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• Old-growth definition criteria greatly impacts amount estimates and maps. • Physically-based old-growth definitions are detectable via lidar remote sensing. • Age-based old-growth definitions are difficult to detect using lidar remote sensing. • Age-based old-growth forests are extremely diverse in physical structure. Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests. [ABSTRACT FROM AUTHOR]

Aim Understanding the drivers of forest structure, function and change is a fundamental problem in both theoretical ecology and applied forestry for carbon mapping and monitoring. An important component of forest ecology research often utilizes allometric equations to scale up local measurements to predict large-scale forest and ecosystem-level properties. However, both applied and theoretical allometries in forest ecology (such as metabolic scaling theory, MST) assume that many scaling relationships are insensitive to broad-scale climate gradients or species life histories. We aim to test these assumptions by mapping continental-scale forest allometry across environmental gradients in the United States. Location United States. Methods We fit exponents to two allometric relationships in c. 100,000 Forest Inventory Analysis (FIA) field plots: (1) the relationship between the height of an individual tree and its diameter, and (2) plot-level tree size distributions. We compare fitted exponents to environmental and life-history variables, such as climate, topography and forest structure, in an attempt to explain allometric variability and deviations from theoretically predicted allometries. Results We find that the structural allometry of forests varies strongly as a function of location in the United States. Allometric exponents appear to asymptote at approximately where MST theory predicts with increasing forest height, while deviations from MST are partially explained as a function of environmentally driven recruitment limitations and successional status. Main conclusions While we find support for invariant tree and stand allometric scaling relationships in forests that are in steady state with regard to demography and resources, we also find considerable spatial variability in forest allometric relationships when steady-state conditions are violated. These findings suggest that extensions of metabolic scaling theory should incorporate variation in demographic dynamics in younger successional forests, and factors influencing recruitment limitation. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Previous studies have developed strong, site-specific relationships between canopy metrics from lidar (light detecting and ranging) remote sensing data and forest structural characteristics such as above-ground biomass (AGBM), but the generality of these relationships is unknown. In this study, we examine the generality of relationships between lidar metrics and forest structural characteristics, including AGBM, from two study areas in Central America with different precipitation patterns. Location A series of tropical moist forest sites in Panama and a tropical wet forest in Costa Rica. Methods Canopy metrics (e.g. canopy height) were calculated from airborne lidar data. Basal area, mean stem diameter and AGBM were calculated from measurements taken as a part of ongoing forest dynamics studies in both areas. We examined the generality of relationship between lidar metrics and forest structure, and possible environmental effects (e.g. leaf phenology). Results We found that lidar metrics were strongly correlated (R 2: 0.65–0.92) with mean stem diameter, basal area and AGBM in both regions. We also show that the relationships differed between these regions. Deciduousness of canopy trees in the tropical moist forest area accounted for the differences in predictive equations for stem diameter and basal area. The relationships between lidar metrics and AGBM, however, remained significantly different between the two study areas even after adjusting for leaf drop. We attribute this to significant differences in the underlying allometric relationships between stem diameter and AGBM in tropical wet and moist forests. Conclusions Important forest structural characteristics can be estimated reliably across a variety of conditions sampled in these closed-canopy tropical forests. Environmental factors such as drought deciduousness have an important influence on these relationships. Future efforts should continue to examine climatic factors that... [ABSTRACT FROM AUTHOR]

The area of tropical secondary forests is increasing rapidly, but data on the physical and biological structure of the canopies of these forests are limited. To obtain such data and to measure the ontogeny of canopy structure during tropical rainforest succession, we studied patch-scale (5 m2) canopy structure in three areas of 18–36 year-old secondary forest in Costa Rica, and compared the results to data from old-growth forest at the same site. All stands were sampled with a stratified random design with complete harvest from ground level to the top of the canopy from a modular portable tower. All canopies were organized into distinct high- and low-leaf-density layers (strata), and multiple strata developed quickly with increasing patch height. The relation of total Leaf Area Index (LAI, leaf area per area of ground) to patch canopy height, the existence of distinct high and low leaf- density layers (strata and free air spaces), the depth and LAI of the canopy strata and free air spaces, and the relation of the number of strata to patch canopy height were remarkably constant across the entire successional gradient. Trees were the most important contributor to LAI at all stages, while contribution of palm LAI increased through succession. We hypothesize that canopy physical structure at the patch scale is driven by light competition and discuss how this hypothesis could be tested. That canopy physical structure was relatively independent of the identity of the species present suggests that canopy physical structure may be conserved even as canopy floristics shift due to changing climate. [ABSTRACT FROM AUTHOR]

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects. [ABSTRACT FROM AUTHOR]