Predicting leaf traits across functional groups using reflectance spectroscopy.

Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2  = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.

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.

Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca).

Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2 = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2 = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.

Effects of climate change on forest plantation productivity in Chile.

Forest plantations in Chile occupy more than 2.2 million ha and are responsible for 2.1% of the GDP of the country's economy. The ability to accurately predictions of plantations productivity under current and future climate has an impact can enhance on forest management and industrial wood production. The use of process-based models to predict forest growth has been instrumental in improving the understanding and quantifying the effects of climate variability, climate change, and the impact of atmospheric CO2 concentration and management practices on forest growth. This study uses the 3-PG model to predict future forest productivity Eucalyptus globulus and Pinus radiata. The study integrates climate data from global circulation models used in CMIP5 for scenarios RCP26 and RCP85, digital soil maps for physical and chemical variables. Temporal and spatial tree growth inventories were used to compare with the 3-PG predictions. The results indicated that forest productivity is predicted to potentially increase stand volume (SV) over the next 50 years by 26% and 24% for the RCP26 scenario and between 73% and 62% for the RCP85 scenario for E. globulus and P. radiata, respectively. The predicted increases can be explained by a combination of higher level of atmospheric CO2 , air temperatures closer to optimum than current, and increases in tree water use efficiency. If the effect of CO2 is not considered, the predicted differences of SV for 2070 are 16% and 14% for the RCP26 scenario and 22% and 14% for RCP85 for the two species. While shifts in climate and increasing CO2 are likely to benefit promote higher productivity, other factors such as lack insufficient availability of soil nutrients, events such as increasing frequency and duration of droughts, longer periods of extreme temperatures, competing vegetation, and occurrence of new pests and diseases may compromise these potential gains.

Assessing representation of remote sensing derived forest structure and land cover across a network of protected areas.

Protected areas (PA) are an effective means of conserving biodiversity and protecting suites of valuable ecosystem services. Currently, many nations and international governments use proportional area protected as a critical metric for assessing progress towards biodiversity conservation. However, the areal and other common metrics do not assess the effectiveness of PA networks, nor do they assess how representative PA are of the ecosystems they aim to protect. Topography, stand structure, and land cover are all key drivers of biodiversity within forest environments, and are well-suited as indicators to assess the representation of PA. Here, we examine the PA network in British Columbia, Canada, through drivers derived from freely-available data and remote sensing products across the provincial biogeoclimatic ecosystem classification system. We examine biases in the PA network by elevation, forest disturbances, and forest structural attributes, including height, cover, and biomass by comparing a random sample of protected and unprotected pixels. Results indicate that PA are commonly biased towards high-elevation and alpine land covers, and that forest structural attributes of the park network are often significantly different in protected versus unprotected areas (426 out of 496 forest structural attributes found to be different; p < 0.01). Analysis of forest structural attributes suggests that establishing additional PA could ensure representation of various forest structure regimes across British Columbia's ecosystems. We conclude that these approaches using free and open remote sensing data are highly transferable and can be accomplished using consistent datasets to assess PA representations globally.

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance.

Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software's over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.

Changing spring snow cover dynamics and early season forage availability affect the behavior of a large carnivore.

Changing climates are altering wildlife habitats and wildlife behavior in complex ways. Here, we examine how changing spring snow cover dynamics and early season forage availability are altering grizzly bear (Ursus arctos) behavior postden emergence. Telemetry data were used to identify spring activity dates for 48 individuals in the Yellowhead region of Alberta, Canada. Spring activity date was related to snow cover dynamics using a daily percent snow cover dataset. Snow melt end date, melt rate, and melt consistency explained 45% of the variation in spring activity date. We applied this activity date model across the entire Yellowhead region from 2000 to 2016 using simulated grizzly bear home ranges. Predicted spring activity date was then compared with a daily spring forage availability date dataset, resulting in "wait time" estimates for four key early season forage species. Temporal changes in both spring activity date and early season forage "wait times" were assessed using non-parametric regression. Grizzly bear activity date was found to have either remained constant (95%) or become earlier (5%) across the study area; virtually no areas with significantly later spring activity dates were detected. Similarly, the majority of "wait times" did not change (85%); however, the majority of significant changes in "wait times" for the four early season forage species indicated that "wait times" were lessening where changes were detected. Our results show that spring activity date is largely dictated by snow melt characteristics and that changing snow melt conditions may result in earlier spring activity. However, early season food stress conditions are likely to remain unchanged or improve as vegetation phenology also becomes earlier. Our findings extend the recent work examining animal movement in response to changing phenology from migratory birds and ungulates to an apex predator, further demonstrating the potential effects of changing climates on wildlife species.

Relationships between individual-tree mortality and water-balance variables indicate positive trends in water stress-induced tree mortality across North America.

Accounting for water stress-induced tree mortality in forest productivity models remains a challenge due to uncertainty in stress tolerance of tree populations. In this study, logistic regression models were developed to assess species-specific relationships between probability of mortality (Pm ) and drought, drawing on 8.1 million observations of change in vital status (m) of individual trees across North America. Drought was defined by standardized (relative) values of soil water content (Ws,z ) and reference evapotranspiration (ETr,z ) at each field plot. The models additionally tested for interactions between the water-balance variables, aridity class of the site (AC), and estimated tree height (h). Considering drought improved model performance in 95 (80) per cent of the 64 tested species during calibration (cross-validation). On average, sensitivity to relative drought increased with site AC (i.e. aridity). Interaction between water-balance variables and estimated tree height indicated that drought sensitivity commonly decreased during early height development and increased during late height development, which may reflect expansion of the root system and decreasing whole-plant, leaf-specific hydraulic conductance, respectively. Across North America, predictions suggested that changes in the water balance caused mortality to increase from 1.1% yr-1 in 1951 to 2.0% yr-1 in 2014 (a net change of 0.9 ± 0.3% yr-1 ). Interannual variation in mortality also increased, driven by increasingly severe droughts in 1988, 1998, 2006, 2007 and 2012. With strong confidence, this study indicates that water stress is a common cause of tree mortality. With weak-to-moderate confidence, this study strengthens previous claims attributing positive trends in mortality to increasing levels of water stress. This 'learn-as-we-go' approach - defined by sampling rare drought events as they continue to intensify - will help to constrain the hydraulic limits of dominant tree species and the viability of boreal and temperate forest biomes under continued climate change.

An ecoregion assessment of projected tree species vulnerabilities in western North America through the 21st century.

Forest ecosystems across western North America will likely see shifts in both tree species dominance and composition over the rest of this century in response to climate change. Our objective in this study was to identify which ecological regions might expect the greatest changes to occur. We used the process-based growth model 3-PG, to provide estimates of tree species responses to changes in environmental conditions and to evaluate the extent that species are resilient to shifts in climate over the rest of this century. We assessed the vulnerability of 20 tree species in western North America using the Canadian global circulation model under three different emission scenarios. We provided detailed projections of species shifts by including soil maps that account for the spatial variation in soil water availability and soil fertility as well as by utilizing annual climate projections of monthly changes in air temperature, precipitation, solar radiation, vapor pressure deficit and frost at a spatial resolution of one km. Projected suitable areas for tree species were compared to their current ranges based on observations at >40 000 field survey plots. Tree species were classified as vulnerable if environmental conditions projected in the future appear outside that of their current distribution ≥70% of the time. We added a migration constraint that limits species dispersal to <200 m yr-1 to provide more realistic projections on species distributions. Based on these combinations of constraints, we predicted the greatest changes in the distribution of dominant tree species to occur within the Northwest Forested Mountains and the highest number of tree species stressed will likely be in the North American Deserts. Projected climatic changes appear especially unfavorable for species in the subalpine zone, where major shifts in composition may lead to the emergence of new forest types.

Barren-ground caribou (Rangifer tarandus groenlandicus) behaviour after recent fire events; integrating caribou telemetry data with Landsat fire detection techniques.

Fire regimes are changing throughout the North American boreal forest in complex ways. Fire is also a major factor governing access to high-quality forage such as terricholous lichens for barren-ground caribou (Rangifer tarandus groenlandicus). Additionally, fire alters forest structure which can affect barren-ground caribou's ability to navigate in a landscape. Here, we characterize how the size and severity of fires are changing across five barren-ground caribou herd ranges in the Northwest Territories and Nunavut, Canada. Additionally, we demonstrate how time since fire, fire severity, and season result in complex changes in caribou behavioural metrics estimated using telemetry data. Fire disturbances were identified using novel gap-free Landsat surface reflectance composites from 1985 to 2011 across all herd ranges. Burn severity was estimated using the differenced normalized burn ratio. Annual area burned and burn severity were assessed through time for each herd and related to two behavioural metrics: velocity and relative turning angle. Neither annual area burned nor burn severity displayed any temporal trend within the study period. However, certain herds, such as the Ahiak/Beverly, have more exposure to fire than other herds (i.e. Cape Bathurst had a maximum forested area burned of less than 4 km2 ). Time since fire and burn severity both significantly affected velocity and relative turning angles. During fall, winter, and spring, fire virtually eliminated foraging-focused behaviour for all 26 years of analysis while more severe fires resulted in a marked increase in movement-focused behaviour compared to unburnt patches. Between seasons, caribou used burned areas as early as 1-year postfire, demonstrating complex, nonlinear reactions to time since fire, fire severity, and season. In all cases, increases in movement-focused behaviour were detected postfire. We conclude that changes in caribou behaviour immediately postfire are primarily driven by changes in forest structure rather than changes in terricholous lichen availability.

Predicting carbon benefits from climate-smart agriculture: High-resolution carbon mapping and uncertainty assessment in El Salvador.

Agroforestry management in smallholder agriculture can provide climate change mitigation and adaptation benefits and has been promoted as 'climate-smart agriculture' (CSA), yet has generally been left out of international and voluntary carbon (C) mitigation agreements. A key reason for this omission is the cost and uncertainty of monitoring C at the farm scale in heterogeneous smallholder landscapes. A largely overlooked alternative is to monitor C at more aggregated scales and develop C contracts with groups of land owners, community organizations or C aggregators working across entire landscapes (e.g., watersheds, communities, municipalities, etc.). In this study we use a 100-km2 agricultural area in El Salvador to demonstrate how high-spatial resolution optical satellite imagery can be used to map aboveground woody biomass (AGWB) C at the landscape scale with very low uncertainty (95% probability of a deviation of less than 1%). Uncertainty of AGWB-C estimates remained low (<5%) for areas as small as 250 ha, despite high uncertainties at the farm and plot scale (34-99%). We estimate that CSA adoption could more than double AGWB-C stocks on agricultural lands in the study area, and that utilizing AGWB-C maps to target denuded areas could increase C gains per unit area by 46%. The potential value of C credits under a plausible adoption scenario would range from $38,270 to $354,000 yr-1 for the study area, or about $13 to $124 ha-1 yr-1, depending on C prices. Considering farm sizes in smallholder landscapes rarely exceed 1-2 ha, relying solely on direct C payments to farmers may not lead to widespread CSA adoption, especially if farm-scale monitoring is required. Instead, landscape-scale approaches to C contracting, supported by satellite-based monitoring methods such as ours, could be a key strategy to reduce costs and uncertainty of C monitoring in heterogeneous smallholder landscapes, thereby incentivizing more widespread CSA adoption.

Technological Advancement in Tower-Based Canopy Reflectance Monitoring: The AMSPEC-III System.

Understanding plant photosynthesis, or Gross Primary Production (GPP), is a crucial aspect of quantifying the terrestrial carbon cycle. Remote sensing approaches, in particular multi-angular spectroscopy, have proven successful for studying relationships between canopy-reflectance and plant-physiology processes, thus providing a mechanism to scale up. However, many different instrumentation designs exist and few cross-comparisons have been undertaken. This paper discusses the design evolution of the Automated Multiangular SPectro-radiometer for Estimation of Canopy reflectance (AMSPEC) series of instruments. Specifically, we assess the performance of the PP-Systems Unispec-DC and Ocean Optics JAZ-COMBO spectro-radiometers installed on an updated, tower-based AMSPEC-III system. We demonstrate the interoperability of these spectro-radiometers, and the results obtained suggest that JAZ-COMBO can successfully be used to substitute more expensive measurement units for detecting and investigating photosynthesis and canopy spectra. We demonstrate close correlations between JAZ-COMBO and Unispec-DC measured canopy radiance (0.75 ≤ R² ≤ 0.85) and solar irradiance (0.95 ≤ R² ≤ 0.96) over a three month time span. We also demonstrate close agreement between the bi-directional distribution functions obtained from each instrument. We conclude that cost effective alternatives may allow a network of AMSPEC-III systems to simultaneously monitor various vegetation types in different ecosystems. This will allow to scale and improve our understanding of the interactions between vegetation physiology and spectral characteristics, calibrate broad-scale observations to stand-level measurements, and ultimately lead to improved understanding of changing vegetation spectral features from satellite.

Disentangling linkages between satellite-derived indicators of forest structure and productivity for ecosystem monitoring.

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems.

Linking microphytobenthos distribution and mudflat geomorphology under varying sedimentary regimes using unoccupied aerial vehicle (UAV)-acquired multispectral reflectance and photogrammetry.

Microphytobenthic (MPB) biofilms play significant roles in the ecology of coastal mudflats, including provision of essential food resources to shorebird species. In these ecosystems, water-divergence structures like jetties and causeways can drastically alter sedimentation patterns and mudflat topography, yet their effects on MPB biofilm biomass and distribution are poorly understood. Here, we used a combination of unoccupied aerial vehicle (UAV) technologies, photogrammetric processing, and sediment field samples to compare biofilm and mudflat characteristics between areas of the Fraser River Estuary with varying sedimentary regimes and shorebird use. Our aims were to: (1) demonstrate the use of fine spatial resolution UAV-acquired multispectral imagery (cm2) with extensive spatial coverage (>km2) and a co-alignment photogrammetric processing techniques to survey MPB biofilm and mudflat topography at spatial scales and detail relevant to foraging shorebirds; and, (2) investigate the effects of water-divergence structures on mudflat elevation and microtopography, as well as MPB biofilm biomass, distribution, and spatial patterning. From a technical perspective, co-alignment allowed us to analyze aligned and continuous fine-resolution elevation models and orthomosaics for large areas of the estuary, while the normalized difference vegetation index was a good predictor of sediment chlorophyll-a (R2 = 0.9). Using these data products, we found that mudflats in close proximity to water-divergence structures have cross-shore profiles characteristic of low sediment supply as well as decreased microtopographic variability. At disturbed sites, elevation and microtopography had a weaker influence on biofilm biomass compared to intact estuarine ecosystem sites. Analysis of biofilm patch showed that sites either had a relatively small number of large, contiguous patches, or a large number of smaller, isolated patches; however, less disturbed sites did not necessarily have larger biofilm patches than more disturbed sites. We conclude that UAV-acquired multispectral imagery and co-alignment-based workflow are promising new tools for ecologists to map, monitor, and understand MPB biofilm dynamics in ecologically sensitive estuaries.

Quantifying forest disturbance regimes within caribou (Rangifer tarandus) range in British Columbia.

Habitat disturbance is a major driver of the decline of woodland caribou (Rangifer tarandus caribou) in Canada. Different disturbance agents and regimes negatively impact caribou populations to different degrees. It is therefore critical that land managers and scientists studying caribou have a detailed understanding of the disturbance regimes affecting caribou habitat. In this work we use recent advances in satellite-based disturbance detection to quantify polygonal forest disturbance regimes affecting caribou ecotypes and herds in British Columbia (BC) from 1985 to 2019. Additionally, we utilize this data to investigate harvesting rates since the implementation of the Species at Risk Act (SARA) and publication of recovery strategies for caribou in BC. Southern Mountain caribou herds are the most threatened yet experienced the highest rates of disturbance, with 22.75% of forested habitat within their ranges disturbed during the study period. Over the study period, we found that in total, 16.4% of forested area was disturbed across all caribou herd ranges. Our findings indicate that caribou in BC face high, and in many cases increasing, levels of habitat disturbance. Our results provide a detailed understanding of the polygonal disturbance regimes affecting caribou in BC at the herd scale, and highlight the need for effective implementation of policies aimed at preserving caribou habitat.

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning.

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43-90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1-2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.