Movement Ecology Using RMove | PR Statistics

Master movement ecology by analyzing spatial movement data using the RMove package in R. Learn essential workflows for ecological data analysis with expert instruction.

Movement Ecology Using RMove | PR Statistics

Master movement ecology by analyzing spatial movement data using the RMove package in R. Learn essential workflows for ecological data analysis with expert instruction.

Arthro-Pod EP 172: Bugs, Maps, and Math with Jeff Holland

  On this episode of Arthro-Pod, join Mike and Jon as they catch up with Jeff Holland of Bugs, Maps, and Math. Jeff was previously a profes...

Exploration of spatial patterns in raster data

GEO-OPEN-HACK-2024: Big Geospatial Data Hackathon with Open Infrastructure and Tools

GEO-OPEN-HACK-2024 is a comprehensive and informative event designed for advanced geo-coders to explore various open tools and approaches for upscaling geospatial analysis on open High-Performance Computing (HPC) infrastructure.

Comparing spatial patterns using moving window approach

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Spatial ecology - Wikipedia

Looking forwards and backwards: dynamics and genealogies of locally regulated populations

We introduce a broad class of spatial models to describe how spatially heterogeneous populations live, die, and reproduce. Individuals are represented by points of a point measure, whose birth and death rates can depend both on spatial position and local population density, defined via the convolution of the point measure with a nonnegative kernel. We pass to three different scaling limits: an interacting superprocess, a nonlocal partial differential equation (PDE), and a classical PDE. The classical PDE is obtained both by first scaling time and population size to pass to the nonlocal PDE, and then scaling the kernel that determines local population density; and also (when the limit is a reaction-diffusion equation) by simultaneously scaling the kernel width, timescale and population size in our individual based model. A novelty of our model is that we explicitly model a juvenile phase: offspring are thrown off in a Gaussian distribution around the location of the parent, and reach (instant) maturity with a probability that can depend on the population density at the location at which they land. Although we only record mature individuals, a trace of this two-step description remains in our population models, resulting in novel limits governed by a nonlinear diffusion. Using a lookdown representation, we retain information about genealogies and, in the case of deterministic limiting models, use this to deduce the backwards in time motion of the ancestral lineage of a sampled individual. We observe that knowing the history of the population density is not enough to determine the motion of ancestral lineages in our model. We also investigate the behaviour of lineages for three different deterministic models of a population expanding its range as a travelling wave: the Fisher-KPP equation, the Allen-Cahn equation, and a porous medium equation with logistic growth.