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Remote spectral detection of biodiversity effects on forest biomass


Quantifying how biodiversity affects ecosystem functions through time over large spatial extents is needed for meeting global biodiversity goals yet is infeasible with field-based approaches alone. Imaging spectroscopy is a tool with potential to help address this challenge. Here, we demonstrate a spectral approach to assess biodiversity effects in young forests that provides insight into its underlying drivers. Using airborne imaging of a tree-diversity experiment, spectral differences among stands enabled us to quantify net biodiversity effects on stem biomass and canopy nitrogen. By subsequently partitioning these effects, we reveal how distinct processes contribute to diversity-induced differences in stand-level spectra, chemistry and biomass. Across stands, biomass overyielding was best explained by species with greater leaf nitrogen dominating upper canopies in mixtures, rather than intraspecific shifts in canopy structure or chemistry. Remote imaging spectroscopy may help to detect the form and drivers of biodiversity–ecosystem function relationships across space and time, advancing the capacity to monitor and manage Earth’s ecosystems.

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Fig. 1: Simulations to assess spectral diversity effects.
Fig. 2: Prediction of stem biomass from spectra.
Fig. 3: Spectral diversity effects on stem biomass and the field-measured NBE.
Fig. 4: Diversity effects on spectrally predicted canopy nitrogen and the field-measured NBE on stem biomass.

Data availability

AVIRIS-NG data can be downloaded from Image level spectra, canopy nitrogen predictions and field-based measurements along with coefficients for PLSR and PLS-DA models are available on the Data Repository for the University of Minnesota68 (

Code availability

Code for the PLSR and PLS-DA models developed here along with code for simulating spectra, applying PLSR models and calculating spectral diversity effects are available at the Data Repository for the University of Minnesota68 (


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We thank K. Rice, R. Bermudez, J. Gamon, A. Mazur, A. Schweiger, M. Sinnen, R. Wang and numerous interns for field assistance. We also thank E. Butler, J. Ditmer, B. Fallon, S. Hobbie, F. Isbell, S. Kothari, J. E. Meireles, R. Putnam, G. Sapes and A. Schweiger for comments. The project was funded by a National Science Foundation and National Aeronautic and Space Administration grant awarded to J.C.-B. (grant no. DEB-1342872) and P.A.T. (grant no. DEB-1342778) through the Dimensions of Biodiversity program, the Hubachek Wilderness Research endowment (University of Minnesota), the Canada Research Chairs program and the National Science Foundation’s Biology Integration Institutes program (grant no. NSF-DBI-2021898).

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Authors and Affiliations



This work is part of the Dimensions of Biodiversity project ‘Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes’, conceptualized by J.C.-B. and P.A.T. J.C.-B. and L.J.W. conceptualized this study. P.B.R., C.M. and A.S. designed and implemented the broader IDENT study. J.J.C., A.S. and L.J.W. collected data. Z.W. prepared spectral data and mapped canopy nitrogen. L.J.W. analysed the data with assistance from J.C.-B., J.J.C. and Z.W. L.J.W. wrote the first draft of the manuscript. All authors contributed to revisions and further manuscript development.

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Correspondence to Laura J. Williams or Jeannine Cavender-Bares.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Fits of the PLSR model of stem biomass.

Partial least squares regression (PLSR) model combining data from the fifth and sixth growing seasons, showing fits for calibration (Cal.), cross-validation (Val.) and independent validation (Ind. val.) data subsets. Spectra were vector-normalized. n = number of stands, n comp. = number of components in model.

Extended Data Fig. 2 Wavelengths of importance in the PLSR model of stem biomass.

The variable importance index represents the reduction of sums of squares63. Solid line indicates the mean and shading indicates the 95% confidence intervals around the mean importance value for each wavelength across the 1000 model iterations. Noisy and water absorption wavelengths are omitted.

Extended Data Fig. 3 Examples of spectral reflectance and diversity effects on spectral reflectance.

a, Spectral reflectance of four stands, representing the range in stem biomass in each growing season, and important wavelengths in the PLSR model of stem biomass. Important wavelengths (number of iterations where the wavelength was among the 20 most important based on the reduction of the sums of squares63) are indicated with the intensity of vertical lines. Shading around spectra indicates 95% confidence intervals among pixels within stands. b, Examples of diversity effects on the spectral reflectance of stands, showing the difference between observed spectral reflectance (Obs) and simulated spectral reflectance (SimT) (that is, the spectral net biodiversity effect, sNBE) separated into the additive contributions of spectral dominance (sDE) and spectral plasticity (sPE) (see Fig. 1). These stands are from the fifth growing season and illustrate strongly positive, moderately positive, and negative field-measured NBE on stem biomass (top to bottom panels, respectively). Noisy and water absorption wavelengths are omitted.

Extended Data Fig. 4 Spectrally determined diversity effects on stem biomass and the spectral net biodiversity effect.

Contributions of the spectral dominance effect (sDE) (a, c) and spectral plasticity effect (sPE) (b, d) on stem biomass to the spectrally predicted net biodiversity effect (sNBE) on stem biomass in the fifth (a,b) and sixth (c,d) growing seasons. Error bars show 95% confidence intervals among the 1000 model iterations. Thick line represents the regression line (significant for sDE in both years and sPE in the fifth growing season, P ≤ 0.002, but not for sPE in the sixth growing season, P = 0.117). Dark grey lines represent the 95% prediction interval and light grey lines the 95% confidence interval of the models. Dashed grey line shows 1:1. e, Mean contributions of sDE and sPE on stem biomass to sNBE on stem biomass for each species mixture, showing effects in the fifth growing season and the increase (or decrease) in effects in the sixth growing season. Error bars for sNBE represent standard deviations among blocks (n = 3; an additional five mixed-species stands measured in one block in the fifth growing season are omitted). 6 NA = all six species of North American origin, 6 EU = all six species of European origin, 6 angio = all six angiosperms, 6 gymno = all six gymnosperms, Ap = Acer platanoides, As = Acer saccharum, Bpa = Betula papyrifera, Bpe = Betula pendula, Ld = Larix decidua, Ll = Larix laricina, Pa = Picea abies, Pg = Picea glauca, Pst = Pinus strobus, Psy = Pinus sylvestris, Qro = Quercus robur and Qru = Quercus rubra.

Extended Data Fig. 5 Maps of canopy nitrogen.

Canopy nitrogen concentration (Nmass, %) estimated from spectra using PLSR for the (a) fifth growing season and (b) sixth growing season. Location of stands indicated with black boxes.

Extended Data Fig. 6 Canopy nitrogen and stem biomass.

Field-measured canopy nitrogen concentration was positively associated with spectrally predicted canopy nitrogen concentration in both (a) the fifth growing season and (d) the sixth growing season. Field-measured stem biomass was positively associated with both (b) field-measured canopy nitrogen concentration and (c) spectrally predicted canopy nitrogen concentration in the fifth growing season, and with both (e) field-measured canopy nitrogen concentration and (f) spectrally predicted canopy nitrogen concentration in the sixth growing season. Thick line represents the regression line (P < 0.001).

Extended Data Fig. 7 Wavelengths of importance within PLS-DA models.

Wavelengths of importance in distinguishing species within partial least squares discriminant analysis (PLS-DA) models (red) shown alongside the PLSR model of stem biomass (grey, unchanged in all panels). The variable importance index represents the reduction of sums of squares49. Solid lines indicate the mean and shading indicates the 95% confidence intervals around the mean importance value for each wavelength across the 1000 model iterations. Vertical lines highlight the 20 most important wavelengths on average across the model iterations. Noisy and water absorption wavelengths are omitted.

Extended Data Fig. 8 Spectral assignments of the species composition of stands.

Species assignments based on PLS-DA. Two-species compositions were not present on all four blocks (indicated by asterisks): Pg-Qru was planted in place of Pg-Qro on Block B, and Pg-As was planted on two stands in Block D with one stand in place of Pa-As.

Extended Data Fig. 9 Confusion matrix for PLS-DA species assignments in monoculture.

The reference species identity of pixels (columns) and the predicted species identity of pixels (rows) from PLS-DA calibrated with pixels drawn from monoculture stands in their fifth and sixth growing seasons. Values are the mean proportion of pixels assigned to a given species using the validation data subset in each iteration. Presented in coarse phylogenetic order, separating angiosperms from gymnosperms.

Extended Data Fig. 10 Spectrally determined diversity effects on stem biomass calculated with remotely sensed species composition.

Models whereby spectra were first used to predict the species composition of stands before calculating the net biodiversity effect (sNBEsID) (a, d), spectral dominance effect (sDEsID) (b, e) and spectral plasticity effect (sPEsID) (c, f) on stem biomass were each associated with their counterparts that were spectrally predicted using the known species composition of stands (sNBE, sDE and sPE, respectively). The top row (a–c) shows the fifth growing season and the bottom row (d–f) shows the sixth growing season. Error bars show 95% confidence intervals among the 1000 model iterations. Thick line represents the regression line (P < 0.001), dark grey lines represent the 95% prediction interval, and light grey lines represent the 95% confidence interval of the models. Dashed grey line shows 1:1. Sample sizes differ among panels; all panels are limited to the subset of stands that were not monocultures or spectrally identified as such, and sDE and sPE are also limited to those stands where leaf area was measured.

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Williams, L.J., Cavender-Bares, J., Townsend, P.A. et al. Remote spectral detection of biodiversity effects on forest biomass. Nat Ecol Evol 5, 46–54 (2021).

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