Integrating immersion with GPS data improves behavioural classification for wandering albatrosses and shows scavenging behind fishing vessels mirrors natural foraging

In eg a ing imme sion wi h GPS da a imp o es beha iou al classi ica ion o wande ing alba osses and shows sca enging behind ishing essels mi o s na u al o aging A P B Ca nei o 1 ,MPDias 1,2 , S Oppel 3 , E J Pea main 1 , B L Cla k 1 , A G Wood 4 , T Cla elle 5 & R A Phillips 4 1 Bi dLi e In e na ional, Camb idge, UK 2 Cen e o Ecology, E olu ion and En i onmen al Changes, cE3c & Depa men o Animal Biology, Faculdade de Ci^ encias, Uni e sidade de Lisboa, Lisbon, Po ugal 3 Royal Socie y o he P o ec ion o Bi ds, The Da id A enbo ough Building, Camb idge, UK 4 B i ish An a c ic Su ey, Na u al En i onmen Resea ch Council, Camb idge, UK 5 Global Fishing Wa ch, Washing on, Dis ic o Columbia, USA Keywo ds ine-scale; ishe ies; hidden Ma ko models; imme sion; ada ; andom o es models; seabi ds; essel. Co espondence Ana P. B. Ca nei o, Bi dLi e In e na ional, The Da id A enbo ough Building, Pemb oke S ee , Camb idge CB2 3QZ, UK. Email: [email p o ec ed] Richa d A. Phillips, B i ish An a c ic Su ey, Na u al En i onmen Resea ch Council, High C oss, Madingley Road, Camb idge CB3 0ET, UK. Email: [email p o ec ed] Edi o : Ka l E ans Associa e Edi o : Jaime Ramos Recei ed 13 June 2021; accep ed 04 Janua y 2022 doi:10.1111/ac .12768 Abs ac Ad ances in biologging echniques and he a ailabili y o high- esolu ion fishe ies da a ha e imp o ed ou abili y o unde s and he in e ac ions be ween seabi ds and fishe ies and o e alua e mo ali y isk due o byca ch. Howe e , i emains unclea whe he mo emen pa e ns and beha iou di e be ween bi ds o aging na u ally o sca enging behind essels and whe he his could be diagnos ic o fishe ies in e ac ions. We deployed no el logge s ha eco d he GPS posi ion o bi ds a sea and scan he su oundings o de ec ada ansmissions om essels and imme sion (ac i i y) logge s on wande ing alba osses Diomedea exulans om Sou h Geo gia. We ma ched hese da a o emo ely sensed fishing essel posi ions and used a combina ion o hidden Ma ko and andom o es models o in es iga e whe he i was possible o de ec a cha ac e is ic signa u e om he seabi d ack- ing and ac i i y da a ha would indica e fine-scale essel o e lap and in e ac ions. Including imme sion da a in ou hidden Ma ko models allowed wo dis inc o ag- ing beha iou s o be iden ified, bo h indica i e o A ea Res ic ed Sea ch (ARS) bu wi h o wi hou landing beha iou (likely p ey cap u e a emp s) ha would no be de ec able wi h loca ion da a alone. Bi ds app oached essels du ing all beha- iou al s a es, and he e was no clea pa e n associa ed wi h his ype o sca eng- ing beha iou . The andom o es models had e y low sensi i i y, pa ly because o aging e en s a essels occu ed e y a ely, and did no con ain any diagnos ic mo emen o ac i i y pa e n ha was dis inc om na u al beha iou s away om essels. Thus, we we e unable o p edic accu a ely whe he o aging bou s occu ed in he icini y o a fishing essel, o na u ally, based on beha iou alone. Ou me hod p o ides a cohe en and gene alizable amewo k o segmen ips using auxilia y biologging (imme sion) da a and o efine he classifica ion o o - aging s a egies o seabi ds. These esul s ne e heless unde line he alue o using ada de ec o s ha de ec essel p oximi y o emo ely sensed essel loca ions o a be e unde s anding o seabi d–fishe y in e ac ions. In oduc ion Inciden al mo ali y (byca ch) in fishe ies is one o he majo h ea s o seabi d popula ions wo ldwide and pa icula ly o alba osses and pe els (Phillips e al., 2016; Dias e al., 2019). Ad ances in biologging echniques and he a ailabili y o high- esolu ion fishe ies da a h ough essel moni o ing sys ems (VMS) and he au oma ic iden ifica ion sys em (AIS) ha e ad anced ou unde s anding o in e ac- ions be ween seabi ds and essels and associa ed mo ali y isks (Vo ie e al., 2010; G anadei o e al., 2011; To es e al., 2013). Howe e , he e is s ill an u gen need o be e Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. 1 This is an open access a icle unde he e ms o he C ea i e Commons A ibu ion-NonComme cial-NoDe i s License, which pe mi s use and dis ibu ion in any medium, p o ided he o iginal wo k is p ope ly ci ed, he use is non-comme cial and no modi ica ions o adap a ions a e made. Animal Conse a ion. P in ISSN 1367-9430 quan i a i e p edic ions o he isk o seabi ds om fishe y in e ac ions a fine scales, and no s udies o da e ha e de el- oped models based on biologging da a alone ha p edic eeding on disca ds, bai ed hooks o o he an h opogenic esou ces om essels. Such models could ep esen a majo s ep-change in his esea ch field, conside ing he apid inc ease in he a ailabili y o biologging da a o seabi ds (Be na d e al., 2021). Ma ine op p eda o s, including seabi ds ha usually eed on na u ally agg ega ed p ey, will o en display A ea Res ic ed Sea ch (ARS) beha iou when hey encoun e p ofi able pa ches. This beha iou al model is cha ac e ised by inc eased sinuosi y and slowe speed (Fauchald & T e aa, 2003; Weime ski ch e al., 2007; Pi o a e al., 2018) and inc eased esidence ime in p oduc i e a eas (Weime ski ch, Gaul , & Che el, 2005; Ga he e al., 2016). Seabi ds a e also known o change hei fine-scale mo emen s when hey in e ac wi h fishing essels, which also esul s in ARS beha- iou (To es e al., 2011; Bodey e al., 2014; Co beau e al., 2019; G  emille e al., 2019). To disc imina e be ween na u- al o aging and a ge ing o essels equi es independen , fine-scale da a on seabi d mo emen s and he loca ions o indi idual essels. The la e , howe e , a e gene ally a ail- able only wi hin Exclusi e Economic Zones (EEZs). Hence, mos analyses o seabi d–fishe ies o e lap o da e ha e used agg ega ed da a on fishing e o , such as o al longline hooks deployed pe 5°g id cell, by mon h, which is o en he highes esolu ion a ailable om he Regional Fishe ies Managemen O ganiza ions (RFMOs) which manage fishing in he High Seas (Tuck e al., 2011; Small, Waugh, & Phil- lips, 2013; Clay e al., 2019; Ca nei o e al., 2020). When da a a e a ailable on bi d and essel mo emen s, a common app oach o y o dis inguish na u al o aging om essel in e ac ions is o iden i y pe iods o ac i e o aging wi hin acks (based on fi s -passage ime o speed- o uosi y h esholds), o e lap hose in space and ime wi h essels, and di e en ia e na u al o aging om essel in e ac ion based on he p esence o absence o nea by fishing essels (Vo ie e al., 2010; To es e al., 2011; Co beau e al., 2019; Co beau, Colle , Pajo , e al., 2021b). A second app oach ocuses on iden i ying beha iou al changes in he icini y o afishing essel by examining he p obabili y o swi ching om one beha iou al s a e o ano he (e.g. edi ec fligh a- jec o ies when in he icini y o a fishing essel; Bodey e al., 2014; Colle , Pa ick, & Weime ski ch, 2015; Cianche i-Benede i e al., 2018; Le Bo , Lesc o€ el, & G  emille , 2018; Cla k e al., 2020b). Al hough hese s udies demons a ed ha essels could a ec he mo emen s and beha iou o seabi ds, i emains unclea whe he he e is a dis inguishable mo emen pa e n, su ficien o p edic i e disc imina ion, which is diagnos ic o whe he he bi d is ei he o aging na u ally o sca enging behind a essel. An app oach ha o e s conside able p omise is he use o hid- den Ma ko models (HMMs), which a e used inc easingly o cha ac e ising animal beha iou (McClin ock & Michelo , 2018). In seabi d ecology, HMMs ha e been used almos exclusi ely on loca ion da a o dis inguish be ween ARS and ansi mo emen s, howe e , hey can also inco po a e auxilia y biologging and en i onmen al da a (Leos-Ba ajas e al., 2017; Pa e son e al., 2019; Clay e al., 2020; Con- ne s e al., 2021). In pa icula , addi ional senso s could help dis inguish sea ching beha iou om landings o cap u e p ey, he la e po en ially signalling a byca ch isk i hey ake place nea essels and in ol e a ge ing o longline bai s du ing se ing o hauling. One po en ial applica ion o hese models is o iden i y cha ac e is ic essel- ollowing beha iou om he complex pa e ns o ac i i y o seabi ds a sea ha could imp o e p edic ions o seabi d–fishe y in e - ac ions in he absence o independen in o ma ion on essel loca ions. Wande ing alba osses Diomedea exulans a Sou h Geo gia ha e declined ca as ophically since he 1970s (Ponce e al., 2017), leading o he de elopmen o a conse a ion Ac ion Plan by he Go e nmen o Sou h Geo gia and he Sou h Sand- wich Islands (GSGSSI), and hei lis ing as a P io i y Popula- ion by he Ag eemen on he Conse a ion o Alba osses and Pe els (ACAP). Byca ch in fishe ies is conside ed o be hei main h ea (Pa do e al., 2017; Ponce e al., 2017). Analyses o he spa ial o e lap a la ge scales be ween hei a -sea dis i- bu ion and pelagic and deme sal longline fishe ies indica ed ha he popula ion was a highes isk in he B azil-Falklands confluence zone, pa icula ly om he Japanese and Taiwanese dis an -wa e una flee s (Jim enez e al., 2016; Clay e al., 2019). Howe e , o e lap me ics a e scale dependen and he assump ion ha co-occu ence o seabi ds and fishe ies in a egion leads o in e ac ion and mo ali y isk has been egula ly highligh ed as a po en ial pi all (To es e al., 2013; Weime - ski ch e al., 2020; Co beau, Colle , O ge e , e al., 2021a) because he e is a lack o fine-scale da a simul aneously a ail- able o bo h bi ds and essels. As such, a ho ough unde - s anding o seabi d–fishe y in e ac ions a much fine scales is highly ele an o conse a ion and o be e a ge manage- men ac ions. He e we used ecen ly de eloped logge s ha eco d he GPS posi ion o bi ds a sea and egula ly scan he su ound- ings o de ec he p esence o ada ansmissions om essels (Weime ski ch e al., 2020). These da a may allow ecognis- able beha iou s o be delinea ed om he mo emen s o he bi d ha can be ma ched o he p oximi y o fishing essels a ha ime. Combining hese da a wi h imme sion (ac i i y) da a om geoloca o -imme sion de ices (Phalan e al., 2007; Mackley e al., 2010; G anadei o e al., 2011; Dean e al., 2013), we used hidden Ma ko models o cha ac e ise o ag- ing beha iou (including landings) in mo e de ail han a loca ion-only model (Cla k, Handby, e al., 2020a). We ma ched hese da a wi h he posi ion o indi idual essels ob ained om he au oma ic iden ifica ion sys em (AIS) and used andom o es models o in es iga e whe he wande ing alba osses b eeding a Sou h Geo gia exhibi clea ly iden ifi- able pa e ns o mo emen and beha iou ha can be associ- a ed wi h ei he na u al o aging o in e ac ions wi h essels. I diagnos ic, such pa e ns could hen be applied o exis ing, ex ensi e bi d- acking da ase s o quan i y he equency, du a ion and p opensi y o indi idual bi ds, sexes and li e- his o y s ages o in e ac wi h fishing essels, and he e o e mo e accu a ely assess hei isk o byca ch. 2Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. Sca enging behind essels mi o s na u al o aging in alba osses A. P. B. Ca nei o e al. Ma e ials and me hods Da a collec ion We used Tesa ape o a ach 85 GPS- ada logge s (XSpu - nik; Sex an Technology) o he man le ea he s o b eeding adul wande ing alba osses a Bi d Island, Sou h Geo gia (54°00’S, 38°03’W) and le hem a ached o one o mo e o aging ips du ing incuba ion (Janua y–Feb ua y), b ood- gua d (Ma ch–Ap il) and pos -gua d chick- ea ing (July– Augus ) pe iods in 2020. Logge s eco ded a GPS posi ion e e y 10 min and es ed o ada ansmissions (wi hin 5 km; Weime ski ch e al., 2018) o 5 min. The same bi ds we e also equipped wi h a geoloca o -imme sion logge (In i- geo C-330; Mig a e Technology), which eco ded sal -wa e imme sion e en s ha las ed ≥3 s, p o iding in o ma ion on ac i i y pa e ns ( iming and du a ion o fligh s and wa e landings) h oughou he o aging ip. Imme sion logge s we e a ached o a plas ic ing on he a sus. Bi ds we e cap- u ed on he nes du ing changeo e wi h he pa ne (incu- ba ion o b ood-gua d) o a e eeding he chick (pos - gua d). A achmen o he de ices always ook less han 10 min, and bi ds ypically le he colony sho ly a e being eleased o o age a sea. To acili a e logge e ie al du ing pos -gua d chick- ea ing, a ence was buil a ound he nes on he 3 d day a e he adul depa ed, and nes s we e is- i ed wice a day he ea e un il he adul e u ned and de ices we e e ie ed (Xa ie e al., 2003). I he pa ne a i ed be o e he ins umen ed bi d e u ned, he ence was opened empo a ily o allow i o deli e he meal o he chick. To al ins umen load was c. 55 g (0.6 and 0.7 o male and emale mean adul body mass, espec i ely), which was well below he h eshold o 3% a which de ice e ec s end o become appa en (Phillips, Xa ie , & C oxall, 2003). T acked bi ds comp ised an e en sp ead o sexes and ages (9–44 yea s). B eeding success o bi ds fi ed wi h de ices was ex emely high (97%) because we a ge ed expe ienced b eede s (which ha e highe success han new ec ui s; F oy e al. 2013) and did no deploy un il a leas he end o he second incuba ion s in , a oiding he ini ial pe iod o highe ailu e pos -laying. As such, he e was no e idence ha he deploymen s had dele e ious e ec s. Da a p ocessing GPS da a we e fi s fil e ed o emo e loca ions a he nes and o aging ips we e defined as he loca ion be ween he las GPS fix in he colony p io o depa u e and he fi s GPS fix a e a e u n. Un ealis ic loca ions in ol ing a el speeds abo e 120 km h 1 we e emo ed using he SDLfil e R package (Shimada e al., 2012). Posi ions we e linea ly in e pola ed o a 10-min sampling equency o egula ise he da a and fill in occasional gaps. The imme sion da a we e summa ised as he numbe o landings ( he o al numbe o d y–we ansi ions) and he p opo ion o ime spen on he wa e su ace (we ) in he 10-min in e al p eceding each GPS loca ion. The numbe o landings and he p opo ion o ime spen on he wa e su ace we e hen ma ched empo ally wi h GPS da a. Alba osses ha e low cos s o fligh and landings, bu ake-o s om he wa e su ace (we –d y ansi ions) in ol e high ene ge ic cos ; bi ds will only land o eed o o es , and he e o e, he landing a e p o ides a good indica ion o o aging e o (Weime ski ch e al., 2000; Phalan e al., 2007; G anadei o e al., 2011). Loca ions o indi idual fishing essels wi hin he s udy a ea we e ob ained om Global Fishing Wa ch (GFW), which com- bine public essel egis ies and machine-lea ning models o (i) iden i y fishing essels in he AIS da a and (ii) de ec when hey a e ac i ely fishing, wi h a fishing de ec ion accu acy o >90% (K oodsma e al., 2018). Fo each essel (AIS) loca ion, he ollowing in o ma ion was a ailable: unique essel iden i- fie , da e, ime, la i ude, longi ude and fishing sco e (i.e. he likelihood o fishing om he GFW fishing de ec ion model). All essel loca ions wi hin 5 km and 5 min o each in e po- la ed loca ion o he acked alba osses we e ex ac ed, bu only essels classified as ‘ac i ely fishing’we e included in he analysis. The empo al esolu ion o AIS da a associa ed wi h bi d loca ions wi hin he s udy a ea was 7.8 101.9 min (mean SD). P elimina y analyses indica ed ha including he ype o fishing ac i i y ( awling, long-lining e c.) had no e ec on he models, and hence he esul s p esen ed he e a e om all ypes o fishing ac i i y pooled. Beha iou al s a e classi ica ion HMMs a e ypically used o iden i y beha iou al s a es om animal mo emen based on s ep leng h and u ning angle be ween subsequen loca ions, wi h sho -medium s ep leng hs and high u ning angles conside ed o ep esen o - aging (McClin ock & Michelo , 2018; Conne s e al., 2021). The addi ion o o he da a p o ided by he concu en deploymen o addi ional senso s may p o ide inc eased powe o esol e beha iou al s a es (Dean e al., 2013; McClin ock & Michelo , 2018; Cla k, Handby, e al., 2020). We he e o e combined s ep leng hs and u ning angles wi h he numbe o landings and p opo ion o ime spen on he wa e su ace (ex ac ed om he imme sion da a) o cha ac- e ise he p incipal ypes o a -sea beha iou o wande ing alba osses using he momen uHMM package in R (McClin- ock & Michelo , 2018). We modelled he beha iou o all indi iduals combined, as he inclusion o indi idual e ec s in he hidden s a e p ocess makes li le di e ence in e ms o in e ence (McClin ock, 2021). HMMs equi e he use o define s a e-dependen dis ibu- ion classes and o p o ide s a ing alues o acili a e pa am- e e es ima ion. The la e we e selec ed using k-means clus e ing (wi h k=numbe o s a es [2–5]; Dean e al., 2013; Cla k, Handby, e al., 2020a), wi h he excep ion o he p opo ion o ime on he wa e su ace, which was con- e ed in o a ca ego ical a iable ep esen ing low, in e medi- a e and high p obabili y using s a ing pa ame e s based on ob ious b eaks in he equency his og am (Michelo & Lan- g ock, 2019). A gamma dis ibu ion was chosen o s ep leng hs, w apped Cauchy o u ning angles, Poisson o numbe o landings and ca ego ical o he p opo ion o ime on he wa e su ace. We compa ed he fi be ween Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. 3 A. P. B. Ca nei o e al. Sca enging behind essels mi o s na u al o aging in alba osses simple ( ew s a es) and complex (many s a es) models based on he Akaike In o ma ion C i e ion (AIC) and used he elbow c i e ion (i.e. he poin a which adding addi ional s a es no longe esul s in subs an ial educ ions in AIC) o selec he bes ade-o be ween model accu acy and com- plexi y (Dean e al., 2013; Cla k, Handby, e al., 2020a). The Vi e bi algo i hm was hen used o es ima e he mos likely sequence o unde lying s a es om he selec ed model o each ack (McClin ock & Michelo , 2018). An expe - d i en app oach and anima ions o he ips we e used o de e mine i he models we e assigning app op ia e sequences o beha iou s, ha is o a andom selec ion o ips, we compa ed beha iou s a es assigned by he models and hose om isualisa ion (Clay e al., 2020). Beha iou al bou iden i ica ion We used he esul s o he HMM o iden i y beha iou al bou s as sequences o posi ions in he same beha iou al s a e (see Resul s). In o de o smoo h he da a, bou s ha consis ed o a single loca ion we e eassigned o he same HMM s a e as he p e ious and subsequen loca ion i he HMM s a es o he p e- ious and subsequen loca ions we e iden ical. Wande ing alba- osses a end fishing essels o se e al hou s (Weime ski ch e al., 2020); he e o e, we excluded beha iou al bou s o sho du a ion (<5 loca ions) om he analyses ( his only accoun ed o 8% o he da a), as hese we e likely o indica e ansi h ough he same a ea as essels, and no an in e ac ion. We hen cha ac e ised each beha iou al bou using se e al me ics, including he ype o bou (based on HMM classifica ion), he mean, maximum and minimum o e all speeds, du a ion, cumu- la i e and maximum dis ances a elled and s aigh ness o mo emen (a measu e o maximum dis ance di ided by cumu- la i e dis ance). Based on he majo i y o ime spen , he bou was assigned o dayligh (including wiligh ) o da kness, acco ding o he iming o ci il wiligh (when he sun is 6° below he ho izon) ha we e p e iously assigned o each GPS posi ion. We calcula ed he o al numbe o landings and he p opo ion o loca ions wi h landing a emp s, and he p opo - ion o ime spen on he wa e , o he du a ion o he bou . We also ex ac ed om GFW whe he any o he essels wi hin 5 km we e ac i ely fishing a any ime du ing he bou . Disc imina ing be ween na u al o aging s. o aging behind essels using machine lea ning We used a machine-lea ning algo i hm o es whe he we could accu a ely p edic i a bi d was o aging in he icini y o a essel using he esul s o he HMM analysis and he beha iou al bou classifica ion. Ou esponse a iable was bina y and indica ed whe he each bou coincided wi h de ec- ion o ada om a essel and i ha essel was ac i ely fish- ing wi hin he bou (de ec ed by AIS). Fo his, we calcula ed he p opo ion o GPS loca ions in each bou ha coincided wi h ada de ec ion and he p opo ion o loca ions in each bou ha we e classified as ac i ely fishing. We hen used a - ious h esholds o de e mine whe he a beha iou al bou was associa ed wi h an ac i ely fishing essel o o e come he unce ain y inhe en in bo h he ada de ec ion and AIS clas- sifica ions. Nine y-one pe cen o bou s wi h ada de ec ions we e also associa ed wi h AIS da a, whe eas only 49% o bou s wi h ac i ely fishing essels in e ed om AIS we e associa ed wi h ada de ec ion. This is simila o esul s in a p e ious s udy using he same echnology, which indica ed ha 46.6% o AIS loca ions wi hin 5 km o bi ds esul ed in ada de ec ion (Weime ski ch e al., 2020). We conside ed a bou o be indica i e o a ending a fishing essel a di e en h esholds; hese we e when 0%, 5%, 10%, 20% o 50% o loca ions in ha bou we e associa ed wi h ada de ec ion, and when 0%, 5%, 10%, 20% o 50% o loca ions in ha bou we e associa ed wi h a nea by essel classified as ac i ely fishing. We epo he p e alence (p opo ion o bou s classified as ‘a ending a fishing essel’) o each combina ion o h esholds in TABLE 2. We hen fi ed each andom o es model based on a condi ional in e ence amewo k accoun ing o co ela ed p edic o s (Ho ho n, Ho nik, & Zeileis, 2006a) o es whe he he beha iou al s a es de i ed om he HMM had p edic i e powe o dis inguish be ween bou s wi h and wi hou nea by essels ha we e ac i ely fishing. We fi ed his model in a bina y classifica ion amewo k wi h he R package pa y (Ho ho n, Ho nik, & Zeileis, 2006b). We speci- fied an in e nal c oss- alida ion s uc u e o ensu e ha da a om he same indi iduals we e used ei he o fi ing o e al- ua ing ees in he o es , which emula es a andom e ec in linea models and accoun s o he se ial au oco ela ion o bou s pe o med by he same indi idual (Bus on & Eli h, 2011). We used a andom subse o 65% o da a wi hou eplacemen o build single ees and alida ed ou model by applying he ou pu o he emaining da a o es ima e he accu acy o p edic ions. We p esen he p edic i e accu acy o each andom o es model as he p opo ion o c oss- alida ed bou s ha we e co ec ly p edic ed and he sensi i i y o he andom o es models as he p opo ion o bou s wi h ac i ely fishing nea by essels ha we e co ec ly p edic ed. The impo ance o a iables was calcula ed using a pe - mu a ion p ocedu e ha assesses he loss in model p edic i e accu acy (S obl e al., 2008; Jani za, S obl, & Boules eix, 2013; Hap elmeie e al., 2014). Fo easie in e p e a ion, he a iable impo ance was s anda dised, wi h he mos impo - an a iable assigned a ela i e impo ance o 100% (Oppel, Powell, & Dickson, 2009; Oppel e al., 2017). I beha iou al me ics de i ed om acking da a ha e su ficien p edic i e in o ma ion o dis inguish o aging behind essels in sea- bi ds, we would expec he sensi i i y o he model o be high (>75% co ec classifica ions). All analyses we e pe - o med in R 3.6.3 (R Co e Team, 2019). Unless indica ed o he wise, all da a a e p esen ed as means SD. Resul s Fo aging ip cha ac e is ics We acked 30, 28 and 27 indi idual wande ing alba osses du ing incuba ion, b ood-gua d and pos -gua d chick- ea ing, espec i ely (Fig. 1). GPS and ada da a we e incomple e 4Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. Sca enging behind essels mi o s na u al o aging in alba osses A. P. B. Ca nei o e al. o a ew ips because o ba e y deple ion (one in incuba- ion, h ee in b ood-gua d and six in pos -gua d chick- ea ing). Some indi iduals we e acked o mul iple ips in b ood-gua d and pos -gua d chick- ea ing. O e all, we ob ained a o al o 29, 29 and 23 comple e ips ha las ed o 12.6 6.4 days du ing incuba ion ( ange 3–39 days), 3.2 1.8 du ing b ood-gua d ( ange 2–9 days) and 8.0 6.2 days du ing pos -gua d chick- ea ing ( ange 1– 24 days), espec i ely. O he 85 indi iduals acked, de ices on 45 bi ds (53%) de ec ed essel ada du ing a leas one ip, and 26 (58%) o hese bi ds encoun e ed ac i ely fishing essels (Fig. 1). Vessel ada was no de ec ed by de ices on wel e bi ds ha encoun e ed ac i ely fishing essels acco d- ing o he AIS da a. Beha iou al models The inspec ion o AIC o candida e HMMs wi h 2–5 s a es sugges ed ha a ou -s a e HMM adequa ely desc ibed he dominan beha iou al pa e ns o wande ing alba osses a sea (Addi ional File 1). The ou beha iou al s a es consis ed o a a elling s a e cha ac e ised by di ec ed fligh wi h high speeds and low a iance in u ning angles, ew landings and no imme sion; a es ing s a e wi h e y slow a el speeds, in e media e a iance in u ning angles, no landings and always we ; and wo ARS s a es cha ac e ised by slow speeds and high a iance in u ning angles, ei he wi hou imme sion (ARS wi hou landings) o a leas one landing (ARS wi h landings) (Fig. 2; TABLE 1). We classified a o al o 4349 bou s o consis en beha iou om he 85 acked indi iduals. The du a ion o bou s o ARS wi h land- ings was c. h ee imes sho e han hose o ARS wi hou landings. On a e age, he p opo ion o o aging ips ha was spen a elling, es ing, in ARS wi hou landings and ARS wi h landings was 40%, 23%, 23% and 14%, espec- i ely (TABLE 1). Na u al o aging s. o aging behind essels Vessel encoun e s ( ada de ec ions and AIS loca ions om ac i ely fishing essels) occu ed du ing all beha iou al s a es (Fig. 3), wi h a highe numbe o ARS wi h landings bou s associa ed wi h essel encoun e s han expec ed (TABLE 1; 2 =98.404; p<0.001). Howe e , despi e he unequal equency o essel encoun e s among di e en beha iou al bou s, we ound no p edic able pa e n associa ed wi h o aging behind essels because he dis ibu ions o a- el speeds, u ning angles, landings and imme sion alues o e lapped when compa ing bou s o beha iou al s a es wi h and wi hou essel encoun e s (Fig. 3). The use o me ics ex ac ed om combined GPS and imme sion da a in he HMMs did no e eal a s a e indica i e o o aging behind essels (as indica ed by ada and AIS loca ions om ac i ely fishing essels). Ou esul s showed a highe pe - cen age o bou s associa ed wi h essel encoun e s o he ARS wi h landings s a e (30.8%) when compa ed o he ARS wi hou landings s a e (9.6%); none heless, he majo i y o bou s in he ARS wi h landings s a e we e no associa ed wi h essels, sugges ing ha his s a e mos ly eflec ed na u- al o aging beha iou . Figu e 1 Fo aging ips o wande ing alba osses acked om Bi d Island, Sou h Geo gia, du ing incuba ion, b ood-gua d and pos - gua d chick- ea ing in 2020, o e laid on ba hyme y. Red do s ep- esen loca ions whe e alba osses encoun e ed essels, ei he de ec ed by bi d-bo ne ada o om sa elli e AIS da a indica ing ac i ely ishing essels. Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. 5 A. P. B. Ca nei o e al. Sca enging behind essels mi o s na u al o aging in alba osses The andom o es models had collec i ely e y low sensi- i i y and we e unable o iden i y bou s associa ed wi h nea by, ac i ely fishing essels ega dless o he h esholds used o he p opo ion o ada de ec ions and he p opo ion o loca ions o which a nea by essel was classified as ac i ely fishing (TABLE 2). Howe e , e en unde he lowes h esholds, an ac i ely fishing essel was nea by only o abou 7.4% o he 4349 bou s included in he analysis (TABLE 2); his low p e a- lence esul ed in an o e all high p edic i e accu acy because a model p edic ing he e was no fishing essel nea by would co - ec ly classi y >95% o bou s. Thus, we we e unable o accu- a ely p edic whe he o aging bou s occu in he icini y o a fishing essel, o na u ally, based on mo emen me ics. Discussion This s udy in es iga ed whe he i is possible o de ec a cha ac e is ic signa u e om mo emen and ac i i y da a ha would indica e he in e ac ion o wande ing alba osses wi h fishing essels, a he han na u al o aging. Al hough in e- g a ing imme sion and GPS da a allowed us o iden i y wo di e en ARS- ype beha iou s, nei he could unambiguously indica e essel ollowing and associa ed sca enging in his species. Ou esul s sugges ha wande ing alba osses app oach ac i ely fishing essels in equen ly, and hei mo emen and ac i i y me ics du ing hose encoun e s a e no undamen ally di e en om hei b oad ange o na u al o aging beha iou s du ing di e en s ages o he b eeding season (Phalan e al., 2007; F oy e al., 2015; Jim enez e al., 2016). We he e o e conclude ha i is cu en ly no possible o in e essel a endance and hus byca ch isk om seabi d mo emen and ac i i y da a alone using he me hods p e- sen ed he e. Rega dless, bi d-bo ne ada logge s clea ly imp o e ou abili y o in e in e ac ions (Weime ski ch e al., 2018, 2020; Co beau e al., 2019; G  emille e al., 2019; his s udy). Figu e 2 Example o one o aging ip o a wande ing alba oss acked om Bi d Island, Sou h Geo gia, du ing incuba ion in 2020, wi h eg- ula ised GPS loca ions colou ed by beha iou s de i ed om he 4-s a e hidden Ma ko model (HMM). Table 1 Pa ame e es ima es o he s a e-dependen p obabili y dis ibu ions om he ou -s a e hidden Ma ko model (HMM) o acked wande ing alba osses om Sou h Geo gia based on GPS and imme sion (ac i i y) da a. The able also includes he p opo ion o ime spen in each s a e (% loca ions) and mean SD o bou du a ions and he p opo ion o bou s wi h essel encoun e s ( ada de ec ions and AIS loca ions om ac i ely ishing essels). Beha iou al s a e Bou du a ion (h s) % Bou s wi h essel encoun e Speed (km/h) Angle conc. N landings P obabili y o we % Loca ions[0–0.2] [0.2–0.8] [0.8–1] T a el 3.31 3.05 3.87 44.68 15.5 0.92 0.02 1 0 0 40 Res 3.41 3.01 12.93 1.59 1.08 0.8 0 0 0 1 23 ARS w/o landings 4.48 5.91 9.60 5.63 6.10 0.77 0 1 0 0 23 ARS wi h landings 1.55 1.22 30.80 13.80 16.19 0.72 2.62 0.12 0.36 0.52 14 T a el speeds a e de i ed om s ep leng hs and do no ep esen ligh (g ound) speeds o bi ds in he espec i e beha iou al s a es. Tu n- ing angle is he angle concen a ion pa ame e (highe alues indica e less a iance and less con olu ed acks). The numbe o landings ep- esen s he o al numbe o d y–we ansi ions. The p obabili y o we indica es he p obabili ies om low (0) o high (1) ha ime was spen on he sea su ace. 6Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. Sca enging behind essels mi o s na u al o aging in alba osses A. P. B. Ca nei o e al. Al hough seabi d–fishe ies in e ac ions ha e been he ocus o many s udies wo ldwide (Vo ie e al., 2010; G ana- dei o e al., 2011; Pa ick e al., 2015; Weime ski ch e al., 2020), as a as we a e awa e, p e ious s udies ha e no iden ified a diagnos ic pa e n o mo emen associa ed wi h sca enging behind essels ha would allow o p edic ions. Indeed, he ew s udies explo ing his opic ha e ound con- as ing esul s. G anadei o e al. (2011) ound no di e ence in mo emen o a -sea ac i i y pa e ns o black-b owed alba- osses Thalassa che melanoph is when o aging na u ally o in close p oximi y o awl essels a ound he Falkland Islands. In con as , To es e al. (2011) ound ha whi e- capped alba osses T. s eadi om he Auckland Islands mo ed in s aigh e pa hs and a slowe speeds when ollow- ing a squid awle han when o aging na u ally. Simila ly, wande ing alba osses om he C oze Islands mo ed wi h g ea e sinuosi y in he p esence o longline fishing essels (Weime ski ch e al., 2018; Co beau e al., 2019). Howe e , e en hough mean alues o mo emen me ics may di e significan ly be ween bi ds o aging na u ally o behind es- sels, p edic ing essel in e ac ions is p oblema ic when hese occu e y in equen ly and bi ds display mo emen pa e ns ha also occu du ing na u al o aging. Wande ing alba osses a e known o adop di e se s a e- gies, including in-fligh sea ching o p ey, and si -and-wai on he sea su ace (Weime ski ch, Wilson, & Lys, 1997; Pha- lan e al., 2007; Weime ski ch e al., 2007). When o aging in fligh , alba osses o en adop ARS (To es e al., 2011). Howe e , p e ious s udies showed ha ARS beha iou in wande ing alba osses a e p ey cap u e did no las long and only occu ed a e inges ion o a la ge i em (Weime - ski ch e al., 2007). Indeed, o e a la ge scale, he mos e ec i e sea ch s a egy o wande ing alba osses is appa - en ly o ollow a nea ly s aigh pa h, using ARS only when Figu e 3 S a e-dependen densi y his og ams o a) obse ed speed, b) angle concen a ion, c) he numbe o landings, and d) p opo ion o ime spen on he wa e su ace o wande ing alba osses acked om Sou h Geo gia o each beha iou bou . Di e en colou s indica e he p opo ion o loca ions wi hin each s a e ha we e associa ed (g een) o no (pink) wi h essels (ei he de ec ed by bi d-bo ne ada o om sa elli e AIS da a indica ing ac i ely ishing essels). Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. 7 A. P. B. Ca nei o e al. Sca enging behind essels mi o s na u al o aging in alba osses hey encoun e a pa icula ly a ou able en i onmen (Weime ski ch e al., 2007; Co beau e al., 2019). The inclu- sion o imme sion da a in ou HMM allowed wo ARS s a es o be iden ified, bo h in ol ing slow speeds and high u ning angles, bu wi h one s a e including landings indica- i e o p ey cap u e a emp s; his would no o he wise be de ec able wi h loca ion da a only. The e o e, ou models ep esen an imp o emen on p e ious app oaches using aux- ilia y biologging da a o alida ion pu poses only. Landings (de e mined om imme sion da a) p o ide a eliable indica- o o a emp s a p ey cap u e, as alba osses should o he - wise a oid incu ing he high ene ge ic cos o he subsequen ake-o (Weime ski ch e al., 2000). Res ing on he wa e – he only o he eason o land on he wa e su - ace –appea s o be confined o da kness when low ligh le els limi he oppo uni ies o in-fligh sea ching o p ey (Phalan e al., 2007; Mackley e al., 2010). This will be he case whe he sca enging behind a fishing essel o a ge ing na u al p ey. Tha he speed o he s a e ARS wi h landings was conside ably highe on a e age, and mo e a iable, han he speed du ing ARS wi hou landings, may be explained by a ew apid mo emen s o ac i ely o aging bi ds, and also by he esolu ion o he GPS loca ions (10 min, possibly ailing o cap u e he o al dis ance a elled [and hus speed] du ing ci cula mo emen s in he s a e ARS wi hou land- ings). Ou inabili y o de e mine om mo emen s o ac i i y whe he pa icula o aging bou s occu ed only in he icin- i y o an ac i ely fishing essel sugges s ha wande ing alba osses may pe cei e and eac o fishing essels and a ou able o aging pa ches essen ially in he same way, using isual o ol ac o y cues (Ne i , 2000; Weime ski ch e al., 2007; Colle , Pa ick, & Weime ski ch, 2015). Fo ag- ing behind a essel may be simply an ex ension o na u al o aging o a gene alis species such as he wande ing alba- oss. Indeed, pa allels ha e been d awn be ween sca enging beha iou and in e ac ions wi h o he ypes o p edic able an h opogenic ood sou ces (Colle , Pa ick, & Weime ski ch, 2017). Species ha ha e mo e specialised o aging s a egies may espond di e en ly o essels. Fo ins ance, Colle , Pa ick, & Weime ski ch, (2017) showed ha black-b owed alba osses we e mo e s ongly a ac ed o fishing essels and showed a highe le el o ac i e in e ac ion han wande - ing alba osses which o e lapped wi h he same flee . Mo e- o e , many s udies in es iga ing seabi d–fishe ies in e ac ions show di e ences in he deg ee o in e ac ion wi h essels acco ding o species (Colle , Pa ick, & Weime ski ch, 2017), popula ion (G anadei o e al., 2011), sex (Jim enez e al., 2016) and indi idual (Vo ie e al., 2010; G anadei o, B ickle, & Ca y, 2014; Pa ick e al., 2015). The la ge a i- abili y among indi iduals may obscu e pa e ns ha may indica e o aging in associa ion wi h a essel based on mo emen and ac i i y da a alone. Wande ing alba osses om Sou h Geo gia ange widely in he sou hwes A lan ic du ing he b eeding season, mos ly in pelagic wa e s excep du ing b ood-gua d when ips end o be es ic ed o he Sou h Geo gia shel and shel -slope (Handley e al., 2020). Thei ex ensi e o aging ange is such ha bi ds encoun e many fishing flee s wi h a a ie y o ope a ional and gea cha ac e is ics (Jim enez e al., 2014, 2020; Phillips e al., 2016). High o e laps be ween o aging wande ing alba oss and pelagic longline fishe ies a e epo ed in he B azil-Falklands confluence (Bugoni e al., 2008; Jim enez e al., 2014, 2016), pa icula ly wi h he Tai- wanese and Japanese flee s (Jim enez e al., 2016; Clay e al., 2019). They also o e lap wi h deme sal longline fishe ies, bu his is gene ally es ic ed o a con inen al shel o shel - b eak habi a s (Clay e al., 2019). The di e si y o fishe ies, flee s and ope a ional cha ac e is ics, including ime o se - ing in ela ion o day/nigh ime and he use o mi iga ion measu es, combined wi h en i onmen al condi ions, a e known o influence he likelihood o in e ac ions (Jim enez e al., 2014). The e o e, a ia ion in a endance, mo emen and landing pa e ns behind di e en ypes o fishing essel, and when o aging on di e se na u al p ey, may gene a e beha iou al signa u es which a e specific o each o hese ci cums ances. The di e si y o fishing ope a ions ha may be a ended by alba osses is likely o u he educe he p e- dic i e powe o any gene ic model ying o dis inguish be ween essel ollowing and o aging on na u al p ey. Conclusions An inabili y o p edic essel a endance based on mo emen and ac i i y da a alone unde lines he alue o using mul iple sou ces o in o ma ion, including bi d-bo ne ada and fine- scale essel mo emen s o a be e unde s anding o seabi d–fishe y in e ac ions. Fu he mo e, ou me hod p o- ides a cohe en and gene alizable amewo k o segmen ips using auxilia y biologging (imme sion) da a, which Table 2 P e alence (P) and p edic i e accu acy (A) o andom o es models aiming o dis inguish be ween beha iou al bou s o acked wande ing alba osses om Sou h Geo gia wi h and wi hou ac i ely ishing nea by essels as iden i ied om ada ansmissions and essel AIS loca ions om Global Fishing Wa ch (GFW) Rada h eshold AIS h eshold 0 0.05 0.1 0.2 0.5 0–P: 0.074 P: 0.068 P: 0.057 P: 0.036 A: 0.926 A: 0.932 A: 0.943 A: 0.964 0.05 P: 0.064 P: 0.033 P: 0.031 P: 0.027 P: 0.019 A: 0.936 A: 0.967 A: 0.969 A: 0.973 A: 0.981 0.1 P: 0.056 P: 0.028 P: 0.027 P: 0.024 P: 0.018 A: 0.944 A: 0.972 A: 0.973 A: 0.976 A: 0.982 0.2 P: 0.043 P: 0.023 P: 0.023 P: 0.021 P: 0.016 A: 0.957 A: 0.977 A: 0.976 A: 0.979 A: 0.984 0.5 P: 0.023 P: 0.014 P: 0.014 P: 0.014 P: 0.011 A: 0.977 A: 0.986 A: 0.986 A: 0.986 A: 0.989 The sensi i i y o all models was ze o. Di e en h esholds o % ada ansmissions and % o loca ions wi h a nea by essel classi- ied as ac i ely ishing we e used o classi y a bou as a ending an ac i ely ishing essel; he p e alence (P) indica es he p opo ion o hese bou s ou o he o al sample o 4349 bou s om 85 indi- iduals. 8Animal Conse a ion  (2022) – ª2022 The Au ho s. Animal Conse a ion published by John Wiley & Sons L d on behal o Zoological Socie y o London. Sca enging behind essels mi o s na u al o aging in alba osses A. P. B. Ca nei o e al. allow p ey cap u e a emp s o be quan ified. O he senso s such as accele ome e s and magne ome e s may also p o ide use ul da a o imp o ing p edic ions. Inco po a ion o da a on wind condi ions migh also be aluable, as his modula es fligh and o aging decisions o alba osses and o he sea- bi ds (Clay e al., 2019). We cau ion ha he bi d-bo ne ada did no always eco d essels ha we e nea by acco d- ing o he AIS da a, indica ing ha di ec de ec ion o in e - ac ions is no s aigh o wa d. None heless, ada de ec o s can p o ide in aluable ools when fine-scale da a on essels a e una ailable and hey can imp o e ou unde s anding o he scale o undecla ed fishing. They can also be used o suppo emo e su eillance o he oceans. The abili y o p e- dic seabi d–fishe ies in e ac ions would be in aluable o isk assessmen s and o imp o ing he a ge ing o esou ces o byca ch mi iga ion and compliance-moni o ing, con ibu - ing o he conse a ion o many highly h ea ened alba osses and pe els (Phillips e al., 2016). Acknowledgemen s We a e g a e ul o all hose in ol ed in da a collec ion a Bi d Island, in pa icula , Rosie Hall, Alex Dodds and James C ymble. This pape is a con ibu ion o he Ecosys ems componen o he B i ish An a c ic Su ey Pola Science o Plane Ea h P og amme, unded by he Na u al En i onmen Resea ch Council and he Bi dLi e In e na ional Ma ine p o- g amme. Funding was p o ided by Da win Plus 092 (DPLUS092). Re e ences Be na d, A., Rod igues, A.S.L., Cazalis, V. & G  emille , D. 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