SearcharxivSearch

arXiv subjects

Anudeep Surendran

Publications and source records attributed to Anudeep Surendran.

2 recordsLinked to original sources

Range residency determines how movement persistence shapes encounter rates

Encounters between individuals link movement behavior to population-level processes such as predation and disease transmission. For many animal species, movement can be modeled as a multiscale stochastic process, dominated by directional persistence at short time scales and range residency at long time scales. Their separate effects on encounters are well understood: range residency can raise or lower encounter rates depending on home-range overlap, whereas moving with higher directional persistence systematically increases them. However, how directional persistence and range residency jointly determine encounters remains unknown. We present an analytical encounter theory for movement models that combine both features. In this framework, we derive a threshold in home-range overlap above which directional persistence diminishes, rather than enhances, encounters. At shorter time scales, attraction toward the home-range center displaces individual locations, altering encounter rates even when range residency is not measurable in movement tracks. Movement models fitted to short tracks can therefore describe trajectories accurately yet under- or overestimate the encounters derived from those trajectories, depending on home range spatial configuration. Encounter rates are a more demanding target for inference than movement parameters themselves.

q-bio.PE

Spatial moment dynamics and biomass density equations provide complementary, yet limited, descriptions of pattern formation in individual-based simulations

Spatial patterning is common in ecological systems and has been extensively studied via different modeling approaches. Individual-based models (IBMs) accurately describe nonlinear interactions at the organism level and the stochastic spatial dynamics that drives pattern formation, but their computational cost scales quickly with system complexity, limiting their practical use. Population-level approximations such as spatial moment dynamics (SMD) -- which describe the moments of organism distributions -- and coarse-grained biomass density models have been developed to address this limitation. However, the extent to which these approximated descriptions accurately capture the spatial patterns and population sizes emerging from individual-level simulations remains an open question. We investigate this issue considering a prototypical population dynamics IBM with long-range dispersal and intraspecific competition, for which we derive both its SMD and coarse-grained density approximations. We systematically compare the performance of these two approximations at predicting IBM population abundances and spatial patterns. Our results highlight that SMD and density-based approximations complement each other by correctly capturing these two population features within different parameter regimes. Importantly, we identify regions of the parameter space in which neither approximation performed well, which should encourage the development of more refined IBM approximation approaches.

q-bio.PE