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Current evidence suggests that tree-fall gaps can influence forest structure and dynamics by enabling certain species guilds to persist over the long term. Here we assessed the development of local size hierarchies and asymmetric competition for light in tree-fall gaps, and the role played by these two processes for the persistence of rare light-demanding species in the Barro Colorado Island Forest Dynamics Plot (Panama). We performed spatial point pattern analysis, considering both the spatial locations (x,y) and the diameter at breast height (DBH) of all the woody plant recruits from the 1985 and 2000 censuses located in tree-fall gaps, and followed their fate up to the 1990–2010 and 2005–2010 censuses, respectively. For these two recruit cohorts, we found that, from the initial census until 5–10 yr later, close neighbors presented a larger DBH than the mean DBH of all individuals within gaps, which points to a positive growth response of recruits to the increased light levels in the gap centers. However, close neighbors of the 1985 cohort also showed larger than expected DBH differences that disappeared in subsequent censuses, indicating an enhancement of size differences between neighbors and the mortality of the smaller individuals. Finally, for both recruit cohorts, we found that 10–15 yr after gap formation, surviving individuals of rare light-demanding species had a negative impact on survival of neighboring individuals of other species. Our results indicate that gaps favor the persistence of rare light-demanding species through the development of local size hierarchies and asymmetric competition for light. The strength of this process, however, apparently depends upon gap size and the role played by the woody plants already existing at the time of gap formation in early colonization. Moreover, our findings suggest that in this forest, gaps may enhance colonization of plant species typical of nearby dry tropical areas, and that, over the coming decades, similar processes could strongly modify the structure and dynamics of moist tropical forests in the region.

Satellite data and modelling reveal that tropical forest fragments have similar size distributions across continents, and that forest fragmentation is close to a critical point, beyond which fragment numbers will strongly increase. Forest fragmentation patterns Agriculture, logging and urban growth have caused unprecedented losses of tropical forest in the past few decades. Franziska Taubert and colleagues examine patterns of tropical forest fragmentation using high-resolution satellite data. They identify 130 million forest fragments across three continental regions, which each have size frequency distributions that are similar, being described by power laws with almost identical exponents. The principles of percolation theory provide one explanation for the observed patterns, and suggest that forest fragmentation is close to a critical threshold, beyond which fragmentation can be expected to accelerate strongly. Numerical modelling supports this hypothesis, suggesting that additional forest loss will strongly increase the total number of forest fragments over the next 50 years. However, the simulations also suggest that reforestation and reductions in deforestation can mitigate this projected increase in fragmentation. Remote sensing enables the quantification of tropical deforestation with high spatial resolution 1 , 2 . This in-depth mapping has led to substantial advances in the analysis of continent-wide fragmentation of tropical forests 1 , 2 , 3 , 4 . Here we identified approximately 130 million forest fragments in three continents that show surprisingly similar power-law size and perimeter distributions as well as fractal dimensions. Power-law distributions 5 , 6 , 7 have been observed in many natural phenomena 8 , 9 such as wildfires, landslides and earthquakes. The principles of percolation theory 7 , 10 , 11 provide one explanation for the observed patterns, and suggest that forest fragmentation is close to the critical point of percolation; simulation modelling also supports this hypothesis. The observed patterns emerge not only from random deforestation, which can be described by percolation theory 10 , 11 , but also from a wide range of deforestation and forest-recovery regimes. Our models predict that additional forest loss will result in a large increase in the total number of forest fragments—at maximum by a factor of 33 over 50 years—as well as a decrease in their size, and that these consequences could be partly mitigated by reforestation and forest protection.

The dynamics of spatially structured populations is characterized by within- and between-patch processes. The available theory describes the latter with simple distance-dependent functions that depend on landscape properties such as interpatch distance or patch size. Despite its potential role, we lack a good mechanistic understanding of how the movement of individuals between patches affects the dynamics of these populations. We used the theoretical framework provided by movement ecology to make a direct representation of the processes determining how individuals connect local populations in a spatially structured population of Iberian lynx. Interpatch processes depended on the heterogeneity of the matrix where patches are embedded and the parameters defining individual movement behavior. They were also very sensitive to the dynamic demographic variables limiting the time moving, the within-patch dynamics of available settlement sites (both spatiotemporally heterogeneous) and the response of individuals to the perceived risk while moving. These context-dependent dynamic factors are an inherent part of the movement process, producing connectivities and dispersal kernels whose variability is affected by other demographic processes. Mechanistic representations of interpatch movements, such as the one provided by the movement-ecology framework, permit the dynamic interaction of birth-death processes and individual movement behavior, thus improving our understanding of stochastic spatially structured populations.

Assessing the relative importance of different processes that determine the spatial distribution of species and the dynamics in highly diverse plant communities remains a challenging question in ecology. Previous modelling approaches often focused on single aggregated forest diversity patterns that convey limited information on the underlying dynamic processes. Here, we use recent advances in inference for stochastic simulation models to evaluate the ability of a spatially explicit and spatially continuous neutral model to quantitatively predict six spatial and non-spatial patterns observed at the 50 ha tropical forest plot on Barro Colorado Island, Panama. The patterns capture different aspects of forest dynamics and biodiversity structure, such as annual mortality rate, species richness, species abundance distribution, beta-diversity and the species–area relationship (SAR). The model correctly predicted each pattern independently and up to five patterns simultaneously. However, the model was unable to match the SAR and beta-diversity simultaneously. Our study moves previous theory towards a dynamic spatial theory of biodiversity and demonstrates the value of spatial data to identify ecological processes. This opens up new avenues to evaluate the consequences of additional process for community assembly and dynamics.

Vegetation gap patterns in arid grasslands, such as the “fairy circles” of Namibia, are one of nature’s greatest mysteries and subject to a lively debate on their origin. They are characterized by small-scale hexagonal ordering of circular bare-soil gaps that persists uniformly in the landscape scale to form a homogeneous distribution. Pattern-formation theory predicts that such highly ordered gap patterns should be found also in other water-limited systems across the globe, even if the mechanisms of their formation are different. Here we report that so far unknown fairy circles with the same spatial structure exist 10,000 km away from Namibia in the remote outback of Australia. Combining fieldwork, remote sensing, spatial pattern analysis, and process-based mathematical modeling, we demonstrate that these patterns emerge by self-organization, with no correlation with termite activity; the driving mechanism is a positive biomass–water feedback associated with water runoff and biomass-dependent infiltration rates. The remarkable match between the patterns of Australian and Namibian fairy circles and model results indicate that both patterns emerge from a nonuniform stationary instability, supporting a central universality principle of pattern-formation theory. Applied to the context of dryland vegetation, this principle predicts that different systems that go through the same instability type will show similar vegetation patterns even if the feedback mechanisms and resulting soil–water distributions are different, as we indeed found by comparing the Australian and the Namibian fairy-circle ecosystems. These results suggest that biomass–water feedbacks and resultant vegetation gap patterns are likely more common in remote drylands than is currently known.

Over the last two decades spatial point pattern analysis (SPPA) has become increasingly popular in ecological research. To direct future work in this area we review studies using SPPA techniques in ecology and related disciplines. We first summarize the key elements of SPPA in ecology (i.e. data types, summary statistics and their estimation, null models, comparison of data and models, and consideration of heterogeneity); second, we review how ecologists have used these key elements; and finally, we identify practical difficulties that are still commonly encountered and point to new methods that allow current key questions in ecology to be effectively addressed. Our review of 308 articles published over the period 1992–2012 reveals that a standard canon of SPPA techniques in ecology has been largely identified and that most of the earlier technical issues that occupied ecologists, such as edge correction, have been solved. However, the majority of studies underused the methodological potential offered by modern SPPA. More advanced techniques of SPPA offer the potential to address a variety of highly relevant ecological questions. For example, inhomogeneous summary statistics can quantify the impact of heterogeneous environments, mark correlation functions can include trait and phylogenetic information in the analysis of multivariate spatial patterns, and more refined point process models can be used to realistically characterize the structure of a wide range of patterns. Additionally, recent advances in fitting spatially-explicit simulation models of community dynamics to point pattern summary statistics hold the promise for solving the longstanding problem of linking pattern to process. All these newer developments allow ecologists to keep up with the increasing availability of spatial data sets provided by newer technologies, which allow point patterns and environmental variables to be mapped over large spatial extents at increasingly higher image resolutions.

We used point pattern analysis to examine the spatial distribution of 46 common tree species (diameter at breast height >10 cm) in a fully mapped \\documentclass{aastex} \\usepackage{amsbsy} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{bm} \\usepackage{mathrsfs} \\usepackage{pifont} \\usepackage{stmaryrd} \\usepackage{textcomp} \\usepackage{portland,xspace} \\usepackage{amsmath,amsxtra} \\usepackage[OT2,OT1]{fontenc} \\newcommand\\cyr{ \\renewcommand\\rmdefault{wncyr} \\renewcommand\\sfdefault{wncyss} \\renewcommand\\encodingdefault{OT2} \\normalfont \\selectfont} \\DeclareTextFontCommand{\\textcyr}{\\cyr} \\pagestyle{empty} \\DeclareMathSizes{10}{9}{7}{6} \\begin{document} \\landscape $500\\times 500$ \\end{document} ‐m tropical forest plot in Sinharaja, Sri Lanka. We aimed to disentangle the effect of species interactions (second‐order effects) and environment (first‐order effects) on the species’ spatial distributions. To characterize first‐order associations (segregation, overlap), we developed a classification scheme based on Ripley’sKand nearest‐neighbor statistics. We subsequently used heterogeneous Poisson null models, accounting for possible environmental heterogeneity, to reveal significant uni‐ and bivariate second‐order interactions (regularity, aggregation and repulsion, attraction). First‐order effects were strong; overall, 53% of all species pairs occupied largely disjoint areas (segregation), 40% showed partial overlap, and 6% overlapped. Only 5% of all species pairs showed significant second‐order effects, but about half of the species showed significant intraspecific effects. Significant plant‐plant interactions occurred mostly within 2–4 m and disappeared within 15–20 m of the focal plant. While lack of significant species interactions suggests support for the unified neutral theory, species’ observed spatial segregation does not support the assumptions of the neutral theory. The strong observed tendency of species to segregate may have supplementary effects on other processes promoting species coexistence.

Understanding the structure and dynamics of highly diverse tropical forests is challenging. Here we investigate the factors that drive the spatio-temporal variation of local tree numbers and species richness in a tropical forest (including 1250 plots of 20 × 20 m2). To this end, we use a series of dynamic models that are built around the local spatial variation of mortality and recruitment rates, and ask which combination of processes can explain the observed spatial and temporal variation in tree and species numbers. We find that processes not included in classical neutral theory are needed to explain these fundamental patterns of the observed local forest dynamics. We identified a large spatio-temporal variability in the local number of recruits as the main missing mechanism, whereas variability of mortality rates contributed to a lesser extent. We also found that local tree numbers stabilize at typical values which can be explained by a simple analytical model. Our study emphasized the importance of spatio-temporal variability in recruitment beyond demographic stochasticity for explaining the local heterogeneity of tropical forests.

The mysterious ‘fairy circles’ are vegetation-free discs that cover vast areas along the pro-Namib Desert. Despite 30 yr of research their origin remains unknown. Here we adopt a novel approach that focuses on analysis of the spatial patterns of fairy circles obtained from representative 25-ha aerial images of north-west Namibia. We use spatial point pattern analysis to quantify different features of their spatial structures and then critically inspect existing hypotheses with respect to their ability to generate the observed circle patterns. Our working hypothesis is that fairy circles are a self-organized vegetation pattern. Finally, we test if an existing partial-differential-equation model, that was designed to describe vegetation pattern formation, is able to reproduce the characteristic features of the observed fairy circle patterns. The model is based on key-processes in arid areas such as plant competition for water and local resource-biomass feedbacks. The fairy circles showed at all three study areas the same regular spatial distribution patterns, characterized by Voronoi cells with mostly six corners, negative correlations in their size up to a distance of 13 m, and remarkable homogeneity over large spatial scales. These results cast doubts on abiotic gas-leakage along geological lines or social insects as causal agents of their origin. However, our mathematical model was able to generate spatial patterns that agreed quantitatively in all of these features with the observed patterns. This supports the hypothesis that fairy circles are self-organized vegetation patterns that emerge from positive biomass-water feedbacks involving water transport by extended root systems and soil-water diffusion. Future research should search for mechanisms that explain how the different hypotheses can generate the patterns observed here and test the ability of self-organization to match the birth- and death dynamics of fairy circles and their regional patterns in the density and size with respect to environmental gradients.

Many functional summary characteristics such as Ripley's K function have been used in ecology to describe the spatial structure of point patterns to aid understanding of the underlying processes. However, their use is poorly guided in ecology because little is understood how well single summary characteristics, or a combination of them, capture the spatial structure of real world patterns. Here, we systematically tested the performance of combinations of eight summary characteristics [i.e. pair correlation function g(r), K-function K(r), the proportion E(r) of points with no neighbor at distance r, the nearest neighbor distribution function D(r), the spherical contact distribution Hs (r), the kth nearest-neighbor distribution functions Dk (r), the mean distance nn(k) to the kth neighbor, and the intensity function λ(x)]. To this end we used point pattern data covering a wide range of spatial structures including simulated (stationary) as well as real, possibly non-stationary, patterns on tree species in a tropical forest in Panama. To measure the information contained in a given combination of summary characteristics we used simulated annealing to reconstruct the observed patterns based only on the limited information provided by this combination and assessed how well other characteristics of the observed pattern were recovered. We found that the number of summary characteristics required to capture the spatial structure of stationary patterns varied between one (for patterns with near random structures) and three (for patterns with complex cluster and superposition structures), but with a robust ranking g(r), Dk (r), and Hs (r) that was largely independent on pattern idiosyncrasies. Stationary summary characteristics [with ranking g(r), D(r), Hs (r), E(r)] captured small- to intermediate scale properties of non-stationary patterns, but for describing large-scale spatial structures the intensity function was required. Our finding revealed that the current practice in ecology of using only one or two summary characteristics bears danger that essential characteristics of more complex patterns would not be detected. The technique of pattern reconstruction presented here has wide applications in ecology.