Biology subjects

Townsend, P. A.

Publications and source records attributed to Townsend, P. A..

7 recordsLinked to original sources

bioRxiv2023

Mapping oak wilt disease using phenological observations from space

Protecting the future of forests relies on our ability to observe changes in forest health. Thus, developing tools for sensing diseases in a timely fashion is critical for managing threats at broad scales. Oak wilt --a disease caused by a pathogenic fungus (Bretziella fagacearum)-- is threatening oaks, killing thousands yearly while negatively impacting the ecosystem services they provide. Here we propose a novel workflow for mapping oak wilt by targeting temporal disease progression through symptoms using land surface phenology (LSP) from spaceborne observations. By doing so, we hypothesize that phenological changes in pigments and photosynthetic activity of trees affected by oak wilt can be tracked using LSP metrics derived from the Chlorophyll/Carotenoid Index (CCI). We used dense time-series observations from Sentinel-2 to create Analysis Ready Data across Minnesota and Wisconsin and to derive three LSP metrics: the value of CCI at the start and end of the growing season, and the coefficient of variation of the CCI during the growing season. We integrate high-resolution airborne imagery in multiple locations to select pixels (n = 3,872) from the most common oak tree health conditions: healthy, symptomatic for oak wilt, and dead. These pixels were used to train an iterative Partial Least Square Discriminant (PLSD) model and derive the probability of an oak tree (i.e., pixel) in one of these conditions and the associated uncertainty. We assessed these models spatially and temporally on testing datasets revealing that it is feasible to discriminate among the three health conditions with overall accuracy between 80-82%. Within conditions, our models suggest that spatial variations among three CCI-derived LSP metrics can predict healthily (Area Under the Curve (AUC) = 0.98), symptomatic (AUC = 0.89), and dead (AUC = 0.94) oak trees with low false positive rates. The model performance was robust across different years as well. The predictive maps were used to guide local stakeholders in locating disease hotspots for ground verification and subsequent decision-making for treatment. Our results highlight the capabilities of LSP metrics from dense spaceborne observations to map diseases and their importance for monitoring changes in biodiversity at large scales.

ecology↗

bioRxiv2023

Variability in Forest Plant Traits along the Western Ghats of India and Their Environmental Drivers at Different Resolutions

Identifying key environmental drivers for plant functional traits is an important step to understanding and predicting ecosystem responses to a changing climate. Imaging spectroscopy offers great potential to map plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least-squares regression to map seven key foliar chemical and morphological traits using NASAs Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) for six sites spanning the Western Ghats of India. We studied the variation of trait space using principal components analysis at spatial resolutions from the plot level (4m), community level (30m and 100m) to the ecosystem level (1000m). We observed a consistent pattern of trait space across different resolutions, with one axis representing the traditional leaf economic spectrum defined by foliar nitrogen concentration and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment-trait correlations across resolutions with generally higher predictive capacity of our environment-traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin showed strong environmental dependencies across sites, while calcium, sugar, and nitrogen were significantly affected by site conditions. Models incorporating site as a fixed effect explained more than 50% of the trait variance at 1000m resolution. LMA showed little dependence on both environment and site conditions, implying other factors such as species composition and perhaps site history strongly affect variation in LMA. Our results show that reliable trait-trait relationships can be identified in coarse resolution imagery, but that local scale trait-trait relationships (resolutions finer than 30m) are not sensitive to broad-scale abiotic/biotc factors.

ecology↗

bioRxiv2021

Remotely detected plant function in two midwestern prairie grassland experiments reveals belowground processes

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 grassland systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling, providing a potential link between remote sensing and belowground processes. 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 characterize 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. Specifically, 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 and 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. In contrast, at Wood River, where plant diversity and productivity were consistently higher, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates, while aboveground biomass did not. The strong, contrasting associations between the quantity and chemistry of aboveground inputs with belowground soil processes and properties 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.

ecology↗

bioRxiv2021

Plastic mulches reduce adult and larval populations of Drosophila suzukii in fall-bearing raspberry

The invasive spotted-wing drosophila, Drosophila suzukii, is a major pest of fruit crops world-wide. Management of D. suzukii relies heavily on chemical control in both organic and conventional systems, and there is a need to develop more sustainable management practices. We evaluated the efficacy of three colors of plastic mulches at reducing populations of D. suzukii in fall-bearing raspberry and assessed the mulches impacts on canopy microclimate factors relevant to D. suzukii. Black, white, and metallic plastic mulches reduced adult D. suzukii populations by 42-51% and larval populations by 52-72% compared to the grower standard. The mulches did not change canopy temperature or relative humidity, but metallic mulches increased canopy light intensity compared to the black mulch. Radiance in the visible spectrum (401-680 nm) was higher for the white and metallic mulch plots, but the black mulch plots did not differ from the control. In the UV spectrum (380-400 nm), all three plastic mulches had higher radiance than the control plots. Future studies will determine whether changes in radiance are associated with the observed reduction in D. suzukii populations. Plastic mulches are a promising cultural practice for managing D. suzukii since they can reduce adult and larval populations and could be incorporated into an integrated pest management program in both organic and conventional systems.

ecology↗

bioRxiv2021

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.

ecology↗

bioRxiv2020

Integrating remote sensing and jurisdictional observation networks to improve the resolution of ecological management

The emergence of citizen science, passive sensors (e.g., trail cameras and acoustic monitoring), and satellite remote sensing have enabled biological data to be collected at unprecedented spatial and temporal scales. There is growing interest in networking these datastreams to expedite the collection and synthesis of environmental and biological data to improve broad-scale ecological monitoring, but there are no examples of such networks being developed to directly inform decision-making by managing agencies. Here, we present the implementation of one such jurisdictional observation network (JON), Snapshot Wisconsin (SW), that links satellite remote sensing (RS) with a volunteer-based trail camera network to generate new insights into wildlife distributions and improve their management by the state agency. SW relies on citizen scientists to deploy trail cameras across the state and classify images of wildlife. As of early 2020 SW comprises nearly 1800 volunteers hosting >2100 active cameras recording >37 million images across a sampling effort of >2000 combined trap-years at >3300 distinct camera locations. We use a set of case studies to demonstrate the potential power of a JON to monitor wildlife with unprecedented combinations of spatial, temporal, and biological resolution and extent. Specifically, we demonstrate that SW markedly improves the spatial and temporal resolution with which black bear distributions can be monitored or forecast, in turn improving the resolution of decision-making. Enhancing the biological resolution of monitoring (e.g., monitoring the distribution of species traits or behaviors) may provide new insights into population drivers, such as the connection between vegetation productivity and white-tailed deer foraging behaviors. Enhanced taxonomic extent provided by trail cameras and other passive sensor networks provide managers new information for a wide range of species and communities that are not otherwise monitored. Our cases further show that JONs synergize existing monitoring practices by serving as a complementary and independent line of evidence or as a tool to enhance the extent and precision of existing models through integrated modeling approaches. SW and other JONS are a powerful new tool for agencies to better achieve their missions and reshape the nature of environmental decision-making.

ecology↗

bioRxiv2020

Spectral niches reveal taxonomic identity and complementarity in plant 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, spectral and resource-use complementarity that contribute to ecosystem productivity. In both a prairie grassland and a forest diversity experiment, we delineated N-dimensional hypervolumes using either wavelength-bands of reflectance spectra or foliar traits. First, we compared the hypervolume fraction unique to each species in either spectral or trait space with increasing dimensionality. Then, we investigated the association between the spectral space occupied by individual plants and their growth, as well as the spectral space occupied by plant communities and ecosystem productivity. We show that species are better distinguished in spectral space than in trait space, providing a conceptual basis for identifying plant taxa spectrally. In addition, the spectral space occupied by individuals increased with plant 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.

ecology↗