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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. 

Abstract Understanding ecosystem processes on our rapidly changing planet requires integration across spatial, temporal, and biological scales. We propose that spectral biology, using tools that enable near‐ to far‐range sensing by capturing the interaction of energy with matter across domains of the electromagnetic spectrum, will increasingly enable ecological insights across scales from cells to continents. Here, we focus on advances using spectroscopy in the visible to short‐wave infrared, chlorophyll fluorescence‐detecting systems, and optical laser scanning (light detection and ranging, LiDAR) to introduce the topic and special feature. Remote sensing using these tools, in conjunction with in situ measurements, can powerfully capture ecological and evolutionary processes in changing environments. These tools are amenable to capturing variation in life processes across biological scales that span physiological, evolutionary, and macroecological hierarchies. We point out key areas of spectral biology with high potential to advance understanding and monitoring of ecological processes across scales—particularly at large spatial extents—in the face of rapid global change. These include: the detection of plant and ecosystem composition, diversity, structure, and function as well as their relationships; detection of the causes and consequences of environmental stress, including disease and drought, for ecosystems; and detection of change through time in ecosystems over large spatial extents to discern variation in and mechanisms underlying their resistance, recovery, and resilience in the face of disturbance. We discuss opportunities for spectral biology to discover previously unseen variation and novel processes and to prepare the field of ecology for novel computational tools on the horizon with vast new capabilities for monitoring the ecology of our changing planet. 

Abstract The stability of forest productivity is a widely studied phenomenon often associated with tree species diversity. Yet, drivers of stability in forest structure and its consequences for forest productivity remain poorly understood. Using a large (10 ha) young tree diversity experiment, we evaluated how forest structure and multiple dimensions of diversity and composition are related to remotely sensed structural metrics and their stability through the growing season. We then examined whether structural stability (SS) across the growing season (April–October) could explain overyielding (i.e., the net biodiversity effect, NBE) in annual wood productivity. Using Uncrewed Aerial Vehicle‐Light Detecting and Ranging (UAV‐LiDAR), we surveyed experimental tree communities eight times at regular intervals from before bud break to after leaf senescence to derive metrics associated with canopy height heterogeneity, fractional plant cover, and forest structural complexity (based on fractal geometry). The inverse coefficients of variation for each of these three metrics through the season were used as measures of SS. These metrics were then coupled with annual tree inventories to evaluate their relationships with the NBE. Our findings indicate that wood volume and, to some extent, multiple dimensions of diversity and composition (i.e., taxonomic, phylogenetic, and functional) explain remotely sensed metrics of forest structure and their SS. Increases in wood volume as well as functional and phylogenetic diversity and variability (a measure of diversity independent of species richness) were linked to higher SS of forest complexity and canopy height heterogeneity. We further found that higher SS of forest complexity and fractional plant cover were associated with increased overyielding, which was mostly attributable to the complementarity effect. Structural equation models indicate that the stability of structural complexity explains more variation in NBE among plots than dimensions of diversity or variability, highlighting its value as an informative metric that likely integrates multiple drivers associated with overyielding. This study highlights the potential to integrate remote sensing and ecology to disentangle the role of forest SS in shaping ecological processes. 

Freezing tolerance plays a pivotal role in shaping the distribution and diversification of organisms. We investigated the dynamics of adaptation to climate and potential trade‐offs between stem freezing tolerance and growth rate in 48Quercusspecies. Species from colder regions exhibited higher freezing tolerance, lower growth rates and higher winter‐acclimation potential than species from warmer climates. Despite an evolutionary lag, freezing tolerance in oaks is closely aligned with its optimal state. Deciduous species showed marked variability in freezing tolerance across their broad climatic range, while evergreen species, confined to warm climates, displayed low freezing tolerance. Annual growth rates were constrained in all deciduous species, but those that evolved in warm latitudes lost freezing tolerance, precluding a trade‐off between freezing tolerance and growth. We provide evidence that the capacity to adapt to a wide range of thermal environments was critical to adaptive radiation and the current dominance of the North American oaks. 

Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not. We found that spectral diversity consistently predicts analogous metrics of plant taxonomic, functional, and phylogenetic dimensions of biodiversity across biomes. The approach demonstrates promise for monitoring dimensions of biodiversity globally by integrating ground-based measures of biodiversity with imaging spectroscopy and advances capacity toward a Global Biodiversity Observing System. 

This data was primarily collected to assess forest quality within the Minneapolis-St. Paul (MSP) Metropolitan Area and to link above-ground and below-ground properties as part of the goals of the MSP-LTER Urban Tree Canopy research group. Here, we sampled vegetation on 48 circular plots with a 12.5 m radius distributed across 18 parks, registering the date of sampling, park and management agency names, the plot number, and geolocation (latitude, longitude, and elevation). The plots were randomly selected based on GEDI (Global Ecosystem Dynamics Investigation instrument) 2021 footprints in the MSP Metropolitan Area along accessible forested areas inside public parks, where the management agency allowed sampling. In each plot, we measured forest structure and diversity metrics, species names and abundance, DBH, height, distance from the plot center, the height where each individual canopy starts, and the relative position, exposure, and density of each canopy. We also measured understory plant structure and diversity in 4 subplots per plot, totaling 192 subplots. In these subplots, we surveyed all individual plants with heights over 20 cm, recording species names and abundance, plant basal diameter, plant height, and the total number of branches. Furthermore, we assessed the canopy openness above each subplot by calculating percent DIFN (diffuse non-interceptance) from fish eye pictures of the canopy at 1.3 meters over the subplot. 

Abstract Anthropogenic climate change, particularly changes in temperature and precipitation, affects plants in multiple ways. Because plants respond dynamically to stress and acclimate to changes in growing conditions, diagnosing quantitative plant‐environment relationships is a major challenge. One approach to this problem is to quantify leaf responses using spectral reflectance, which provides rapid, inexpensive, and nondestructive measurements that capture a wealth of information about genotype as well as phenotypic responses to the environment. However, it is unclear how warming and drought affect spectra. To address this gap, we used an open‐air field experiment that manipulates temperature and rainfall in 36 plots at two sites in the boreal‐temperate ecotone of northern Minnesota, USA. We collected leaf spectral reflectance (400–2400 nm) at the peak of the growing season for three consecutive years on juveniles (two to six years old) of five tree species planted within the experiment. We hypothesized that these mid‐season measurements of spectral reflectance capture a snapshot of the leaf phenotype encompassing a suite of physiological, structural, and biochemical responses to both long‐ and short‐time scale environmental conditions. We show that the imprint of environmental conditions experienced by plants hours to weeks before spectral measurements is linked to regions in the spectrum associated with stress, namely the water absorption regions of the near‐infrared and short‐wave infrared. In contrast, the environmental conditions plants experience during leaf development leave lasting imprints on the spectral profiles of leaves, attributable to leaf structure and chemistry (e.g., pigment content and associated ratios). Our analyses show that after accounting for baseline species spectral differences, spectral responses to the environment do not differ among the species. This suggests that building a general framework for understanding forest responses to climate change through spectral metrics may be possible, likely having broader implications if the common responses among species detected here represent a widespread phenomenon. Consequently, these results demonstrate that examining the entire spectrum of leaf reflectance for environmental imprints in contrast to single features (e.g., indices and traits) improves inferences about plant‐environment relationships, which is particularly important in times of unprecedented climate change. 

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.