A cross‐scale assessment of productivity–diversity relationships

Machine learning approach shows that spatial grain mediates relationships between biodiversity and ecosystem-level productivity

Data resources used via GBIF : 344 species
Calocedrus decurrens
Calocedrus decurrens (Torr.) Florin observed in California, USA by Jonny Sperling (CC BY-NC 4.0)

This study attempts to assess the strength and directions of relationships between ecosystem-level productivity and species richness through modelling at three different spatial scales (fine, intermediate and coarse).

The models of the study were based on a large dataset of woody plant species in forests in the contiguous United States, filtered against GBIF-mediated occurrences to eliminate non-forest and alien species and to account for variation in climate, management and age.

Overall, higher values of species richness and productivity were reported in the eastern USA than in the western USA. At intermediate and coarse scales, species richness and productivity were positively correlated. The structural equation models found a direct effect of richness on productivity and vice versa—of similar strength, increasing with spatial grain.

Richness, however, was found to be a relatively weak predictor of productivity compared to other variables such as forest age, management and biomass. With productivity and richness sharing many environmental and geographical drivers, the study is unable to distinguish between causation and correlation in the relationships.

Craven D, Sande MT, Meyer C, Gerstner K, Bennett JM, Giling DP, Hines J, Phillips HRP, May F, Bannar‐Martin KH, Chase JM and Keil P (2020) A cross‐scale assessment of productivity–diversity relationships. Global Ecology and Biogeography. Wiley 29(11): 1940–1955. Available at: https://doi.org/10.1111/geb.13165