Spatial Pattern Of Trees Research Articles

Characterizing Tree Spatial Distribution Patterns Using Discrete Aerial Lidar Data

Tree spatial distribution patterns such as random, regular, and clustered play a crucial role in numerical simulations of carbon and water cycles and energy exchanges between forest ecosystems and the atmosphere. An efficient approach is needed to characterize tree spatial distribution patterns quantitatively. This study aims to employ increasingly available aerial laser scanning (ALS) data to capture individual tree locations and further characterize their spatial distribution patterns at the landscape or regional levels. First, we use the pair correlation function to identify the categories (i.e., random, regular, and clustered) of tree spatial distribution patterns, and then determine the unknown parameters of statistical models used for approximating each tree spatial distribution pattern using ALS-based metrics. After applying the proposed method in both natural and urban forest sites, our results show that ALS-based tree crown radii can capture 58%–77% (p < 0.001) variations of visual-based measurements depending on forest types and densities. The root mean squared errors (RMSEs) of ALS-based tree locations increase from 1.46 m to 2.51 m as the forest densities increasing. The Poisson, soft-core, and hybrid-Gibbs point processes are determined as the optimal models to approximate random, regular, and clustered tree spatial distribution patterns, respectively. This work provides a solid foundation for improving the simulation accuracy of forest canopy bidirectional reflectance distribution function (BRDF) and further obtain a better understanding of the processes of carbon and water cycles of forest ecosystems.

Burn weather and three-dimensional fuel structure determine post-fire tree mortality

Post-fire tree mortality is a spatially structured process driven by interacting factors across multiple scales. However, empirical models of fire-caused tree mortality are generally not spatially explicit, do not differentiate among scales, and do not differentiate immediate from delayed mortality. We aimed to quantify cross-scale linkages between forest structure—including spatial patterns of trees—and the progression of mortality 1–4 years post-fire in terms of rates, causes, and underlying demography. We used data from a long-term study site in the Sierra Nevada, California to build a post-fire tree mortality model predicted by lidar-measured estimates of structure. We calculated structural metrics at scales from individual trees to 90 × 90 m neighborhoods and combined them with metrics for topography, site water balance, and burn weather to predict immediate and delayed post-fire tree mortality. Mortality rates decreased while average diameter of newly killed trees increased each year post-fire. Burn weather predictors as well as interactive terms across scales improved model fit and parsimony. Including landscape-scale information improved finer-scale predictions but not vice versa. The amount of fuel, fuel configuration, and burning conditions predicted total mortality at broader scales while tree group-scale fuel connectivity, tree species fire tolerance, and local stresses predicted the fine-scale distribution, timing, and agents of mortality. Landscape-scale conditions provide the template upon which finer-scale variation in post-fire tree mortality is arranged. Post-fire forest structure is associated with the etiologies of different mortality agents, and so landscape-level heterogeneity is a key part of ecosystem stability and resilience.

Topological acoustic sensing of spatial patterns of trees in a model forest landscape

Remote sensing of forest environments through sound holds promise for a renewed and extended effort in monitoring vegetation distribution and associated characteristics. Here we introduce a new acoustic sensing approach that exploits the geometric phase of ground-supported acoustic waves, such as seismic waves, resulting from their scattering by trees in forest environments. Using a simulated model forest with different spatial arrangements of trees as a testbed, we numerically calculate the geometric phase of acoustic waves and show that it is exquisitely sensitive to the spatial pattern of trees. This topological acoustic sensing modality provides a novel, quantitative, insightful way to characterize global properties of tree spatial distribution, which is fundamental to forest management. Sound has virtually never been used to support quantitative forest monitoring, and here we elaborate on the wider implications and applications of topological acoustic sensing in remote landscapes as a very promising concept.

Ecological drivers of tree assemblage in tropical, subtropical and subalpine forests

AbstractAimsIt is widely accepted that deterministic and neutral dispersal processes are two of the principal mechanisms driving community assembly. However, the relative importance of these ecological drivers between distinct life stages both at community and individual species levels remains poorly understood in different vegetation types. Here, we address the following questions: (a) is there a ubiquitous process shaping tree assemblage; (b) how does the relative importance of environmental and dispersal processes vary through ontogeny in different vegetation types; and (c) will the spatial patterns of trees at community and individual species levels reflect similar contribution between ecological processes?LocationYunnan province, southwest China.MethodsWe used the Homogeneous Poisson, Homogeneous Thomas, Inhomogeneous Poisson, and Inhomogeneous Thomas point process models to predict the effect of stochastic, dispersal and/or environmental processes, respectively, on the distribution of trees across ontogeny in tropical, subtropical and subalpine forests. To evaluate the relative importance of models, we compared the observed and simulated patterns of the species–area relationship and the pair correlation function g(r) at community and species level, respectively.ResultsThe Homogeneous Thomas model was the model with the lowest Akaike information criterion (AIC) scores across ontogeny and forest types at community level. At the species level, however, Homogeneous Thomas, and Inhomogeneous Thomas predicted the distribution of large and small trees, respectively, with lower AIC values in two of our three plots. In the third plot, the Homogeneous Thomas model is dominant across ontogeny at the species level.ConclusionsTree communities are assembled mainly by the dispersal process at community level. The relative importance of ecological processes for species distribution varies across life stages at the species level, suggesting that there is an ontogenetic shift of ecological processes in shaping tree distribution. Using different summary statistics at both community and species levels helps to discern the different spatial structures of species and increases our appreciation of the processes underpinning community assembly.

Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data

Competitive interactions are important predictors of tree growth. Spatial and temporal changes in resource availability, and variation in species and spatial patterning of trees alter competitive interactions, thus affecting tree growth and, hence, biomass. Competition indices are used to quantify the level of competition among trees. As these indices are normally computed only over small areas, where field measurements are done, it would be useful to have a tool to predict them over large areas. On this regard, remote sensing, and in particular light detection and ranging (lidar) data, could be the perfect tool. The objective of this study was to use lidar metrics to predict competition (on the basis of distance-dependent competition indices) of individual trees and to relate them with tree aboveground biomass (AGB). The selected study area was a mountain forest area located in the Italian Alps. The analyses focused on the two dominant species of the area: Silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). The results showed that lidar metrics could be used to predict competition indices of individual trees (R2 above 0.66). Moreover, AGB decreased as competition increased, suggesting that variations in the availability of resources in the soil, and the ability of plants to withstand competition for light may influence the partitioning of biomass.

Under one canopy? Assessing the distributional environmental justice implications of street tree benefits in Barcelona

Street trees are an important component of green infrastructure in cities, providing multiple ecosystem services (ES) and hence contributing to urban resilience, sustainability and livability. Still, access to these benefits may display an uneven distribution across the urban fabric, potentially leading to socio-environmental inequalities. Some studies have analyzed the distributional justice implications of street tree spatial patterns, but generally without quantifying the associated ES provision. This research estimated the amount of air purification, runoff mitigation and temperature regulation provided by circa 200,000 street trees in Barcelona, Spain, using the i-Tree Eco tool. Results were aggregated at neighborhood (n = 73) and census tract (n = 1068) levels to detect associations with the distribution of five demographic variables indicating social vulnerability, namely: income, residents from the Global South, residents with low educational attainment, elderly residents, and children. Associations were evaluated using bivariate, multivariate and cluster analyses, including a spatial autoregressive model. Unlike previous studies, we found no evidence of a significant and positive association between the distribution of low income or Global South residents and a lower amount of street tree benefits in Barcelona. Rather, higher ES provision by street trees was associated with certain types of vulnerable populations, especially elderly citizens. Our results also suggest that street trees can play an important redistributive role in relation to the local provision of regulating ES due to the generally uneven and patchy distribution of other urban green infrastructure components such as urban forests, parks or gardens in compact cities such as Barcelona. In the light of these findings, we contend that just green infrastructure planning should carefully consider the distributive implications associated with street tree benefits.

Tree and opening spatial patterns vary by tree density in two old-growth remnant ponderosa pine forests in Northern Arizona, USA

Forest spatial patterns influence many ecological processes in dry conifer forests. Thus, understanding and replicating spatial patterns is critically important in order to make these forests sustainable and more resilient to fire and other disturbances. The labor and time required to stem-map trees and the large plot size (>0.5 ha) needed to study tree spatial patterns have limited our examination of how these patterns change as a function of site conditions and tree densities. We stem-mapped all trees >40 cm DBH within two large relict (minimally logged) pure ponderosa pine study sites on experimental forests at Long Valley (73 ha) on sedimentary soils and Fort Valley (32 ha) on basalt soils in northern Arizona, USA. We also simulated 1,000 4-ha plots from models of each study site incorporating field data parameters. Using cluster analysis and field data, we found that an inter-tree distance (ITD) of 9–11 m best separated single trees and groups within our study sites. Using a fixed 10-m ITD, the more productive Long Valley (LV) site had 62 trees ha−1 and groups of up to 113 trees, compared to the Fort Valley (FV) site, which averaged 41 trees ha−1 and had 22 trees in the largest group. However, the sites differed only slightly in terms of single trees ha−1 (LV 7.3; FV 5.6) and group of tree ha−1 (LV 7.2; FV 8.1). Simulation results indicated that when tree densities are equal, the spatial patterns were very similar between the two sites, suggesting that tree spatial pattern variability is a function of tree densities and only indirectly related to site productivity. As the number of trees increased, the additional trees integrated into existing groups rather than creating new groups. In addition to tree spatial patterns, we quantified gaps (defined as >30 m wide stem-to-stem) and openings (defined as ≥30 m wide stem-to-stem) within the two study sites. Although both sites were dominated by small openings most of the open area was found within a few large openings. Our large plots allowed us to incorporate variability and capture a larger range of tree and openings spatial patterns than have been captured in previous studies to provide insights on spatial heterogeneity that can inform management of this important forest type in North America.

Spatial Analysis of Forest–Tundra Ecotones Reveals the Influence of Topography and Vegetation on Alpine Treeline Patterns in the Subarctic

The response of vegetation to recent climate change is a central theme in contemporary biogeography. A tenet of this research is that forests will advance upslope and northward as climate warms, replacing tundra communities. There has been considerable variation, however, in the pace and extent of recent change, particularly in alpine regions. The objectives of this study were to determine how and why tree spatial patterns vary across different topographic features, and to use spatial patterns to infer the mechanisms governing treeline dynamics in an alpine region of subarctic Canada. We mapped trees across different elevations, slope aspects, and slope angles in two QuickBird satellite images, and in field plots established within the bounds of each image. We then quantified the degree of clustering among trees using Ripley’s K(t) statistic. We also classified each image into vegetation classes and used class-level landscape metrics to quantify the degree of treeline abruptness in different topographic settings. We found that clustering of stems was more common on south than north aspects, likely due to the high occurrence of overwinter damage on the former. Treelines were also more abrupt on south aspects, likely because high tall shrub abundance on these slopes inhibits tree seedling establishment. We conclude that the spatial patterns of subarctic alpine treelines are strongly influenced by both physical and biological factors that vary strongly with slope aspect. The response of treelines to future climate change will likely be highly variable at the landscape scale, despite experiencing similar climatic conditions. Key Words: forest–tundra ecotone, FragStats, landscape metrics, QuickBird, spatial pattern analysis, Yukon.

Asymmetric Competition Can Shape the Size Distribution of Trees in a Natural Tropical Forest

AbstractThe architecture (here, the size distribution combined with the spatial pattern of individuals) of natural forest at demographic equilibrium can be used to infer the demographic processes that drive the forest dynamics. In particular, a constant growth rate and a constant mortality rate for all trees would generate an exponential distribution of their size, whereas the metabolic scaling theory predicts a power distribution. In an undisturbed tropical rainforest in French Guiana, the diameter distribution was significantly steeper than the best-fit exponential distribution and significantly flatter than the best-fit power distribution. A simple individual-based model of forest dynamics with asymmetric competition between trees, where the strength of competition was regulated by a single parameter, was able to predict the observed distribution. Competition drove the forest into a self-organized state with stronger inequalities of size among trees, a lower mean competition index, and a spatial pattern of trees that deviated from complete spatial randomness.

Role of mycorrhizal associations in tree spatial distribution patterns based on size class in an old-growth forest.

An important factor controlling tree species diversity is conspecific density dependence (CDD). Adult trees associated with arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM) can exhibit negative and positive CDD effects on conspecific recruitment, respectively. However, the extent to which these mycorrhizal associations affect spatial distributions of individual trees and their relative abundances within forests through CDD remains uncertain. We analysed changes in spatial correlations between adults and conspecific juveniles at different growth stages of five hardwood species in a 6-ha plot of an old-growth forest using a point pattern analysis. The clump sizes of large individuals were also evaluated using the Iδ index (a measure of individual dispersion) in 24 species. In two AM-associated species, juveniles were distributed at greater distances with increasing size or were always distributed at a distance from adults, resulting in small clumps of adults. In contrast, juveniles of two ECM-associated species were distributed close to adults during early or late growth stage, resulting in large clumps of adults. Juveniles of an ECM-associated species disappeared with increasing size, probably due to shade intolerance. In 24 tree species with large numbers of individuals within a plot, the relative basal area was related to both mycorrhizal type and maximum diameter, suggesting that the relative abundance of a species is largely related to its mycorrhizal associations and maximum plant size. This study strongly demonstrated that mycorrhizal associations play an important role in determining the spatial distribution patterns and community structure of tree species through CDD.

Tree spatial patterns modulate peak snow accumulation and snow disappearance

Forests and snow covered regions frequently co-occur across the northern hemisphere. In these environments, forests are structurally and spatially complex mosaics of tree neighborhoods that are intrinsically linked to ecosystem functions. Tree and canopy structures influence snow accumulation and disappearance processes through interception and radiation attenuation. However, it is unclear if spatial heterogeneity within the forest canopy induces heterogeneity in snow accumulation and persistence. We quantitatively identified different tree neighborhoods and tested the differential effects of these within-stand neighborhoods on snow processes. Neighborhood types included individual ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii) and western larch (Larix occidentalis) trees, dense overstory tree clumps, openings, and regeneration patches. Intensive measurements of snow accumulation (density and depth) and persistence (disappearance date) were made within replicate neighborhoods for three years. Overall, neighborhood type and year had a significant effect on accumulation and snow disappearance. Openings were significantly different from clumps and individuals, always accumulating more snow. Openings retained snow significantly later than clumps but were not significantly different from individuals. Within the individual tree neighborhood type, a nested species effect indicated no differences in accumulation but significant differences in disappearance between deciduous and evergreen conifers, with snow persisting longer beneath deciduous western larch. Our results suggest that canopy interception is the primary mechanism driving the accumulation phase, while snow disappearance patterns are largely a consequence of spatial variation of longwave radiation. Reducing canopy interception and longwave radiation by increasing the abundance of widely spaced single trees and small openings with silvicultural treatments should increase snow depth and duration, and thus snow water storage. Maintaining a heterogeneous canopy structure that includes tree clumps can be used to meet multiple objectives such as provision of ungulate winter range habitat, and heterogenous understory plant phenology.

The effect of spatial structure of forests on the precision and costs of plot-level forest resource estimation

BackgroundWe investigated how the precision and costs of forest resource estimates for sample plots of different type and size depend on the spatial structure of forests and jointly studied the effects of tree density and size distribution. Statistically thinking, the trees in a forest can be regarded as a point pattern. Based on the spatial properties of the point pattern, we classified the forests into clustered, random, and regular. We used empirical data from 396 mapped forest plots from Finland. The variance of the unbiased Horvitz-Thompson estimator and expected costs of the basal area and tree density estimation were calculated for 99 different sample plots of different type and size in each of the 396 forest plots. Further, we considered the estimation of the change between two time points for a subset of the data.ResultsThe precision and expected cost depended on the tree size distribution and spatial pattern of trees. While large sample plots are advisable for clustered forests or the monitoring of young forests with small trees, we see potential for measuring smaller sample plots in regular forests. The choice of sample plot was more important in clustered forests, where also the variability of the expected costs was higher.ConclusionsIf the spatial structure of forests could be predicted accurately and precisely prior to field measurements, for instance from remote sensing data, the precision of forest inventories could potentially be improved or costs decreased by allowing the sample plot size and type to vary from one forest stand to another. When using a compromise sample plot over a large region and a long inventory rotation, optimizing the sample plot for one time point ignores possible changes in forest structures caused by changes in forest management practices.

沙地樟子松天然林南缘分布区林木竞争、空间格局及其更新特征

沙地樟子松天然林南缘分布区林木竞争、空间格局及其更新特征

Multi-scale assessment of post-fire tree mortality models

Post-fire tree mortality models are vital tools used by forest land managers to predict fire effects, estimate delayed mortality and develop management prescriptions. We evaluated the performance of mortality models within the First Order Fire Effects Model (FOFEM) software, and compared their performance to locally-parameterised models based on five different forms. We evaluated all models at the individual tree and stand levels with a dataset comprising 34174 trees from a mixed-conifer forest in the Sierra Nevada, California that burned in the 2013 Rim Fire. We compared stand-level accuracy across a range of spatial scales, and we used point pattern analysis to test the accuracy with which mortality models predict post-fire tree spatial pattern. FOFEM under-predicted mortality for the three conifers, possibly because of the timing of the Rim Fire during a severe multi-year drought. Locally-parameterised models based on crown scorch were most accurate in predicting individual tree mortality, but tree diameter-based models were more accurate at the stand level for Abies concolor and large-diameter Pinus lambertiana, the most abundant trees in this forest. Stand-level accuracy was reduced by spatially correlated error at small spatial scales, but stabilised at scales ≥1ha. The predictive error of FOFEM generated inaccurate predictions of post-fire spatial pattern at small scales, and this error could be reduced by improving FOFEM model accuracy for small trees.

Sensitivity of Codispersion to Noise and Error in Ecological and Environmental Data

Understanding relationships among tree species, or between tree diversity, distribution, and underlying environmental gradients, is a central concern for forest ecologists, managers, and management agencies. The spatial processes underlying observed spatial patterns of trees or edaphic variables often are complex and violate two fundamental assumptions—isotropy and stationarity—of spatial statistics. Codispersion analysis is a new statistical method developed to assess spatial covariation between two spatial processes that may not be isotropic or stationary. Its application to data from large forest plots has provided new insights into mechanisms underlying observed patterns of species distributions and the relationship between individual species and underlying edaphic and topographic gradients. However, these data are not collected without error, and the performance of the codispersion coefficient when there is noise or measurement error (“contamination”) in the data heretofore has been addressed only theoretically. Here, we use Monte Carlo simulations and real datasets to investigate the sensitivity of codispersion to four types of contamination commonly seen in many forest datasets. Three of these involved comparing codispersion of a spatial dataset with a contaminated version of itself. The fourth examined differences in codispersion between tree species and soil variables, where the estimates of soil characteristics were based on complete or thinned datasets. In all cases, we found that estimates of codispersion were robust when contamination was relatively low (<15%), but were sensitive to larger percentages of contamination. We also present a useful method for imputing missing spatial data and discuss several aspects of the codispersion coefficient when applied to noisy data to gain more insight about the performance of codispersion in practice.

Density-dependent and distance-dependent effects in a 60-ha tropical mountain rain forest in the Jianfengling mountains, Hainan Island, China: Spatial pattern analysis

To understand the density-dependent and distance-dependent effects on the spatial patterns of trees in tropical mountain rain forest, we used the recent developed O-ring statistic, which is a point pattern analysis method, to explore spatial distribution patterns and spatial associations of trees of different size-classes in a 60-ha tropical mountain rain forest plot in the Jianfengling Nature Reserve, Hainan Island, China. We describe four main findings. (1) As the size-classes of live trees increased, the live trees became more regular. (2) Densities of saplings, small trees, and dead trees tended to decrease with increasing distances, while densities of medium trees, big trees, and old-growth trees initially increased and then decreased at greater distances, showing a skewed inverted V-shaped pattern. (3) Densities of saplings, small trees, and medium trees first increased rapidly from 0 m to 5 m to 7 m radius (distance from old-growth trees) and then decreased continuously away from old-growth trees, showing consistent patterns expect from the distance-dependent effect. (4) Mortality was non-random, and dead trees were significantly aggregated with live trees at scales <2 m, showing clustering of dead (or surviving) trees and clear density dependence where stems with more neighbors had a higher risk of mortality. We concluded that size and spacing effects play important roles in determining spatial distribution patterns of the trees in the Jianfengling tropical mountain rain forest plot.

Spatial patterns of trees from different development stages in mixed temperate forest in the Hyrcanian region of Iran

Mixed beech (Fagus orientalis Lipsky) forests are very important to the Hyrcanian region of Iran because of their high degree of naturalness and as a source of wood production. Determination of tree spatial patterns over development stages is a first step in understanding underlying processes driving regeneration and forest dynamics. Spatial patterns of trees from three development stages (initial, optimal and decay) of an untouched mixed beech forest were quantified within three 1-ha plots. To prepare the stem maps, we measured each tree with a DBH larger than 7.5 cm using the azimuth-distance method. Spatial patterns were then analysed using Ripley’s K-function. Results indicated that the number of trees decreased from the initial stage to the decay stage, with tree spatial patterns being aggregated, slightly aggregated and highly randomized for initial, optimal and decay stages, respectively. Differences in spatial patterns among the development stages are not unexpected considering that such unmanaged forests are driven by gap dynamics which results in interactions between gap filling individuals.

Size-growth relationship, tree spatial patterns, and tree-tree competition influence tree growth and stand complexity in a 160-year red pine chronosequence

Extended rotations have been suggested as a strategy for balancing timber production and ecological objectives. By lengthening the period of stand development, extended rotations may increase tree size inequality and other elements of structural complexity, thus reducing the disparity between managed and old-growth stands. A potential limitation of extended rotations is the tradeoff between reduced stand-level productivity and greater large-tree growth that typically occurs with stand age. The mechanisms driving this tradeoff have not been fully explored. To fill this knowledge gap, we investigated the size-growth relationship (SGR), tree spatial patterns, and tree-tree competition along an established 160-yr chronosequence of 19 single-cohort, unthinned red pine (Pinus resinosa) stands in northern Minnesota, USA. We analyzed SGR, a stand-level metric used to estimate the relative efficiency with which different sized trees utilize available resources, to assess how the relationship between tree size and growth changed over an extended period of stand development. We performed spatial analysis to examine whether tree spatial clustering, a criterion of structural complexity, increased with stand age. We modeled individual-tree biomass increment to test whether competition along the chronosequence was size-symmetric (access of individual trees to resources is directly proportional to size) or size-asymmetric (larger trees suppress the growth of smaller individuals by preempting resources), and how SGR, tree spatial patterns, and competition together influenced individual-tree growth. We found low SGR (i.e., disproportionately slow growth of larger trees compared to smaller trees) across the chronosequence, a finding that contrasts with hypothesized models of SGR during stand development but is consistent with previous research on pine-dominated systems. Tree spatial patterns trended towards clustering with stand age, indicating higher structural complexity over time. In agreement with our SGR findings, competition across the chronosequence was size-symmetric, suggesting that competition reduced individual-tree growth while maintaining relative size equality. Individual-tree biomass increment was strongly dependent on tree size, with the growth of small trees appearing relatively less affected by competition. Differences in SGR did not translate into individual-tree growth, and tree spatial clustering was associated with reduced growth, especially in larger trees. Our results indicate that disproportionately slow large-tree growth and size-symmetric competition throughout stand development may delay the emergence of stand structural complexity in extended rotation red pine stands. Silvicultural treatments may be required to promote stand structural complexity and increase large crop-tree growth.

Modeling and simulation of tree spatial patterns in an oak-hickory forest with a modular, hierarchical spatial point process framework

Modeling and simulating tree spatial distribution in complex forests is important to ecologists and applied scientists who seek to both understand pattern-creating biological processes and create realistic model forests that can be used for hypothesis testing and sampling experiments. Several patterns of tree spatial distribution can co-occur in a forest. Clustering can occur due to localized patterns of growth and mortality of larger trees and corresponding regeneration of smaller trees, while trees of medium size can exhibit more uniform patterns. Inter-tree interaction may be characterized by asymmetry of competitive strength, with larger individuals having a disproportionate influence on smaller individuals.Many point process modeling approaches exist, but few have incorporated hierarchical principles that describe inter-tree competition. Those that do sometimes assume symmetric interaction among trees, which can be unrealistic. None of the existing models allow for the use of different model types at each level of the hierarchy, something that could provide a more realistic representation of the patterns displayed by trees of different size. In this study, we model and simulate a forest using a novel, modular, hierarchical approach that allows for the use of different model types at each hierarchy level, and incorporates asymmetrical interactions as well as the effects of environmental covariates. The forest is a mid-successional 8-ha stem-mapped oak-hickory watershed in Pennsylvania, USA. Results suggest that asymmetrical interactions based on tree size do exist, and these are mediated by the effects of topography. The hierarchical models reproduce the spatial patterns found in the original data better than non-hierarchical versions of the same models. The flexibility afforded by the modularity of our modeling framework will allow simulation of forests with varying levels of complexity as well as the testing of ecological hypotheses about drivers of spatial pattern creation.

Quantifying the effect of non-spatial and spatial forest stand structure on rainfall partitioning in mountain forests, Southern China

Forest stand structure plays an important role in rainfall interception and is a focal point in forest hydrology. Previous studies mainly looked at the effect of non-spatial attributes of stands while a few studies addressed the influence of spatial features. The aim of this study was to quantify the effect of stand structure on rainfall partitioning using diameter, height, leaf area index (LAI), neighbourhood comparison, mingling index and uniform angle index. The results revealed that the average accumulative throughfall, stemflow and interception loss accounted for 72.8%–83.2%, 0.5%–11.3% and 13.3%–26.2% of total precipitation, respectively, and significant differences existed in rainfall partitioning. The accumulative interception loss was negatively related to uniform angle index (a measure of tree spatial distribution patterns) as stand structure attribute was not available at each rainfall event. The effects of stand structure on throughfall, stemflow and interception loss varies considerably under different rainfall conditions. The LAI was significantly associated with interception loss for heavy rainfalls. The mingling index was negatively related to stemflow; however, significant relationships existed between mingling index, throughfall, and interception loss for light rainfall (drizzle). Significant positive relationships existed between uniform angle index and stemflow, while significant relationships existed for interception loss for light and heavy rainfalls. The results highlight that stand structure in combination with rainfall patterns influence rainfall partitioning.