@article{fdi:010085217,
  title = {{C}orrelated {R}andom {W}alk of tuna in arrays of {F}ish {A}ggregating {D}evices : a field-based model from passive acoustic tagging},
  author = {{P}erez, {G}eraldine and {D}upaix, {A}. and {D}agorn, {L}aurent and {D}eneubourg, {J}. {L}. and {H}olland, {K}. and {B}eeharry, {S}. and {C}apello, {M}anuela},
  editor = {},
  language = {{ENG}},
  abstract = {{F}or centuries fishers have exploited the propensity for tuna to associate with floating objects, yet the reasons and mechanisms behind this behavior remain unclear. {T}he number of man-made floating objects ({FAD}s, {F}ish {A}ggregating {D}evices) undergone a dramatic increase in recent decades, with the development of industrial tuna purse seine fishing. {H}owever, current knowledge does not allow for the evaluation of the consequences of this increase on the ecology of tuna. {H}ere, we developed a model of tuna movements in an array of {FAD}s, using passive acoustic tagging data. {T}he model was built using four behavioral rules: (1) when no {FAD} is perceived, tuna exhibit a random search behavior, (2) individuals can orient directly to a {FAD} when they perceive it (within a given orientation radius), (3) the associative dynamics of tuna follow a daily rhythm and (4) {C}ontinuous {R}esidence {T}ime ({CRT}s - time spend at {FAD} by tuna) are independent from previous {C}ontinuous {A}bsent {T}ime ({CAT}s-time between two consecutive {CRT}s). {T}he model is based on only four parameters: swimming speed, path sinuosity, orientation distance and a loss term to account for natural and fishing mortality events. {T}he model was calibrated on 70 +/- 10 cm yellowfin tuna ({T}hunnus albacares), acoustically tagged in two different networks of anchored {FAD}s ({O}ahu, {H}awaii, {U}.{S}.{A}. and {M}auritius) with different {FAD} densities. {O}ur results show that the model can reproduce the time tuna spent traveling between {FAD}s (i.e., time away from the {FAD}s), as well as the total time spent by the fish in the {FAD} array (total residence time) at both study sites. {T}he parameter sets that best reproduce the experimental data correspond to a steering radius between 2 and 5 km, a sinuosity (correlated random walk parameter) between 0.9 and 0.995 and mortality rates between 1 and 3% per day. {T}his model, thus parameterized, could be used in future studies to predict tuna movements in arrays of different {FAD} densities and thus provide scientific advice for their management. {T}he same approach can be used for modeling the movements of marine and terrestrial animals detected near aggregation sites.},
  keywords = {{C}orrelated {R}andom {W}alk ; {T}ropical tuna ; {S}patial model ; {F}ish {A}ggregating ; {D}evice ; {A}coustic tagging ; {S}urvival curve ; {T}una motion ; {R}esidency ; {ETATS} {UNIS} ; {HAWAII} ; {MAURICE} ; {OAHU}},
  booktitle = {},
  journal = {{E}cological {M}odelling},
  volume = {470},
  numero = {},
  pages = {110006 [12 ]},
  ISSN = {0304-3800},
  year = {2022},
  DOI = {10.1016/j.ecolmodel.2022.110006},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085217},
}