Abstract Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape‐scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top of canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the three ‐dimensional spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure‐function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field‐collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top‐of‐canopy percent N and total canopy N (i.e., the total amount of N [g/m2] within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Our resulting maps suggest that, in contrast to top of canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.
more »
« less
Defining a spectrum of integrative trait‐based vegetation canopy structural types
Abstract Vegetation canopy structure is a fundamental characteristic of terrestrial ecosystems that defines vegetation types and drives ecosystem functioning. We use the multivariate structural trait composition of vegetation canopies to classify ecosystems within a global canopy structure spectrum. Across the temperate forest sub‐set of this spectrum, we assess gradients in canopy structural traits, characterise canopy structural types (CST) and evaluate drivers and functional consequences of canopy structural variation. We derive CSTs from multivariate canopy structure data, illustrating variation along three primary structural axes and resolution into six largely distinct and functionally relevant CSTs. Our results illustrate that within‐ecosystem successional processes and disturbance legacies can produce variation in canopy structure similar to that associated with sub‐continental variation in forest types and eco‐climatic zones. The potential to classify ecosystems into CSTs based on suites of structural traits represents an important advance in understanding and modelling structure–function relationships in vegetated ecosystems.
more »
« less
- PAR ID:
- 10525960
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 22
- Issue:
- 12
- ISSN:
- 1461-023X
- Format(s):
- Medium: X Size: p. 2049-2059
- Size(s):
- p. 2049-2059
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Vegetation species mapping using airborne imaging spectroscopy yields accurate results and is important for advancing conservation objectives and biogeographic studies. As these data become more readily available owing to the upcoming launch of spaceborne imaging spectrometers, it is necessary to understand how these data can be used to consistently classify species across large geographic scales. However, few studies have attempted to map species across multiple ecosystems; therefore, little is known regarding the effect of intra-specific variation on the mapping of a single species across a wide range of environments and among varying backgrounds of other non-target species. To explore this effect, we developed and tested species classification models forMetrosideros polymorpha, a highly polymorphic canopy species endemic to Hawai’i, which is found in a diverse array of ecosystems. We compared the accuracies of support vector machine (SVM) and random forest models trained on canopy reflectance data from each of eight distinct ecosystems (ecosystem-specific) and a universal model trained on data from all ecosystems. When applied to ecosystem-specific test datasets, the ecosystem-specific models outperformed the universal model; however, the universal model retained high (>81%) accuracies across all ecosystems. Additionally, we found that models from ecosystems with broad variation inM. polymorphacanopy traits, as estimated using chemometric equations applied to canopy spectra, accurately predictedM. polymorphain other ecosystems. While species classifications across ecosystems can yield accurate results, these results will require sampling procedures that capture the intra-specific variation of the target species.more » « less
-
Abstract Structural diversity—the volume and physical arrangement of vegetation within the three‐dimensional (3D) space of ecosystems—is a predictor of ecosystem function that can be measured at large scales with remote sensing. However, the landscape composition and configuration of structural diversity across macrosystems have not been well described. Using a relatively recently developed method to quantify landscape composition and configuration of continuous habitat or terrain, we propose the application of gradient surface metrics (GSMs) to quantify landscape patterns of structural diversity and provide insights into how its spatial pattern relates to ecosystem function. We first applied an example set of GSMs that represent landscape heterogeneity, dominance, and edge density to Lidar‐derived structural diversity within 28 forested landscapes at National Ecological Observatory Network (NEON) sites. Second, we tested for forest type, geographic location, and climate drivers of macroscale variation in GSMs of structural diversity (GSM‐SD). Third, we demonstrated the utility of these metrics for understanding spatial patterns of ecosystem function in a case study with NDVI, a proxy of productivity. We found that GSM‐SD varied in landscapes within macrosystems, with forest type, geographic location, and climate being significantly related to some but not all metrics. We also found that dominance of high peaks of height and vertical complexity of canopy vegetation and the heterogeneity of the vertical complexity and coefficient of variation of canopy vegetation height within 120‐m patches were negatively correlated with NDVI across the 28 NEON sites. However, forest type always had a significant interaction term between these GSM‐SD and NDVI relationships. Our study demonstrates that GSMs are useful to describe the landscape composition and configuration of structural diversity and its relationship with productivity that warrants further consideration for spatially motivated management decisions.more » « less
-
Abstract AimCanopy structural complexity, which describes the degree of heterogeneity in vegetation density, is strongly tied to a number of ecosystem functions, but the community and structural characteristics that give rise to variation in complexity at site to subcontinental scales are poorly defined. We investigated how woody plant taxonomic and phylogenetic diversity, maximum canopy height, and leaf area index (LAI) relate to canopy rugosity, a measure of canopy structural complexity that is correlated with primary production, light capture, and resource‐use efficiency. LocationOur analysis used 122 plots distributed across 10 ecologically and climatically variable forests spanning a > 1,500 km latitudinal gradient within the National Ecological Observatory Network (NEON) of the USA. Time period2016–2018. Taxa studiedWoody plants. MethodsWe used univariate and multivariate modelling to examine relationships between canopy rugosity, and community and structural characteristics hypothesized to drive site and subcontinental variation in complexity. ResultsSpatial variation in canopy rugosity within sites and across the subcontinent was strongly and positively related to maximum canopy height (r2 = .87 subcontinent‐wide), with the addition of species richness in a multivariate model resolving another 2% of the variation across the subcontinent. Individually, woody plant species richness and phylogenetic diversity (r2 = .17 to .44, respectively) and LAI (r2 = .16) were weakly to moderately correlated with canopy rugosity at the subcontinental scale, and inconsistently explained spatial variation in canopy rugosity within sites. Main conclusionsWe conclude that maximum canopy height is a substantially stronger predictor of complexity than diversity or LAI within and across forests of eastern North America, suggesting that canopy volume places a primary constraint on the development of structural complexity. Management and land‐use practices that encourage and sustain tall temperate forest canopies may support greater complexity and associated increases in ecosystem functioning.more » « less
-
Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that described heterogeneity of the outermost layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicated that aerial LiDAR could be of use in quantifying broad-scale variation in structural diversity across macroscales.more » « less
An official website of the United States government
