@article{fdi:010064851, title = {{G}eneralized {P}areto for pattern-oriented random walk modelling of organisms' movements}, author = {{B}ertrand, {S}ophie and {J}oo, {R}ocio and {F}ablet, {R}onan}, editor = {}, language = {{ENG}}, abstract = {{H}ow organisms move and disperse is crucial to understand how population dynamics relates to the spatial heterogeneity of the environment. {R}andom walk ({RW}) models are typical tools to describe movement patterns. {W}hether {L}evy or alternative {RW} better describes forager movements is keenly debated. {W}e get around this issue using the {G}eneralized {P}areto {D}istribution ({GPD}). {GPD} includes as specific cases {N}ormal, exponential and power law distributions, which underlie {B}rownian, {P}oisson-like and {L}evy walks respectively. {W}hereas previous studies typically confronted a limited set of candidate models, {GPD} lets the most likely {RW} model emerge from the data. {W}e illustrate the wide applicability of the method using {GPS}-tracked seabird foraging movements and fishing vessel movements tracked by {V}essel {M}onitoring {S}ystem ({VMS}), both collected in the {P}eruvian pelagic ecosystem. {T}he two parameters from the fitted {GPD}, a scale and a shape parameter, provide a synoptic characterization of the observed movement in terms of characteristic scale and diffusive property. {T}hey reveal and quantify the variability, among species and individuals, of the spatial strategies selected by predators foraging on a common prey field. {T}he {GPD} parameters constitute relevant metrics for (1) providing a synthetic and pattern-oriented description of movement, (2) using top predators as ecosystem indicators and (3) studying the variability of spatial behaviour among species or among individuals with different personalities.}, keywords = {}, booktitle = {}, journal = {{P}los {O}ne}, volume = {10}, numero = {7}, pages = {e0132231 [16 ]}, ISSN = {1932-6203}, year = {2015}, DOI = {10.1371/journal.pone.0132231}, URL = {https://www.documentation.ird.fr/hor/fdi:010064851}, }