Multidimensional Structural Characterization is Required to Detect and Differentiate Among Moderate Disturbance Agents

Abstract

The study of vegetation community and structural change has been central to ecology for over a century, yet how disturbances reshape the physical structure of forest canopies remains relatively unknown. Moderate severity disturbance including fire, ice storms, insect and pathogen outbreaks, affects different canopy strata and plant species, which may give rise to variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits with known linkages to ecosystem functioning. We used lidar-derived canopy structural measured within a machine learning framework to detect and differentiate among various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that disturbance agents such as fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, pathogen and insect infestation, and senescence altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, indicating that this commonly quantified canopy feature is insufficient to characterize many moderate severity disturbances. Rather, measures of canopy structure, including those that describe multidimensional change, are needed to characterize disturbance at moderate severities because structural changes from these events are spatially and quantitatively variable. Our findings suggest that standard disturbance detection methods, such as optical based remote sensing platforms, may currently be limited in their ability to detect, differentiate, and characterize disturbance. Further, we conclude that a more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structure change will improve the modeling, detection, and prediction of functional implications of moderate severity disturbance.

Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

“…The full-waveform LiDAR data enable detailed characterization of topography and structure of the observed landscape. Airborne LiDAR data are primarily used for generating bare-earth digital terrain models (DTMs), modeling hillslope hydrology and geomorphology, measuring and characterizing vegetation structure (Atkins et al 2019), and estimating aboveground biomass (Lefsky et al 2002). However, the data can also be used for urban land-cover classification (Yan et al 2015), measuring snow depth (Deems et al 2013), and many other applications.…”

Aboveground Wood Production Is Sustained in the First Growing Season after Phloem-Disrupting Disturbance

“…In May of 2019, we initiated the Forest Resilience Threshold Experiment (FoRTE) to examine C cycling responses to multiple disturbance severities and two disturbance types. The experiment used stem girdling (n > 3600 trees) to implement four replicated factorial combinations of disturbance severity at 0% (control), 45, 65 or 85% targeted gross leaf area index (LAI) loss, and a top-down or bottom-up disturbance type treatment meant to simulate structural changes in response to disturbances like beech bark disease or low severity subcanopy fire [6] that disproportionately impact large and small diameter trees, respectively. We acknowledge, however, that our experimental approach, intended to isolate the effects of disturbance severity and type, does not perfectly emulate the structural and compositional changes associated with natural disturbance.…”

“…Each year in the United States alone, an estimated 2.4 Mha of forestland is invaded by phloem-disrupting insects [2], giving rise to large gradients of stand-scale disturbance severity [1,[3][4][5]. While C cycling responses to stand-replacing disturbance are well-studied, the mechanisms underlying whole ecosystem C cycling responses to less severe phloem-disrupting disturbances are poorly understood [2,[5][6][7][8][9]. Notably, phloem disruption may result in a more gradual reduction in tree growth relative to disturbances from fire, extreme weather and partial harvesting [10,11].…”

Vegetation Structural Complexity and Biodiversity Across Elevation Gradients in the Great Smoky Mountains

“…Southern Appalachian forests, such as those of Great Smoky Mountains National Park (GRSM), are among the most diverse temperate forests in the world with over 100 woody plant species (Latham & Ricklefs 1993). As such, these forests have long been studied for insights into biodiversity (Whittaker 1956), and related topics including forest productivity (Whittaker 1966;Gough et al 2019), disturbance (Harmon et al 1984;Atkins et al 2020), resource acquisition (Atkins et al 2018), habitat fragmentation (Ambrose & Bratton 1990), pollution (Mathias & Thomas 2018), and land cover change (Turner et al 2003). Perhaps surprisingly, then, we are unaware of earlier studies relating biodiversity and forest structure in this high-diversity, topographically complex system.…”

“…Advances in remote sensing, specifically light detection and ranging (LiDAR), have enabled mapping of forest structure and complexity with unprecedented precision, at plot-level to regional scales (LaRue et al 2020). Ecological applications of LiDAR have broadened our understanding of resource acquisition (Stark et al 2015;Atkins et al 2018), allocation strategies (Stovall et al 2017;Stovall et al 2018b;Stovall & Shugart 2018;Stovall et al 2018a), use efficiencies (Hardiman et al 2013), drought response (Atkins & Agee 2019;Smith et al 2019;Stovall et al 2019), productivity (Gough et al 2019), and disturbance history (Fahey et al 2015;Atkins et al 2020). However, recent studies of forest structural complexity either focus on broad, landscape to continental scale patterns (LaRue et al 2018;Atkins et al 2018;Gough et al 2019;Fahey et al 2019), or are limited to characterizations of stand-scale phenomena (Hardiman et al 2018;Hickey et al 2019), without fully considering how forest complexity varies at the stand to regional scale in response to ecological gradients.…”