Optimal Lévy-flight foraging in a finite landscape

Abstract

We present a simple model to study Lévy-flight foraging with a power-law step-size distribution in a finite landscape with countable targets. We find that different optimal foraging strategies characterized by a wide range of power-law exponent μopt, from ballistic motion (μopt → 1) to Lévy flight (1 < μopt < 3) to Brownian motion (μopt ≥ 3), may arise in adaptation to the interplay between the termination of foraging, which is regulated by the number of foraging steps, and the environmental context of the landscape, namely the landscape size and number of targets. We further demonstrate that stochastic returning can be another significant factor that affects the foraging efficiency and optimality of foraging strategy. Our study provides a new perspective on Lévy-flight foraging, opens new avenues for investigating the interaction between foraging dynamics and the environment and offers a realistic framework for analysing animal movement patterns from empirical data.

System-wide organization of actin cytoskeleton determines organelle transport in hypocotyl plant cells

“…3 K ; in particular, truncated power laws yielded higher likelihoods than exponential distributions (49)]. These exponents are larger than those typically reported for foraging animals or bacteria, $1<\alpha <3$ (46–48), but might indicate search strategies in small areas with a limited number of targets (50) or in the presence of obstacles or preferred areas (51–53), e.g., other organelles or delivery sites. Again, these findings were highly consistent across cells (Fig.…”

Autonomous surveillance for biosecurity

“…The technology can provide data in near real time, which allows dissemination of timely biosecurity alerts in the affected areas. Spatiotemporal movement data are also an important step toward developing accurate behavioral models [30] of the animal species under study. Agent-based modeling and simulations of animal populations [31] have applications in prediction of the disease risks that the animals carry and the management of pest animal species, which we discuss in more detail below.…”

“…These data can then drive predictive models of biosecurity-related risks such as transmission of diseases or crop damage and their distribution within a landscape. Predictive modeling is a complex task that requires an understanding of how the disease vectors (e.g., flying foxes) respond, in terms of their movement and choice of migration routes and foraging locations, to the structure of landscapes and the distribution of resources within them and how this varies across landscapes, seasons, and individuals [30,31]. A common approach is to develop agent-based models characterizing Box 2.…”

Learning abstract snippet detectors with Temporal embedding in convolutional neural Networks

“…For example, the ability to predict a day's power consumption of many individual houses at midday will be profoundly beneficial for the smart grid to manage dynamically its power supply resources. While in the scenario of smart location tracking [14], [37], [29], with a replenish-able energy budget the system either aims to minimize the energy efficiency of location tracking, or attempts to maximize the tracking accuracy given a fixed energy budget [30]. A crucial challenge involved in such a smart tracking system is to estimate at any time of day how much further the moving entities will move for the remainder of the day.…”