Local Orientation and the Evolution of Foraging: Changes in Decision Making Can Eliminate Evolutionary Trade-offs (original) (raw)
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Quantifying the Adaptive Value of Learning in Foraging Behavior
The American Naturalist, 2009
The value of acquiring environmental information depends on the costs of collecting it and its utility. Foragers that search for patchily distributed resources may use experiences in previous patches to learn the habitat quality and adjust their behavior. We map the ecological landscape for the evolution of learning under a range of conditions, including both spatial and temporal heterogeneity. We compare the learning strategy with genetically fixed patchleaving rules and with strategies of foragers that have free and perfect information about their environment. The model reveals that the efficiency of learning is highest when low encounter stochasticity results in reliable estimates of patch quality, when there is no or little temporal change, and when there is little spatial variability. This partially contrasts with the value of learning, which is highest when there is temporal change, because flexible strategies may track the environmental trend, and when there is spatial variability, because there is a need to distinguish between good and bad patches. Learning rules with short-term memory are beneficial when patch information is accurate and when there is temporal change, whereas learning rules that update slowly are generally more robust to spatial variability.
How a simple adaptive foraging strategy can lead to emergent home ranges and increased food intake
2013
Animals often alternate between searching for food locally and moving over larger distances depending on the amount of food they find. This ability to switch between movement modes can have large implications on the fate of individuals and populations, and a mechanism that allows animals to find the optimal balance between alternative movement strategies is therefore selectively advantageous. Recent theory suggests that animals are capable of switching movement mode depending on heterogeneities in the landscape, and that different modes may predominate at different temporal scales. Here we develop a conceptual model that enables animals to use either an area-concentrated food search behavior or undirected random movements. The model builds on the animals’ ability to remember the profitability and location of previously visited areas. In contrast to classical optimal foraging models, our model does not assume food to be distributed in large, well-defined patches, and our focus is on animal movement rather than on how animals choose between foraging patches with known locations and value. After parameterizing the fine-scale movements to resemble those of the harbor porpoise Phocoena phocoena we investigate whether the model is capable of producing emergent home ranges and use pattern-oriented modeling to evaluate whether it can reproduce the large-scale movement patterns observed for porpoises in nature. Finally we investigate whether the model enables animals to forage optimally. We found that the model was indeed able to produce either stable home ranges or movement patterns that resembled those of real porpoises. It enabled animals to maximize their food intake when fine-tuning the memory parameters that controlled the relative contribution of area concentrated and random movements.
Sources of variation in search and foraging: A theoretical perspective
Quarterly Journal of Experimental Psychology, 2021
Search-the problem of exploring a space of alternatives in order to identify target goals-is a fundamental behaviour for many species. Although its foundation lies in foraging, most studies of human search behaviour have been directed towards understanding the attentional mechanisms that underlie the efficient visual exploration of two-dimensional scenes. With this review, we aim to characterise how search behaviour can be explained across a wide range of contexts, environments, spatial scales, and populations, both typical and atypical. We first consider the generality of search processes across psychological domains. We then review studies of interspecies differences in search. Finally, we explore in detail the individual and contextual variables that affect visual search and related behaviours in established experimental psychology paradigms. Despite the heterogeneity of the findings discussed, we identify that variations in control processes, along with the ability to regulate behaviour as a function of the structure of search space and the sampling processes adopted, to be central to explanations of variations in search behaviour. We propose a tentative theoretical model aimed at integrating these notions and close by exploring questions that remain unaddressed.
Near—Far search: An evolutionarily stable foraging strategy
Journal of Theoretical Biology, 1995
This study addresses the momentary rules of nectar foraging behavior on carpet-type, small inflorescences. It has been suggested that patchiness in the distribution of nectar can give an advantage to ''near-far'' type of foraging strategies; that is, to foragers which search ''near'' (in the neighborhood of the last visited flower) as long as the nectar yield is high enough and go ''far'' otherwise. To explore the evolutionary stability of near-far search, various foraging strategies were compared, according to two, slightly different optimality criteria: the number of flowers emptied during a fixed length bout and the number of flowers visited until total extraction of the entire inflorescence. With long bouts (in the case of a single forager) or a substantial probability of revisits to the same inflorescence (in the case of multiple foragers), a near-far foraging strategy is an ESS. Furthermore, prior patchiness in the nectar distribution is not a necessary condition for the evolutionary stability of near-far search. It turns out that during near-far foraging some patchiness is created by the foraging process itself, which the near-far forager can exploit later on.
Research Square (Research Square), 2022
Consumers must track and acquire resources in complex landscapes. Much discussion has focused on the concept of a 'resource gradient' and the mechanisms by which consumers can take advantage of such gradients as they navigate their landscapes in search of resources. However, the concept of tracking resource gradients means different things in different contexts. Here we take a synthetic approach and consider six different definitions of what it means to search for resources based on density or gradients in density. These include scenarios where consumers change their movement behavior based on the density of conspecifics, on the density of resources, and on spatial or temporal gradients in resources. We also consider scenarios involving non-local perception and a form of memory. Using a continuous space, continuous time model that allows consumers to switch between resource-tracking and random motion, we investigate the relative performance of these six different strategies. Consumers' success in matching the spatiotemporal distributions of their resources differs starkly across the six scenarios. Movement strategies based on perception and response to temporal (rather than spatial) resource gradients afforded consumers with the best opportunities to match resource distributions. All scenarios would allow for optimization of resource matching in terms of the underlying parameters, providing opportunities for evolutionary adaptation, and links back to classical studies of foraging ecology.
Optimal search behavior and classic foraging theory
Journal of Physics A-mathematical and Theoretical, 2009
Random walk methods and diffusion theory pervaded ecological sciences as methods to analyze and describe animal movement. Consequently, statistical physics was mostly seen as a toolbox rather than as a conceptual framework that could contribute to theory on evolutionary biology and ecology. However, the existence of mechanistic relationships and feedbacks between behavioral processes and statistical patterns of movement suggests that, beyond movement quantification, statistical physics may prove to be an adequate framework to understand animal behavior across scales from an ecological and evolutionary perspective. Recently developed random search theory has served to critically re-evaluate classic ecological questions on animal foraging. For instance, during the last few years, there has been a growing debate on whether search behavior can include traits that improve success by optimizing random (stochastic) searches. Here, we stress the need to bring together the general encounter problem within foraging theory, as a mean for making progress in the biological understanding of random searching. By sketching the assumptions of optimal foraging theory (OFT) and by summarizing recent results on random search strategies, we pinpoint ways to extend classic OFT, and integrate the study of search strategies and its main results into the more general theory of optimal foraging.
2009
Question: How does the ability to improve foraging skills by learning, and to transfer that learned knowledge, affect the development of intra-population foraging specializations? Features of the model: We use both a state-dependent life-history model implemented by stochastic dynamic programming (SDPM) and an individual-based model (IBM) to capture the dynamic nature of behavioural preferences in feeding. Variables in the SDPM include energy reserves, skill levels, energy and handling time per single prey item, metabolic rate, the rates at which skills are learned and forgotten, the effect of skills on handling time, and the relationship between energy reserves and fitness. Additional variables in the IBM include the probability of successful weaning, the logistic dynamics of the prey species with stochastic recruitment, the intensity of top-down control of prey by predators, the mean and variance in skill levels of new recruits, and the extent to which learned information can be transmitted via matrilineal social learning. Key range of variables: We explore the effects of approaching the time horizon in the SDPM, changing the extent to which skills can improve with experience, increasing the rates of learning or forgetting of skills, changing whether the learning curve is constant, accelerating ('J'-shaped) or decelerating ('r'-shaped), changing both mean and maximum possible energy reserves, changing metabolic costs of foraging, and changing the rate of encounter with prey. Conclusions: The model results show that the following factors increase the degree of prey specialization observed in a predator population: (1) Experience handling a prey type can substantially improve foraging skills for that prey. (2) There is limited ability to retain complex learned skills for multiple prey types. (3) The learning curve for acquiring new foraging skills is accelerating, or J-shaped. (4) The metabolic costs of foraging are high relative to available energy reserves. (5) Offspring can learn foraging skills from their mothers (matrilineal social
Theoretical Ecology, 2019
Animals use different modes of movement at different times, in different locations, and on different scales. Incorporating such context dependence in mathematical models represents a substantial increase in complexity, but creates an opportunity to more fully integrate key biological features. Here, we consider the spatial dynamics of a population of foragers with two subunits. In one subunit, foragers move via diffusion (random search) whereas in the other, foragers move via advection (gradient-following search). Foragers switch back and forth between the subunits as functions of their spatial context (i.e., depending on whether they are inside or outside of a patch, or depending on whether or not they can detect a gradient in resource density). We consider a onedimensional binary landscape of resource patches and non-habitat and gauge success in terms of how well the mobile foragers overlap with the distribution of resources. Actively switching between dispersal modes can sometimes greatly enhance this spatial overlap relative to the spatial overlap possible when foragers merely blend advection and diffusion modes at all times. Switching between movement modes is most beneficial when organism's gradient-following abilities are weak compared to its overall capacity for movement, but switching can actually be quite detrimental for organisms that can rapidly follow resource gradients. An organism's perceptual range plays a critical role in determining the conditions under which switching movement modes benefits versus disadvantages foragers as they seek out resources.