New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.
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.. Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to Ecology.Abstract. Ecologists need a better understanding of how animals make decisions about moving across landscapes. To this end, we developed computer simulations that contrast the effectiveness of various search strategies at finding habitat patches in idealized landscapes (uniform, random, or clumped patches), where searchers have different energy reserves and face different mortality risks. Nearly straight correlated random walks always produced better dispersal success than relatively uncorrelated random walks. However, increasing patch density decreased the degree of correlation that maximized dispersal success. Only under high mortality and low energy reserves in a uniform landscape did absolutely straight-line search perform better than any random walk. With low mortality risks and high energy reserves, exhaustive systematic search was superior to the best correlated random walk; an increase in the perceptual range of the searcher (i.e., patch delectability) also favored exhaustive search over relatively straight random walks. For all conditions examined, the "average distance rule," a hybrid search rule incorporating both straightline and systematic search, was best. Overall, however, our results suggest that a simple and effective search rule for many landscape-explicit models would involve straight or nearly straight movements.
Ecologists need a better understanding of how animals make decisions about moving across landscapes. To this end, we developed computer simulations that contrast the effectiveness of various search strategies at finding habitat patches in idealized landscapes (uniform, random, or clumped patches), where searchers have different energy reserves and face different mortality risks. Nearly straight correlated random walks always produced better dispersal success than relatively uncorrelated random walks. However, increasing patch density decreased the degree of correlation that maximized dispersal success. Only under high mortality and low energy reserves in a uniform landscape did absolutely straight-line search perform better than any random walk. With low mortality risks and high energy reserves, exhaustive systematic search was superior to the best correlated random walk; an increase in the perceptual range of the searcher (i.e., patch detectability) also favored exhaustive search over relatively straight random walks. For all conditions examined, the ''average distance rule,'' a hybrid search rule incorporating both straightline and systematic search, was best. Overall, however, our results suggest that a simple and effective search rule for many landscape-explicit models would involve straight or nearly straight movements.
Summary 1.Increasing urbanization and recreational activities around and within biodiversity hotspots require an understanding of how to reduce the impacts of human disturbance on more than a single species; however, we lack a general framework to study multiple species. One approach is to expand on knowledge about the theory of anti-predator behaviour to understand and predict how different species might respond to humans. 2. We reviewed the literature and found that only 21% of studies that used a behavioural approach to study human disturbance focused on multiple species. These studies identified a number of potential predictive variables. 3.We developed a simulation model that investigates interspecific variation in different parameters of disturbance with variation in human visitation. We found that fitness-related responses, such as the quantity of food consumed by a species, are relatively sensitive to the distance at which animals detect humans, the frequency of disturbance by humans and the interaction of these factors, but are less sensitive to other characteristics. 4. We examined avian alert distance (the distance animals first orientated to an approaching threat, a proxy for detection distance) across 150 species, controlling for phylogenetic effects. We found that larger species had greater alert distances than smaller species, which could increase local spatial and temporal limitations on suitable habitat with increasing human visitation. 5. Synthesis and applications . Our results suggest that body size could be a potential predictor of responses to human disturbance across species, and could be used by managers to make conservation decisions regarding levels of human visitation to a protected site. We suggest that three things are essential to develop predictive models of how different species will respond to human disturbance. First, multiple indicators of disturbance should be studied to select those with lower intraspecific variation for a given study system. Secondly, the species-specific nature of responses should be identified. Thirdly, life history, natural history and other correlates with these species-specific responses must be assessed.
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.
In the absence of evidence to the contrary, population models generally assume that the dispersal trajectories of animals are random, but systematic dispersal could be more efficient at detecting new habitat and may therefore constitute a more realistic assumption. Here, we investigate, by means of simulations, the properties of a potentially widespread systematic dispersal strategy termed "foray search." Foray search was more efficient in detecting suitable habitat than was random dispersal in most landscapes and was less subject to energetic constraints. However, it also resulted in considerably shorter net dispersed distances and higher mortality per net dispersed distance than did random dispersal, and it would therefore be likely to lead to lower dispersal rates toward the margins of population networks. Consequently, the use of foray search by dispersers could crucially affect the extinction-colonization balance of metapopulations and the evolution of dispersal rates. We conclude that population models need to take the dispersal trajectories of individuals into account in order to make reliable predictions.
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