Changepoint analysis: a new approach for revealing animal ... et... · Changepoint analysis (CPA)...

Changepoint analysis: a new approach for revealinganimal movements and behaviors from satellite telemetry data

SAMIR H. PATEL,1,7,� STEPHEN J. MORREALE,2 ALIKI PANAGOPOULOU,1,3 HELEN BAILEY,4 NATHAN J. ROBINSON,5,6

FRANK V. PALADINO,5,6 DIMITRIS MARGARITOULIS,3 AND JAMES R. SPOTILA1,5

1Drexel University, Philadelphia, Pennsylvania 19104 USA2Cornell University, Ithaca, New York 14850 USA

3ARCHELON, The Sea Turtle Protection Society of Greece, 104 32 Athens Greece4University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, Solomons, Maryland 20688 USA

5The Leatherback Trust, Monterey, California 93940 USA6Indiana University-Purdue University Fort Wayne, Fort Wayne, Indiana 46805 USA

Citation: Patel, S. H., S. J. Morreale, A. Panagopoulou, H. Bailey, N. J. Robinson, F. V. Paladino, D. Margaritoulis, and J. R.

Spotila. 2015. Changepoint analysis: a new approach for revealing animal movements and behaviors from satellite

telemetry data. Ecosphere 6(12):291. http://dx.doi.org/10.1890/ES15-00358.1

Abstract. While telemetry is an invaluable tool for tracking animal movement patterns, the data generated

by this technique is often challenging to interpret. Here, we addressed this issue by developing a novel

method, based on changepoint analysis, which incorporated both the horizontal and vertical movement

metrics and compared this output to that from a switching state-space model (SSSM) that categorized

behavior based on horizontal movement metrics. We deployed 20 satellite transmitters on postnesting

loggerhead turtles at Rethymno, Crete, Greece between 2010 and 2011 to monitor their at-sea behavior. We

used both models to identify behavioral changes, such as the switches from migration to foraging, and from

foraging to overwintering. The satellite-tracked turtles exhibited three discrete migratory strategies, with 9

turtles migrating southwards to the coast of northern Africa, 6 turtles migrating northwards into the Aegean

Sea, and 4 turtles remaining resident in the waters of Crete. The SSSM readily identified the switch from

transiting to ARS behavior in most animals, but the CPA model was able to distinguish multiple modes and

more subtle shifts in behavior corresponding with shifts from migration to foraging to overwintering

behaviors. We have shown that by incorporating vertical movement metrics into the analysis of telemetry

data, previously hidden shifts in behavior can be revealed. The resulting increase in ability to discern

complex behavioral patterns of animals remotely will likely yield better management and conservation

decisions for a wide array of organisms.

Key words: Aegean Sea; Caretta caretta; Crete; foraging; Gulf of Gabes; loggerhead sea turtle; Mediterranean Sea;

migration; overwintering; switching state-space model.

Received 9 June 2015; revised 10 July 2015; accepted 15 July 2015; published 21 December 2015. Corresponding Editor:

D. P. C. Peters.

Copyright: � 2015 Patel et al. This is an open-access article distributed under the terms of the Creative Commons

Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the

original author and source are credited. http://creativecommons.org/licenses/by/3.0/7 Present address: Coonamessett Farm Foundation, East Falmouth, Massachusetts 02536 USA.

� E-mail: [email protected]

INTRODUCTION

Identifying the behavioral states of animals

from satellite telemetry data is becoming increas-

ingly common as new statistical approaches are

developed (Jonsen et al. 2013). However, rarely

do these approaches account for the full suite of

data available through modern satellite telemetry

v www.esajournals.org 1 December 2015 v Volume 6(12) v Article 291

devices (Bestley et al. 2013, 2015). A commonapproach for discerning different behavioralstates from satellite telemetry data is to use aswitching state-space model (SSSM; Jonsen et al.2005). SSSM identifies a switch between twobehaviors: transiting, which is characterized bymovement at high rate and low turn angle; andarea restricted search, which is characterized bylocalized movement of low rate and high turnangle (Bailey et al. 2009). However, using SSSMbased on horizontal metrics alone is not univer-sally applicable to all species, as some speciesmay forage while exhibiting wandering move-ments (Hays et al. 2006), and residential move-ments do not always correspond to foraging(Bestley et al. 2015).

Due to the computational complexities ofexpanding state-space models (SSM) to incorpo-rate more variables beyond turn angle and rate,limited work has been done to include additionalmetrics available through telemetry systems(Jonsen et al. 2013, but see Bestley et al. 2013,2015). SSM requires a predetermined scale to beincorporated into the model to identify thespecific behaviors (Jonsen et al. 2005). As aresult, thresholds must be determined prior torunning the SSM to calculate a change inbehavior (Bailey et al. 2008). This becomescomplicated when not all behaviors are knownor not enough information about known behav-iors exists to create thresholds within the variousavailable metrics (Bestley et al. 2015). Wedeveloped a model, based on the changepointmethod (Killick and Eckley 2014), that identifieschange in multiple horizontal and vertical move-ment metrics simultaneously without the prereq-uisite of determining thresholds.

Changepoint analysis (CPA) is a tool used toestimate a point change in the mean and/orvariance of time-series data (Killick and Eckley2014). CPA has been used in a variety of fieldsranging from finance (Zeileis et al. 2010) tooceanography (Killick et al. 2010). Currently,the most common search method to find change-points is binary segmentation (Killick and Eckley2014). To find changepoints, binary segmentationfirst splits the data into two segments based onuser decided statistical properties. This step isthen repeated on the two new segments, and if achangepoint is identified within the segments,then they are split further. This process continues

until no more changepoints are found. Since CPAwas not developed for a unique data type, it isvery flexible in its applications, with the simpleassumption that the data are in chronologicalorder (Killick and Eckley 2014). This flexibilityallowed us to develop a model, incorporatingCPA, using the full suite of data availablethrough modern telemetry systems.

We focused our research on the third largestnesting population of loggerheads in Greece,located at Rethymno, Crete (Margaritoulis et al.2003, 2009). Previous tracking of loggerheads inthe Mediterranean show they migrate in roughlystraight lines to their foraging locations wherethey take up residence (Schofield et al. 2013,Hays et al. 2014). As these movement patterns fitclosely with the assumptions that area restrictedsearch is linked to foraging, these data should bereadily interpretable using SSSM. Typically theseturtles exhibited a transiting behavior of directedmovement away from the nesting beach, fol-lowed by area restricted search behavior uponarrival at foraging grounds (Schofield et al. 2013).However, not all turtles exhibited a postnestingmigration and at the foraging grounds, turtleshave been documented exhibiting overwintering,which involves long periods of inactivity andvery long dive durations, in addition to foraging(Hochscheid et al. 2005, 2007, Broderick et al.2007). As a result, with the range of potentialbehaviors that exist for this population, develop-ing a model to account for this variety isessential. An analysis combining horizontal andvertical movement data may provide a clearpicture and reveal more precisely how theseturtles make use of the Mediterranean Sea.

Here we present the first use of CPA toinvestigate the at-sea behavior of sea turtles. Weincorporated nine movement metrics, calculatedfrom the satellite transmitter data from 19loggerheads, into the CPA model to identify thebehavioral states of loggerheads at-sea. We alsoused SSSM to analyze the raw telemetry data anddetermine behavioral modes for each turtle.When possible, we compared the results fromthe SSSM and the CPA to identify the similaritiesand differences between our model and the mostcommonly used method to determine animalbehavior from satellite transmitters.


PATEL ET AL.

METHODS

During 2010 and 2011, we deployed 20 satellitetransmitters on adult female loggerhead turtlesthat were encountered opportunistically duringnightly patrols of Rethymno beach (latitude35.3858, longitude 24.5908). Those sections ofbeach have historically been patrolled by theGreek sea turtle conservation organization AR-CHELON, and were observed to have the highestdensity of nesting activity in that region.

Satellite transmitter configurationWe used Wildlife Computers’ (Redmond,

Washington, USA) tag models Mk10-PAT for 19turtles and Mk10-AF (with Fastloc GPS capabil-ities) for 1 individual. The satellite transmitterswere attached based on methods described inPatel (2013). We obtained location, dive, andtemperature data, taking advantage of the PATtags’ capabilities for opportunistic transmissionsas the turtles swam. To prolong battery life, in2010, we set the transmitters to a 6 hour on: offduty cycle, with a maximum of 75 transmissionsper day, with unused transmits carried over tothe next day. For the 2011 season, we pro-grammed the transmitters with a 24 hour dutycycle with the overall number of transmissionslimited to 52 per day. All transmitters sampledand summarized diving data (dive depth, diveduration, and time at depth) in pre-assigned bins.A dive was classified as a movement deeper than1 m and lasting longer than 1 minute. Thehistogram bins for dive depth and time at depthwere 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100,200 and .200 m of depth. Dive durations wereplaced into bins of 2, 5, 10, 20, 30, 40, 50, 60, 90and .90 minutes. In addition, maximum andminimum temperatures were recorded at the seasurface and at intervals of depths of 8 m. Thetransmitters were programmed to compile andtransmit dive and temperature data, formatted ashistograms summarizing 4-hour periods.

Postnesting movements and behaviorsWe conducted all mapping and plotting of

spatially referenced data using ArcGIS 10.2 (ESRI2011). We determined the start of postnestingbehavior as the time after ultrasonographyrevealed an empty ovary (Patel et al. 2015), orby receiving successive locations as the turtle was

obviously moving away from the nesting beach.

Switching state-space model (SSSM)To generate daily position estimates at evenly

spaced time intervals from irregular satellitelocations, we applied a Bayesian switchingstate-space model (Jonsen et al. 2007, Bailey etal. 2008) to all raw location data, Argos LC 3 – B,for each track (n ¼ 19). Location estimates wereinferred by coupling a statistical model of theobservation method (measurement equation)with a model of the movement dynamics(transition equation; Patterson et al. 2008). Themeasurement equation accounted for errors inobserved satellite locations (Vincent et al. 2002).When satellite positions were missing, linearlyinterpolated positions were used as initial values(Bailey et al. 2008). The transition equation wasbased on a first-difference correlated randomwalk model.

For each of two behavioral modes, behavioralmode 1 was considered to represent transitingbehavior (e.g., migration) and behavioral mode 2represented area-restricted search behavior (e.g.,foraging, breeding and overwintering; Bailey etal. 2009). Transiting was characterized by havinga turn angle of closer to 0 with autocorrelationhigher than during area-restricted search (Jonsenet al. 2007). Calculated values of ,1.25 werecategorized as behavioral mode 1, while those.1.75 were considered behavioral mode 2. Allvalues in-between were regarded as uncertainbehavioral mode (Jonsen et al. 2007, Bailey et al.2012).

The model was fitted using the R softwarepackage (R Core Team 2014) and Winbugssoftware (Lunn et al. 2000). Two chains wererun in parallel, each for a total of 30,000 MarkovChain Monte Carlo Samples, with the first 10,000samples discarded as burn-in, and the remainingsamples thinned, retaining every fifth sample toreduce autocorrelation (Blanco et al. 2012).

Changepoint analysis (CPA)One of our primary objectives was to expand

on analysis techniques for telemetry data, be-yond merely analyzing location or distinguishingbetween two behavioral states. To accomplishthis, we took advantage of the full suite ofmetrics provided by the transmitters and incor-porated many horizontal and vertical movement


PATEL ET AL.

data for each turtle into a series of changepointanalyses. The model was run using the change-point package for R with binary segmentation(Killick et al. 2012, Killick and Eckley 2014). Tomore fully interpret the at-sea behavior of theseturtles, we used a total of 9 separate measuredvariables that we could detect remotely. Thesewere turn angle and rate of travel, percentage oftime at surface (above 5 m of depth), percentageof time above median dive depth for that period,mean number of dives, max dive depth, meandive duration, max dive duration, and variancein dive duration. The two horizontal movementmetrics, were calculated by feeding raw locationdata from ARGOS into algorithms and script wegenerated using dBase Plus. To account for Argoslocation error, we filtered locations when the rateexceeded 50 km day�1, and recalculated rate andturn angle until we returned no rates exceeding50 km day�1 (Freitas et al. 2008, Lowther et al.2015). We chose 50 km day�1 as the upper limitthreshold as Schofield et al. (2010) found logger-heads in the Eastern Mediterranean migrate at amean (6 SD) rate of 1.5 6 0.57 km h�1.

Using the changepoint analysis we calculatedwhen any shift in the mean and variance withineach behavioral parameter occurred. For eachselected metric, we calculated a maximum of 20changepoints, well above the number of actualdistinct behavioral modes we would expect for asea turtle. The changepoints for each turtle werethen ranked to reflect the relative strength of thedetected shift. Since changepoints are calculatedalong a timeline, when the 9 parameters werealigned we could determine at which dates andtimes there were discrete shifts in many behav-ioral metrics simultaneously. When there weresimultaneous shifts (within a day) in severalmetrics, it was deemed that an individual turtlehad undergone a major change in at-sea behav-ior. We associated the calculated behaviors withcommon sea turtle postnesting behaviors ofmigration, foraging and overwintering based onchronological order, and previous assessments ofeach (Godley et al. 2003, Broderick et al. 2007).We deemed the other behaviors (n ¼ 2) astransitional between the common behaviors dueto the short duration of each, along with thechronology.

Combining the two analytical methods, weused results from both the switching state-space

model and the changepoint analyses to deter-mine the dates and locations of shifts andoccurrence of various at-sea behaviors, such asmigration, foraging and overwintering, and tocharacterize the movement and dive patternsassociated with each major behavioral mode. Inaddition, we compared dive behavior of eachturtle for foraging and overwintering, as well asthe location of residency in the Mediterranean.For statistical comparisons, we performed one-way ANOVAs with a statistical significance at alevel of 0.05. Statistical analyses were performedin R (R Core Team 2014).

RESULTS

We successfully tracked 19 of the 20 taggedturtles as they moved away from the beachesafter nesting in Rethymno during the two yearsof study. In all, the tracking durations duringpostnesting periods ranged from 11 to 250 days(mean 6 SD: 136 6 74.3 days), making acumulative total of 2718 turtle days of trackingdata. Five transmitters from 2010 averaged 104 6

68.3 days, while the 15 transmitters from 2011with the updated duty cycle averaged 147 6 75.2days. We received a total of 4066 location pointsalong with dive behavior histograms for 2601four-hour time periods and temperature histo-grams for 1065 separate periods.

After completing nesting for the season, fourturtles remained in the coastal waters of Crete forthe remainder of their tracking durations, whichranged from 171 to 250 days. Three of theseturtles resided at separate sites along the northcoast of Crete and the fourth moved to the islandof Gavdos, 35 km south of Crete. Migrationdistances for the 15 turtles that traveled awayfrom Crete, as calculated using the unfiltered rawArgos satellite data, ranged from 237 to 2347 km,traveling at speeds ranging from 36.0 to 52.8 kmper day. Nine individuals traveled to the north-ern African coast, with 8 ultimately settling in theGulf of Gabes region of Tunisia, and 1 apparentlyestablishing residency along the northeasternLibyan coast. The remaining six turtles thatmigrated, traveled north into the Aegean Sea.Two traveled to the Saronikos Gulf, near Athens;two turtles apparently established residency nearthe central Aegean islands, Ikaria and Naxos;and two migrated to the coastal waters of Turkey


PATEL ET AL.

near Izmir and Bodrum.

Switching state-space model analysisUsing the switching state-space model to

analyze horizontal movement patterns, we wereable to distinguish two separate modes ofbehavior from the modeled location data: behav-ior mode 1 (transiting), and behavior mode 2(area-restricted search; Table 1; Figs. 1 and 2).Combining all 2712 daily modeled data pointsfor all turtles, we estimated that collectively

turtles spent 11.4% of the time in behavior mode1, and 76.3% of the time in behavior mode 2, withthe remaining 12.3% calculated as uncertainbehavior (1.25 , bmode value , 1.75).

When viewed individually, the SSSM analysisdesignated that 14 of the 15 turtles that migratedaway from Crete exhibited behavior mode 1 forsome portion of their time at sea. While turtleswere in this transiting mode, calculated rates oftravel ranged from 32.5 to 53.6 km per day (mean¼ 27.5 6 6.0). The individuals that traveled to the

Table 1. Summary data of results from the SSSM and CPA analyses.

Mode and description

Turtles (n)%

totaltime No. days

Travel rate(km/day) No. dives

Diveduration

% timeabove5 m

AegeanSea

NorthAfrican

Cretanresident

SSSM behavioral modesB1, transiting 6 8 0 11.40 24.3 6 19.1 27.5 6 5.96 11.4 6 7.7 18.5 6 17.4 55.80B2, area restricted search 5 6 4 76.30 132.9 6 19.2 11.3 6 3.05 10.6 6 9.6 18.6 6 20.0 30.40

CPA behavioral modesCP1, migration 6 9 0 19.20 36.2 6 12.9 23.0 6 8.93 14.1 6 8.7 16.0 6 14.9 52.30CP2, foraging 5 8 4 45.80 70.3 6 33.0 10.7 6 2.74 11.2 6 9.8 19.1 6 18.5 32.00CP3, overwintering 2 3 3 21.30 71.9 6 38.2 7.36 6 1.89 2.2 6 2.6 64.1 6 40.7 11.00

T1–2, transition 1–2 4 7 3 10.70 20.8 6 10.8 15.6 6 4.95 12.5 6 8.9 16.6 6 15.6 44.70T2–3, transition 2–3 0 1 1 3.03 41.0 6 8.48 12.9 6 1.48 12.7 6 15.7 16.6 6 19.7 21.20

Note: Results show mean 6 SD.

Fig. 1. Results from the SSSM for the 15 loggerhead turtles that migrated away from Crete. Each track line is

colored to represent a different turtle and each circle represents a daily location estimate from the SSSM. The

circles are colored based on the inferred behavior mode at each location.


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African coast spent a mean of 33.8 days (SD 6

9.3) in transiting mode, compared to those that

ultimately settled in the Aegean who only spent amean of only 7.7 days (SD 6 2.3) transiting.According to the SSSM analysis, two individualsthat migrated to Tunisian waters, turtles 1 and 8,

only exhibited behavior mode 1, even thoughboth turtles clearly stopped migrating after theyreached the Gulf of Gabes region. For turtle 2, on

the other hand, the SSSM calculated that it onlyexhibited uncertain behavior throughout thetracking duration; while it also clearly exhibiteda directed migration towards Tunisia.

During transiting behavior, as calculated bythe SSSM, turtles (n¼ 14) averaged (mean 6 SD)11.4 6 7.7 dives per four hour sample period,

with 55.8% of dive time spent between 1 and 5 mof depth. Dive durations during SSSM behaviormode 1 averaged 18.5 6 17.3 minutes. Individ-

uals that traveled to the coast of Africa averaged

11.5 6 7.8 dives per sample period, while thosemigrating northwards into the Aegean Sea

averaged slightly fewer, at 10.3 6 6.1 dives persample period. Turtles migrating southwardsalso took slightly shorter dives on average (mean6 SD ¼ 18.2 6 16.9 minutes) than those

migrating to the north (24.2 6 21.2 minutes)and spent less time at the surface (Africa: 54.5%

of dive time and Aegean: 71.8% of dive time).

During SSSM-designated area-restricted search(behavior mode 2), turtles (n ¼ 15) averaged (6SD) 10.6 6 9.6 dives per sample period, 18.6 6

20.0 minute dives and spent 30.4% of dive timeabove 5 m of depth. There were slight differencesin the dive behavior during SSSM behavior mode2 for turtles from the 3 different regions.

Individuals that maintained residency in theAegean Sea performed the most dives on average(Aegean: mean 6 SD¼ 12.3 6 9.5 dives; African:

9.88 6 11.3 dives; Cretan: 8.27 6 5.9 dives), with

Fig. 2. Dive behavior during SSSM behavioral modes 1 and 2 for all turtles. Sample periods were 4 hours.

Horizontal bars¼median; box¼ 50%; whiskers¼ range of observations within 1.5 times the interquartile range

from edge of the box; circles ¼ observations farther than 1.5 times the interquartile range.


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the slightly shortest mean dive duration (Aegean:17.3 6 18.9 minutes; African: 17.4 6 20.3minutes; Cretan: 24.5 6 21.5 minutes) and therewas a significant difference in the amount of divetime spent closest to the surface (Aegean: 37.1%;African: 25.3%; Cretan: 25.2%; p , 0.001, F2, 736¼14.7). These regional differences, however, aremost likely due to the duration of available data,as the transmitters on the Cretan turtles lastedthe longest. Thus, the data for Cretan turtlesincluded a higher amount of overwinteringbehavior (i.e., fewest dives per sample period,longest dive durations and shortest amount oftime spent at the surface), compared to the otherregions.

CPA analysisUsing changepoint analysis on the complete

data set of rates and directions of movement ofthe postnesting turtles, along with seven mea-sures of dive depths and diving activity patterns,we were able to distinguish three separate anddistinct modes of behavior along with transition

phases between each major behavioral shift(Table 1; Figs. 3a, b and 4). We classified the fiveidentifiable behaviors as the three behavioralmodes migration, foraging, and overwinteringand two transitional phases between thesemodes.

For the 15 turtles that traveled away fromCrete, the first behavioral mode (CPA1) aftercompletion of the nesting season was categorizedas migration. Although this behavioral modemay have included some days of residentforaging for the turtles that migrated north-wards, turtles in this mode averaged rates oftravel 23.0 km per day (68.9), only slightly lessthan the SSSM transiting mode. In furthercomparisons, there were more dives (14.1 6 8.7dives per sample period), shorter dive durations(16.0 6 14.9 minutes), and slightly less dive timespent above 5 m of depth (52.3%) in the CPAmigration mode than the SSSM transiting mode.

Similar to the SSSM analysis, there was asignificant difference in the number of dives persample period between regions, with the north-

Fig. 3. (a) Argos location data (LC: A, 0, 1, 2, or 3) for the loggerhead turtles that remained resident of Crete. (b)

Argos location data (LC: A, 0, 1, 2, or 3) for the turtles that migrated away from Crete. Each track line is colored to

represent a different turtle and each circle is colored to represent a CPA behavior mode exhibited by the turtle at

that location.


PATEL ET AL.

ern migrating turtles averaging the most dives(Aegean: mean 6 SD: 15.4 6 8.7 dives; African:10.9 6 7.8 dives; p , 0.0001; F1, 288 ¼ 17.4),shorter dive durations, and slightly less timecloser to the surface (Aegean: 14.7 6 13.1minutes; 51.5% of dive time; African: 20.0 6

18.9 minutes; 53.8% of dive time).Behavior mode T1-2, as calculated by the CPA,

represented the first observed transition phase,which occurred between migration (or nesting)and foraging. Such a transition phase wasobserved for 14 turtles, while 3 individualsexhibited an abrupt change to apparent foragingand far more localized movement. For severalturtles this transition behavior included a slow-ing of travel to a mean rate of 11.0 6 5.8 km perday, but not a change in turn angle. The periodsof transition in CPA mode T1-2 ranged from 6 to39 days (20.8 6 10.8), with turtles that travelednorthwards exhibiting the shortest transitionperiods, and those remaining near Cretan watersexhibiting the longest.

The CPA-identified transition mode T1-2 alsowas characterized by a decrease in mean divesper sample period (mean 6 SD¼ 12.5 6 8.9) anda slight increase in mean dive duration (16.6 6

15.6 minutes), along with a significant decline inthe amount of time spent above 5 meters (43.4%of dive time; p ¼ 0.01, F1, 416 ¼ 6.11).

Behavioral mode 2 (CPA2), as calculated byCPA, was categorized as foraging. For the turtlesthat we were able to track into overwintering (n¼8), foraging lasted an average of 70.3 6 33.0 days;for the remaining turtles (n ¼ 9), foragingapparently continued for the remainder of theduration of transmissions. Foraging, CPA2, wascharacterized by a greatly diminished andproscribed rate of travel (10.7 6 2.7 km perday), a slight decrease in mean rate of diving(11.2 6 9.83 dives/period), an increase in meandive duration (19.1 6 18.5 minutes) and asignificant decline in the amount of time spentabove 5 m of depth when compared withmigration and the transiting 1-2 behaviors(32.0%; p , 0.0001; F2, 749 ¼ 32.0). During theCPA foraging mode there was a significantdifference regionally in the number of dives persample period (p ¼ 0.01, F2, 415 ¼ 4.50), withturtles migrating to Africa averaging the highestnumber of dives (African: 12.5 6 11.3 dives;Aegean: 10.2 6 8.4 dives; Cretan: 9.12 6 6.0

dives) and taking shorter dives (African 16.0 6

15.3 minutes; Aegean: 22.8 6 23.1 minutes;Cretan: 22.7 6 17.8 minutes) than those remain-ing in more northern waters. Throughout theforaging months for all regions, sea surfacetemperatures averaged 25.58 6 2.28C; southernwaters were warmer, averaging 26.28 6 2.38C, theAegean Sea averaged temperatures of 24.58 6

1.88C.Eight turtles were tracked beyond the CPA

foraging period, as they moved into an apparentoverwintering behavioral mode. Two individualsexhibited a type of transition behavior betweenforaging and overwintering (T2-3) starting inOctober, and 6 turtles shifted directly into anoverwintering behavior, CPA3. The dates whenCPA overwintering began showed no distinctregional trend, and varied considerably betweenthe dates of 13 October and 22 January. Averagesea surface temperature during late October was21.08 6 1.58C, with the Gulf of Gabes regionbeing typically 28C warmer than the Aegean Sea.During the CPA overwintering period, mean seasurface temperature in the Gulf of Gabes regionwas 15.98 6 2.38C; near Crete it was 18.58 6

2.18C; and in the Aegean Sea was 17.28 6 1.48C.The lowest sea surface temperature recorded bythe transmitters during CPA overwintering was13.48C, recorded in February near the Gulf ofGabes.

The transition phase between foraging andoverwintering, T2-3, was characterized by in-creased standard deviations in both dives persample period (mean 6 SD: 12.7 6 15.7) and diveduration (16.6 6 19.7). Additionally, a significantreduction (p , 0.0001, F3, 775 ¼ 25.5) in theamount of time spent in the upper 5 m of thewater column during this behavior indicated aswitch to a more sedentary phase (21.2% of time).

This pattern of reduced time in the upperwater column was more extreme during the CPAoverwintering phase, with a mean of only 11.0%of dive time spent in the upper 5 m. In addition,mean dive durations increased greatly to 64.1 6

40.7 minutes, accompanied by a reduction in themean dives per sample period to only 2.2 6 2.6.Both parameters were significantly lower thanthose observed in all previous CPA behaviormodes (p , 0.001, F4, 906 ¼ 50.8 and p , 0.001,F4,1067 ¼ 44.2, respectively). All combined, theturtles exhibiting this CPA overwintering mode


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appeared to be very inactive and mostly seden-tary, presumably resting on the bottom.

DISCUSSION

With the addition of multiple sensors, moderntransmitters can measure and record much morethan two-dimensional location data. The use ofchangepoint analysis, which combined multiplehorizontal and vertical movement parameters,revealed a detailed and comprehensive view ofthe at-sea behavior of loggerhead turtles in theeastern Mediterranean. Using a Bayesian switch-ing state-space model (SSSM) on Argos satellitelocation data, we were able to detect shifts inbehavioral states between transiting, and whathas been termed area restricted search (Jonsen etal. 2007, Bailey et al. 2009). These behavioralstates were interpreted in our study as migrationand foraging. However, the distinction between

behaviors was likely limited by the number ofparameters that were incorporated into thisSSSM analysis.

In CPA, we were able to easily incorporateadditional measurements of dive depths, divedurations, and proportion of time at specificpositions in the water column, along withderived parameters such as ranges, variability,and frequency of diving and movement metrics.In effect, the application of CPA on severalsimultaneous metrics enabled an effective use ofthe available telemetry data and revealed 5unique behaviors, with 3 categorized as majorbehaviors and 2 as transition phases; yielding amore detailed account of at-sea behavior andhow behavioral changes can have differentmovement characteristics. The CPA approachallowed us to identify the number of change-points based on a suite of movement parameters.A general limitation for both models was the

Fig. 4. Dive behavior during each CPA behavior mode for all turtles. On the x-axis, 1¼ ‘‘CP 1’’, 2¼ ‘‘T 1-2’’, 3¼‘‘CP 2’’, 4¼ ‘‘T 2-3’’ and 5¼ ‘‘CP 3’’. Sample periods were 4 hours. Horizontal bars¼median; box¼ 50%; whiskers

¼ range of observations within 1.5 times the interquartile range from edge of the box; circles ¼ observations

farther than 1.5 times the interquartile range.


PATEL ET AL.

overall lifespan of the transmitters, for example,the number of turtles we identified exhibiting theCPA overwintering phase (CPA3) was directlyrelated to the number of transmitters stillfunctioning during the late autumn and wintermonths. In our study we do not suspect thecessation of transmissions corresponded to turtlemortality, but simply battery exhaustion (Hays etal. 2007) as limiting transmissions per day in2011 resulted in a substantial increase in theoverall lifespan of the transmitters.

Where the results of the two analyticalmethods overlapped, the largest difference foundbetween SSSM and the CPA was the interpreta-tion of the migration phase. The SSSM calculatedthat this first behavior lasted an average of 24.3 6

19.1 days, whereas behavior mode CPA1 wascalculated to be 36.2 6 12.9 days. Most of thediscrepancy could be attributed to several turtlesthat traveled northwards into the Aegean Sea forwhich the behavior CPA1 duration was calculat-ed to be, on average, 40 days longer than thecomparable SSSM migration behavioral mode.Thus, although the Aegean turtles had reachedthe general area in which they ultimatelyestablished residency, their dive behavior, ac-cording to the CPA was still characteristic ofmigration for several more days, meaning a highnumber of short shallow dives. As a result,despite the wide range in migration distances,which differed by as much as 2300 km, theamount of days it took for the Aegean andAfrican turtles to migrate and complete thetransition phase was not significantly different(p¼0.1, F1,11¼2.9), nor were the starting dates offoraging between destination sites (CPA2 startdate: p ¼ 0.4; F1,11 ¼ 0.670). However, the turtlesthat remained in the waters around Crete beganforaging significantly quicker after nesting (p ¼0.02, F2,13 ¼ 5.3) and there was a significantdifference in starting dates (CPA2 start date: p¼0.002, F2,13¼ 9.89). Thus, without the necessity ofmigrating, foraging could begin much sooner forthose four turtles that stayed in waters nearby tothe nesting site.

Loggerhead turtles foraging in North Africanwaters of the Mediterranean exhibited the high-est number of dives per sample period perforaging area, suggesting they had the highestactivity levels. Increased diving activity maycorrespond to higher water temperatures, which

averaged 28C warmer in North African watersthan in Aegean. We observed that turtlesremaining in Aegean and Cretan waters, wherewater temperatures were similarly lower, dis-played similar levels of diving activity while theAfrican turtles were far more active duringforaging. Similar results were reported by God-ley et al. (2003), where two loggerheads trackedby satellite from Cyprus exhibited longer sub-mergence times during foraging than individualsin waters approximately 28C warmer. Increaseddiving activity may reflect higher basal metabo-lism due to increased temperatures, or couldreflect more searching in less food-rich waters(Patel et al. 2015).

As our analytical capabilities improve withtechniques such as SSSM and CPA, we candistinguish much more subtle differences inbehavior from remotely sensed data. By deter-mining shifts in at-sea behavior, we can examinecorrelated patterns of important environmentalfactors and estimate and predict interactions withfisheries and other potentially disruptive humanactivities. Finer resolution of when and where seaturtles and other marine animals migrate, feedand reproduce can be used immediately todevelop effective conservation plans and toimplement best management decisions thatregulate spatial and temporal use of specificregions and habitats. For example, loggerheadscross the Eastern Mediterranean Sea annuallymigrating from nesting beaches to feeding areasduring August and September. During theirmigratory behavior, these turtles take severalshort dives, spending more than 50% of dive timein waters shallower than five meters. As a result,regulating longline and net fisheries during thesemonths in terms of specific locations, placementand depth of set of hooks and nets could greatlyreduce undesired bycatch. Similarly, by deter-mining specifically when and where overwinter-ing behavior begins and ends, bottom trawlingcould be limited in certain areas during thosemonths.

Two of the postnesting migration patternsobserved here have been reported for logger-heads from other nesting beaches in the Medi-terranean. Postnesting females from Zakynthos,Greece and from Cyprus were tracked to NorthAfrica, including several individuals that settledin Tunisian shelf waters (Margaritoulis et al.


PATEL ET AL.

2003, Broderick et al. 2007, Zbinden et al. 2008,2011, Schofield et al. 2013). In addition severalloggerheads from Zakynthos were tracked asthey migrated to Aegean waters (Schofield et al.2013). Notably, the third postnesting pattern, inwhich turtles remained resident in Cretan waters,has not been documented before. However, thiswas similar to the behavioral pattern of aloggerhead nesting on Cyprus which was ob-served to migrate only a short distance to remainwithin Cypriot waters in consecutive postnestingmigrations two years apart (Broderick et al.2007). Additionally, unlike western Greek nest-ing populations, we did not track turtles migrat-ing into the Adriatic Sea. Flipper tag return datahas also shown a general lack of migration fromRethymno into the Adriatic Sea with only 1 tagrecovered (Margaritoulis and Rees 2011). Thismay indicate unique oceanographic conditionsimpacting the dispersal of turtles from this beach(Hays et al. 2010).

Overall, the majority of turtles tracked in thevarious telemetry studies in the Eastern Mediter-ranean Sea traveled through the ExclusiveEconomic Zones of seven countries, Cyprus,Egypt, Greece, Italy, Libya, Tunisia and Turkey.These countries are responsible for 63.7% of thecaptures of sea turtles by fishing gear annually inthe Mediterranean (Casale 2011). For the bestsolutions to such pressing problems, we need tomake the most effective use of our remotetelemetry data using advanced analytical tech-niques, such as presented here. For both fisheriesmanagement and conservation of biodiversity, itis crucial, and indeed an achievable goal, to findan acceptable balance among limited bycatch,sustainable harvest, and economic gain (Howellet al. 2015).

ACKNOWLEDGMENTS

This project was funded by The Betz Chair ofEnvironmental Science at Drexel University and byThe Leatherback Trust, Monterey, California. Assis-tance in the collection of these data was provided bythe many generous leaders and volunteers of ARCHE-LON, the Sea Turtle Protection Society of Greece.Specifically, those at the ARCHELON site in Rethym-no, Crete during the 2010–2011 seasons providedgenerous assistance in ensuring proper data collection.Patrick J. Sullivan provided assistance in the dataanalyses. Michael P. O’Connor, Harold W. Avery,

Susan S. Kilham and the reviewer comments fromGraeme Hays provided critical feedback in improvingthis manuscript.

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