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Plant diversity effects on community productivity often increase over time. Whether the strengthening of diversity effects is caused by temporal shifts in species-level overyielding (i.e., higher species-level productivity in diverse communities compared with monocultures) remains unclear. Here, using data from 65 grassland and forest biodiversity experiments, we show that the temporal strength of diversity effects at the community scale is underpinned by temporal changes in the species that yield. These temporal trends of species-level overyielding are shaped by plant ecological strategies, which can be quantitatively delimited by functional traits. In grasslands, the temporal strengthening of biodiversity effects on community productivity was associated with increasing biomass overyielding of resource-conservative species increasing over time, and with overyielding of species characterized by fast resource acquisition either decreasing or increasing. In forests, temporal trends in species overyielding differ when considering above- versus belowground resource acquisition strategies. Overyielding in stem growth decreased for species with high light capture capacity but increased for those with high soil resource acquisition capacity. Our results imply that a diversity of species with different, and potentially complementary, ecological strategies is beneficial for maintaining community productivity over time in both grassland and forest ecosystems.

 

This dataset is a compilation of spatially explicit, species-specific urban tree inventories from across the seven-county Minneapolis-St. Paul (MSP) metropolitan area in Minnesota, U.S.A. The dataset was compiled to examine fine-scale patterns of tree biodiversity across MSP. Existing tree inventories were solicited from all municipalities, counties, park systems, and relevant non-profit organizations in the region for which we were able to find contact information, resulting in inventories from 37 municipalities, two counties, one park system, and one non-profit, along with two datasets from prior academic research efforts. The spatial and temporal scope of the inventories varies; for example, the inventories from some municipalities include data from a subset of only street trees at one timepoint, while other municipal inventories were continuously updated datasets with spatially comprehensive data for street trees in addition to some trees in parks and private lands. No inventory was fully comprehensive of all trees in an area. Data are assumed to have been collected between 2012-2022, although the timestamp on each data point is not explicit. Individual inventories were combined into one uniform database. 

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. 

Abstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using satellite remote sensing products as covariates, we constructed stacked species distribution models (S-SDMs) under a Bayesian framework to build next-generation biodiversity models. Model performance of these models was assessed using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON). This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found that applying constraints to assemblage predictions, such as using the probability ranking rule, does not improve biodiversity prediction models. Furthermore, we found that independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), these kinds of next-generation biodiversity models do not accurately recover the observed species composition at the plot level or ecological-community scales (NEON plots are 400 m 2 ). However, these models do return reasonable predictions at macroecological scales, i.e., moderately to highly correct assignments of species identities at the scale of NEON sites (mean area ~ 27 km 2 ). Our results provide insights for advancing the accuracy of prediction of assemblage diversity and composition at different spatial scales globally. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models that accurately predict and monitor ecological assemblages through time and space.