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1. A causal inference framework for climate change attribution in ecology

   https://doi.org/10.22541/au.173349929.91948075/v1  

   Dudney, Joan; Dee, Laura; Heilmayr, Robert; Byrnes, Jarrett; Siegel, Katherine (December 2024, Authorea Inc.)

   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 impacts on ecological systems, experimental results are difficult to generalize to real-world scenarios. To better capture realized 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 data sets, 3) design a causal inference analysis, 4) estimate a counterfactual scenario, and 5) evaluate assumptions and results using robustness checks. We then demonstrate this framework using a case study focused on detecting climate change impacts on whitebark pine growth in California’s Sierra Nevada. We conclude with a discussion of challenges and frontiers in ecological climate change attribution. Our aim is to provide an accessible foundation for applying observational causal inference to climate change attribution in ecology. 

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2. Riparian vegetation reduces coastal turbidity

   https://doi.org/10.1038/s44458-025-00031-5  

   Brumberg, Hilary D; Dee, Laura E; Murayama, Hikari; Alvarado_Barrientos, Juan José; Bessesen, Brooke; Bouffard, Marie G; Burgess, Matthew G; Cortés, Jorge; Furey, Samuel; Hernández, Noelia; et al (December 2026, Communications Sustainability)

   Abstract Marine ecosystems worldwide are increasingly degraded by upstream land use activities, compounding climate change impacts. However, empirically quantifying causal land-sea linkages remains challenging. Using remote sensing data (1987-2019) and four causal inference methods, here we developed an empirical and scalable framework to estimate how land use affects coastal turbidity across spatial scales in southern Costa Rica. We found that riparian natural vegetation (15 m buffer) significantly reduced gulf turbidity up to 800 m offshore, which overlaps with coral reefs and seagrass habitats. In contrast, pasture and gravel roads increased coastal turbidity. Effects were greatest for rivers that are short, steep, or have low discharge. Watershed-scale land uses showed no significant effects. We provide a replicable, scalable framework to identify causal pathways from land to sea, particularly valuable in data-limited regions. Riparian conservation and restoration could serve as effective strategies to align human land use needs with terrestrial, freshwater, and marine conservation. 

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3. Causal Language and Statistics Instruction: Evidence from a Randomized Experiment

   https://doi.org/10.52041/serj.v23i1.673  

   HILL, JENNIFER; PERRETT, GEORGE; HANCOCK, STACEY; WIN, LE; BERGNER, YOAV (July 2024, Statistics Education Research Journal)

   Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation. 

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4. Causal Inference With Observational Data and Unobserved Confounding Variables

   https://doi.org/10.1111/ele.70023  

   Byrnes, Jarrett_E K; Dee, Laura E (January 2025, Ecology Letters)

   ABSTRACT Experiments have long been the gold standard for causal inference in Ecology. As Ecology tackles progressively larger problems, however, we are moving beyond the scales at which randomised controlled experiments are feasible. To answer causal questions at scale, we need to also use observational data —something Ecologists tend to view with great scepticism. The major challenge using observational data for causal inference is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders—known or unknown—lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this omitted variable bias, other disciplines have developed rigorous approaches for causal inference from observational data that flexibly control for broad suites of confounding variables. We show how ecologists can harness some of these methods—causal diagrams to identify confounders coupled with nested sampling and statistical designs—to reduce risks of omitted variable bias. Using an example of estimating warming effects on snails, we show how current methods in Ecology (e.g., mixed models) produce incorrect inferences due to omitted variable bias and how alternative methods can eliminate it, improving causal inferences with weaker assumptions. Our goal is to expand tools for causal inference using observational and imperfect experimental data in Ecology. 

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5. Foundations and Future Directions for Causal Inference in Ecological Research

   https://doi.org/10.1111/ele.70053  

   Siegel, Katherine; Dee, Laura E (January 2025, Ecology Letters)

   ABSTRACT Ecology often seeks to answer causal questions, and while ecologists have a rich history of experimental approaches, novel observational data streams and the need to apply insights across naturally occurring conditions pose opportunities and challenges. Other fields have developed causal inference approaches that can enhance and expand our ability to answer ecological causal questions using observational or experimental data. However, the lack of comprehensive resources applying causal inference to ecological settings and jargon from multiple disciplines creates barriers. We introduce approaches for causal inference, discussing the main frameworks for counterfactual causal inference, how causal inference differs from other research aims and key challenges; the application of causal inference in experimental and quasi‐experimental study designs; appropriate interpretation of the results of causal inference approaches given their assumptions and biases; foundational papers; and the data requirements and trade‐offs between internal and external validity posed by different designs. We highlight that these designs generally prioritise internal validity over generalisability. Finally, we identify opportunities and considerations for ecologists to further integrate causal inference with synthesis science and meta‐analysis and expand the spatiotemporal scales at which causal inference is possible. We advocate for ecology as a field to collectively define best practices for causal inference. 

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