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1. Structural diversity as a predictor of ecosystem function

   https://doi.org/10.1088/1748-9326/ab49bb  

   LaRue, Elizabeth A. ; Hardiman, Brady S. ; Elliott, Jessica M. ; Fei, Songlin ( October 2019 , Environmental Research Letters)

   Biodiversity is believed to be closely related to ecosystem functions. However, the ability of existing biodiversity measures, such as species richness and phylogenetic diversity, to predict ecosystem functions remains elusive. Here, we propose a new vector of diversity metrics, structural diversity, which directly incorporates niche space in measuring ecosystem structure. We hypothesize that structural diversity will provide better predictive ability of key ecosystem functions than traditional biodiversity measures. Using the new lidar-derived canopy structural diversity metrics on 19 National Ecological Observation Network forested sites across the USA, we show that structural diversity is a better predictor of key ecosystem functions, such as productivity, energy, and nutrient dynamics than existing biodiversity measures (i.e. species richness and phylogenetic diversity). Similar to existing biodiversity measures, we found that the relationships between structural diversity and ecosystem functions are sensitive to environmental context. Our study indicates that structural diversity may be as good or a better predictor of ecosystem functions than species richness and phylogenetic diversity.
2. Assessing the effects of payments for ecosystem services programs on forest structure and species biodiversity

   https://doi.org/10.1007/s10531-020-01953-3  

   Chen, H.L. ( February 2020 , Biodiversity and conservation)

   Globally, biodiversity has declined at an unprecedented rate, challenging the viability of ecosystems, species, and ecological functions and their corresponding services. Payments for ecosystem services (PES) programs have been established and implemented worldwide to combat the degradation or loss of essential ecosystems and ecosystem services with-out sacrificing the well-being of people. With an overarching goal of reducing soil ero-sion, China’s Grain-to-Green program (GTGP) converts cropland to forest or grassland. As one of the largest PES programs in the world, GTGP has great potential to offer biodi-versity conservation co-benefits. To consider how GTGP may influence biodiversity, we measured forest structure and plant and wildlife species diversity at both GTGP forest and natural forest sites in Fangjingshan National Nature Reserve, China. We also evaluated the relationship between canopy cover and biodiversity measures to test whether forest cover, the most commonly measured and reported ecological metric of PES programs, might act as a good proxy for other biodiversity related parameters. We found that forest cover and species diversity increased after GTGP implementation as understory and overstory plant cover, and understory and midstory plant diversity at GTGP sites were similar to natural forest. Our results suggest that GTGP may also have been associated withmore »increased habitat for protected and vulnerable wildlife species including Elliot’s pheasant (Syrmaticus elli-oti), hog badger (Arctonyx collaris), and wild boar (Sus scrofa). Nevertheless, we identi-fied key differences between GTGP forest and natural forest, particularly variation in forest types and heterogeneity of overstory vegetation. As a result, plant overstory diversity and wildlife species richness at GTGP forest were significantly lower than at natural forest. Our findings suggest, while forest cover may be a good proxy for some metrics of forest struc-ture, it does not serve as a robust proxy for many biodiversity parameters. These findings highlight the need for and importance of robust and representative indicators or proxy vari-ables for measuring ecological effects of PES programs on compositional and structural diversity. We demonstrate that PES may lead to biodiversity co-benefits, but changes in program implementation could improve the return on investment of PES programs to sup-port conservation of biodiversity.« less
3. The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

   https://doi.org/10.1111/2041-210X.13836  

   Valle, Denis ; Silva, Carlos Alberto ; Longo, Marcos ; Brando, Paulo ( January 2022 , Methods in Ecology and Evolution)

   Record, Sydne (Ed.)

   1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high-dimensionality of these data. 2. We show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source R package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as themore »strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least seven years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g., canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.« less
4. Spatial Variation in Canopy Structure across Forest Landscapes

   https://doi.org/10.3390/f9080474  

   Hardiman, Brady ; LaRue, Elizabeth ; Atkins, Jeff ; Fahey, Robert ; Wagner, Franklin ; Gough, Christopher ( August 2018 , Forests)

   Forest canopy structure (CS) controls many ecosystem functions and is highly variable across landscapes, but the magnitude and scale of this variation is not well understood. We used a portable canopy LiDAR system to characterize variation in five categories of CS along N = 3 transects (140–800 m long) at each of six forested landscapes within the eastern USA. The cumulative coefficient of variation was calculated for subsegments of each transect to determine the point of stability for individual CS metrics. We then quantified the scale at which CS is autocorrelated using Moran’s I in an Incremental Autocorrelation analysis. All CS metrics reached stable values within 300 m but varied substantially within and among forested landscapes. A stable point of 300 m for CS metrics corresponds with the spatial extent that many ecosystem functions are measured and modeled. Additionally, CS metrics were spatially autocorrelated at 40 to 88 m, suggesting that patch scale disturbance or environmental factors drive these patterns. Our study shows CS is heterogeneous across temperate forest landscapes at the scale of 10 s of meters, requiring a resolution of this size for upscaling CS with remote sensing to large spatial scales.
5. Combining airborne lidar and acoustic remote sensing to characterize the impacts of Amazon forest degradation

   Rappaport, D.I. ; Morton, D.C. ( May 2017 , Anais do Simpósio Brasileiro de Sensoriamento Remoto)

   Frontier forests in the Brazilian Amazon have been heavily altered by nearly a half-century of deforestation for agriculture and degradation from fire and logging. The long-term effects of forest degradation on habitat structure and habitat use remain poorly understood, largely due to the limitations of traditional field methods for characterizing heterogeneity at relevant spatial and temporal scales. This work demonstrates the opportunity to assess degradation impacts on ecosystem structure and biodiversity at landscape scales (200 km2) by combining airborne lidar and acoustic remote sensing across two municipalities in Mato Grosso, Feliz Natal and Nova Ubiratã. Among degradation classes, our results indicate that repeated fire events have the most destructive legacy for both habitat structure and habitat use. Lidar analyses reveal that repeated fire events can result in a total loss of original canopy trees. Similarly, our acoustic analyses suggest that repeated fires may fundamentally transform animal community composition. The combination of remote sensing approaches bridges the scale gap between ground-based and satellite observations to support a regional-scale investigation into the complex consequences of Amazon forest degradation.