Previous methods of encounter rate estimation have relied on the assumption that animals travel everywhere with equal probability. By incorporating information on how animals use their home ranges unevenly, Smithsonian scientists are able to estimate encounter rates with greater accuracy than ever before.

Encounter locations are important for understanding animal behavior because animals are likely to act differently at locations where they may encounter predators, competitors, or mates (Fig. 1).

Figure 1: The distributions of territorial capuchin monkey groups on Barro Colorado, Panama. The orange polygon represents the areas where groups are predicted to encounter one another, while the black points depict independently observed encounters.

Smithsonian scientists have developed a new method for describing the spatial distribution of encounters called the ‘conditional distribution of encounters’ (CDE; Fig. 2).

Figure 2: Mongolian gazelle have overlapping ranges in the Eastern Steppe of Mongolia, which can be seen from the GPS locations in (a), where each color represents a different individual. A network diagram based on spatial overlap can be used to visualize potential social interactions in the population (b). Depending on the data quality and animal movement behavior, the estimated spatial overlap between two individuals may or may not be statistically significant. For example, (c) depicts two home-range estimates where there might be substantial spatial overlap, but the data quality is so poor that the estimates are not statistically significant. This indicates that while the animals may live closely together, the chance of individuals encountering one another may be low. (Figure taken from Winner et al. 2018)

RESOURCES

1. M. J. Noonan, R. Martinez-Garcia, G. H. Davis, M. C. Crofoot, R. Kays, B. T. Hirsch, E. Payne et al. “Estimating encounter location distributions from animal tracking data.” biorxiv (2020).
2. R. Martinez-Garcia, C. H. Fleming, R. Seppelt, W. F. Fagan, and J. M. Calabrese. “How range residency and long-range perception change encounter rates.” Journal of Theoretical Biology (2020):110267.
3. K. Winner, M. J. Noonan, C. H. Fleming, K. Olson, T. Mueller, D. Sheldon, J. M. Calabrese, “Statistical inference for home range overlap”, Methods in Ecology and Evolution, 9:7, 1679-1691 (2018)