Convergence of marine megafauna movement patterns in coastal and open oceans

The extent of increasing anthropogenic impacts on large marine vertebrates partly depends on the animals’ movement patterns. Effective conservation requires identification of the key drivers of movement including intrinsic properties and extrinsic constraints associated with the dynamic nature of the environments the animals inhabit. However, the relative importance of intrinsic versus extrinsic factors remains elusive. We analyze a global dataset of ∼2.8 million locations from >2,600 tracked individuals across 50 marine vertebrates evolutionarily separated by millions of years and using different locomotion modes (fly, swim, walk/paddle). Strikingly, movement patterns show a remarkable convergence, being strongly conserved across species and independent of body length and mass, despite these traits ranging over 10 orders of magnitude among the species studied. This represents a fundamental difference between marine and terrestrial vertebrates not previously identified, likely linked to the reduced costs of locomotion in water. Movement patterns were primarily explained by the interaction between species-specific traits and the habitat(s) they move through, resulting in complex movement patterns when moving close to coasts compared with more predictable patterns when moving in open oceans. This distinct difference may be associated with greater complexity within coastal microhabitats, highlighting a critical role of preferred habitat in shaping marine vertebrate global movements. Efforts to develop understanding of the characteristics of vertebrate movement should consider the habitat(s) through which they move to identify how movement patterns will alter with forecasted severe ocean changes, such as reduced Arctic sea ice cover, sea level rise, and declining oxygen content.
Workshop funding was granted by the University of Western Australia (UWA) Oceans Institute, the Australian Institute of Marine Science (AIMS), and King Abdullah University of Science and Technology (KAUST). A.M.M.S. was supported by Australian Research Council Grant DE170100841 and an Indian Ocean Ocean Marine Research Centre (UWA, AIMS, Commonwealth of Scientific and Industrial Research Organisation) fellowship. J.P.R., V.M.E., and J.F.G. were supported by Agencia Estatal de Investigación (AEI, Spain) and Fondo Europeo de Desarrollo Regional (FEDER) through project Spatiotemporality in Sociobological Interactions, Models and Methods (SPASIMM) (FIS2016-80067-P AEI/FEDER, European Union), and by research funding from KAUST. J.P.R. was supported by Ministerio de Educación, Cultura y Deporte (Formación de Profesorado Universitario Grant, Spain). D.W.S. was supported by the UK Natural Environment Research Council and Save Our Seas Foundation. N.Q. was supported by Fundação para a Ciência e Tecnologia (Portugal). M.M.C.M. was supported by a Coordenação de Aperfeiçoamento de pessoal de Nível Superior fellowship (Ministry of Education).