","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1086/726143","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/9/29 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"ECOLOGY","Score":null,"Total":0}
引用次数: 1
Abstract
AbstractSensitivity analysis is often used to help understand and manage ecological systems by assessing how a constant change in vital rates or other model parameters might affect the management outcome. This allows the manager to identify the most favorable course of action. However, realistic changes are often localized in time-for example, a short period of culling leads to a temporary increase in the mortality rate over the period. Hence, knowing when to act may be just as important as knowing what to act on. In this article, we introduce the method of time-dependent sensitivity analysis (TDSA) that simultaneously addresses both questions. We illustrate TDSA using three case studies: transient dynamics in static disease transmission networks, disease dynamics in a reservoir species with seasonal life history events, and endogenously driven population cycles in herbivorous invertebrate forest pests. We demonstrate how TDSA often provides useful biological insights, which are understandable on hindsight but would not have been easily discovered without the help of TDSA. However, as a caution, we also show how TDSA can produce results that mainly reflect uncertain modeling choices and are therefore potentially misleading. We provide guidelines to help users maximize the utility of TDSA while avoiding pitfalls.
期刊介绍:
Since its inception in 1867, The American Naturalist has maintained its position as one of the world''s premier peer-reviewed publications in ecology, evolution, and behavior research. Its goals are to publish articles that are of broad interest to the readership, pose new and significant problems, introduce novel subjects, develop conceptual unification, and change the way people think. AmNat emphasizes sophisticated methodologies and innovative theoretical syntheses—all in an effort to advance the knowledge of organic evolution and other broad biological principles.