Learning is consistent, for example, with frequent visits to certain locations, or site fidelity ( Bonnell et al., 2013 Falcón-Cortés et al., 2017), and with the emergence of home range behavior or preferential travel routes ( Van Moorter et al., 2009 Boyer and Walsh, 2010). For instance, an animal can make decisions based on past successful experiences, resulting in a change of behavior and improved resource exploitation ( Leonard, 1990 Bracis and Mueller, 2017 Jesmer et al., 2018 Merkle et al., 2019). Adaptive behaviors based on learning can occur thanks to an interdependence between the acquisition of information over time and movement decisions ( Falcón-Cortés et al., 2017, 2019). Memory is also critical in the emergence of spatial learning, which results from interactions with the environment and can be detected through changes in movement patterns ( Mueller and Fagan, 2008). Identifying how animals use memory to make decisions is fundamental to developing a general theory of animal movement and space use ( Gautestad and Mysterud, 2005 Morales et al., 2010 Spencer, 2012). The combination of these two types of information allows animals to choose among alternative movement paths as has been observed in bumblebees ( Lihoreau et al., 2011) or large herbivores ( Avgar et al., 2013 Merkle et al., 2014). For example, food patch quality can be spatially encoded: patch quality is an attribute and its location is spatial information ( Fagan et al., 2013). The information stored as attribute memory may be the abundance or types of food, and can be linked to spatial information. While spatial memory allows animals to reduce uncertainty about the location of geographical features, attribute memory reduces uncertainty concerning location-independent features of objects ( Fagan et al., 2013). (2013), encodes the attributes of landscape features under the name of attribute memory. Another type of memory, described for the first time by Schacter (1992) and retaken by Fagan et al. These representations may allow animals to move directly to important sites in their environment that lie outside of their perceptual range ( Normand and Boesch, 2009 Presotto and Izar, 2010), such as resource patches, sites that connect with other high quality sites in space, or safe spots to avoid predators, and may also allow them to estimate the travel cost to reach a particular place ( Lanner, 1996 Janson, 2007 Janson and Byrne, 2007 Noser and Byrne, 2007). For example, humans, non-human primates and other large-brained vertebrates make movement decisions based on spatial representations of their environments ( Wills et al., 2010). The use of spatial memory is well-documented in many animal species. The fact that individual elk rapidly become used to a relatively small number of patches was consistent with the hypothesis that they seek places with predictable resources and reduced mortality risks such as predation. Memory decay was mild or negligible over the study period. Almost all the observed animals exhibited preferential returns to previously visited patches, such that memory and random exploration phases occurred. Here we propose several mobility models based on memory and perform hierarchical Bayesian inference on 11-month trajectories of 21 elk after they were released in a completely new environment. Identifying how animals use memory to make beneficial decisions is fundamental to developing a general theory of animal movement and space use. The study of how spatio-ecological knowledge is constructed throughout the life of an individual has not been carried out in a quantitative and comprehensive way, hindered by the lack of knowledge of the information an animal already has of its environment at the time monitoring begins. Adaptive behaviors based on learning can emerge thanks to an interdependence between the acquisition of information over time and movement decisions. The use of spatial memory is well-documented in many animal species and has been shown to be critical for the emergence of spatial learning. 4Grupo de Ecología Cuantitativa, INIBIOMA-CONICET, Universidad Nacional del Comahue, San Carlos de Bariloche, Argentina.3Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.2Instituto de Física, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.1Instituto de Ciencias Físicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico.Andrea Falcón-Cortés 1 * Denis Boyer 2 Evelyn Merrill 3 Jacqueline L.
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