Ralf Seppelt | Searcharxiv

arXiv2020

Harmonise and integrate heterogeneous areal data with the R package arealDB

Many relevant applications in the environmental and socioeconomic sciences use areal data, such as biodiversity checklists, agricultural statistics, or socioeconomic surveys. For applications that surpass the spatial, temporal or thematic scope of any single data source, data must be integrated from several heterogeneous sources. Inconsistent concepts, definitions, or messy data tables make this a tedious and error-prone process. To date, a dedicated tool to address these challenges is still lacking. Here, we introduce the R package arealDB that integrates heterogeneous areal data and associated geometries into a consistent database, in an easy-to-use workflow. It is useful for harmonising language and semantics of variables, relating data to geometries, and documenting metadata and provenance. We illustrate the functionality by integrating two disparate datasets (Brazil, USA) on the harvested area of soybean. arealDB promises quality-improvements to downstream scientific, monitoring, and management applications but also substantial time-savings to database collation efforts.

cs.DB↗

arXiv2019

How range residency and long-range perception change encounter rates

Encounter rates link movement strategies to intra- and inter-specific interactions, and therefore translate individual movement behavior into higher-level ecological processes. Indeed, a large body of interacting population theory rests on the law of mass action, which can be derived from assumptions of Brownian motion in an enclosed container with exclusively local perception. These assumptions imply completely uniform space use, individual home ranges equivalent to the population range, and encounter dependent on movement paths actually crossing. Mounting empirical evidence, however, suggests that animals use space non-uniformly, occupy home ranges substantially smaller than the population range, and are often capable of nonlocal perception. Here, we explore how these empirically supported behaviors change pairwise encounter rates. Specifically, we derive novel analytical expressions for encounter rates under Ornstein-Uhlenbeck motion, which features non-uniform space use and allows individual home ranges to differ from the population range. We compare OU-based encounter predictions to those of Reflected Brownian Motion, from which the law of mass action can be derived. For both models, we further explore how the interplay between the scale of perception and home range size affects encounter rates. We find that neglecting realistic movement and perceptual behaviors can systematically bias encounter rate predictions.

q-bio.PE↗