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Biodiversity is ultimately the outcome of millions of years of evolution; however, due to increasing human domination of the Earth, biodiversity in its multiple dimensions is changing rapidly. Here, we present “phylogenetic completeness” (PC) as a concept and method for safeguarding Earth's evolutionary heritage by maintaining all branches of the tree of life. Using data for five major terrestrial clades, we performed a global evaluation of the PC approach and compared the results to an approach in which species are conserved or lost at random. We demonstrate that under PC, for a given number of species extinctions, it is possible to maximize the protection of evolutionary innovations in every clade. The PC approach is flexible, may be used to conduct a phylogenetic audit of biodiversity under different conservation scenarios, complements existing conservation efforts, and is linked to the post‐2020 UN Convention on Biodiversity targets.

 

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.

 

Urban tree canopy cover is often unequally distributed across cities such that more socially vulnerable neighborhoods often have lower tree canopy cover than less socially vulnerable neighborhoods. However, how the diversity and composition of the urban canopy affect the nature of social‐ecological benefits (and burdens), including the urban forest's vulnerability to climate change, remains underexamined. Here, we synthesize tree inventories developed by multiple organizations and present a species‐specific, geolocated database of more than 600,000 urban trees across the 7‐county Minneapolis‐St. Paul (MSP) metropolitan area in the Upper Midwest of the United States. We find that tree diversity across the MSP is variable yet dominated by a few species (e.g.,Fraxinus pennsylvanica,Acer platanoides, andGleditsia triacanthos), contributing to the vulnerability of the MSP urban forest to future climate change and disturbances. In contrast to tree canopy cover, tree diversity was not well predicted by socioeconomic or demographic factors. However, our analysis identified areas where both climate and social vulnerability are high. Our results add to a growing body of literature emphasizing the importance of considering how complex and interacting social and ecological factors drive urban forest diversity and composition when pursuing management objectives.

 

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.