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Summary Reflectance spectroscopy is a rapid method for estimating traits and discriminating species. Spectral libraries from herbarium specimens represent an untapped resource for generating broad phenomic datasets across space, time, and taxa.We conducted a proof‐of‐concept study using trait data and spectra from herbarium specimens up to 179 yr old, alongside data from recently dried and pressed leaves. We validated model accuracy and transferability for trait prediction and taxonomic discrimination.Trait models from herbarium spectra predicted leaf mass per area (LMA) withR² = 0.94 and %RMSE = 4.86%. Models for LMA prediction were transferable between herbarium and pressed spectra, achievingR² = 0.88, %RMSE = 8.76% for herbarium to pressed spectra, andR² = 0.76, %RMSE = 10.5% for the reverse transfer. Discriminant models classified leaf spectra from 25 species with 74% accuracy, and classification probabilities were significantly associated with several herbarium specimen quality metrics.The results validate herbarium spectral data for trait prediction and taxonomic discrimination, and demonstrate that trait modeling can benefit from the complementary use of pressed‐leaf and herbarium‐leaf spectral datasets. These promising advancements help to justify the spectral digitization of plant biodiversity collections and support their application in broad ecological and evolutionary investigations. 

Measuring the chemical traits of leaf litter is important for understanding plants’ influence on nutrient cycles, including through nutrient resorption and litter decomposition, but conventional leaf trait measurements are often destructive and labor-intensive. Here, we develop and evaluate the performance of partial least-squares regression models that use reflectance spectra of intact or ground leaves to estimate leaf litter traits, including carbon and nitrogen concentration, carbon fractions, and leaf mass per area (LMA). Our analyses included more than 300 samples of senesced foliage from 11 species of temperate trees, including both needleleaf and broadleaf species. Across all samples, we could predict each trait with moderate-to-high accuracy from both intact-leaf litter spectra (validation R² = 0.543–0.941; %root mean squared error (RMSE) = 7.49–18.5) and ground-leaf litter spectra (validation R² = 0.491–0.946; %RMSE = 7.00–19.5). Notably, intact-leaf spectra yielded better predictions of LMA. Our results support the feasibility of building models to estimate multiple chemical traits from leaf litter of a range of species. In particular, intact-leaf spectral models allow non-destructive trait estimation in a matter of seconds, which could enable researchers to measure the same leaves over time in studies of nutrient resorption. 

Abstract We introduce a new “ecosystem‐scale” experiment at the Cedar Creek Ecosystem Science Reserve in central Minnesota, USA to test long‐term ecosystem consequences of tree diversity and composition. The experiment—the largest of its kind in North America—was designed to provide guidance on forest restoration efforts that will advance carbon sequestration goals and contribute to biodiversity conservation and sustainability.The new Forest and Biodiversity (FAB2) experiment uses native tree species in varying levels of species richness, phylogenetic diversity and functional diversity planted in 100 m²and 400 m²plots at 1 m spacing, appropriate for testing long‐term ecosystem consequences. FAB2 was designed and established in conjunction with a prior experiment (FAB1) in which the same set of 12 species was planted in 16 m²plots at 0.5 m spacing. Both are adjacent to the BioDIV prairie‐grassland diversity experiment, enabling comparative investigations of diversity and ecosystem function relationships between experimental grasslands and forests at different planting densities and plot sizes.Within the first 6 years, mortality in 400 m²monoculture plots was higher than in 100 m²plots. The highest mortality occurred inTilia americanaandAcer negundomonocultures, but mortality for both species decreased with increasing plot diversity. These results demonstrate the importance of forest diversity in reducing mortality in some species and point to potential mechanisms, including light and drought stress, that cause tree mortality in vulnerable monocultures. The experiment highlights challenges to maintaining monoculture and low‐diversity treatments in tree mixture experiments of large extent.FAB2 provides a long‐term platform to test the mechanisms and processes that contribute to forest stability and ecosystem productivity in changing environments. Its ecosystem‐scale design, and accompanying R package, are designed to discern species and lineage effects and multiple dimensions of diversity to inform restoration of ecosystem functions and services from forests. It also provides a platform for improving remote sensing approaches, including Uncrewed Aerial Vehicles (UAVs) equipped with LiDAR, multispectral and hyperspectral sensors, to complement ground‐based monitoring. We aim for the experiment to contribute to international efforts to monitor and manage forests in the face of global change. 

More than ever, ecologists seek to employ herbarium collections to estimate plant functional traits from the past and across biomes. However, many trait measurements are destructive, which may preclude their use on valuable specimens. Researchers increasingly use reflectance spectroscopy to estimate traits from fresh or ground leaves, and to delimit or identify taxa. Here, we extend this body of work to non-destructive measurements on pressed, intact leaves, like those in herbarium collections. Using 618 samples from 68 species, we used partial least-squares regression to build models linking pressed-leaf reflectance spectra to a broad suite of traits, including leaf mass per area (LMA), leaf dry matter content (LDMC), equivalent water thickness, carbon fractions, pigments, and twelve elements. We compared these models to those trained on fresh- or ground-leaf spectra of the same samples. The traits our pressed-leaf models could estimate best were LMA (R2 = 0.932; %RMSE = 6.56), C (R2 = 0.855; %RMSE = 9.03), and cellulose (R2 = 0.803; %RMSE = 12.2), followed by water-related traits, certain nutrients (Ca, Mg, N, and P), other carbon fractions, and pigments (all R2 = 0.514–0.790; %RMSE = 12.8–19.6). Remaining elements were predicted poorly (R2 < 0.5, %RMSE > 20). For most chemical traits, pressed-leaf models performed better than fresh-leaf models, but worse than ground-leaf models. Pressed-leaf models were worse than fresh-leaf models for estimating LMA and LDMC, but better than ground-leaf models for LMA. Finally, in a subset of samples, we used partial least-squares discriminant analysis to classify specimens among 10 species with near-perfect accuracy (>97%) from pressed- and ground-leaf spectra, and slightly lower accuracy (>93%) from fresh-leaf spectra. These results show that applying spectroscopy to pressed leaves is a promising way to estimate leaf functional traits and identify species without destructive analysis. Pressed-leaf spectra might combine advantages of fresh and ground leaves: like fresh leaves, they retain some of the spectral expression of leaf structure; but like ground leaves, they circumvent the masking effect of water absorption. Our study has far-reaching implications for capturing the wide range of functional and taxonomic information in the world’s preserved plant collections. 

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n -dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity. 

Abstract Planting diverse forests has been proposed as a means to increase long‐term carbon (C) sequestration while providing many co‐benefits. Positive tree diversity–productivity relationships are well established, suggesting more diverse forests will lead to greater aboveground C sequestration. However, the effects of tree diversity on belowground C storage have the potential to either complement or offset aboveground gains, especially during early stages of afforestation when potential exists for large losses in soil C due to soil decomposition. Thus, experimental tests of the effects of planted tree biodiversity on changes in whole‐ecosystem C balance are needed. Here, we present changes in above‐ and belowground C pools 6 years after the initiation of the Forests and Biodiversity experiment (FAB1), consisting of high‐density plots of one, two, five, or 12 tree species planted in a common garden. The trees included a diverse range of native species, including both needle‐leaf conifer and broadleaf angiosperm species, and both ectomycorrhizal and arbuscular mycorrhizal species. We quantified the effects of species richness, phylogenetic diversity, and functional diversity on aboveground woody C, as well as on mineral soil C accumulation, fine root C, and soil aggregation. Surprisingly, changes in aboveground woody C pools were uncorrelated to changes in mineral soil C pools, suggesting that variation in soil C accumulation was not driven by the quantity of plant litter inputs. Aboveground woody C accumulation was strongly driven by species and functional identity; however, plots with higher species richness and functional diversity accumulated more C in aboveground wood than expected based on monocultures. We also found weak but significant effects of tree species richness, identity, and mycorrhizal type on soil C accumulation. To assess the role of the microbial community in mediating these effects, we further compared changes in soil C pools to phospholipid fatty acid (PLFA) profiles. Soil C pools and accumulation were more strongly correlated with specific microbial clades than with total microbial biomass or plant diversity. Our results highlight rapidly emerging and microbially mediated effects of tree biodiversity on soil C storage in the early years of afforestation that are independent of gains in aboveground woody biomass. 

As the effects of anthropogenic climate change become more severe, several approaches for deliberate climate intervention to reduce or stabilize Earth’s surface temperature have been proposed. Solar radiation modification (SRM) is one potential approach to partially counteract anthropogenic warming by reflecting a small proportion of the incoming solar radiation to increase Earth’s albedo. While climate science research has focused on the predicted climate effects of SRM, almost no studies have investigated the impacts that SRM would have on ecological systems. The impacts and risks posed by SRM would vary by implementation scenario, anthropogenic climate effects, geographic region, and by ecosystem, community, population, and organism. Complex interactions among Earth’s climate system and living systems would further affect SRM impacts and risks. We focus here on stratospheric aerosol intervention (SAI), a well-studied and relatively feasible SRM scheme that is likely to have a large impact on Earth’s surface temperature. We outline current gaps in knowledge about both helpful and harmful predicted effects of SAI on ecological systems. Desired ecological outcomes might also inform development of future SAI implementation scenarios. In addition to filling these knowledge gaps, increased collaboration between ecologists and climate scientists would identify a common set of SAI research goals and improve the communication about potential SAI impacts and risks with the public. Without this collaboration, forecasts of SAI impacts will overlook potential effects on biodiversity and ecosystem services for humanity.