Data from: Suite of simple metrics reveals common movement syndromes across vertebrate taxa

• Main Author: Abrahms, Briana
• Format: Dataset
• Language: English
• Published: Movebank Data Repository 2017
• Subjects: African buffalo; African elephant; animal tracking; Antidorcas marsupialis; black-backed jackal; California sea lion; Canis mesomelas; central place foraging; classification scheme; cluster analysis; Equus quagga; Galapagos albatross; Galapagos tortoise; Geochelone nigra; Gyps africanus; Loxodonta africana; migration; Mirounga angustirostris; movement ecology; nomadism; northern elephant seal; Phoebastria irrorata; Plains zebra; springbok; Syncerus caffer; territoriality; white-backed vulture; Zalophus californianus; Dougherty; Galapagos; Getz; Elephant Seal
• Online Access: https://dx.doi.org/10.5441/001/1.hm5nk220; https://www.datarepository.movebank.org/handle/10255/move.670

Abrahms B, Seidel DP, Dougherty E, Hazen EL, Bograd SJ, Wilson AM, McNutt JW, Costa DP, Blake S, Brashares JS, Getz WM (2017) Suite of simple metrics reveals common movement syndromes across vertebrate taxa. Movement Ecology 5:12. doi:10.1186/s40462-017-0104-2 : Background: Because empirical studies of animal movement are most-often site- and species-specific, we lack understanding of the level of consistency in movement patterns across diverse taxa, as well as a framework for quantitatively classifying movement patterns. We aim to address this gap by determining the extent to which statistical signatures of animal movement patterns recur across ecological systems. We assessed a suite of movement metrics derived from GPS trajectories of thirteen marine and terrestrial vertebrate species spanning three taxonomic classes, orders of magnitude in body size, and modes of movement (swimming, flying, walking). Using these metrics, we performed a principal components analysis and cluster analysis to determine if individuals organized into statistically distinct clusters. Finally, to identify and interpret commonalities within clusters, we compared them to computer-simulated idealized movement syndromes representing suites of correlated movement traits observed across taxa (migration, nomadism, territoriality, and central place foraging). Results: Two principal components explained 70% of the variance among the movement metrics we evaluated across the thirteen species, and were used for the cluster analysis. The resulting analysis revealed four statistically distinct clusters. All simulated individuals of each idealized movement syndrome organized into separate clusters, suggesting that the four clusters are explained by common movement syndrome. Conclusions: Our results offer early indication of widespread recurrent patterns in movement ecology that have consistent statistical signatures, regardless of taxon, body size, mode of movement, or environment. We further show that a simple set of metrics can be used to classify broad-scale movement patterns in disparate vertebrate taxa. Our comparative approach provides a general framework for quantifying and classifying animal movements, and facilitates new inquiries into relationships between movement syndromes and other ecological processes.