Associations of Leaf Spectra with Genetic and Phylogenetic Variation in Oaks: Prospects for Remote Detection of Biodiversity

Harnessing Raman spectroscopy for the analysis of plant diversity

Here, we explore the application of Raman spectroscopy for the assessment of plant biodiversity. Raman spectra from 11 vascular plant species commonly found in forest ecosystems, specifically angiosperms (both monocots and eudicots) and pteridophytes (ferns), were acquired in vivo and in situ using a Raman leaf-clip. We achieved an overall accuracy of 91% for correct classification of a species within a plant group and identified lignin Raman spectral features as a useful discriminator for classification. The results demonstrate the potential of Raman spectroscopy in contributing to plant biodiversity assessment.

Monitoring vegetation- and geodiversity with remote sensing and traits

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal

PREMISE

Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data.

METHODS

Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400-2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model.

RESULTS

Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species.

CONCLUSIONS

Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.

Making the Genotypic Variation Visible: Hyperspectral Phenotyping in Scots Pine Seedlings

Hyperspectral reflectance contains valuable information about leaf functional traits, which can indicate a plant's physiological status. Therefore, using hyperspectral reflectance for high-throughput phenotyping of foliar traits could be a powerful tool for tree breeders and nursery practitioners to distinguish and select seedlings with desired adaptation potential to local environments. We evaluated the use of 2 nondestructive methods (i.e., leaf and proximal/canopy) measuring hyperspectral reflectance in the 350- to 2,500-nm range for phenotyping on 1,788 individual Scots pine seedlings belonging to lowland and upland ecotypes of 3 different local populations from the Czech Republic. Leaf-level measurements were collected using a spectroradiometer and a contact probe with an internal light source to measure the biconical reflectance factor of a sample of needles placed on a black background in the contact probe field of view. The proximal canopy measurements were collected under natural solar light, using the same spectroradiometer with fiber optical cable to collect data on individual seedlings' hemispherical conical reflectance factor. The latter method was highly susceptible to changes in incoming radiation. Both spectral datasets showed statistically significant differences among Scots pine populations in the whole spectral range. Moreover, using random forest and support vector machine learning algorithms, the proximal data obtained from the top of the seedlings offered up to 83% accuracy in predicting 3 different Scots pine populations. We conclude that both approaches are viable for hyperspectral phenotyping to disentangle the phenotypic and the underlying genetic variation within Scots pine seedlings.

Evaluating potential of leaf reflectance spectra to monitor plant genetic variation

Remote sensing of vegetation by spectroscopy is increasingly used to characterize trait distributions in plant communities. How leaves interact with electromagnetic radiation is determined by their structure and contents of pigments, water, and abundant dry matter constituents like lignins, phenolics, and proteins. High-resolution ("hyperspectral") spectroscopy can characterize trait variation at finer scales, and may help to reveal underlying genetic variation-information important for assessing the potential of populations to adapt to global change. Here, we use a set of 360 inbred genotypes of the wild coyote tobacco Nicotiana attenuata: wild accessions, recombinant inbred lines (RILs), and transgenic lines (TLs) with targeted changes to gene expression, to dissect genetic versus non-genetic influences on variation in leaf spectra across three experiments. We calculated leaf reflectance from hand-held field spectroradiometer measurements covering visible to short-wave infrared wavelengths of electromagnetic radiation (400-2500 nm) using a standard radiation source and backgrounds, resulting in a small and quantifiable measurement uncertainty. Plants were grown in more controlled (glasshouse) or more natural (field) environments, and leaves were measured both on- and off-plant with the measurement set-up thus also in more to less controlled environmental conditions. Entire spectra varied across genotypes and environments. We found that the greatest variance in leaf reflectance was explained by between-experiment and non-genetic between-sample differences, with subtler and more specific variation distinguishing groups of genotypes. The visible spectral region was most variable, distinguishing experimental settings as well as groups of genotypes within experiments, whereas parts of the short-wave infrared may vary more specifically with genotype. Overall, more genetically variable plant populations also showed more varied leaf spectra. We highlight key considerations for the application of field spectroscopy to assess genetic variation in plant populations.

Climate change impacts the distribution of Quercus section Cyclobalanopsis (Fagaceae), a keystone lineage in East Asian evergreen broadleaved forests

East Asian evergreen broadleaved forests (EBFLs) harbor high species richness, but these ecosystems are severely impacted by global climate change and deforestation. Conserving and managing EBLFs requires understanding dominant tree distribution dynamics. In this study, we used 29 species in Quercus section Cyclobalanopsis-a keystone lineage in East Asian EBLFs-as proxies to predict EBLF distribution dynamics using species distribution models (SDMs). We examined climatic niche overlap, similarity, and equivalency among seven biogeographical regions' species using 'ecospat'. We also estimated the effectiveness of protected areas in the predicted range to elucidate priority conservation regions. Our results showed that the climatic niches of most geographical groups differ. The western species under the Indian summer monsoon regime were mainly impacted by temperature factors, whereas precipitation impacted the eastern species under the East Asian summer monsoon regime. Our simulation predicted a northward range expansion of section Cyclobalanopsis between 2081 and 2100, except for the ranges of the three Himalayan species analyzed, which might shrink significantly. The greatest shift of highly suitable areas was predicted for the species in the South Pacific, with a centroid shift of over 300 km. Remarkably, only 7.56% of suitable habitat is currently inside protected areas, and the percentage is predicted to continue declining in the future. To better conserve Asian EBLFs, establishing nature reserves in their northern distribution ranges, and transplanting the populations with predicted decreasing numbers and degraded habitats to their future highly suitable areas, should be high-priority objectives.

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

Leaf spectroscopy of resistance to Ceratocystis wilt of 'Ōhi'a

Plant pathogens are increasingly compromising forest health, with impacts to the ecological, economic, and cultural goods and services these global forests provide. One response to these threats is the identification of disease resistance in host trees, which with conventional methods can take years or even decades to achieve. Remote sensing methods have accelerated host resistance identification in agricultural crops and for a select few forest tree species, but applications are rare. Ceratocystis wilt of 'ōhi'a, caused by the fungal pathogen Ceratocystis lukuohia has been killing large numbers of the native Hawaiian tree, Metrosideros polymorpha or 'Ōhi'a, Hawaii's most common native tree and a biocultural keystone species. Here, we assessed whether resistance to C. lukuohia is detectable in leaf-level reflectance spectra (400-2500 nm) and used chemometric conversion equations to understand changes in leaf chemical traits of the plants as indicators of wilt symptom progression. We collected leaf reflectance data prior to artificially inoculating 2-3-year-old M. polymorpha clones with C. lukuohia. Plants were rated 3x a week for foliar wilt symptom development and leaf spectra data collected at 2 to 4-day intervals for 120 days following inoculation. We applied principal component analysis (PCA) to the pre-inoculation spectra, with plants grouped according to site of origin and subtaxon, and two-way analysis of variance to assess whether each principal component separated individuals based on their disease severity ratings. We identified seven leaf traits that changed in susceptible plants following inoculation (tannins, chlorophyll a+b, NSC, total C, leaf water, phenols, and cellulose) and leaf chemistries that differed between resistant and early-stage susceptible plants, most notably chlorophyll a+b and cellulose. Further, disease resistance was found to be detectable in the reflectance data, indicating that remote sensing work could expedite Ceratocystis wilt of 'ōhi'a resistance screenings.

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as "lineage functional types" or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.

Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species

Leaf spectra are integrated foliar phenotypes that capture a range of traits and can provide insight into ecological processes. Leaf traits, and therefore leaf spectra, may reflect belowground processes such as mycorrhizal associations. However, evidence for the relationship between leaf traits and mycorrhizal association is mixed, and few studies account for shared evolutionary history. We conduct partial least squares discriminant analysis to assess the ability of spectra to predict mycorrhizal type. We model the evolution of leaf spectra for 92 vascular plant species and use phylogenetic comparative methods to assess differences in spectral properties between arbuscular mycorrhizal and ectomycorrhizal plant species. Partial least squares discriminant analysis classified spectra by mycorrhizal type with 90% (arbuscular) and 85% (ectomycorrhizal) accuracy. Univariate models of principal components identified multiple spectral optima corresponding with mycorrhizal type due to the close relationship between mycorrhizal type and phylogeny. Importantly, we found that spectra of arbuscular mycorrhizal and ectomycorrhizal species do not statistically differ from each other after accounting for phylogeny. While mycorrhizal type can be predicted from spectra, enabling the use of spectra to identify belowground traits using remote sensing, this is due to evolutionary history and not because of fundamental differences in leaf spectra due to mycorrhizal type.

Foliar functional and genetic variation in a keystone Hawaiian tree species estimated through spectroscopy

Imaging spectroscopy has the potential to map closely related plant taxa at landscape scales. Although spectral investigations at the leaf and canopy levels have revealed relationships between phylogeny and reflectance, understanding how spectra differ across, and are inherited from, genotypes of a single species has received less attention. We used a common-garden population of four varieties of the keystone canopy tree, Metrosideros polymorpha, from Hawaii Island and four F1-hybrid genotypes derived from controlled crosses to determine if reflectance spectra discriminate sympatric, conspecific varieties of this species and their hybrids. With a single exception, pairwise comparisons of leaf reflectance patterns successfully distinguished varieties of M. polymorpha on Hawaii Island as well as populations of the same variety from different islands. Further, spectral variability within a single variety from Hawaii Island and the older island of Oahu was greater than that observed among the four varieties on Hawaii Island. F1 hybrids most frequently displayed leaf spectral patterns intermediate to those of their parent taxa. Spectral reflectance patterns distinguished each of two of the hybrid genotypes from one of their parent varieties, indicating that classifying hybrids may be possible, particularly if sample sizes are increased. This work quantifies a baseline in spectral variability for an endemic Hawaiian tree species and advances the use of imaging spectroscopy in biodiversity studies at the genetic level.

Insights into the Taxonomically Challenging Hexaploid Alpine Shrub Willows of Salix Sections Phylicifoliae and Nigricantes (Salicaceae)

The complex genomic composition of allopolyploid plants leads to morphologically diverse species. The traditional taxonomical treatment of the medium-sized, hexaploid shrub willows distributed in the Alps is difficult based on their variable morphological characters. In this study, RAD sequencing data, infrared-spectroscopy, and morphometric data are used to analyze the phylogenetic relationships of the hexaploid species of the sections Nigricantes and Phylicifoliae in a phylogenetic framework of 45 Eurasian Salix species. Both sections comprise local endemics as well as widespread species. Based on the molecular data, the described morphological species appeared as monophyletic lineages (except for S. phylicifolia s.str. and S. bicolor, which are intermingled). Both sections Phylicifoliae and Nigricantes are polyphyletic. Infrared-spectroscopy mostly confirmed the differentiation of hexaploid alpine species. The morphometric data confirmed the molecular results and supported the inclusion of S. bicolor into S. phylicifolia s.l., whereas the alpine endemic S. hegetschweileri is distinct and closely related to species of the section Nigricantes. The genomic structure and co-ancestry analyses of the hexaploid species revealed a geographical pattern for widespread S. myrsinifolia, separating the Scandinavian from the alpine populations. The newly described S. kaptarae is tetraploid and is grouped within S. cinerea. Our data reveal that both sections Phylicifoliae and Nigricantes need to be redefined.

Exploring plant responses to abiotic stress by contrasting spectral signature changes

In this study, daily changes over a short period and diurnal progression of spectral reflectance at the leaf level were used to identify spring wheat genotypes (Triticum aestivum L.) susceptible to adverse conditions. Four genotypes were grown in pots experiments under semi-controlled conditions in Chile and Spain. Three treatments were applied: i) control (C), ii) water stress (WS), and iii) combined water and heat shock (WS+T). Spectral reflectance, gas exchange and chlorophyll fluorescence measurements were performed on flag leaves for three consecutive days at anthesis. High canopy temperature ( H _CT ) genotypes showed less variability in their mean spectral reflectance signature and chlorophyll fluorescence, which was related to weaker responses to environmental fluctuations. While low canopy temperature ( L _CT ) genotypes showed greater variability. The genotypes spectral signature changes, in accordance with environmental fluctuation, were associated with variations in their stomatal conductance under both stress conditions (WS and WS+T); L _CT genotypes showed an anisohydric response compared that of H _CT , which was isohydric. This approach could be used in breeding programs for screening a large number of genotypes through proximal or remote sensing tools and be a novel but simple way to identify groups of genotypes with contrasting performances.

Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites

This paper focuses on vegetation health conditions (VHC) assessment and mapping using high resolution airborne hyperspectral AVIRIS-NG imagery and validated with field spectroscopy-based vegetation spectral data. It also quantified the effect of mining on vegetation health for geo-environmental impact assessment at a fine level scale. In this study, we have developed and modified vegetation indices (VIs) based model for VHC assessment and mapping in coal mining sites. We have used thirty narrow banded VIs based on the statistical measurement for suitable VIs identification. The highest Pearson's r, R², lowest RMSE, and P values indices have been used for VIs combined pixels analysis. The highest different (Healthy vs. unhealthy) vegetation combination index (VCI) has been selected for VHC assessment and mapping. We have also compared VIs model-based VHC results to ENVI (software) forest health tool and Spectral-based SAM classification results. The 1st VCI result showed the highest difference (72.07%) from other VCI. The AUC values of the ROC curve have shown a better fit for the VIs model (0.79) than Spectral classification (0.74), and ENVI FHT (0.68) based on VHC results. The VHC results showed that unhealthy vegetation classes are located at low distances from mine sites, and healthy vegetation classes are situated at high distances. It is also seen that there is a highly significant positive relationship (R² =0.70) between VHC classes and distance from mines. These results will provide a guideline for geo-environmental impact assessment in coal mining sites.

Revealing the Complex Relationship Among Hyperspectral Reflectance, Photosynthetic Pigments, and Growth in Norway Spruce Ecotypes

Norway spruce has a wide natural distribution range, harboring substantial physiological and genetic variation. There are three altitudinal ecotypes described in this species. Each ecotype has been shaped by natural selection and retains morphological and physiological characteristics. Foliar spectral reflectance is readily used in evaluating the physiological status of crops and forest ecosystems. However, underlying genetics of foliar spectral reflectance and pigment content in forest trees has rarely been investigated. We assessed the reflectance in a clonal bank comprising three ecotypes in two dates covering different vegetation season conditions. Significant seasonal differences in spectral reflectance among Norway spruce ecotypes were manifested in a wide-ranging reflectance spectrum. We estimated significant heritable variation and uncovered phenotypic and genetic correlations among growth and physiological traits through bivariate linear models utilizing spatial corrections. We confirmed the relative importance of the red edge within the context of the study site's ecotypic variation. When interpreting these findings, growth traits such as height, diameter, crown length, and crown height allowed us to estimate variable correlations across the reflectance spectrum, peaking in most cases in wavelengths connected to water content in plant tissues. Finally, significant differences among ecotypes in reflectance and other correlated traits were detected.

Representing plant diversity in land models: An evolutionary approach to make "Functional Types" more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere-atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of "Plant Functional Types" (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution-based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages ("Lineage Functional Types") will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next-generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.

A spectral analysis of stem bark for boreal and temperate tree species

The woody material of forest canopies has a significant effect on the total forest reflectance and on the interpretation of remotely sensed data, yet research on the spectral properties of bark has been limited. We developed a novel measurement setup for acquiring stem bark reflectance spectra in field conditions, using a mobile hyperspectral camera. The setup was used for stem bark reflectance measurements of ten boreal and temperate tree species in the visible (VIS) to near‐infrared (NIR) (400–1000 nm) wavelength region. Twenty trees of each species were measured, constituting a total of 200 hyperspectral reflectance images. The mean bark spectra of species were similar in the VIS region, and the interspecific variation was largest in the NIR region. The intraspecific variation of bark spectra was high for all studied species from the VIS to the NIR region. The spectral similarity of our study species did not correspond to the general phylogenetic lineages. The hyperspectral reflectance images revealed that the distributions of per‐pixel reflectance values within images were species‐specific. The spectral library collected in this study contributes toward building a comprehensive understanding of the spectral diversity of forests needed not only in remote sensing applications but also in, for example, biodiversity or land surface modeling studies.

Early detection of a tree pathogen using airborne remote sensing

Native forests of Hawai'i Island are experiencing an ecological crisis in the form of Rapid 'Ōhi'a Death (ROD), a recently characterized disease caused by two fungal pathogens in the genus Ceratocystis. Since approximately 2010, this disease has caused extensive mortality of Hawai'i's keystone endemic tree, known as 'ōhi'a (Metrosideros polymorpha). Visible symptoms of ROD include rapid browning of canopy leaves, followed by death of the tree within weeks. This quick progression leading to tree mortality makes early detection critical to understanding where the disease will move at a timescale feasible for controlling the disease. We used repeat laser-guided imaging spectroscopy (LGIS) of forests on Hawai'i Island collected by the Global Airborne Observatory (GAO) in 2018 and 2019 to derive maps of foliar trait indices previously found to be important in distinguishing between ROD-infected and healthy 'ōhi'a canopies. Data from these maps were used to develop a prognostic indicator of tree stress prior to the visible onset of browning. We identified canopies that were green in 2018, but became brown in 2019 (defined as "to become brown"; TBB), and a corresponding set of canopies that remained green. The data mapped in 2018 showed separability of foliar trait indices between TBB and green 'ōhi'a, indicating early detection of canopy stress prior to the onset of ROD. Overall, a combination of linear and non-linear analyses revealed canopy water content (CWC), foliar tannins, leaf mass per area (LMA), phenols, cellulose, and non-structural carbohydrates (NSC) are primary drivers of the prognostic spectral capability which collectively result in strong consistent changes in leaf spectral reflectance in the near-infrared (700-1300 nm) and shortwave-infrared regions (1300-2500 nm). Results provide insight into the underlying foliar traits that are indicative of physiological responses of M. polymorpha trees infected with Ceratocycstis and suggest that imaging spectroscopy is an effective tool for identifying trees likely to succumb to ROD prior to the onset of visible symptoms.

Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.

Reading light: leaf spectra capture fine-scale diversity of closely related, hybridizing arctic shrubs

Leaf reflectance spectroscopy is emerging as an effective tool for assessing plant diversity and function. However, the ability of leaf spectra to detect fine-scale plant evolutionary diversity in complicated biological scenarios is not well understood. We test if reflectance spectra (400-2400 nm) can distinguish species and detect fine-scale population structure and phylogenetic divergence - estimated from genomic data - in two co-occurring, hybridizing, ecotypically differentiated species of Dryas. We also analyze the correlation among taxonomically diagnostic leaf traits to understand the challenges hybrids pose to classification models based on leaf spectra. Classification models based on leaf spectra identified two species of Dryas with 99.7% overall accuracy and genetic populations with 98.9% overall accuracy. All regions of the spectrum carried significant phylogenetic signal. Hybrids were classified with an average overall accuracy of 80%, and our morphological analysis revealed weak trait correlations within hybrids compared to parent species. Reflectance spectra captured genetic variation and accurately distinguished fine-scale population structure and hybrids of morphologically similar, closely related species growing in their home environment. Our findings suggest that fine-scale evolutionary diversity is captured by reflectance spectra and should be considered as spectrally-based biodiversity assessments become more prevalent.

Detection of the metabolic response to drought stress using hyperspectral reflectance

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

Coupling spectral and resource-use complementarity in experimental grassland and forest communities

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.

Predicting species distributions and community composition using satellite remote sensing predictors

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 m2). 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 km2). 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.

Priority list of biodiversity metrics to observe from space

Monitoring global biodiversity from space through remotely sensing geospatial patterns has high potential to add to our knowledge acquired by field observation. Although a framework of essential biodiversity variables (EBVs) is emerging for monitoring biodiversity, its poor alignment with remote sensing products hinders interpolation between field observations. This study compiles a comprehensive, prioritized list of remote sensing biodiversity products that can further improve the monitoring of geospatial biodiversity patterns, enhancing the EBV framework and its applicability. The ecosystem structure and ecosystem function EBV classes, which capture the biological effects of disturbance as well as habitat structure, are shown by an expert review process to be the most relevant, feasible, accurate and mature for direct monitoring of biodiversity from satellites. Biodiversity products that require satellite remote sensing of a finer resolution that is still under development are given lower priority (for example, for the EBV class species traits). Some EBVs are not directly measurable by remote sensing from space, specifically the EBV class genetic composition. Linking remote sensing products to EBVs will accelerate product generation, improving reporting on the state of biodiversity from local to global scales.

BII-Implementation: The causes and consequences of plant biodiversity across scales in a rapidly changing world

The proposed Biology Integration Institute will bring together two major research institutions in the Upper Midwest—the University of Minnesota (UMN) and University of Wisconsin-Madison (UW)—to investigate the causes and consequences of plant biodiversity across scales in a rapidly changing world—from genes and molecules within cells and tissues to communities, ecosystems, landscapes and the biosphere. The Institute focuses on plant biodiversity, defined broadly to encompass the heterogeneity within life that occurs from the smallest to the largest biological scales. A premise of the Institute is that life is envisioned as occurring at different scales nested within several contrasting conceptions of biological hierarchies, defined by the separate but related fields of physiology, evolutionary biology and ecology. The Institute will emphasize the use of ‘spectral biology’—detection of biological properties based on the interaction of light energy with matter—and process-oriented predictive models to investigate the processes by which biological components at one scale give rise to emergent properties at higher scales. Through an iterative process that harnesses cutting edge technologies to observe a suite of carefully designed empirical systems—including the National Ecological Observatory Network (NEON) and some of the world’s longest running and state-of-the-art global change experiments—the Institute will advance biological understanding and theory of the causes and consequences of changes in biodiversity and at the interface of plant physiology, ecology and evolution.

INTELLECTUAL MERIT

The Institute brings together a diverse, gender-balanced and highly productive team with significant leadership experience that spans biological disciplines and career stages and is poised to integrate biology in new ways. Together, the team will harness the potential of spectral biology, experiments, observations and synthetic modeling in a manner never before possible to transform understanding of how variation within and among biological scales drives plant and ecosystem responses to global change over diurnal, seasonal and millennial time scales. In doing so, it will use and advance state-of-the-art theory. The institute team posits that the designed projects will unearth transformative understanding and biological rules at each of the various scales that will enable an unprecedented capacity to discern the linkages between physiological, ecological and evolutionary processes in relation to the multi-dimensional nature of biodiversity in this time of massive planetary change. A strength of the proposed Institute is that it leverages prior federal investments in research and formalizes partnerships with foreign institutions heavily invested in related biodiversity research. Most of the planned projects leverage existing research initiatives, infrastructure, working groups, experiments, training programs, and public outreach infrastructure, all of which are already highly synergistic and collaborative, and will bring together members of the overall research and training team.

BROADER IMPACTS

A central goal of the proposed Institute is to train the next generation of diverse integrative biologists. Post-doctoral, graduate student and undergraduate trainees, recruited from non-traditional and underrepresented groups, including through formal engagement with Native American communities, will receive a range of mentoring and training opportunities. Annual summer training workshops will be offered at UMN and UW as well as training experiences with the Global Change and Biodiversity Research Priority Program (URPP-GCB) at the University of Zurich (UZH) and through the Canadian Airborne Biodiversity Observatory (CABO). The Institute will engage diverse K-12 audiences, the general public and Native American communities through Market Science modules, Minute Earth videos, a museum exhibit and public engagement and educational activities through the Bell Museum of Natural History, the Cedar Creek Ecosystem Science Reserve (CCESR) and the Wisconsin Tribal Conservation Association.

Current and near-term advances in Earth observation for ecological applications

There is an unprecedented array of new satellite technologies with capabilities for advancing our understanding of ecological processes and the changing composition of the Earth's biosphere at scales from local plots to the whole planet. We identified 48 instruments and 13 platforms with multiple instruments that are of broad interest to the environmental sciences that either collected data in the 2000s, were recently launched, or are planned for launch in this decade. We have restricted our review to instruments that primarily observe terrestrial landscapes or coastal margins and are available under free and open data policies. We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum. The suite of instruments we describe measure land surface characteristics, including land cover, but provide a more detailed monitoring of ecosystems, plant communities, and even some species then possible from historic sensors. The newer instruments have potential to greatly improve our understanding of ecosystem functional relationships among plant traits like leaf mass area (LMA), total nitrogen content, and leaf area index (LAI). They provide new information on physiological processes related to photosynthesis, transpiration and respiration, and stress detection, including capabilities to measure key plant and soil biophysical properties. These include canopy and soil temperature and emissivity, chlorophyll fluorescence, and biogeochemical contents like photosynthetic pigments (e.g., chlorophylls, carotenoids, and phycobiliproteins from cyanobacteria), water, cellulose, lignin, and nitrogen in foliar proteins. These data will enable us to quantify and characterize various soil properties such as iron content, several types of soil clays, organic matter, and other components. Most of these satellites are in low Earth orbit (LEO), but we include a few in geostationary orbit (GEO) because of their potential to measure plant physiological traits over diurnal periods, improving estimates of water and carbon budgets. We also include a few spaceborne active LiDAR and radar imagers designed for quantifying surface topography, changes in surface structure, and 3-dimensional canopy properties such as height, area, vertical profiles, and gap structure. We provide a description of each instrument and tables to summarize their characteristics. Lastly, we suggest instrument synergies that are likely to yield improved results when data are combined.

Application of remote sensing technology to estimate productivity and assess phylogenetic heritability

PREMISE

Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives.

METHODS

We explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it.

RESULTS

The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone-based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession.

DISCUSSION

Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.

Leaf reflectance spectra capture the evolutionary history of seed plants

Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.

Foliar Spectra and Traits of Bog Plants across Nitrogen Deposition Gradients

Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.

Intraspecific genetic variation of a Fagus sylvatica population in a temperate forest derived from airborne imaging spectroscopy time series

The growing pace of environmental change has increased the need for large-scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.We used multi-annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full-analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling.

Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning

Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual symptoms would improve management by greatly reducing disease potential and spread as well as improve both the financial and environmental sustainability of potato farms. In-vivo foliar spectroscopy offers the capacity to rapidly and non-destructively characterize plant physiological status, which can be used to detect the effects of necrotizing pathogens on plant condition prior to the appearance of visual symptoms. Here, we tested differences in spectral response of four potato cultivars, including two cultivars with a shared genotypic background except for a single copy of a resistance gene, to inoculation with Phytophthora infestans clonal lineage US-23 using three statistical approaches: random forest discrimination (RF), partial least squares discrimination analysis (PLS-DA), and normalized difference spectral index (NDSI). We find that cultivar, or plant genotype, has a significant impact on spectral reflectance of plants undergoing P. infestans infection. The spectral response of four potato cultivars to infection by Phytophthora infestans clonal lineage US-23 was highly variable, yet with important shared characteristics that facilitated discrimination. Early disease physiology was found to be variable across cultivars as well using non-destructively derived PLS-regression trait models. This work lays the foundation to better understand host-pathogen interactions across a variety of genotypic backgrounds, and establishes that host genotype has a significant impact on spectral reflectance, and hence on biochemical and physiological traits, of plants undergoing pathogen infection.

Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes

Hyperspectral reflectance tools have been used to detect multiple pathogens in agricultural settings and single sources of infection or broad declines in forest stands. However, differentiation of any one disease from other sources of tree stress is integral for stand and landscape-level applications in mixed species systems. We tested the ability of spectral models to differentiate oak wilt, a fatal disease in oaks caused by Bretziella fagacearum ``Bretz'', from among other mechanisms of decline. We subjected greenhouse-grown oak seedlings (Quercus ellipsoidalis ``E.J. Hill'' and Quercus macrocarpa ``Michx.'') to chronic drought or inoculation with the oak wilt fungus or bur oak blight fungus (Tubakia iowensis ``T.C. Harr. & D. McNew''). We measured leaf and canopy spectroscopic reflectance (400-2400 nm) and instantaneous photosynthetic and stomatal conductance rates, then used partial least-squares discriminant analysis to predict treatment from hyperspectral data. We detected oak wilt before symptom appearance, and classified the disease with high accuracy in symptomatic leaves. Classification accuracy from spectra increased with declines in photosynthetic function in oak wilt-inoculated plants. Wavelengths diagnostic of oak wilt were only found in non-visible spectral regions and are associated with water status, non-structural carbohydrates and photosynthetic mechanisms. We show that hyperspectral models can differentiate oak wilt from other causes of tree decline and that detection is correlated with biological mechanisms of oak wilt infection and disease progression. We also show that within the canopy, symptom heterogeneity can reduce detection, but that symptomatic leaves and tree canopies are suitable for highly accurate diagnosis. Remote application of hyperspectral tools can be used for specific detection of disease across a multi-species forest stand exhibiting multiple stress symptoms.

How to learn to stop worrying and love environmental DNA monitoring

Environmental DNA is one of the most promising new tools in the aquatic biodiversity monitoring toolkit, with particular appeal for applications requiring assessment of target taxa at very low population densities. And yet there persists considerable anxiety within the management community regarding the appropriateness of environmental DNA monitoring for certain tasks and the degree to which environmental DNA methods can deliver information relevant to management needs. This brief perspective piece is an attempt to address that anxiety by offering some advice on how end-users might best approach these new technologies. I do not here review recent developments in environmental DNA science, but rather I explore ways in which managers and decision-makers might become more comfortable adopting environmental DNA tools-or choosing not to adopt them, should circumstances so dictate. I attempt to contextualize the central challenges associated with acceptance of environmental DNA detection by contrasting them with traditional "catch-and-look" approaches to biodiversity monitoring. These considerations lead me to recommend the cultivation of four "virtues," attitudes that can be brought into engagement with environmental DNA surveillance technologies that I hope will increase the likelihood that those engagements will be positive and that the future development and application of environmental DNA tools will further the cause of wise management.

Partitioning plant spectral diversity into alpha and beta components

Plant spectral diversity - how plants differentially interact with solar radiation - is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (β; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ-, β-, and α-diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify 'hotspots' of spectral α-diversity within a region, and discover spectrally unique areas that contribute strongly to β-diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.

Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

In-vivo foliar spectroscopy, also known as contact hyperspectral reflectance, enables rapid and non-destructive characterization of plant physiological status. This can be used to assess pathogen impact on plant condition both prior to and after visual symptoms appear. Challenging this capacity is the fact that dead tissue yields relatively consistent changes in leaf optical properties, negatively impacting our ability to distinguish causal pathogen identity. Here, we used in-situ spectroscopy to detect and differentiate Phytophthora infestans (late blight) and Alternaria solani (early blight) on potato foliage over the course of disease development and explored non-destructive characterization of contrasting disease physiology. Phytophthora infestans, a hemibiotrophic pathogen, undergoes an obligate latent period of two–seven days before disease symptoms appear. In contrast, A. solani, a necrotrophic pathogen, causes symptoms to appear almost immediately when environmental conditions are conducive. We found that respective patterns of spectral change can be related to these differences in underlying disease physiology and their contrasting pathogen lifestyles. Hyperspectral measurements could distinguish both P. infestans-infected and A. solani-infected plants with greater than 80% accuracy two–four days before visible symptoms appeared. Individual disease development stages for each pathogen could be differentiated from respective controls with 89–95% accuracy. Notably, we could distinguish latent P. infestans infection from both latent and symptomatic A. solani infection with greater than 75% accuracy. Spectral features important for late blight detection shifted over the course of infection, whereas spectral features important for early blight detection remained consistent, reflecting their different respective pathogen biologies. Shortwave infrared wavelengths were important for differentiation between healthy and diseased, and between pathogen infections, both pre- and post-symptomatically. This proof-of-concept work supports the use of spectroscopic systems as precision agriculture tools for rapid and early disease detection and differentiation tools, and highlights the importance of careful consideration of underlying pathogen biology and disease physiology for crop disease remote sensing.

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.

Early Detection of Sage (Salvia officinalis L.) Responses to Ozone Using Reflectance Spectroscopy

Advancements in techniques to rapidly and non-destructively detect the impact of tropospheric ozone (O3) on crops are required. This study demonstrates the capability of full-range (350-2500 nm) reflectance spectroscopy to characterize responses of asymptomatic sage leaves under an acute O3 exposure (200 ppb for 5 h). Using partial least squares regression, spectral models were developed for the estimation of several traits related to photosynthesis, the oxidative pressure induced by O3, and the antioxidant mechanisms adopted by plants to cope with the pollutant. Physiological traits were well predicted by spectroscopic models (average model goodness-of-fit for validation (R2): 0.65-0.90), whereas lower prediction performances were found for biochemical traits (R2: 0.42-0.71). Furthermore, even in the absence of visible symptoms, comparing the full-range spectral profiles, it was possible to distinguish with accuracy plants exposed to charcoal-filtered air from those exposed to O3. An O3 effect on sage spectra was detectable from 1 to 5 h from the beginning of the exposure, but ozonated plants quickly recovered after the fumigation. This O3-tolerance was confirmed by trends of vegetation indices and leaf traits derived from spectra, further highlighting the capability of reflectance spectroscopy to early detect the responses of crops to O3.

Demystifying dominant species

The pattern of a few abundant species and many rarer species is a defining characteristic of communities worldwide. These abundant species are often referred to as dominant species. Yet, despite their importance, the term dominant species is poorly defined and often used to convey different information by different authors. Based on a review of historical and contemporary definitions we develop a synthetic definition of dominant species. This definition incorporates the relative local abundance of a species, its ubiquity across the landscape, and its impact on community and ecosystem properties. A meta-analysis of removal studies shows that the loss of species identified as dominant by authors can significantly impact ecosystem functioning and community structure. We recommend two metrics that can be used jointly to identify dominant species in a given community and provide a roadmap for future avenues of research on dominant species. In our review, we make the case that the identity and effects of dominant species on their environments are key to linking patterns of diversity to ecosystem function, including predicting impacts of species loss and other aspects of global change on ecosystems.

Diversification, adaptation, and community assembly of the American oaks (Quercus), a model clade for integrating ecology and evolution

Contents Summary 669 I. Model clades for the study and integration of ecology and evolution 670 II. Oaks: an important model clade 671 III. Insights from the history of the American oaks for understanding community assembly and ecosystem dominance 673 IV. Bridging the gap between micro- and macroevolutionary processes relevant to ecology 679 V. How do we reconcile evidence for adaptive evolution with niche conservatism and long-term stasis? 682 VI. High plasticity and within-population genetic variation contribute to population persistence 683 VII. Emerging technologies for tracking functional change 685 VIII. Conclusions 685 Acknowledgements 686 References 686 SUMMARY: Ecologists and evolutionary biologists are concerned with explaining the diversity and composition of the natural world and are aware of the inextricable linkages between ecological and evolutionary processes that maintain the Earth's life support systems. Yet examination of these linkages remains challenging due to the contrasting nature of focal systems and research approaches. Model clades provide a critical means to integrate ecology and evolution, as illustrated by the oaks (genus Quercus), an important model clade, given their ecological dominance, remarkable diversity, and growing phylogenetic, genomic, and ecological data resources. Studies of the clade reveal that their history of sympatric parallel adaptive radiation continues to influence community assembly today, highlighting questions on the nature and extent of coexistence mechanisms. Flexible phenology and hydraulic traits, despite evolutionary stasis, may have enabled adaptation to a wide range of environments within and across species, contributing to their high abundance and diversity. The oaks offer fundamental insights at the intersection of ecology and evolution on the role of diversification in community assembly processes, on the importance of flexibility in key functional traits in adapting to new environments, on factors contributing to persistence of long-lived organisms, and on evolutionary legacies that influence ecosystem function.

The CCB-ID approach to tree species mapping with airborne imaging spectroscopy

BACKGROUND

Biogeographers assess how species distributions and abundances affect the structure, function, and composition of ecosystems. Yet we face a major challenge: it is difficult to precisely map species across landscapes. Novel Earth observations could overcome this challenge for vegetation mapping. Airborne imaging spectrometers measure plant functional traits at high resolution, and these measurements can be used to identify tree species. In this paper, I describe a trait-based approach to species identification with imaging spectroscopy, the Center for Conservation Biology species identification (CCB-ID) method, which was developed as part of an ecological data science evaluation competition.

METHODS

These methods were developed using airborne imaging spectroscopy data from the National Ecological Observatory Network (NEON). CCB-ID classified tree species using trait-based reflectance variation and decision tree-based machine learning models, approximating a morphological trait and dichotomous key method inspired by botanical classification. First, outliers were removed using a spectral variance threshold. The remaining samples were transformed using principal components analysis (PCA) and resampled to reduce common species biases. Gradient boosting and random forest classifiers were trained using the transformed and resampled feature data. Prediction probabilities were calibrated using sigmoid regression, and sample-scale predictions were averaged to the crown scale.

RESULTS

CCB-ID received a rank-1 accuracy score of 0.919, and a cross-entropy cost score of 0.447 on the competition test data. Accuracy and specificity scores were high for all species, but precision and recall scores varied for rare species. PCA transformation improved accuracy scores compared to models trained using reflectance data, but outlier removal and data resampling exacerbated class imbalance problems.

DISCUSSION

CCB-ID accurately classified tree species using NEON data, reporting the best scores among participants. However, it failed to overcome several species mapping challenges like precisely identifying rare species. Key takeaways include (1) selecting models using metrics beyond accuracy (e.g., recall) could improve rare species predictions, (2) within-genus trait variation may drive spectral separability, precluding efforts to distinguish between functionally convergent species, (3) outlier removal and data resampling can exacerbate class imbalance problems, and should be carefully implemented, (4) PCA transformation greatly improved model results, and (5) targeted feature selection could further improve species classification models. CCB-ID is open source, designed for use with NEON data, and available to support species mapping efforts.

Reflectance spectroscopy: a novel approach to better understand and monitor the impact of air pollution on Mediterranean plants

The Mediterranean basin can be considered a hot spot not only in terms of climate change (CC) but also for air quality. Assessing the impact of CC and air pollution on ecosystem functions is a challenging task, and adequate monitoring techniques are needed. This paper summarizes the present knowledge on the use of reflectance spectroscopy for the evaluation of the effects of air pollution on plants. First, the history of this technique is outlined. Next, we describe the vegetation reflectance spectrum, how it can be scaled from leaf to landscape levels, what information it contains, and how it can be exploited to understand plant and ecosystem functions. Finally, we review the literature concerning this topic, with special attention to Mediterranean air pollutants, showing the increasing interest in this technique. The ability of spectroscopy to detect the influence of air pollution on plant function of all major and minor Mediterranean pollutants has been evaluated, and ozone and its interaction with other gases (carbon dioxide, nitrogen oxides, and sulfur dioxide) have been the most studied. In the recent years, novel air pollutants, such as particulate matter, nitrogen deposition, and heavy metals, have drawn attention. Although various vegetation types have been studied, few of these species are representative of the Mediterranean environment. Thus, major emphasis should be placed on using vegetation spectroscopy for better understanding and monitoring the impact of air pollution on Mediterranean plants in the CC era.

Genetic, morphological, and spectral characterization of relictual Niobrara River hybrid aspens (Populus × smithii)

PREMISE OF THE STUDY

Aspen groves along the Niobrara River in Nebraska have long been a biogeographic curiosity due to morphological differences from nearby remnant Populus tremuloides populations. Pleistocene hybridization between P. tremuloides and P. grandidentata has been proposed, but the nearest P. grandidentata populations are currently several hundred kilometers east. We tested the hybrid-origin hypothesis using genetic data and characterized putative hybrids phenotypically.

METHODS

We compared nuclear microsatellite loci and chloroplast sequences of Niobrara River aspens to their putative parental species. Parental species and putative hybrids were also grown in a common garden for phenotypic comparison. On the common garden plants, we measured leaf morphological traits and leaf-level spectral reflectance profiles, from which chemical traits were derived.

KEY RESULTS

The genetic composition of the three unique Niobrara aspen genotypes is consistent with the hybridization hypothesis and with maternal chloroplast inheritance from P. grandidentata. Leaf margin dentition and abaxial pubescence differentiated taxa, with the hybrids showing intermediate values. Spectral profiles allowed statistical separation of taxa in short-wave infrared wavelengths, with hybrids showing intermediate values, indicating that traits associated with internal structure of leaves and water absorption may vary among taxa. However, reflectance values in the visible region did not differentiate taxa, indicating that traits related to pigments are not differentiated.

CONCLUSIONS

Both genetic and phenotypic results support the hypothesis of a hybrid origin for these genetically unique aspens. However, low genetic diversity and ongoing ecological and climatic threats to the hybrid taxon present a challenge for conservation of these relictual boreal communities.