DOI: 10.5061/dryad.r7sqv9srx
title: Complementarity of ecosystem types drives landscape-wide productivity in North America

description: 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.
description: 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.
description: # Complementarity of ecosystem types drives landscape-wide productivity in
 North America Dataset DOI:
 [10.5061/dryad.r7sqv9srx](https://doi.org/10.5061/dryad.r7sqv9srx) ##
 Description of the data and file structure 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. Here, we analyzed a
 15-year time series of Landsat-satellite-derived primary productivity
 covering North America. Productivity was quantified as normalized
 difference vegetation index (NDVI), integrated over the growing season
 (NDVIgs, dimensionless). We established a network of over 50,000 plots
 ~25ha in size. These plots were chosen so that environmental conditions in
 these were decorrelated from land-cover (LC) type diversity, our variable
 of interest. Mixed LC type plots were on average more productive than
 single LC type plots. We decomposed this effect of LC type diversity into
 complementarity and selection effects using the additive partitioning
 method by Loreau and Hector (2001) . We also partitioned these effects
 using an extension of the additive partitioning proposed by Isbell et al.
 (2018), who further considers spatial, temporal (inter-annual), and
 spatio-temporal components of selection effects. These spatio-temporal
 components are also known as 'insurance effects'. Temporal
 variation among years was considered by classifying years into dry and
 non-dry using a hydrological model; this classification predicted a large
 fraction of the observed inter-annual variance in productivity. ### Files
 and variables The diversity of a plot (`ID`) is quantified as land-cover
 (LC) type composition (column `comp`), indicating a combination of LC
 types, indicated by the letters A (agriculture), F (forest), G
 (grassland), S (shrubland), U (urban), and W (wetland). For example,
 "FG" indicates a mixture of the two LC types grassland and
 forest. ## ecoregions.csv | Column | Description | | --------------- |
 ---------------------- | | ecoregion | numeric ecoregion code | |
 ecoregion\_name | ecoregion name | ## scaling.csv | Column | Description |
 | --------- | ---------------------- | | ecoregion | numeric ecoregion
 code | | factor | factor | ## additive_partitioning.csv Additive
 partitioning components (complementarity effect, `CE`; selection effect,
 `SE`) calculated according to the partitioning scheme by Loreau &
 Hector (2001). Note the the net diversity effect NE = `CE` + `SE`. |
 Column | Description | | --------- | ---------------------- | | ecoregion
 | numeric ecoregion code | | block | block of study design | | ID | study
 plot | | comp | LC type composition | | CE | complementarity effect | | SE
 | selection effect | 
 
 ## additive_partitioning_by_season.csv As `additive_partitioning.csv`,
 but calculated separately for the first quarter of the growing season
 ("start"), the last quarter of the growing season
 ("end"), and the main growing season in between
 ("center"). | Column | Description | | --------- |
 ----------------------------------------------- | | ecoregion | numeric
 ecoregion code | | block | block of study design | | ID | study plot | |
 comp | LC type composition | | season | part of the growing season (start,
 center, end) | | CE | complementarity effect | | SE | selection effect |
 ## spatio_temporal_partitioning.csv Spatio-temporal partitioning according
 to Isbell et al. (2018). Note that in the original paper, the
 spatio-temporal effects are referred to as 'insurance effects';
 here, we refer to them as selection effect instead, since they are part of
 the total selection effect. `dataset` indicates the dataset the
 partitioning was applied to. See manuscript for details. *
 "overall": the variation across all plots is partitioned *
 "within_ecoregions": the variation within within ecoregion is
 partitioned * "across_ecoregions": using ecoregion averages, the
 variation between ecoregions is partitioned The following relationships
 apply (NE = net biodiversity effect): * NE = TotalSE + TotalCE * TotalSE =
 NonRandOY + AvgSE+TempSE + SpatSE + SpatTempSE | Column | Description | |
 ---------- | --------------------------------------- | | dataset | dataset
 the partitioning was applied to | | comp | LC type composition | | TotalCE
 | total complementarity effect | | TotalSE | total selection effect | |
 TempSE | temporal selection effect | | SpatSE | spatial selection effect |
 | SpatTempSE | spatio-temporal selection effect | | NonRandOY | non-random
 overyielding | | AvgSE | average selection effect | ##
 temporal_variation.csv Interannual variation of productivity of each study
 plot, measured as standard deviation of NDVI data. | Column | Description
 | | --------- | -------------------------------------------------------- |
 | ecoregion | numeric ecoregion code | | block | block of study design | |
 ID | study plot | | comp | LC type composition | | NDVIgs.sd |
 inter-annual variation, quantified as standard deviation | ##
 asynchrony.csv Temporal variation and interannual asynchrony between
 different LC types. Net diversity effects on temporal variation are
 calculated in two ways: * delta.sd = sd.ab-0.5*(sd.a+sd.b) is the net
 effects not considering any temporal asynchrony in productivity between
 component land-cover types in the expected value expected under the null
 hypothesis. * delta.cov.sd = sd.ab-0.5*sqrt(sd.a^2^* *+ sd.b^2^* *+
 2*cov.mono) is the net effect considering the ansynchrony between LC
 types, as observed in single LC landscape plots in the same block
 (covariance stored in cov.mono). | Column | Description | | ------------ |
 ---------------------------------------------------------------------------------------------------------------------------------------- | | ecoregion | numeric ecoregion code | | block | block of study design | | comp | LC type composition | | sd.a | inter-annual variation (as standard deviation) of first LC 'a' | | sd.b | inter-annual variation (as standard deviation) of second LC 'b' | | sd.ab | inter-annual variation (as standard deviation) of binary LC type composition 'ab' | | cov.mono | inter-annual covariance of single LC plots with composition 'a' and 'b', in the same block | | delta.sd | net diversity effect for composition 'ab', based on expectation that the productivity of the single LC type does not vary asynchronously | | delta.cov.sd | net diversity effect, considering the asynchrony in productivity of the two LC types in the expected value | ## References * Loreau M, Hector A (2001) Partitioning selection and complementarity in biodiversity experiments. *Nature* **412**, 72–76, [https://doi.org/10.1038/35083573](https://doi.org/10.1038/35083573)) * Isbell F, Cowles J, Dee LE, Loreau M, Reich PB, Gonzalez A, Hector A, Schmid B (2018) Quantifying effects of biodiversity on ecosystem functioning across times and places[^†^](https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1111%2Fele.12928#ele12928-note-1001). *Ecology Letters* 21:763-778, [https://doi.org/10.1111/ele.12928)](https://doi.org/10.1111/ele.12928) 
 
 ## Code/software All files are comma-separated UTF-8-encoded text files
 that can, for example, be read using the R software for statistical
 computing.