Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high accuracy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non-infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread

Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy datawere used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly

Leaf carbon and nitrogen content of tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016

This data set contains carbon and nitrogen content from combustion–reduction elemental analysis (TruSpec CN Analyzer, LECO) from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. Data were collected as part of the Dimensions of Biodiversity project “Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes”. Samples were collected in or near the old fields chronosequence, the oak savanna, and the Forest and Biodiversity Experiment (FAB 1) plots. We used this data together with leaf-level spectral measurements to build partial least squares regression (PLSR) models for predicting leaf traits from spectra.DEB-1342872DEB-1342778DEB-1342827DEB-1342823DEB-1234162iCORE/AITF (G224150012 and 200700172)NSERC (RGPIN-2015-05129)CFI (26793

Content of leaf pigments of tree and grassland species collected at the Cedar Creek Ecosystem Science Reserve in 2015 and 2016

This data set contains the content of chlorophyll a, chlorophyll b, β-carotene, lutein, neoxanthin, violaxanthin, antheraxanthin and zeaxanthin pigments from tree and grassland species sampled at the Cedar Creek Ecosystem Science Reserve in East Bethel, MN. Mass- and area-based pigment contents were determined using high-performance liquid chromatography (HPLC). Data were collected as part of the Dimensions of Biodiversity project “Linking remotely sensed optical diversity to genetic, phylogenetic and functional diversity to predict ecosystem processes”. Samples were collected in or near the old fields chronosequence, the oak savanna, and the Forest and Biodiversity Experiment (FAB 1) plots. We used this data together with leaf-level spectral measurements to build partial least squares regression (PLSR) models for predicting leaf traits from spectra.DEB-1342872DEB-1342778DEB-1342827DEB-1342823DEB-1234162iCORE/AITF (G224150012 and 200700172)NSERC (RGPIN-2015-05129)CFI (26793

Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes

<p>The datasets in this repository include plant community surveys, hyperspectral reflectance data at the leaf and canopy level, leaf trait data, and soil chemistry data collected at five sites along an elevation gradient of 2400m-3500m in the Chilean Andes (33°S, 70°W). The purpose of this study was to evaluate the environmental drivers of community assembly processes along the elevation gradient.</p><p>Excel, R</p><p>Funding provided by: National Fund for Scientific and Technological Development<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002850<br>Award Number: 1180454</p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: DEB 1342872</p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: DBI 2021898</p><p>Funding provided by: Agencia Nacional de Investigación y Desarrollo<br>Crossref Funder Registry ID: https://ror.org/02ap3w078<br>Award Number: PIA APOYO CCTE AFB170008</p><p>Funding provided by: Agencia Nacional de Investigación y Desarrollo<br>Crossref Funder Registry ID: https://ror.org/02ap3w078<br>Award Number: PIA/BASAL FB210006</p><p>Funding provided by: Agencia Nacional de Investigación y Desarrollo<br>Crossref Funder Registry ID: https://ror.org/02ap3w078<br>Award Number: PIA/BASAL PFB210018</p><p>Funding provided by: National Fund for Scientific and Technological Development<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002850<br>Award Number: 1221540</p><p>Funding provided by: National Fund for Scientific and Technological Development (FONDECYT)*<br>Crossref Funder Registry ID: <br>Award Number: 1230717</p&gt

Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes

<p>The datasets in this repository include plant community surveys, hyperspectral reflectance data at the leaf and canopy level, leaf trait data, and soil chemistry data collected at five sites along an elevation gradient of 2400m-3500m in the Chilean Andes (33°S, 70°W). The purpose of this study was to evaluate the environmental drivers of community assembly processes along the elevation gradient.</p><p>Excel, R</p><p>Funding provided by: National Fund for Scientific and Technological Development<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002850<br>Award Number: 1180454</p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: DEB 1342872</p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: DBI 2021898</p><p>Funding provided by: Agencia Nacional de Investigación y Desarrollo<br>Crossref Funder Registry ID: https://ror.org/02ap3w078<br>Award Number: PIA APOYO CCTE AFB170008</p><p>Funding provided by: Agencia Nacional de Investigación y Desarrollo<br>Crossref Funder Registry ID: https://ror.org/02ap3w078<br>Award Number: PIA/BASAL FB210006</p><p>Funding provided by: Agencia Nacional de Investigación y Desarrollo<br>Crossref Funder Registry ID: https://ror.org/02ap3w078<br>Award Number: PIA/BASAL PFB210018</p><p>Funding provided by: National Fund for Scientific and Technological Development<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100002850<br>Award Number: 1221540</p><p>Funding provided by: National Fund for Scientific and Technological Development (FONDECYT)*<br>Crossref Funder Registry ID: <br>Award Number: 1230717</p&gt

Drivers of plant diversity, community composition, functional traits, and soil processes along an alpine gradient in the central Chilean Andes

Abstract High alpine regions are threatened but understudied ecosystems that harbor diverse endemic species, making them an important biome for testing the role of environmental factors in driving functional trait‐mediated community assembly processes. We tested the hypothesis that plant community assembly along a climatic and elevation gradient is influenced by shifts in habitat suitability, which drive plant functional, phylogenetic, and spectral diversity. In a high mountain system (2400–3500 m) Región Metropolitana in the central Chilean Andes (33°S, 70°W). We surveyed vegetation and spectroscopic reflectance (400–2400 nm) to quantify taxonomic, phylogenetic, functional, and spectral diversity at five sites from 2400 to 3500 m elevation. We characterized soil attributes and processes by measuring water content, carbon and nitrogen, and net nitrogen mineralization rates. At high elevation, colder temperatures reduced available soil nitrogen, while at warmer, lower elevations, soil moisture was lower. Metrics of taxonomic, functional, and spectral alpha diversity peaked at mid‐elevations, while phylogenetic species richness was highest at low elevation. Leaf nitrogen increased with elevation at the community level and within individual species, consistent with global patterns of increasing leaf nitrogen with colder temperatures. The increase in leaf nitrogen, coupled with shifts in taxonomic and functional diversity associated with turnover in lineages, indicate that the ability to acquire and retain nitrogen in colder temperatures may be important in plant community assembly in this range. Such environmental filters have important implications for forecasting shifts in alpine plant communities under a warming climate

Canopy spectral reflectance detects oak wilt at the landscape scale using phylogenetic discrimination

The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high ac- curacy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non- infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread

Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly

Tree diversity reduces variability in sapling survival under drought

1. Enhancing tree diversity may be important to fostering resilience to drought-related climate extremes. So far, little attention has been given to whether tree diversity can increase the survival of trees and reduce its variability in young forest plantations. 2. We conducted an analysis of seedling and sapling survival from 34 globally distributed tree diversity experiments (363,167 trees, 168 species, 3744 plots, 7 biomes) to answer two questions: (1) Do drought and tree diversity alter the mean and variability in plot-level tree survival, with higher and less variable survival as diversity increases? and (2) Do species that survive poorly in monocultures survive better in mixtures and do specific functional traits explain monoculture survival? 3. Tree species richness reduced variability in plot-level survival, while functional diversity (Rao's Q entropy) increased survival and also reduced its variability. Importantly, the reduction in survival variability became stronger as drought severity increased. We found that species with low survival in monocultures survived comparatively better in mixtures when under drought. Species survival in monoculture was positively associated with drought resistance (indicated by hydraulic traits such as turgor loss point), plant height and conservative resource-acquisition traits (e.g. low leaf nitrogen concentration and small leaf size). 4. Synthesis. The findings highlight: (1) The effectiveness of tree diversity for decreasing the variability in seedling and sapling survival under drought; and (2) the importance of drought resistance and associated traits to explain altered tree species survival in response to tree diversity and drought. From an ecological perspective, we recommend mixing be considered to stabilize tree survival, particularly when functionally diverse forests with drought-resistant species also promote high survival of drought-sensitive species