Laura E. Dee

Nov 25, 2024 | biorxiv.org | Laura E. Dee |Kate L. Wootton |François Massol |Aaron Clauset

AbstractNetworks are a powerful way to represent the complexity of complex ecological systems. However, most ecological networks are incompletely observed, e.g., food webs typically contain only partial lists of species interactions. Computational methods for inferring such missing links from observed networks can facilitate field work and investigations of the ecological processes that shape food webs.

Aug 1, 2024 | biorxiv.org | Laura E. Dee

AbstractExperiments have long been the gold standard for causal inference in Ecology. Observational data has been primarily used to validate experimental results or to find patterns that inspire experiments - not for causal inference. As ecology tackles progressively larger problems, we are moving beyond the scales at which randomized controlled experiments are feasible.

Feb 29, 2024 | biorxiv.org | Laura E. Dee

AbstractAs ecology tackles progressively larger problems, we have begun to move beyond the scale at which we can conduct experiments to derive causal inferences. Randomized controlled experiments have long been seen as the gold standard for quantifying causal effects in ecological systems.

Jul 12, 2023 | nature.com | Laura E. Dee |Paul J. Ferraro |Elizabeth T. Borer |Yann Hautier |Andrew L Hector |Peter B. Reich | +12 more

Correction to: Nature Communications https://doi.org/10.1038/s41467-023-37194-5, published online 05 May 2023The original version of this Article contained errors in the Methods section ‘Target causal effect’, in which terms were omitted from the mathematical definitions of the causal effect and average causal effect.

May 5, 2023 | nature.com | Laura E. Dee |Paul J. Ferraro |Elizabeth T. Borer |Yann Hautier |Andrew L Hector |Peter B. Reich | +12 more

AbstractCausal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity.