Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference

Causal 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. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [-4.1, -0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones

Replication data for: "Technology Adoption under Uncertainty: Take-up and Subsequent Investment in Zambia"

Review of Economics and Statistics: Forthcomin

Publisher Correction: Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference (Nature Communications, (2023), 14, 1, (2607), 10.1038/s41467-023-37194-5)

The 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. These sentences incorrectly read “The causal effect of a change in richness from R′ to R″ on productivity P in plot i is defined as [(R″) − (R′)], where Pi(R″) is the potential productivity outcome when R = R″ and P(R′) is the potential productivity outcome when R = R′ (R′ ≠ R″).” and “The average causal effect of a change in biodiversity from R′ to R″ across all plots is [(R″) − P(R′)], where E[·] is the expectation operator.”. The correct version states “[Pi(R′′) − Pi(R′)]” in place of “[(R″) − (R′)]”, “Pi (R′)” in place of “P (R′)”, and “E[Pi(R′′) − Pi(R′)]” in place of “[(R″) − P(R′)]”. This has been corrected in both the PDF and HTML versions of the Article

Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference

Abstract Causal 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. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones