An evaluation of the state of spatial point pattern analysis in ecology

Abstract

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.

Species-specific spatial structure, species coexistence and mortality pattern in natural, uneven-aged Scots pine (Pinus sylvestris L.)-dominated forest

“…This type of spatial pattern results from many factors, e.g., specific dispersal mechanisms and associated seed dispersal limitation, habitat heterogeneity, local disturbance events or clonal growth (Stoll and Prati 2001;Stoll and Newbery 2005;Wiegand and Moloney 2004;Shen et al 2013). To distinguish the importance of habitat heterogeneity and other clustering mechanisms is of vital importance in modern ecology, but such studies are still very scarce in the literature (Velázquez et al 2016). In mixed forests, spatial structure of different species plays an important role in the process of their coexistence (Stoll and Newbery 2005;Wiegand and Moloney 2004;Raventós et al 2010;Shen et al 2013).…”

“…Species coexistence has been commonly described by the theory of spatial segregation (Raventós et al 2010). Interspecific segregation is usually due to the niche differentiation or habitat filtering (Raventós et al 2010;Velázquez et al 2016), and usually it is related to intraspecific aggregation. However, Wilson (2011) indicated twelve theories of different importance explaining the coexistence of different species in plant populations.…”

Scale and strength of oak–mesophyte interactions in a transitional oak–hickory forest

“…The PCF, g(r) , is the density of points in a ring of radius r around a focal point divided by the average density of those points (Velázquez et al, 2016). The bivariate version measures the spatial association of points of two different types, so it measures the density of type A points around focal points of type B divided by the average density of type A points.…”

“…The bivariate version measures the spatial association of points of two different types, so it measures the density of type A points around focal points of type B divided by the average density of type A points. In either the bivariate or univariate case, the PCF is preferable to the more commonly used Ripley’s K because it is non-cumulative (Perry et al, 2006; Velázquez et al, 2016). In cumulative spatial statistics the value of that statistic at radius r is the cumulative effect from 0 to r , rather than the effect just at r .…”

“…We used the inhomogeneous PCF because none of the point patterns was homogenous across the plot. See Velázquez et al (2016) for a discussion of the importance of using inhomogeneous spatial statistics.…”