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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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When Does Form Reflect Function? Acknowledging and Supporting Ecomorphological Assumptions
Abstract Ecomorphology is the study of relationships between organismal morphology and ecology. As such, it is the only way to determine if morphometric data can be used as an informative proxy for ecological variables of interest. To achieve this goal, ecomorphology often depends on, or directly tests, assumptions about the nature of the relationships among morphology, performance, and ecology. We discuss three approaches to the study of ecomorphology: morphometry-driven, function-driven, and ecology-driven and study design choices inherent to each approach. We also identify 10 assumptions that underlie ecomorphological research: 4 of these are central to all ecomorphological studies and the remaining 6 are variably applicable to some of the specific approaches described above. We discuss how these assumptions may impact ecomorphological studies and affect the interpretation of their findings. We also point out some limitations of ecomorphological studies, and highlight some ways by which we can strengthen, validate, or eliminate systematic assumptions.
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- PAR ID:
- 10119936
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Integrative and Comparative Biology
- Volume:
- 59
- Issue:
- 2
- ISSN:
- 1540-7063
- Page Range / eLocation ID:
- p. 358-370
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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