Animal movement ecology and the conservation of highly mobile species

Background/Question/Methods

Real landscapes are dynamic in space and time, and the scales over which such variation occurs can determine the success of different conservation strategies for resident species. Within such landscapes, real species rely on a variety of individual-level behaviors for movement and navigation. Movement behaviors such as long-distance searching and fine-scale foraging are often intermixed, but operate on vastly different spatial and temporal scales.  Individual experience, life-history traits, and resource dynamics combine to shape population-level patterns such as range residency, migration, and nomadism. Combining empirical movement data and powerful statistical approaches provides pathways for understanding animal movement on the large spatial scales that are so critical for conservation. Methods such as ‘animal models’ of pedigree effects, semi-variance functions for animal movement data, and special kernel density estimators that leverage the long term autocorrelations present in animal tracking data are increasingly essential tools at the interface of ecology and conservation.  Such approaches help ecologists interpret the consequences of conservation efforts, aid the analysis of large-scale datasets, and identify priority areas for conservation.

Results/Conclusions

The interface between basic ecology and conservation science is robust, and ecological principles are often critical to effective conservation.  However, the interface is actually a two-way street. For example, data on rare species can yield new insights on ecological processes, and conservation-relevant datasets present analytical challenges that inspire novel ecological techniques. I focus here on these latter connections, whereby conservation efforts spawn new ecological findings and approaches. For example, decades of investment in the conservation of the migratory whooping crane have provided an unparalleled dataset for learning about how the experience level of migrating birds influences group performance. Recent discoveries demonstrated that the migratory trajectories of young birds were more direct when they flew with older, more experienced birds. New results show that similar experience-based processes underlie migratory ‘shortstopping’ and the choice of overwintering sites. Likewise, analytical challenges characteristic of mammalian tracking data necessitate new statistical approaches. Examples include semi-variance approaches, which identify multiple movement modes and solve the sampling rate problem for tracking data, and autocorrelated kernel density estimators, which provide robust approaches for delineating animal ranges. Together these approaches reveal relationships among individual movements, landscape dynamics, and population level patterns, strengthening the bridge from conservation to basic ecology.