Lianas are a key component of tropical forests; however, most surveys are too small to accurately quantify liana community composition, diversity, abundance, and spatial distribution - critical ...components for measuring the contribution of lianas to forest processes. In 2007, we tagged, mapped, measured the diameter, and identified all lianas ≥1 cm rooted in a 50-ha plot on Barro Colorado Island, Panama (BCI). We calculated liana density, basal area, and species richness for both independently rooted lianas and all rooted liana stems (genets plus clones). We compared spatial aggregation patterns of liana and tree species, and among liana species that varied in the amount of clonal reproduction. We also tested whether liana and tree densities have increased on BCI compared to surveys conducted 30-years earlier. This study represents the most comprehensive spatially contiguous sampling of lianas ever conducted and, over the 50 ha area, we found 67,447 rooted liana stems comprising 162 species. Rooted lianas composed nearly 25% of the woody stems (trees and lianas), 35% of woody species richness, and 3% of woody basal area. Lianas were spatially aggregated within the 50-ha plot and the liana species with the highest proportion of clonal stems more spatially aggregated than the least clonal species, possibly indicating clonal stem recruitment following canopy disturbance. Over the past 30 years, liana density increased by 75% for stems ≥1 cm diameter and nearly 140% for stems ≥5 cm diameter, while tree density on BCI decreased 11.5%; a finding consistent with other neotropical forests. Our data confirm that lianas contribute substantially to tropical forest stem density and diversity, they have highly clumped distributions that appear to be driven by clonal stem recruitment into treefall gaps, and they are increasing relative to trees, thus indicating that lianas will play a greater role in the future dynamics of BCI and other neotropical forests.
Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the ...extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.
Mapping the spatial distribution of plant species in savannas provides insight into the roles of competition, fire, herbivory, soils and climate in maintaining the biodiversity of these ecosystems. ...This study focuses on the challenges facing large-scale species mapping using a fusion of Light Detection and Ranging (LiDAR) and hyperspectral imagery. Here we build upon previous work on airborne species detection by using a two-stage support vector machine (SVM) classifier to first predict species from hyperspectral data at the pixel scale. Tree crowns are segmented from the lidar imagery such that crown-level information, such as maximum tree height, can then be combined with the pixel-level species probabilities to predict the species of each tree. An overall prediction accuracy of 76% was achieved for 15 species. We also show that bidirectional reflectance distribution (BRDF) effects caused by anisotropic scattering properties of savanna vegetation can result in flight line artifacts evident in species probability maps, yet these can be largely mitigated by applying a semi-empirical BRDF model to the hyperspectral data. We find that confronting these three challenges—reflectance anisotropy, integration of pixel- and crown-level data, and crown delineation over large areas—enables species mapping at ecosystem scales for monitoring biodiversity and ecosystem function.
Both habitat filtering and dispersal limitation influence the compositional structure of forest communities, but previous studies examining the relative contributions of these processes with ...variation partitioning have primarily used topography to represent the influence of the environment. Here, we bring together data on both topography and soil resource variation within eight large (24–50 ha) tropical forest plots, and use variation partitioning to decompose community compositional variation into fractions explained by spatial, soil resource and topographic variables. Both soil resources and topography account for significant and approximately equal variation in tree community composition (9–34% and 5–29%, respectively), and all environmental variables together explain 13–39% of compositional variation within a plot. A large fraction of variation (19–37%) was spatially structured, yet unexplained by the environment, suggesting an important role for dispersal processes and unmeasured environmental variables. For the majority of sites, adding soil resource variables to topography nearly doubled the inferred role of habitat filtering, accounting for variation in compositional structure that would previously have been attributable to dispersal. Our results, illustrated using a new graphical depiction of community structure within these plots, demonstrate the importance of small-scale environmental variation in shaping local community structure in diverse tropical forests around the globe.
AIM: Termite mounds form small islands of enhanced water and soil nutrient availability on otherwise dry and nutrient‐poor hill crests, which can have important impacts on the plant community. ...However, the way in which termite mounds alter the spatial distribution of particular tree species across broad savanna landscapes is poorly understood. We aimed to understand the nature and extent of the relationship between termite mounds and key woody savanna species at landscape scales through the use of airborne remote sensing. LOCATION: Kruger National Park, South Africa. METHODS: We mapped 9894 termite mounds and 666,679 savanna trees from 15 species across two landscapes with contrasting rainfall regimes using airborne imaging spectroscopy and LiDAR data. We then examined changes in tree species densities and community composition with respect to distance from termite mounds. RESULTS: In both landscapes, termite mounds reduced overall tree densities over distances up to 10 m from mound centres. However, the effect of termite mounds on tree density differed among species, with some species, typically associated with lowland and riparian habitats, showing increased density near termite mounds. Indeed, changes in overall tree community composition revealed that termite mounds harbour tree communities similar to lowland communities, with this similarity decreasing with increased distance from the nearest mound. Termite effects were more pronounced in the savanna landscape receiving higher annual rainfall, whereas a greater percentage of the landscape was affected in the drier landscape due to higher mound densities. MAIN CONCLUSIONS: Termite mounds mediate the spatial distribution of tree species in savanna landscapes, increasing the abundance of tree species typically associated with lowland habitats. This contributes to the spatial heterogeneity of savanna vegetation within landscapes and the maintenance of savanna biodiversity.
Airborne remote sensing has an important role to play in mapping and monitoring biodiversity over large spatial scales. Techniques for applying this technology to biodiversity mapping have focused on ...remote species identification of individual crowns; however, this requires collection of a large number of crowns to train a classifier, which may limit the usefulness of this approach in many study regions. Based on the premise that the spectral variation among sites is related to their ecological dissimilarity, we asked whether it is possible to estimate the beta diversity, or turnover in species composition, among sites without the use of training data. We evaluated alternative methods using simulated communities constructed from the spectra of field-identified tree and shrub crowns from an African savanna. A method based on the k-means clustering of crown spectra produced beta diversity estimates (measured as Bray-Curtis dissimilarity) among sites with an average pairwise correlation of ~0.5 with the true beta diversity, compared to an average correlation of ~0.8 obtained by a supervised species classification approach. When applied to savanna landscapes, the unsupervised clustering method produced beta diversity estimates similar to those obtained from supervised classification. The unsupervised method proposed here can be used to estimate the spatial structure of species turnover in a landscape when training data (e.g., tree crowns) are unavailable, providing top-down information for science, conservation and ecosystem management applications.
Plant species identification and mapping based on remotely-sensed spectral signatures is a challenging task with the potential to contribute enormously to ecological studies. Success in this task ...rests upon the appropriate collection and use of costly field-based training data, and researchers are in need of ways to improve collection efficiency based on quantitative evidence. Using imaging spectrometer data collected by the Carnegie Airborne Observatory for hundreds of field-identified tree crowns in Kruger National Park, South Africa, we developed woody plant species classification models and evaluated how classification accuracy increases with increasing numbers of training crowns. First, we show that classification accuracy must be estimated while respecting the crown as the basic unit of data; otherwise, accuracy will be overestimated and the amount of training data needed to perform successful classification will be underestimated. We found that classification accuracy and the number of training crowns needed to perform successful classification varied depending on the number and spectral separability of species in the model. We also used a modified Michaelis-Menten function to describe the empirical relationship between training crowns and model accuracy, and show how this function may be useful for predicting accuracy. This framework can assist researchers in designing field campaigns to maximize the efficiency of field data collection, and thus the amount of biodiversity information gained from remote species identification models.
High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest ...managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting early outbreaks of invasive plant species that are often hidden beneath the forest canopy. To address these challenges, we investigated the performance of two single-class classification frameworks-Biased Support Vector Machine (BSVM) and Mixture Tuned Matched Filtering (MTMF)-to estimate the degree of Psidium cattleianum incidence over a range of forest vertical strata (relative canopy density). We demonstrate that both BSVM and MTMF have the ability to detect relative canopy density of a single focal plant species in a vertically stratified forest, but they differ in the degree of user input required. Our results suggest BSVM as a promising method to disentangle spectrally-mixed classifications, as this approach generates decision values from a similarity function (kernel), which optimizes complex comparisons between classes using a dynamic machine learning process.
Summary
Fire is a key determinant of woody vegetation structure in savanna ecosystems, acting both independently and synergistically through interactions with herbivores. Fire influences biodiversity ...and ecological functioning, but quantifying its effects on woody structure is challenging at both species and community scales.
Deeper insight into fire effects, and fire–herbivore interactions, can be gained through the examination of species‐specific demographic and dynamic changes occurring across areas with different fire regimes in the presence of large herbivores. We used the Carnegie Airborne Observatory (an integrated LiDAR and imaging spectroscopy system) to map woody tree structure, species and dynamics over a four‐year interval across two adjacent savanna landscapes with contrasting fire histories in Kruger National Park, South Africa.
A history of higher fire frequency was associated with reduced woody canopy cover (17% vs. 23%) and an increased overall rate of treefall (27% vs. 18%). The landscape with a history of higher fire frequency displayed a shift in woody canopy height distribution from a unimodal curve to a bimodal pattern at the community scale, with large reductions in height classes <7 m.
Differences in tree height distributions and treefall rates across sites were underpinned by species‐specific responses to fire frequency. Acacia nigrescens displayed the highest rates of treefall, most likely related to elephant activity, with losses exceeding 40% in the 6‐ to 9‐m height classes.
Synthesis. Our findings indicate that fire history imparts demographic legacies not only on vegetation structure, but also on current vegetation dynamics. Current treefall rates of certain tree species are exacerbated by a history of higher fire frequency. Species‐specific and context‐conscious investigations are critical for elucidating the driving mechanisms underlying broader community patterns.
Lay Summary
Understanding the relative importance of environment and life history strategies in determining leaf chemical traits remains a key objective of plant ecology. We assessed 20 foliar chemical ...properties among 12 African savanna woody plant species and their relation to environmental variables (hillslope position, precipitation, geology) and two functional traits (thorn type and seed dispersal mechanism). We found that combinations of six leaf chemical traits (lignin, hemi-cellulose, zinc, boron, magnesium, and manganese) predicted the species with 91% accuracy. Hillslope position, precipitation, and geology accounted for only 12% of the total variance in these six chemical traits. However, thorn type and seed dispersal mechanism accounted for 46% of variance in these chemical traits. The physically defended species had the highest concentrations of hemi-cellulose and boron. Species without physical defense had the highest lignin content if dispersed by vertebrates, but threefold lower lignin content if dispersed by wind. One of the most abundant woody species in southern Africa, Colophospermum mopane, was found to have the highest foliar concentrations of zinc, phosphorus, and δ(13)C, suggesting that zinc chelation may be used by this species to bind metallic toxins and increase uptake of soil phosphorus. Across all studied species, taxonomy and physical traits accounted for the majority of variability in leaf chemistry.
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