Energy Landscapes Shape Animal Movement Ecology

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Submitted January 5, 2013; Accepted April 11, 2013; Electronically published July 12, 2013 abstract: The metabolic costs of animal movement have been studied extensively under laboratory conditions, although frequently these are a poor approximation of the costs of operating in the natural, heterogeneous environment. Construction of "energy landscapes," which relate animal locality to the cost of transport, can clarify whether, to what extent, and how movement properties are attributable to environmental heterogeneity. Although behavioral responses to aspects of the energy landscape are well documented in some fields (notably, the selection of tailwinds by aerial migrants) and scales (typically large), the principles of the energy landscape extend across habitat types and spatial scales. We provide a brief synthesis of the mechanisms by which environmentally driven changes in the cost of transport can modulate the behavioral ecology of animal movement in different media, develop example cost functions for movement in heterogeneous environments, present methods for visualizing these energy landscapes, and derive specific predictions of expected outcomes from individual-to population-and species-level processes. Animals modulate a suite of movement parameters (e.g., route, speed, timing of movement, and tortuosity) in relation to the energy landscape, with the nature of their response being related to the energy savings available. Overall, variation in movement costs influences the quality of habitat patches and causes nonrandom movement of individuals between them. This can provide spatial and/or temporal structure to a range of population-and specieslevel processes, ultimately including gene flow. Advances in animalattached technology and geographic information systems are opening up new avenues for measuring and mapping energy landscapes that are likely to provide new insight into their influence in animal ecology.

Habitat or matrix: which is more relevant to predict road-kill of vertebrates?

“…More permeable matrix types improve functional connectivity in the landscape, representing more gene flow, less extinction risk and thus, a good conservation strategy under population and community perspectives (Taylor et al, 1993). The association between river proximity and road-kill of all species together and of mammals indicates that rivers may be a preferential route for them, minimizing energy costs (Shepard et al, 2013). In a mountain, rivers located in valleys represent food and water source, and also an easier way to move along them in forests.…”

“…In addition, some landscape characteristics are relevant to understand animal movements, preferential routes and matrix permeability for species (Prevedello and Vieira, 2010). Is expected that species, as possible, move in a straight line, for instance along river margins, because represent "low energy" paths, particularly in mountain areas, where, in contrast, steep slopes represent "high energy" paths (Shepard et al, 2013). Thus, rivers and water bodies, due to movement behavior and resource use (Zeller et al, 2012) may be important landscape characteristics to predict preferred crossing sites and the best location where apply mitigation measures (Schuster et al, 2013).…”

Where eagles soar: Fine‐resolution tracking reveals the spatiotemporal use of differential soaring modes in a large raptor

“…The physical environment influences the energetic cost of animal locomotion and is known to modulate animal movement (Ganskopp, Cruz, & Johnson, 2000; Shepard et al., 2013; Spaar & Bruderer, 1996; Wall, Douglas‐Hamilton, & Vollrath, 2006; Wilson, Quintana, & Hobson, 2011). For soaring birds, the route, method, and cost of flight are greatly affected by the availability of uplift, which provides a harvestable supply of energy for movement (Katzner et al., 2015; Lanzone et al., 2012).…”

“…Given these technological advances and the discrete nature of uplift, we are now in a position to model the spatiotemporal probability of uplift‐induced flight behaviors or availability of uplift as “energy landscapes” for soaring species (Shepard et al., 2013; Wilson et al., 2011). This advance will be important for a number of applications; for example, the ability to predict the distribution and probability of locomotory behaviors will be critical in mitigating anthropogenic risks associated with specific behaviors (Camacho, Palacios, Sáez, Sánchez, & Potti, 2014; Colchero et al., 2011; Péron et al., 2017; Reid, Krüger, Whitfield, & Amar, 2015).…”

“…Landscape features that promote or limit certain movement strategies can affect the energetic cost of locomotion, which are reflected in the “energy landscape” (Shepard et al., 2013; Wilson et al., 2011). At a broad scale, the energy landscape is likely to have implications for species distributions, and at a finer scale, it might impact on individual fitness or breeding performance (Shepard et al., 2011; Weimerskirch, Louzao, de Grissac, & Delord, 2012).…”