Quantifying and Simulating Fine-Scale Spatial Patterns in Plant Populations

<p>Plant ecology as a discipline has increasingly acknowledged the importance of fine-scale spatial patterns in developing our understanding of community/population dynamics. These spatial patterns are largely determined by direct and indirect interactions between plants and their immediate neighbors. Such interactions thus play an important role in the structure and function of plant communities. Study of these types of local interactions has greatly benefitted from simulation based approaches. one such simulation method, agent-based modeling, has increasingly been identified as a useful tool for simulating these fine-scale interactions, and for investigating theoretical descriptions of underlying processes. Similarly, statistical techniques aimed at quantifying and comparing spatial patterns across a range of spatial scales are an active area of research, and have served to greatly increase our understanding of plant communities.</p> <p>Typically underlying these statistical and simulation methods, is a simplified representation of individuals as grid cells, points or circles. Recent work has illustrated that fine-scale spatial patterns may be misrepresented when such assumptions are made, and researchers are increasingly developing methods that do not rely on such geometric simplifications. The work presented in this dissertation shows that important inter-annual changes in spatial pattern occur in <em>Bouteloua gracilis</em> populations at multiple, sub-meter scales, reinforcing the belief that local interactions are important factors in community structure (Chapter 1). It further illustrates that the very notion of 'local' is markedly influenced by the particular data type chosen to represent individuals, and that geometric simplifications change how neighborhood composition is described (Chapter 2). The dissertation presents an extension to traditional point-pattern analysis techniques that allows for more complex geometries in describing randomness, clustering and/or regularity in spatial patterns down to the scale of individual plants (Chapter 3). The proposed method extends a recent advance in the literature for quantifying spatial patterns in polygon data consisting of irregularly shaped objects by considering the physical space occupied by competing individuals, rather than simply the density of neighbors. This provides a useful metric of competition intensity experienced by individuals within a population. Finally, this dissertation presents a proof-of-concept agent-based model that extends previous models by allowing individual plants to respond to local conditions by dynamically changing size and shape (Chapter 4).</p>

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Year dc:date.available

2017

Author dc:creator

• Schulte, Darin

Contributors dc:contributor

• Anna Sher, Ph.D.
• Robin Tinghitella
• Nathan Sturtevant
• Catherine Durso

• Agent-based modeling
• Point-pattern analysis
• Polygons
• Simulation
• Spatial dynamics
• Spatial patterns
• Ecology and Evolutionary Biology

Statement dc:rights

• <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>

Language dc:language

en

Repository record dc:identifier

https://digitalcommons.du.edu/etd/1311

OAI identifier oai:identifier

oai:digitalcommons.du.edu:etd-2311