Restoration and replication: a case study on the value of computational reproducibility assessment

Abstract

Open science is vital to the interdisciplinary field of ecology due to its integrative nature and use of longitudinal datasets that build upon earlier data collections. To highlight the importance of open science in the rapidly growing discipline of restoration ecology, we conducted a “computational reproducibility” assessment of a publication on a mining restoration program spanning several decades and over 250 km2 in a global biodiversity hotspot. Open data and code provided alongside the original publication were assessed for consistency with the results and conclusions of the original publication, as were potential limitations in findings due to the methodology. The impacts of inconsistencies and limitations were qualitatively assessed against the key findings from the publication and data were reanalyzed where impacts were potentially significant. Of the six inconsistencies and limitations identified, two had a significant impact on 5 of the 11 key findings of the original publication and one new key finding was made. The impact of this is of high ecological significance as the findings related to key restoration parameters: species richness (similarity of species richness between forest and 25‐year‐old restoration), functional diversity (correlation of species richness and functional diversity), and the restoration trajectory (long‐term trends and restored areas' resilience to disturbance). These outcomes highlight the importance of open data and the value of detailed third‐party data reviews, particularly in restoration ecology which relies on research findings to inform decision‐making and policy and drive adaptive management.