Stephanie A. Bohlman

Scale‐dependent relationships between tree species richness and ecosystem function in forests

1. The relationship between species richness and ecosystem function, as measured by productivity or biomass, is of long-standing theoretical and practical interest in ecology. This is especially true for forests, which represent a majority of global biomass, productivity and biodiversity.

Quantifying the deciduousness of tropical forest canopies under varying climates

Deciduousness is an important functional attribute of tropical trees, reflecting climatic conditions. Precisely quantifying and mapping deciduousness in tropical forests will be necessary for calibrating remote sensing images which attempt to assess canopy properties such as carbon cycling, productivity, or chlorophyll content. We thus set out to assess the degree of canopy deciduousness in three moist, semi-deciduous tropical forests in central Panama. One site is a 6-ha research plot near the Atlantic coast of Panama, where rainfall is 2830 mm/yr. The second site is a 50-ha plot on Barro Colorado Island, near the center of the isthmus of Panama, where rainfall is 2570 mm/yr, and the final site is a 4-ha plot near the Pacific coast of Panama, where rainfall is 2060 mm/yr. At each site, a random sample of trees from all canopy species (those with individuals ≥ 30 cm DBH) were visited and scored for deciduousness three times during the 1997 dry season. The estimated peak fraction of deciduous individuals in the canopy at the wetter site was 4.8%, at the intermediate site, 6.3%, and at the drier site, 24.3%. The estimated fraction of crown area deciduous peaked at 3.6%, 9.7%, and 19.1% at the wet, medium, and dry sites respec- tively. The percentage of canopy species that was deciduous - 14%, 28%, and 41% - was much higher than the percentage of deciduous individuals, because not all individuals of de- ciduous species were deciduous. During the 1999 dry sea- son, every individual of all the deciduous species was visited at the two drier sites, and the total number of deciduous trees observed closely matched the estimated numbers based on the smaller 1997 samples.

Importance of soils, topography and geographic distance in structuring central Amazonian tree communities

Abstract Question: What is the relative contribution of geographic distance, soil and topographic variables in determining the community floristic patterns and individual tree species abundances in the nutrient-poor soils of central Amazonia? Location: Central Amazonia near Manaus, Brazil. Methods: Our analysis was based on data for 1105 tree species (≥ 10 cm dbh) within 40 1-ha plots over a ca. 1000-km2 area. Slope and 26 soil-surface parameters were measured for each plot. A main soil-fertility gradient (encompassing soil texture, cation content, nitrogen and carbon) and five other uncorrelated soil and topographic variables were used as potential predictors of plant-community composition. Mantel tests and multiple regressions on distance matrices were used to detect relationships at the community level, and ordinary least square (OLS) and conditional autoregressive (CAR) models were used to detect relationships for individual species abundances. Results: Floristic similarity declined rapidly with distance over small spatial scales (0–5 km), but remained constant (ca. 44%) over distances of 5 to 30 km, which indicates lower beta diversity than in western Amazonian forests. Distance explained 1/3 to 1/2 more variance in floristics measures than environmental variables. Community composition was most strongly related to the main soil-fertility gradient and C:N ratio. The main fertility gradient and pH had the greatest impact of species abundances. About 30% of individual tree species were significantly related to one or more soil/topographic parameters. Conclusions: Geographic distance and the main fertility gradient are the best predictors of community floristic composition, but other soil variables, particularly C:N ratio, pH, and slope, have strong relationships with a significant portion of the tree community.

Dominance of the suppressed: Power-law size structure in tropical forests

Size distributions of tropical trees The distribution of tree size in tropical forests follows a power-law regardless of location. This pattern has largely eluded mechanistic explanation. Using 30 years of tree demography and growth data from a forest plot in Panama, Farrior et al. show that the power-law size structure emerges after natural local disturbances such as the gaps formed by falling trees. A model of forest dynamics identifies the structural parameter governing the power-law distribution. A mechanistic understanding of tropical forest structural dynamics will benefit forest carbon cycling studies. Science, this issue p. 155 The consistency of tropical tree size structure is related to the frequency of small-scale disturbance and competition for light. Tropical tree size distributions are remarkably consistent despite differences in the environments that support them. With data analysis and theory, we found a simple and biologically intuitive hypothesis to explain this property, which is the foundation of forest dynamics modeling and carbon storage estimates. After a disturbance, new individuals in the forest gap grow quickly in full sun until they begin to overtop one another. The two-dimensional space-filling of the growing crowns of the tallest individuals relegates a group of losing, slow-growing individuals to the understory. Those left in the understory follow a power-law size distribution, the scaling of which depends on only the crown area–to–diameter allometry exponent: a well-conserved value across tropical forests.

Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

Mapping species through classification of imaging spectroscopy data is facilitating research to understand tree species distributions at increasingly greater spatial scales. Classification requires a dataset of field observations matched to the image, which will often reflect natural species distributions, resulting in an imbalanced dataset with many samples for common species and few samples for less common species. Despite the high prevalence of imbalanced datasets in multiclass species predictions, the effect on species prediction accuracy and landscape species abundance has not yet been quantified. First, we trained and assessed the accuracy of a support vector machine (SVM) model with a highly imbalanced dataset of 20 tropical species and one mixed-species class of 24 species identified in a hyperspectral image mosaic (350–2500 nm) of Panamanian farmland and secondary forest fragments. The model, with an overall accuracy of 62% ± 2.3% and F-score of 59% ± 2.7%, was applied to the full image mosaic (23,000 ha at a 2-m resolution) to produce a species prediction map, which suggested that this tropical agricultural landscape is more diverse than what has been presented in field-based studies. Second, we quantified the effect of class imbalance on model accuracy. Model assessment showed a trend where species with more samples were consistently over predicted while species with fewer samples were under predicted. Standardizing sample size reduced model accuracy, but also reduced the level of species over- and under-prediction. This study advances operational species mapping of diverse tropical landscapes by detailing the effect of imbalanced data on classification accuracy and providing estimates of tree species abundance in an agricultural landscape. Species maps using data and methods presented here can be used in landscape analyses of species distributions to understand human or environmental effects, in addition to focusing conservation efforts in areas with high tree cover and diversity.

Modeling the spatial distribution and fruiting pattern of a key tree species in a neotropical forest: Methodology and potential applications

Background The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we demonstrate how advances in statistics permit the combination of sparse ground sampling with remote sensing imagery to generate biological relevant, spatially and temporally explicit distributions of food resources. We illustrate our procedure by creating a detailed simulation model of fruit production patterns for Dipteryx oleifera, a keystone tree species, on Barro Colorado Island (BCI), Panama. Methodology and Principal Findings Aerial photographs providing GPS positions for large, canopy trees, the complete census of a 50-ha and 25-ha area, diameter at breast height data from haphazardly sampled trees and long-term phenology data from six trees were used to fit 1) a point process model of tree spatial distribution and 2) a generalized linear mixed-effect model of temporal variation of fruit production. The fitted parameters from these models are then used to create a stochastic simulation model which incorporates spatio-temporal variations of D. oleifera fruit availability on BCI. Conclusions and Significance We present a framework that can provide a statistical characterization of the habitat that can be included in agent-based models of animal movements. When environmental heterogeneity cannot be exhaustively mapped, this approach can be a powerful alternative. The results of our model on the spatio-temporal variation in D. oleifera fruit availability will be used to understand behavioral and movement patterns of several species on BCI.

Dispersal limitation drives successional pathways in Central Siberian forests under current and intensified fire regimes.

Fire is a primary driver of boreal forest dynamics. Intensifying fire regimes due to climate change may cause a shift in boreal forest composition toward reduced dominance of conifers and greater abundance of deciduous hardwoods, with potential biogeochemical and biophysical feedbacks to regional and global climate. This shift has already been observed in some North American boreal forests and has been attributed to changes in site conditions. However, it is unknown if the mechanisms controlling fire-induced changes in deciduous hardwood cover are similar among different boreal forests, which differ in the ecological traits of the dominant tree species. To better understand the consequences of intensifying fire regimes in boreal forests, we studied postfire regeneration in five burns in the Central Siberian dark taiga, a vast but poorly studied boreal region. We combined field measurements, dendrochronological analysis, and seed-source maps derived from high-resolution satellite images to quantify the importance of site conditions (e.g., organic layer depth) vs. seed availability in shaping postfire regeneration. We show that dispersal limitation of evergreen conifers was the main factor determining postfire regeneration composition and density. Site conditions had significant but weaker effects. We used information on postfire regeneration to develop a classification scheme for successional pathways, representing the dominance of deciduous hardwoods vs. evergreen conifers at different successional stages. We estimated the spatial distribution of different successional pathways under alternative fire regime scenarios. Under intensified fire regimes, dispersal limitation of evergreen conifers is predicted to become more severe, primarily due to reduced abundance of surviving seed sources within burned areas. Increased dispersal limitation of evergreen conifers, in turn, is predicted to increase the prevalence of successional pathways dominated by deciduous hardwoods. The likely fire-induced shift toward greater deciduous hardwood cover may affect climate-vegetation feedbacks via surface albedo, Bowen ratio, and carbon cycling.

Landscape‐scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon

Wind disturbance can create large forest blowdowns, which greatly reduces live biomass and adds uncertainty to the strength of the Amazon carbon sink. Observational studies from within the central Amazon have quantified blowdown size and estimated total mortality but have not determined which trees are most likely to die from a catastrophic wind disturbance. Also, the impact of spatial dependence upon tree mortality from wind disturbance has seldom been quantified, which is important because wind disturbance often kills clusters of trees due to large treefalls killing surrounding neighbors. We examine (1) the causes of differential mortality between adult trees from a 300-ha blowdown event in the Peruvian region of the northwestern Amazon, (2) how accounting for spatial dependence affects mortality predictions, and (3) how incorporating both differential mortality and spatial dependence affect the landscape level estimation of necromass produced from the blowdown. Standard regression and spatial regression models were used to estimate how stem diameter, wood density, elevation, and a satellite-derived disturbance metric influenced the probability of tree death from the blowdown event. The model parameters regarding tree characteristics, topography, and spatial autocorrelation of the field data were then used to determine the consequences of non-random mortality for landscape production of necromass through a simulation model. Tree mortality was highly non-random within the blowdown, where tree mortality rates were highest for trees that were large, had low wood density, and were located at high elevation. Of the differential mortality models, the non-spatial models overpredicted necromass, whereas the spatial model slightly underpredicted necromass. When parameterized from the same field data, the spatial regression model with differential mortality estimated only 7.5% more dead trees across the entire blowdown than the random mortality model, yet it estimated 51% greater necromass. We suggest that predictions of forest carbon loss from wind disturbance are sensitive to not only the underlying spatial dependence of observations, but also the biological differences between individuals that promote differential levels of mortality.

High Spatial and Spectral Resolution Remote Sensing of Panama Canal Zone Watershed Forests

High spatial resolution airborne and satellite sensors have been used with varying degrees of success to measure deciduousness, canopy structure, and light interception, and to identify tree species in the Panama Canal Watershed. Results reported to date indicate that remotely sensed data have a high degree of accuracy in measuring deciduousness and leaf density in the upper canopy, but less accuracy than in temperate systems in measuring canopy light interception for semi-deciduous lowland tropical forests in the canal watershed. Of particular relevance to evergreen forests like the upper Rio Chagres basin, this work examines whether high spectral resolution data, like that collected by the HYDICE system, can separate canopy species based on hyperspectral signatures in the 0.4 to 2.5-µm wavelength region. If a few well-selected spectral bands could accurately differentiate species, tropical canopies in remote and rugged terrain could be mapped using relatively simple, but optimized sensor systems.

Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine

Abstract. Hyperspectral images can be used to identify savannah tree species at the landscape scale, which is a key step in measuring biomass and carbon, and tracking changes in species distributions, including invasive species, in these ecosystems. Before automated species mapping can be performed, image processing and atmospheric correction is often performed, which can potentially affect the performance of classification algorithms. We determine how three processing and correction techniques (atmospheric correction, Gaussian filters, and shade/green vegetation filters) affect the prediction accuracy of classification of tree species at pixel level from airborne visible/infrared imaging spectrometer imagery of longleaf pine savanna in Central Florida, United States. Species classification using fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction outperformed ATCOR in the majority of cases. Green vegetation (normalized difference vegetation index) and shade (near-infrared) filters did not increase classification accuracy when applied to large and continuous patches of specific species. Finally, applying a Gaussian filter reduces interband noise and increases species classification accuracy. Using the optimal preprocessing steps, our classification accuracy of six species classes is about 75%.