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.

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.

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.