Modeling small-scale spatial interaction of shortgrass prairie species

Abstract

Native grasses interact spatially with themselves and their environment and can therefore be thought of as a system of dependent random variables. One method of modeling the spatial dependence of a multi-species population is a Gibbsian pairwise potential model. Since natural selection operates at the level of individual plants, the information obtained from such a model should provide a greater understanding of the intraspecific interactions in plant populations, while providing a theoretical basis for determining a plants' 'competitive zone' of influence. In this paper we fit a pairwise potential model to describe the spatial dependency of dominant grasses and forbs measured on a 1.5 x 1.5 m study plot located on a shortgrass prairie site near Fort Collins, Colorado. Dominant grasses included blue grama (Bouteloua gracilis), western wheatgrass (Agropyron smithii), Indian ricegrass (Oryzopsis hymenoides), and needle-and-thread grass (Stipa comata). Procedures for introducing spatial heterogeneity in the model is also discussed. © 1997 Elsevier Science B.V.

Discussion of “Marked Spatial Point Processes: Current State and Extensions to Point Processes on Linear Networks”

“…They included the marks in the model by dividing the distance between two points by some function of the marks connected to the points. A similar type of construction can be found in Reich et al (2009). A generalisation of the Strauss process, called Strauss disc process, was given in Goulard et al (1996).…”

“…In the multitype case, a potential function is defined for each type of points to model the interaction within the type as well as the interactions between the types. Simple pairwise interaction models for multitype forest data were presented, for example, in Ogata and Tanemura (1985); Goulard et al (1996); Reich et al (2009); Stoyan and Penttinen (2000). However, pairwise interaction models are typically not suitable for clustered patterns; for clustered patterns, other Markov processes, such as Geyer's saturation process or the area-interaction process, have been used.…”

Quantification of plant cover estimates

“…When this is the case, one may either use a more appropriate distribution such as the Poisson or may correct the effect of correlation by a transformation. It is also known that pattern scales among individual plants tend toward random as the trial areas become smaller in a sample site (Greig‐Smith 1983) and this effect may result in spatial exclusion among plants (Pielou 1969; Reich et al . 1997).…”