| Summary: | Repeatable individual differences often account for large proportions of intraspecific variation in animal movements. However, meta-population models have continued to rely on single species-level and season-specific species-level means for movement prediction. Here, we test the hypothesis that repeatable individual differences can account for a similar proportion of movement distance variation as species differences. We used radio telemetry to generate repeated measures of movement from 504 hetero-specific fish. We tracked 5 large bodied fish species (Salvelinus confluentus, Oncorhynchus mykiss, Prosopium williamsoni, Thymallus arcticus, and Sander vitreus) in the upper reaches of the Peace River, British Columbia, Canada, over 8 years. We applied a hierarchical framework to partition repeatability of movement distances at the intra- and interspecific biological levels, and among short-term (within-season) and long-term (across seasons and years) temporal levels. Our results show that long-term movement distance repeatability was higher at the intraspecific level than at the interspecific level, demonstrating that animal personality can account for more variation in movement than species differences. These findings provide a novel, community level demonstration of the importance of individual variation, highlighting the predictive gains associated with a shift in the focus of spatial ecology, away from species mean and seasonal species-level mean predictive approaches, towards a spatial behavioral types-based predictive approach. Intraspecific variation exceeds interspecific variation in the movement distances of a river fish community. Using radio telemetry, we track individuals from 5 large bodied ecologically diverse river fish species over long periods. We show that individual behavioral patterns account for a greater proportion of movement variation than species- specific patterns and a similar proportion of variation as species-specific seasonal patterns. animal personality, community ecology, ...
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