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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Predicting species distributions and community composition using satellite remote sensing predictors
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AVIRIS-NG data can be downloaded from https://aviris-ng.jpl.nasa.gov/alt_locator/. 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 (https://doi.org/10.13020/s7pf-am91).
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).
Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).
Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).
Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).
Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES, 2019).
Cardinale, B. J. et al. The functional role of producer diversity in ecosystems. Am. J. Bot. 98, 572–592 (2011).
Thompson, P. L., Isbell, F., Loreau, M., O’Connor, M. I. & Gonzalez, A. The strength of the biodiversity–ecosystem function relationship depends on spatial scale. Proc. R. Soc. B 285, 20180038 (2018).
Barry, K. E. et al. A universal scaling method for biodiversity–ecosystem functioning relationships. Preprint at bioRxiv https://doi.org/10.1101/662783 (2019).
O’Connor, M. I. et al. A general biodiversity–function relationship is mediated by trophic level. Oikos 126, 18–31 (2017).
Oehri, J., Schmid, B., Schaepman-Strub, G. & Niklaus, P. A. Biodiversity promotes primary productivity and growing season lengthening at the landscape scale. Proc. Natl Acad. Sci. USA 114, 10160–10165 (2017).
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science 354, aaf8957 (2016).
Chisholm, R. A. et al. Scale-dependent relationships between tree species richness and ecosystem function in forests. J. Ecol. 101, 1214–1224 (2013).
Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).
Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).
Asner, G. P. & Martin, R. E. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front. Ecol. Environ. 7, 269–276 (2009).
Climate Change and Land (eds Shukla, P. R. et al.) (IPCC, in the press).
Fernández, N., Ferrier, S., Navarro, L. M. & Pereira, H. M. in Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 485–501 (Springer, 2020).
Cavender-Bares, J., Schweiger, A. K., Pinto-Ledezma, J. N. & Meireles, J. E. in Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 13–42 (Springer, 2020).
Strategic Plan for Biodiversity 2011–2020, including Aichi BiodiversityTargets (Convention on Biological Diversity, 2010).
Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).
Schimel, D., Schneider, F. D., Carbon, J. P. L. & Ecosystem, P. Flux towers in the sky: global ecology from space. New Phytol. 224, 570–584 (2019).
Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).
Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).
Gholizadeh, H. et al. Remote sensing of biodiversity: soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems. Remote Sens. Environ. 206, 240–253 (2018).
Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).
Ollinger, S. V. et al. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proc. Natl Acad. Sci. USA 105, 19335–19340 (2008).
Singh, A., Serbin, S. P., McNeil, B. E., Kingdon, C. C. & Townsend, P. A. Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecol. Appl. 25, 2180–2197 (2015).
Ustin, S. L., Roberts, D. A., Gamon, J. A., Asner, G. P. & Green, R. O. Using imaging spectroscopy to study ecosystem processes and properties. BioScience 54, 523–534 (2004).
Fallon, B. et al. Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes. Tree Physiol. 40, 377–390 (2020).
Schweiger, A. K. et al. Using imaging spectroscopy to predict above-ground plant biomass in alpine grasslands grazed by large ungulates. J. Veg. Sci. 26, 175–190 (2015).
Caughlin, T. T. et al. A hyperspectral image can predict tropical tree growth rates in single-species stands. Ecol. Appl. 26, 2369–2375 (2016).
Serbin, S. P. et al. Remotely estimating photosynthetic capacity, and its response to temperature, in vegetation canopies using imaging spectroscopy. Remote Sens. Environ. 167, 78–87 (2015).
Tobner, C. M. et al. Functional identity is the main driver of diversity effects in young tree communities. Ecol. Lett. 19, 638–647 (2016).
Grossman, J. J., Cavender-Bares, J., Hobbie, S. E., Reich, P. B. & Montgomery, R. A. Species richness and traits predict overyielding in stem growth in an early-successional tree diversity experiment. Ecology 98, 2601–2614 (2017).
Sapijanskas, J., Paquette, A., Potvin, C., Kunert, N. & Loreau, M. Tropical tree diversity enhances light capture through crown plasticity and spatial and temporal niche differences. Ecology 95, 2479–2492 (2014).
Niklaus, P. A., Baruffol, M., He, J. S., Ma, K. & Schmid, B. Can niche plasticity promote biodiversity–productivity relationships through increased complementarity? Ecology 98, 1104–1116 (2017).
Williams, L. J., Paquette, A., Cavender-Bares, J., Messier, C. & Reich, P. B. Spatial complementarity in tree crowns explains overyielding in species mixtures. Nat. Ecol. Evol. 1, 0063 (2017).
Forrester, D. I. & Bauhus, J. A review of processes behind diversity–productivity relationships in forests. Curr. For. Rep. 2, 45–61 (2016).
Jactel, H. et al. Tree diversity drives forest stand resistance to natural disturbances. Curr. For. Rep. 3, 223–243 (2017).
Wright, A. J., Wardle, D. A., Callaway, R. M. & Gaxiola, A. The overlooked role of facilitation in biodiversity experiments. Trends Ecol. Evol. 32, 383–390 (2017).
Kothari, S., Montgomery, R. & Cavender-Bares, J. Physiological responses to light explain competition and facilitation in a tree diversity experiment. Preprint at bioRxiv https://doi.org/10.1101/845701 (2020).
Reich, P. B. Key canopy traits drive forest productivity. Proc. R. Soc. B 279, 2128–2134 (2012).
Tobner, C. M., Paquette, A., Reich, P. B., Gravel, D. & Messier, C. Advancing biodiversity–ecosystem functioning science using high-density tree-based experiments over functional diversity gradients. Oecologia 174, 609–621 (2014).
Schweiger, A. K. et al. Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evol. 2, 976–982 (2018).
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
Reich, P. B. The world-wide ‘fast-slow’plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
Williams, L. J., Cavender-Bares, J., Paquette, A., Messier, C. & Reich, P. B. Light mediates the relationship between community diversity and trait plasticity in functionally and phylogenetically diverse tree mixtures. J. Ecol. 108, 1617–1634 (2020).
Huete, A. R., Liu, H. & van Leeuwen, W. J. D. The use of vegetation indices in forested regions: issues of linearity and saturation. IEEE Trans. Geosci. Remote Sens. 4, 1966–1968 (1997).
Tjoelker, M. G., Volin, J. C., Oleksyn, J. & Reich, P. B. Interaction of ozone pollution and light effects on photosynthesis in a forest canopy experiment. Plant Cell Environ. 18, 895–905 (1995).
Pacala, S. W. et al. Forest models defined by field measurements: estimation, error analysis and dynamics. Ecol. Monogr. 66, 1–43 (1996).
Reich, P. B., Ellsworth, D. S., Kloeppel, B. D., Fownes, J. H. & Gower, S. T. Vertical variation in canopy structure and CO2 exchange of oak–maple forests: influence of ozone, nitrogen, and other factors on simulated canopy carbon gain. Tree Physiol. 7, 329–345 (1990).
Ollinger, S. V. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytol. 189, 375–394 (2011).
Fox, J. W. Interpreting the ‘selection effect’ of biodiversity on ecosystem function. Ecol. Lett. 8, 846–856 (2005).
Isbell, F. et al. Quantifying effects of biodiversity on ecosystem functioning across times and places. Ecol. Lett. 21, 763–778 (2018).
Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).
Reich, P. B. et al. Geographic range predicts photosynthetic and growth response to warming in co-occurring tree species. Nat. Clim. Change 5, 148–152 (2015).
Thomas, S. C. & Winner, W. E. Leaf area index of an old-growth Douglas-fir forest estimated from direct structural measurements in the canopy. Can. J. Res. 30, 1922–1930 (2000).
Nock, C. A., Caspersen, J. P. & Thomas, S. C. Large ontogenetic declines in intra-crown leaf area index in two temperate deciduous tree species. Ecology 89, 744–753 (2008).
Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).
Serbin, S. P., Singh, A., McNeil, B. E., Kingdon, C. C. & Townsend, P. A. Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol. Appl. 24, 1651–1669 (2014).
Mevik, B.-H., Wehrens, R. & Liland, K. H. pls: Partial least squares and principal component regression. R package version 2.7.0 https://CRAN.R-project.org/package=pls (2018).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
Kuhn, M. caret: Classification and regression training. R package version 6.0.81 https://CRAN.R-project.org/package=caret (2018).
Tucker, C. J. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979).
Serbin, S. P. Spectroscopic Determination of Leaf Nutritional, Morphological, and Metabolic Traits. PhD thesis, Univ. of Wisconsin-Madison (2012).
Williams, L. J. et al. Data and Code for Remote Spectral Detection of Biodiversity Effects on Forest Biomass (Data Repository for the University of Minnesota, 2020); https://doi.org/10.13020/s7pf-am91
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).
The authors declare no competing interests.
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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.
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.
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.
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).
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.
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.
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). https://doi.org/10.1038/s41559-020-01329-4
Nature Ecology & Evolution (2022)
Nature Ecology & Evolution (2022)
Light: Science & Applications (2021)
Predicting species distributions and community composition using satellite remote sensing predictors
Scientific Reports (2021)