Multivariate point processes and spatial variation of species diversity

Shimatani, Kenichiro

doi:10.1016/s0378-1127(00)00352-2

Cited by 47 publications

(42 citation statements)

References 9 publications

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“…van Lieshout and Baddeley (1996) describe an index (the I-function) based on a weighted average of the J-function (where J(w)=(1ÀG(w))/(1ÀF(x)) for multi-mark event sets. Nevertheless, in the area of multivariate point pattern, there is a need for further research and the development of analytical tools (see Podani and Czaran 1997;Podani et al 1998;Shimatani 2001;Shimatani and Kubota 2004 for some attempts in this direction). For bivariate data it may be important to explore the degree of (a)symmetry in the interaction between events of different types.…”

Section: Which Test?mentioning

confidence: 99%

A comparison of methods for the statistical analysis of spatial point patterns in plant ecology

2006

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We describe a range of methods for the description and analysis of spatial point patterns in plant ecology. The conceptual basis of the methods is presented, and specific tests are compared, with the goal of providing guidelines concerning their appropriate selection and use. Simulated and real data sets are used to explore the ability of these methods to identify different components of spatial pattern (e.g. departure from randomness, regularity vs. aggregation, scale and strength of pattern). First-order tests suffer from their inability to characterise pattern at distances beyond those at which local interactions (i.e. nearest neighbours) occur. Nevertheless, the tests explored (first-order nearest neighbour, Diggle's G and F) are useful first steps in analysing spatial point patterns, and all seem capable of accurately describing patterns at these (shorter) distances. Among second-order tests, a density-corrected form of the neighbourhood density function (NDF), a noncumulative analogue of the commonly used Ripley's K-function, most informatively characterised spatial patterns at a range of distances for both univariate and bivariate analyses. Although Ripley's K is more commonly used, it can give very different results to the NDF because of its cumulative nature. A modified form of the K-function suitable for inhomogeneous point patterns is discussed. We also explore the use of local and spatially-explicit methods for point pattern analysis. Local methods are powerful in that they allow variations from global averages to be detected and potentially provide a link to recent spatial ecological theory by taking the 'plant's-eye view'. We conclude by discussing the problems of linking spatial pattern with ecological process using three case studies, and consider some ways that this issue might be addressed.

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Section: Which Test?mentioning

confidence: 99%

A comparison of methods for the statistical analysis of spatial point patterns in plant ecology

2006

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“…The four measures describing between‐species spatial structure that we considered were the individual species–area relationship (ISAR; Wiegand et al . ; which captures similar information to the species–area relationship), the spatial Simpson index (Shimatani ; Rajala & Illian ), the degree of association (Coomes, Rees & Turnbull ) and the cross‐pair overlap distribution (×POD; Brown et al . ).…”

Section: Methodsmentioning

confidence: 99%

“…Many studies of spatial structure have focused on variations in diversity, often using measures of β‐diversity that summarize some aspect of the turnover in species composition with site (Jaccard ; Odum ; Shimatani ; Legendre, Borcard & Peres‐Neto ; Anderson et al . ; Gonzalez‐Caro et al .…”

Section: Introductionmentioning

confidence: 99%

Success of spatial statistics in determining underlying process in simulated plant communities

2015

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Summary Spatial statistics are widely used in studies of ecological processes in plant communities, especially to provide evidence of neutral or non‐neutral mechanisms that might support species coexistence. The contribution of such statistics has been substantial, but their ability to identify any links between underlying processes and emergent patterns is not certain. We investigate the ability of a number of spatial statistics to distinguish theorized mechanisms of species coexistence (spatial and temporal niche differentiation, neutrality, the Janzen–Connell effect and heteromyopia) in a simulated plant community. We find that individual statistics differ substantially in their sensitivity to these mechanisms, with those based on nearest neighbour species identities being the most sensitive. These differences are largely robust to changes in the strength of the modelled mechanisms when simulated independently and in combination. The spatial signal of niche differentiation is always distinct in simulations that combine mechanisms. Synthesis. We describe full spatial signals of modelled coexistence mechanisms that are observed consistently across statistics and simulated strengths and combinations of mechanisms, and identify a set of spatial statistics that holds particular promise for empirical studies designed to investigate mechanisms of these kinds.

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“…plant interactions) occur over fine scales such as local plant neighborhoods (Illian et al 2009), which suggests conducting analyses from the ‘plant's‐eye view’ (Turkington and Harper 1979, Law et al 2009). Community‐level analyses may therefore obscure interesting patterns in diversity that may be fundamental to understanding plant plant interactions (Wiegand et al 2007a), dispersal mechanisms (Wiegand et al 2009), species coexistence (Illian and Burslem 2007, Law et al 2009), or in developing vegetation management strategies (Shimatani 2001). Recently, novel statistics have been developed that allow for more advanced analyses of the spatial patterns of diversity.…”

mentioning

confidence: 99%

“…These statistics consider the pattern of individual plants as well as the number and distribution of species, combining diversity metrics and point pattern analyses in order to quantitatively assess aspects of multispecies spatial patterns from the ‘plant's‐eye view’ (Shimatani 2001, Shimatani and Kubota 2004, Wiegand et al 2007a). These techniques also address the long‐standing challenge in spatial ecology of describing multispecific plant patterns without resorting to numerous bivariate statistics (Wiegand et al 2007a, b, Illian et al 2009), and are being used to elucidate new connections between diversity patterns and ecological processes in plant communities such as mixed hardwood forests (Shimatani 2001), subtropical rain forests (Shimantani and Kubota 2004), tropical rain forests (Wiegand et al 2007a) and high‐diversity semi‐arid shrublands (Illian et al 2009). Such work is an important contribution to the efforts of ecologists to understand the processes that structure communities and promote species coexistence in the face of global declines in biodiversity (Illian and Burslem 2007, Illian et al 2009).…”

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confidence: 99%

Individual species–area relationships and spatial patterns of species diversity in a Great Basin, semi‐arid shrubland

Rayburn¹,

Wiegand²

2012

Ecography

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Traditional biodiversity metrics operate at the level of a plant community but do not capture spatial variation in diversity from a ‘plant's‐eye view’ of a community. Recently‐developed statistics consider the spatial patterns of plants as well as the number and distribution of species in local plant neighborhoods to quantitatively assess multispecies spatial patterns from a ‘plant's‐eye view’. We used one such statistic, the individual species area relationship (ISAR), to assess spatial patterns of species diversity in a Great Basin (USA) semi‐arid shrubland through an analysis of a spatial dataset on shrub species and locations. In conjunction with appropriate null models, the ISAR blends species area relationships with second‐order spatial statistics to measure the expected species richness in local neighborhoods of variable size around the individuals of a focal species within a community. We found that, contrary to a previous analysis using more traditional methods, the community was well‐mixed with a typical shrub surrounded on average by 4.9 shrub neighbors of 2.1 species at a neighborhood scale of 1.0 m. We also found statistically significant fine‐scale variation in diversity patterns, such that neighborhoods of two species were more diverse than expected by a heterogeneous Poisson null model that accounted for larger‐scale habitat heterogeneity. However, this effect was caused by intraspecific aggregation of these species and was not due to positive interspecific association. Contrary to previous findings in other semi‐arid shrublands, our analysis suggests that the spatial pattern of the shrub community was not significantly structured by interspecific facilitation. This result supports growing evidence for balanced species patterns of adult plants in multispecies communities. Our approach may be used in other communities to describe complex multispecies spatial patterns, quantify species‐specific associations with diversity patterns, and to generate hypotheses regarding relationships between patterns and community‐structuring processes.

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Multivariate point processes and spatial variation of species diversity

Cited by 47 publications

References 9 publications

A comparison of methods for the statistical analysis of spatial point patterns in plant ecology

A comparison of methods for the statistical analysis of spatial point patterns in plant ecology

Success of spatial statistics in determining underlying process in simulated plant communities

Individual species–area relationships and spatial patterns of species diversity in a Great Basin, semi‐arid shrubland

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