Testing the independent species’ arrangement assertion made by theories of stochastic geometry of biodiversity

Wiegand, Thorsten; Huth, Andreas; Getzin, Stephan; Wang, Xugao; Hao, Zhanqing; Gunatilleke, C. V. S.; Gunatilleke, I. A. U. N.

doi:10.1098/rspb.2012.0376

Cited by 81 publications

(246 citation statements)

References 37 publications

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“…We can use this model to simultaneously fit the wavelet variance of two individual species and their covariance. However, in species-rich communities such as Barro Colorado, with ∼300 tree species in 50 ha, a dilution effect yields approximate species independence (Wiegand et al 2012). As a result, there is little improvement, if any, in fitting the multivariate model compared with fitting each species independently.…”

Section: Discussionmentioning

confidence: 99%

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Detto

Muller‐Landau

2013

The American Naturalist

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. 7, 2012; Accepted October 29, 2012; Electronically published February 19, 2013 Online enhancement: zip file with appendixes, supplemental figure. abstract: Ecological spatial patterns are structured by a multiplicity of processes acting over a wide range of scales. We propose a new method, based on the scalewise variance-that is, the variance as a function of spatial scale, calculated here with wavelet kernel functions-to disentangle the signature of processes that act at different and similar scales on observed spatial patterns. We derive exact and approximate analytical solutions for the expected scalewise variance under different individual-based, spatially explicit models for sessile organisms (e.g., plants), using moment equations. We further determine the probability distribution of independently observed scalewise variances for a given expectation, including complete spatial randomness. Thus, we provide a new analytical test of the null model of spatial randomness to understand at which scales, if any, the variance departs significantly from randomness. We also derive the likelihood function that is needed to estimate parameters of spatial models and their uncertainties from observed patterns. The methods are demonstrated through numerical examples and case studies of four tropical tree species on Barro Colorado Island, Panama. The methods developed here constitute powerful new tools for investigating effects of ecological processes on spatial point patterns and for statistical inference of process models from spatial patterns.

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Section: Discussionmentioning

confidence: 99%

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Detto

Muller‐Landau

2013

The American Naturalist

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“…Understanding various ecological mechanisms and processes that influence species assemblage are critical to get deep insights on species associations and community structure. Analyzing the spatial patterns of species are therefore of main interest in community ecology to figure out the underlying mechanisms and to test different ecological theories [2][3][4]. Several processes-such as competition or facilitation, dispersal limitation, habitat preference, Janzen-Connell hypothesis-that have been proposed for explaining community structure and species coexistence in species-rich forests are mainly based on spatial characteristics of species associations [5].…”

Section: Introductionmentioning

confidence: 99%

“…Species coexistence in diverse communities such as tropical forests can also be described by neutral theory, which assumes that species associations are assembled by dispersal limitation and demographic stochastics [15]. Similarly, previous studies suggested that stochastic dilution effect that assumes independent placement of individuals of different species can well explain species coexistence in tropical forests [3,16,17]. The stochastic dilution effect assumes hypotheses of intraspecific clustering, independence of interspecific interaction and abundance of species following a hollow curve distribution [18].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we used three structural indices [27], Ripley's K function K 12 (r) and nearest neighbor distribution function D 12 (r) [3]. For a better understanding of structural units, we used bivariate distribution in which combines pairs of two structural units for each species, such as mingling-uniform angle index, mingling-dominance and dominance-uniform angle index.…”

Section: Introductionmentioning

confidence: 99%

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Nearest Neighborhood Characteristics of a Tropical Mixed Broadleaved Forest Stand

Hai

Erfanifard

Petritan

2018

Forests

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Structural complexity and local biodiversity of species-rich tropical forests can be characterized by their spatial patterns, which contribute to species intra-and interspecific interactions. Aiming to describe spatial patterns of species at fine spatial scales, we applied the quantitative analyses based on the relationships of nearest neighbors of conspecific and heterospecific trees. In a two-hectare plot of a tropical broadleaved forest stand in central Vietnam with minimal human influence, all tree individuals with diameter at breast height ≥ 2.5 cm were mapped and their characteristics were recorded. We applied two different types of analyses: (1) Intraspecific structural characteristics using nearest neighbor statistics; (2) overall interspecific associations through a classification scheme based on bivariate nearest neighbor distribution function D 12 (r) and Ripley's K function K 12 (r). The findings showed that: (1) Most of studied species in the forest were highly mixed with other species, while conspecifics were regular to aggregated distribution at small spatial scales. Tree individuals with different diameter values were surrounded by heterospecific trees; (2) The majority of 306 species-species pairs showed spatial independence (66.7%), whereas 29.8% of all species showed an overall positive association and negative association consisted only a small percentage (3.5%) up to spatial scales of 50 m. We found significant evidences of the main ecological theories such as dispersal limitation, Neutral theory, Janzen-Connell hypothesis, and other effects like the stochastic dilution. We suggest using both the bivariate distribution of the structural parameters and the spatial point pattern analysis based on nearest neighbor distance as advantageous approaches for further understanding of population structure, as well as discovering and protecting biodiversity in the future.

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“…Soil characteristics can be strong 72 predictors of plant community composition (Gough et al, 2000;Tilman and Olff, 1991), 73 although the scale of the studies influences the predictive power of soil parameters like pH, 74 carbon, nitrogen or phosphorus content (Sebastiá, 2004). But not only abiotic factors are 75 influenced by the scale of a study; positive and negative species-species associations can occur 76 at small scales and disappear with increasing scale (Wiegand et al, 2012).…”

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confidence: 99%

Plant community assembly at small scales: Spatial vs. environmental factors in a European grassland

Horn¹,

Hempel²,

Ristow³

et al. 2015

Acta Oecologica

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24Dispersal limitation and environmental conditions are crucial drivers of plant species 25 distribution and establishment. As these factors operate at different spatial scales, we asked: Do 26 the environmental factors known to determine community assembly at broad scales operate at 27 fine scales (few meters)? How much do these factors account for community variation at fine 28 scales? In which way do biotic and abiotic interactions drive changes in species composition? 29We surveyed the plant community within a dry grassland along a very steep gradient of soil 30 characteristics like pH and nutrients. We used a spatially explicit sampling design, based on 31 three replicated macroplots of 15x15, 12x12 and 12x12 meters in extent. Soil samples were 32 taken to quantify several soil properties (carbon, nitrogen, plant available phosphorus, pH, 33water content and dehydrogenase activity as a proxy for overall microbial activity). We 34 performed variance partitioning to assess the effect of these variables on plant composition and 35 statistically controlled for spatial autocorrelation via eigenvector mapping. We also applied null 36 model analysis to test for non-random patterns in species co-occurrence using randomization 37 schemes that account for patterns expected under species interactions. 38At a fine spatial scale, environmental factors explained 18% of variation when controlling for 39 spatial autocorrelation in the distribution of plant species, whereas purely spatial processes 40 accounted for 14% variation. Null model analysis showed that species spatially segregated in a 41 non-random way and these spatial patterns could be due to a combination of environmental 42 filtering and biotic interactions. Our grassland study suggests that environmental factors found 43 to be directly relevant in broad scale studies are present also at small scales, but are 44 supplemented by spatial processes and more direct interactions like competition.

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Testing the independent species’ arrangement assertion made by theories of stochastic geometry of biodiversity

Cited by 81 publications

References 37 publications

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Nearest Neighborhood Characteristics of a Tropical Mixed Broadleaved Forest Stand

Plant community assembly at small scales: Spatial vs. environmental factors in a European grassland

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