Complementarity of ecosystem types drives landscape-wide productivity in North America

Landscape mosaics with a greater diversity of ecosystems tend to be more productive, mirroring the well-established relationship between species diversity and productivity observed in plot-scale biodiversity experiments. However, the mechanisms driving this effect at the landscape scale remain unclear. The data presented here was used to analyze a 15-year time series of satellite-derived primary productivity across over 50,000 landscape plots that vary in ecosystem type composition.

Our results demonstrate that more diverse landscapes are more productive and more predictable under environmental stress, especially drought. Using statistical partitioning, we show that these diversity effects are primarily driven by complementarity, with productivity gains that are broadly shared among ecosystem types rather than being dominated by a few. The specific ecosystem types that contributed most to landscape functioning varied regionally, but their role in driving mixture productivity remained unaffected by drought. These findings extend biodiversity theory to the landscape scale, emphasizing the critical role of higher-order diversity in shaping ecosystem function and informing landscape management.

Methods
We used a network of over 50,000 study plots (area ~25 ha) spread across North America to relate landscape functioning to land-cover type diversity. For details of the study design, rationale, and method, we refer to https://dx.doi.org/10.1101/2025.11.17.688810.

In brief, to control for large-scale environmental variation, the plot network was blocked according to 16 ecoregions and 3° latitude ⨯ 6° longitude blocks. Land-cover type richness (LCR) was determined based on the Commission for Environmental Cooperation’s North American land monitoring system map for 2015 (30 m spatial resolution), with land-cover aggregated into the broad, ecologically distinct classes agriculture, forest, grassland, shrubland, wetland, and urban areas. Within each block, land-cover type richness gradients were created in such a way that these were not correlated with important topographic variables (e.g. slope and elevation) that likely also affected landscape productivity. Each plot's productivity was estimated as Normalized Difference Vegetation Index (NDVI), integrated over the growing seasons of the years 2008 to 2022.

Net diversity effects (NE) were calculated by subtracting single-land-cover plot values from the mixed-land-cover-plot data, as is common in biodiversity experiments. NE data was then statistically partitioned into complementarity and selection effects using the additive partitioning method by Loreau and Hector. Further, we applied the spatio-temporal partitioning method proposed by Isbell et al., using the classification of data into dry and non-dry years in lieu of calendar year, because the dry vs non-dry classification explained a large proportion of the observed temporal variation in NDVI.