Ecological information from spatial patterns of plants: insights from point process theory

Abstract

Summary 1.This article reviews the application of some summary statistics from current theory of spatial point processes for extracting information from spatial patterns of plants. Theoretical measures and issues connected with their estimation are described. Results are illustrated in the context of specific ecological questions about spatial patterns of trees in two forests. 2. The pair correlation function, related to Ripley's K function, provides a formal measure of the density of neighbouring plants and makes precise the general notion of a 'plant's-eye' view of a community. The pair correlation function can also be used to describe spatial relationships of neighbouring plants with different qualitative properties, such as species identity and size class. 3. The mark correlation function can be used to describe the spatial relationships of quantitative measures (e.g. biomass). We discuss two types of correlation function for quantitative marks. Applying these functions to the distribution of biomass in a temperate forest, it is shown that the spatial pattern of biomass is uncoupled from the spatial pattern of plant locations. 4. The inhomogeneous pair correlation function enables first-order heterogeneity in the environment to be removed from second-order spatial statistics. We illustrate this for a tree species in a forest of high topographic heterogeneity and show that spatial aggregation remains after allowing for spatial variation in density. An alternative method, the master function, takes a weighted average of homogeneous pair correlation functions computed in subareas; when applied to the same data and compared with the former method, the spatial aggregations are smaller in size. 5. Synthesis. These spatial statistics, especially those derived from pair densities, will help ecologists to extract important ecological information from intricate spatially correlated plants in populations and communities.

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

“…Because plants cannot move and mainly interact with their close neighbours [28], their spacing may conserve an imprint of neighbourhood interactions that could be detected using point pattern analysis [21,22,24]. This approach is promising because the intraspecific spacing of plants is also closely related with potential coexistence mechanisms [30].…”

“…In this study, we use spatial point pattern analysis [28,32,33] to test the assertion of species independence with data from three forests of different species richness including a 50 ha plot of neotropical forest at BCI, Panama (more than 300 species of trees and shrubs), a 25 ha plot of tropical forest in Sinharaja, Sri Lanka (more than 200 species) and a 25 ha plot temperate forest in Changbaishan (CBS), China (52 species). Because habitat association and species interactions may occur simultaneously, we conduct one analysis studying their joint effects and one analysis studying selectively species interactions.…”

Changes in spatial point patterns of pioneer woody plants across a large tropical landslide

“…According to this method, we assumed that the patterns in each of the four plots were independent samples or "replicates" from a larger pattern per subzone (Law et al, 2009). Additionally, this approach enabled us to make inferences in relation to whole subzones rather than merely describing the spatial patterns of individual plots De Luis et al, 2008).…”

Special Issue on Spatial Moment Techniques for Modelling Biological Processes

“…The image in Fig. 1a shows some field data illustrating patchiness and clustering in the spatial distribution of trees in a beech forest (Law et al 2009), while the image in Fig. 1b shows an in vitro growth-to-confluence assay for a breast cancer cell line where the cells are initially distributed uniformly and, as the population grows towards confluence, we observe significant clustering and patchiness in the spatial distribution of cells (Simpson et al 2013;Agnew et al 2014).…”

“…a Dispersal and competition processes in a beech forest give rise to patchiness in the distribution of trees (see Law et al (2009) for full details). b Rapid cell proliferation, relative to the rate of cell motility, in a population of initially spatially uniform breast cancer cells gives rise to patchiness and clustering during the growth-to-confluence process (see Simpson et al 2013 for full details).…”