Behavioral tradeoffs when dispersing across a patchy landscape

Abstract

2005. Behavioral tradeoffs when dispersing across a patchy landscape. Á/ Oikos 108: 219 Á/230.A better understanding of the behavior of dispersing animals will assist in determining the factors that limit their success and ultimately help improve the way dispersal is incorporated into population models. To that end, we used a simulation model to investigate three questions about behavioral tradeoffs that dispersing animals might face: (i) speed of movement against risk of predation, (ii) speed of movement against foraging, and (iii) perceptual range against risk of predation. The first investigation demonstrated that dispersing animals can generally benefit by slowing from maximal speed to perform anti-predatory behavior. The optimal speed was most strongly influenced by the disperser's energetic reserves, the risk of predation it faced, the interaction between these two parameters, and the effectiveness of its anti-predatory behavior. Patch arrangement and the search strategy employed by the dispersers had marginal effects on this tradeoff relative to the above parameters. The second investigation demonstrated that slowing movement to forage during dispersal may increase success and that optimum speed of dispersal was primarily a function of the dispersing animal's energetic reserves, predation risk, and their interaction. The richness (density of food resources) of the interpatch matrix and the patch arrangement had relatively minor impacts on how much time a dispersing animal should spend foraging. The final investigation demonstrated animals may face tradeoffs between dispersing under conditions that involve a low risk of predation but limit their ability to perceive distant habitat (necessitating more time spent searching for habitat) and conditions that are inherently more risky but allow animals to perceive distant habitat more readily. The precise nature of this tradeoff was sensitive to the form of the relationship between predation risk and perceptual range. Our overall results suggest that simple depictions of these behavioral tradeoffs might suffice in spatially explicit population models.

Matrix mediates avian movements in tropical forested landscapes: Inference from experimental translocations

“…Gap-crossing and recent translocation experiments reveal that birds may avoid venturing into open areas and instead preferentially move through landscape features most similar to their optimal habitat at an energetic cost of greater travel time (e.g., Desrochers and Hannon, 1997;Gillies and St. Clair, 2008). A reluctance to cross gaps may be a response to avoid predators (e.g., Lima and Dill, 1990), or the result of limited perceptual range (e.g., Zollner and Lima, 2005) or low motivation (e.g., few suitable resources in the matrix; Bélisle and Desrochers, 2002). Thus, birds in bauxite mining landscapes may have remained longer in release patches (e.g., Castellon and Sieving, 2006), searched a longer time for suitable cover, or taken more circuitous routes back to territories (e.g., Gillies and St. Clair, 2008;Hadley and Betts, 2009).…”

Orientation from open water to settlement habitats by coral reef fish: behavioral flexibility in the use of multiple reliable cues

“…Adaptive be havior that leads to successful orientation and recruitment to seagrass beds can thus ultimately lead to increased survival, which is critical for population maintenance. However, localizing and navigating towards isolated inshore habitats such as seagrass beds that are located beyond the reef might increase the short-term mortality risk associated with an increased search time while residing in the open water column (Zollner & Lima 2005). Nevertheless, at the population level, such a trade-off between an increased predation risk in the short-term and a long-term increase in fitness might be a beneficial strategy.…”

Risky movement increases the rate of range expansion

“…Importantly, the key resultgreater survival of individuals that rapidly orient towards a potential destination-is unaffected by the way the bias is modelled. Whereas numerous previous studies have focused on how individual survival probabilities relate to interpatch movement rules [12,15,34], few have considered the consequences of movement rules for spatial population dynamics [33]. This may be in part because high-quality empirical data on animal movements have only recently become available [35], so our ability to empirically address the question of movement rules is in its infancy [36].…”

“…For (i), we run simulations similar to many performed earlier [12,15,16], where individuals are forced to emigrate from a patch and follow specified dispersal rules. The proportion of individuals successfully reaching a patch is recorded.…”