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

Brown, Calum; Illian, Janine B.; Burslem, David F. R. P.

doi:10.1111/1365-2745.12493

Cited by 36 publications

(45 citation statements)

References 82 publications

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“…() and Brown et al . ()). In the Gibbs model framework, the three‐variate analysis of different size trees by Grabarnik and Särkkä () is perhaps the most extensive modelling approach and closest in spirit to the work of this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Many ecological analyses of large and diverse rainforest data sets have been carried out by using the K -function or similar non-parametric summaries, either directly by comparing the summary values under various null model scenarios, or indirectly as part of minimum contrast model fitting of Cox processes (e.g. Lan et al (2012), Flügge et al (2014), Waagepetersen et al (2016), Yang et al (2016), Velázquez et al (2016) and Brown et al (2016)). In the Gibbs model framework, the three-variate analysis of different size trees by Grabarnik and Särkkä (2009) is perhaps the most extensive modelling approach and closest in spirit to the work of this paper.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Multivariate Interactions in Spatial Point Patterns with Gibbs Models and Variable Selection

Rajala

Murrell

Olhede

2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary We propose a method for detecting significant interactions in very large multivariate spatial point patterns. This methodology thus develops high dimensional data understanding in the point process setting. The method is based on modelling the patterns by using a flexible Gibbs point process model to characterize point‐to‐point interactions at different spatial scales directly. By using the Gibbs framework significant interactions can also be captured at small scales. Subsequently, the Gibbs point process is fitted by using a pseudolikelihood approximation, and we select significant interactions automatically by using the group lasso penalty with this likelihood approximation. Thus we estimate the multivariate interactions stably even in this setting. We demonstrate the feasibility of the method with a simulation study and show its power by applying it to a large and complex rainforest plant population data set of 83 species.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Multivariate Interactions in Spatial Point Patterns with Gibbs Models and Variable Selection

Rajala

Murrell

Olhede

2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…, Stoll and Prati , Brown et al. ), likely because understanding the underlying mechanisms of the spatial distribution of tree species is a fundamental step for elucidating the mechanisms of species coexistence (Law et al. , Lin et al.…”

Section: Discussionmentioning

confidence: 99%

The assembly and interactions of tree species in tropical forests based on spatial analysis

Zhang

Zang

2017

Ecosphere

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Citation: Zhang, S., Y. Huang, and R. Zang. 2017. The assembly and interactions of tree species in tropical forests based on spatial analysis. Ecosphere 8(7):e01903. 10. 1002/ecs2.1903 Abstract. Tree species were assembled into forest communities by various processes, and it was found that trees are spatially arranged in a community by the abiotic and biotic conditions within that community. Here, we explore the abiotic processes and biotic interactions in the spatial patterning of tree species across six old-growth forest types in a tropical nature reserve. Four spatial point process models (the homogeneous Poisson process, inhomogeneous Poisson process, homogeneous Thomas process, and inhomogeneous Thomas process) were used to evaluate the potential contribution of the random processes, habitat heterogeneity, dispersal limitation, and the joint effects of dispersal and habitat heterogeneity to the formation of the spatial patterns of tree species. A combination of the null model and size-distance regression analysis was applied to assess the effect of competition and facilitation on the tree species assembly. We found that the homogeneous and inhomogeneous Thomas processes were the best models for describing the spatial patterns of more than 85% tree species across the six tropical forests. The combination of the null model and size-distance regression analysis showed that 37-44% of tree species were affected by competition and 9-24% of tree species were affected by facilitation across the six tropical forest types. Competition was the dominant form of species interactions in forests with mild abiotic conditions such as the tropical montane rain forest and tropical lowland rain forest. Meanwhile, facilitation was the most important biotic interaction in the abiotically harsh tropical montane dwarf forest on mountaintops. Our study suggests that dispersal limitation and the joint effects of dispersal and habitat heterogeneity were the dominant abiotic processes in controlling the spatial patterning of trees in tropical forests. Moreover, the relative importance of competition and facilitation in the assembly of the tree species varied among the forest types with different abiotic conditions.

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“…The relevance of spatial and spatio-temporal point process models in ecological contexts is becoming increasingly recognised. For example, Law et al (2009), Illian et al (2013), Brown et al (2016) determine underlying processes that support species coexistence in plant communities and Yuan et al (2017) model distance sampling data as a thinned log-Gaussian Cox process (LGCP).…”

Section: Introductionmentioning

confidence: 99%

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

et al. 2019

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Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.

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

Cited by 36 publications

References 82 publications

Detecting Multivariate Interactions in Spatial Point Patterns with Gibbs Models and Variable Selection

Detecting Multivariate Interactions in Spatial Point Patterns with Gibbs Models and Variable Selection

The assembly and interactions of tree species in tropical forests based on spatial analysis

Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

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