Leaf angle as a leaf and canopy trait: Rejuvenating its role in ecology with new technology.

Life on Earth depends on the conversion of solar energy to chemical energy by plants through photosynthesis. A fundamental challenge in optimizing photosynthesis is to adjust leaf angles to efficiently use the intercepted sunlight under the constraints of heat stress, water loss and competition. Despite the importance of leaf angle, until recently, we have lacked data and frameworks to describe and predict leaf angle dynamics and their impacts on leaves to the globe. We review the role of leaf angle in studies of ecophysiology, ecosystem ecology and earth system science, and highlight the essential yet understudied role of leaf angle as an ecological strategy to regulate plant carbon-water-energy nexus and to bridge leaf, canopy and earth system processes. Using two models, we show that leaf angle variations have significant impacts on not only canopy-scale photosynthesis, energy balance and water use efficiency but also light competition within the forest canopy. New techniques to measure leaf angles are emerging, opening opportunities to understand the rarely-measured intraspecific, interspecific, seasonal and interannual variations of leaf angles and their implications to plant biology and earth system science. We conclude by proposing three directions for future research.

TLSLeAF: automatic leaf angle estimates from single-scan terrestrial laser scanning.

Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf-wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts.

Tree height and leaf drought tolerance traits shape growth responses across droughts in a temperate broadleaf forest.

As climate change drives increased drought in many forested regions, mechanistic understanding of the factors conferring drought tolerance in trees is increasingly important. The dendrochronological record provides a window through which we can understand how tree size and traits shape growth responses to droughts. We analyzed tree-ring records for 12 species in a broadleaf deciduous forest in Virginia (USA) to test hypotheses for how tree height, microenvironment characteristics, and species' traits shaped drought responses across the three strongest regional droughts over a 60-yr period. Drought tolerance (resistance, recovery, and resilience) decreased with tree height, which was strongly correlated with exposure to higher solar radiation and evaporative demand. The potentially greater rooting volume of larger trees did not confer a resistance advantage, but marginally increased recovery and resilience, in sites with low topographic wetness index. Drought tolerance was greater among species whose leaves lost turgor (wilted) at more negative water potentials and experienced less shrinkage upon desiccation. The tree-ring record reveals that tree height and leaf drought tolerance traits influenced growth responses during and after significant droughts in the meteorological record. As climate change-induced droughts intensify, tall trees with drought-sensitive leaves will be most vulnerable to immediate and longer-term growth reductions.

FluoSpec 2-An Automated Field Spectroscopy System to Monitor Canopy Solar-Induced Fluorescence.

Accurate estimation of terrestrial photosynthesis has broad scientific and societal impacts. Measurements of photosynthesis can be used to assess plant health, quantify crop yield, and determine the largest CO2 flux in the carbon cycle. Long-term and continuous monitoring of vegetation optical properties can provide valuable information about plant physiology. Recent developments of the remote sensing of solar-induced chlorophyll fluorescence (SIF) and vegetation spectroscopy have shown promising results in using this information to quantify plant photosynthetic activities and stresses at the ecosystem scale. However, there are few automated systems that allow for unattended observations over months to years. Here we present FluoSpec 2, an automated system for collecting irradiance and canopy radiance that has been deployed in various ecosystems in the past years. The instrument design, calibration, and tests are recorded in detail. We discuss the future directions of this field spectroscopy system. A network of SIF sensors, FluoNet, is established to measure the diurnal and seasonal variations of SIF in several ecosystems. Automated systems such as FluoSpec 2 can provide unique information on ecosystem functioning and provide important support to the satellite remote sensing of canopy photosynthesis.

Trees outside forests are an underestimated resource in a country with low forest cover.

Trees outside forests (TOF) are an underrepresented resource in forest poor nations. As a result of their frequent omission from national forest resource assessments and a lack of readily available very-high-resolution remotely sensed imagery, TOF status and characterization has until now, been unknown. Here, we assess the capacity of openly available 10 m ESA Sentinel constellation satellite imagery for mapping TOF extent at the national level in Bangladesh. In addition, we estimate canopy height for TOF using a TanDEM-X DEM. We map 2,233,578 ha of TOF in Bangladesh with a mean canopy height of 7.3 m. We map 31 and 53% more TOF than existing estimates of TOF and forest, respectively. We find TOF in Bangladesh is nationally fragmented as a consequence of agricultural activity, yet is capable of maintaining connectedness between remaining stands. Now, TOF accounting is feasible at the national scale using readily available datasets, enabling the mainstream inclusion of TOF in national forest resource assessments for other countries.

Evaluating the success of treatments that slow spread of an invasive insect pest.

BACKGROUND: Treatments for the suppression and eradication of insect populations undergo substantial testing to ascertain their efficacy and safety, but the generally limited spatial and temporal scope of such studies limit knowledge of how contextual factors encountered in operational contexts shape the relative success of pest management treatments. These contextual factors potentially include ecological characteristics of the treated area, or the timing of treatments relative to pest phenology and weather events. We used an extensive database on over 1000 treatments of nascent populations of Lymantria dispar (L.) (gypsy moth) to examine how place-based and time-varying conditions shape the success of management treatments. RESULTS: We found treatment success to vary across states and years, and to be highest in small treatment blocks that are isolated from other populations. In addition, treatment success tended to be lower in treatment blocks with open forest canopies, possibly owing to challenges of effectively distributing treatments in these areas. CONCLUSIONS: Our findings emphasize the importance of monitoring for early detection of nascent gypsy moth colonies in order to successfully slow the spread of the invasion. Additionally, operations research should address best practices for effectively treating with patchy and open forest canopies. © 2021 Society of Chemical Industry.

Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations.

BACKGROUND: Biomass maps are valuable tools for estimating forest carbon and forest planning. Individual-tree biomass estimates made using allometric equations are the foundation for these maps, yet the potentially-high uncertainty and bias associated with individual-tree estimates is commonly ignored in biomass map error. We developed allometric equations for lodgepole pine (Pinus contorta), ponderosa pine (P. ponderosa), and Douglas-fir (Pseudotsuga menziesii) in northern Colorado. Plot-level biomass estimates were combined with Landsat imagery and geomorphometric and climate layers to map aboveground tree biomass. We compared biomass estimates for individual trees, plots, and at the landscape-scale using our locally-developed allometric equations, nationwide equations applied across the U.S., and the Forest Inventory and Analysis Component Ratio Method (FIA-CRM). Total biomass map uncertainty was calculated by propagating errors from allometric equations and remote sensing model predictions. Two evaluation methods for the allometric equations were compared in the error propagation-errors calculated from the equation fit (equation-derived) and errors from an independent dataset of destructively-sampled trees (n = 285). RESULTS: Tree-scale error and bias of allometric equations varied dramatically between species, but local equations were generally most accurate. Depending on allometric equation and evaluation method, allometric uncertainty contributed 30-75% of total uncertainty, while remote sensing model prediction uncertainty contributed 25-70%. When using equation-derived allometric error, local equations had the lowest total uncertainty (root mean square error percent of the mean [% RMSE] = 50%). This is likely due to low-sample size (10-20 trees sampled per species) allometric equations and evaluation not representing true variability in tree growth forms. When independently evaluated, allometric uncertainty outsized remote sensing model prediction uncertainty. Biomass across the 1.56 million ha study area and uncertainties were similar for local (2.1 billion Mg; % RMSE = 97%) and nationwide (2.2 billion Mg;  % RMSE = 94%) equations, while FIA-CRM estimates were lower and more uncertain (1.5 billion Mg;  % RMSE = 165%). CONCLUSIONS: Allometric equations should be selected carefully since they drive substantial differences in bias and uncertainty. Biomass quantification efforts should consider contributions of allometric uncertainty to total uncertainty, at a minimum, and independently evaluate allometric equations when suitable data are available.

Tree height explains mortality risk during an intense drought.

Forest mortality is accelerating due to climate change and the largest trees may be at the greatest risk, threatening critical ecological, economic, and social benefits. Here, we combine high-resolution airborne LiDAR and optical data to track tree-level mortality rates for ~2 million trees in California over 8 years, showing that tree height is the strongest predictor of mortality during extreme drought. Large trees die at twice the rate of small trees and environmental gradients of temperature, water, and competition control the intensity of the height-mortality relationship. These findings suggest that future persistent drought may cause widespread mortality of the largest trees on Earth.

Terrestrial LiDAR-derived non-destructive woody biomass estimates for 10 hardwood species in Virginia.

This article contains data related to the research article entitled "Assessing terrestrial laser scanning for developing non-destructive biomass allometry" (Stovall et al., 2018 [1]) and presents 258 terrestrial LiDAR-derived estimates of tree volume and biomass. The terrestrial LiDAR acquisitions were completed in the Center for Tropical Forest Science - Forest Global Earth Observatory (CTFS-ForestGEO) plot in Front Royal, Virginia, USA. The data includes tree diameter at breast height (DBH), total tree height, tree length (correcting for tree lean), average wood density, estimated wood volume, and dry weight or biomass for all trees. These data were used to develop aboveground biomass models [1] and the reader is referred to this study for additional information, interpretation, and reflection on applying this data.