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Models of heterogeneity, contextuality and self-interaction in ordered spatial point patterns with applications to animal movement and forest inventory

ordSpat SA no 310072

Consortium ordSpat develops novel statistical methods for analyzing the effects of memory, learning, and interactions on animal movement and for conducting stand-level forest inventories by remote sensing. A new class of statistical models for ordered spatial point patterns, originally developed for eye-movement research serves as a flexible framework for both applications. The research is conducted at Natural Resources Institute Finland (Luke) and University of Eastern Finland, and is based on the existing extensive GPS telemetry data and accurately measured forest plots of these institutes. The expected results include widely applicable methodology for spatio-temporal point patterns, deeper understanding of animal behavior, and cost-efficient forest inventory estimates and their standard errors. The results can be utilized in the assessment and protection of wildlife populations, in the determination of human-wildlife conflicts, and in small and large scale forest inventories.