A Causal Inference Framework for Climate Change Attribution in Ecology

ABSTRACT As climate change increasingly affects biodiversity and ecosystem services, a key challenge in ecology is accurate attribution of these impacts. Though experimental studies have greatly advanced our understanding of climate change effects, experimental results are difficult to generalise to...

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Vydáno v:	Ecology letters Ročník 28; číslo 8; s. e70192 - n/a
Hlavní autoři:	Dudney, Joan, Dee, Laura E., Heilmayr, Robert, Byrnes, Jarrett, Siegel, Katherine
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Blackwell Publishing Ltd 01.08.2025
Témata:	adaptation and acclimation Biodiversity Climate Change climate change detection confounding variables counterfactual analysis directed acyclic graph (DAG) Ecological effects ecological forecasting Ecology Ecology - methods Ecosystem Ecosystem services extreme events Inference Models, Biological omitted variable bias panel regression Pinus - physiology quasi‐experimental design Research design adaptation and acclimation quasi‐experimental design omitted variable bias directed acyclic graph (DAG) confounding variables extreme events ecological forecasting climate change detection counterfactual analysis panel regression
ISSN:	1461-023X, 1461-0248, 1461-0248
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Shrnutí:	ABSTRACT As climate change increasingly affects biodiversity and ecosystem services, a key challenge in ecology is accurate attribution of these impacts. Though experimental studies have greatly advanced our understanding of climate change effects, experimental results are difficult to generalise to real‐world scenarios. To better capture realised impacts, ecologists can use observational data. Disentangling cause and effect using observational data, however, requires careful research design. Here we describe advances in causal inference that can improve climate change attribution in observational settings. Our framework includes five steps: (1) describe the theoretical foundation, (2) choose appropriate observational datasets, (3) estimate the causal relationships of interest, (4) simulate a counterfactual scenario and (5) evaluate results and assumptions using robustness checks. We demonstrate this framework using a pinyon pine case study in North America, and we conclude with a discussion of frontiers in climate change attribution. Our aim is to provide an accessible foundation for applying observational causal inference to estimate climate change effects on ecological systems. Accurately attributing ecological shifts to climate change remains a significant challenge. Here, we present an accessible causal inference framework designed for climate change attribution in observational settings. Using a case study and a discussion of key frontiers, we provide ecologists with robust tools to better quantify and manage ecosystem responses in a rapidly warming world.
Bibliografie:	Funding This work was supported by Greater Atlantic Regional Fisheries Office. Directorate for Biological Sciences, 2340606. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1461-023X 1461-0248 1461-0248
DOI:	10.1111/ele.70192