MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 648: 207–219, 2020https://doi.org/10.3354/meps13412

Published August 27

1. INTRODUCTION

Movement ecology research provides valuableknowl edge to inform conservation management(Allen & Singh 2016) and can help to better under-stand the mechanisms influencing population dy -namics (Mora les et al. 2010). Animals may switch be -tween distinct movement modes, allowing them toswiftly pass through unfavourable areas or to spendmore time on important foraging grounds (Fryxell etal. 2008, Owen-Smith et al. 2010, Gurarie et al. 2016).On foraging grounds, animals typically use convo-

luted, area-restricted (AR) movements, and recentadvances in animal tracking technology have enabledus to use movement data to pinpoint areas of partic-ular importance for many species (Cagnacci et al.2010, Allen & Singh 2016, Hindell et al. 2020). How-ever, to extrapolate such findings beyond the areaswhere animal tracks are available and to understandthe proximate cause of variations in animal move-ments, it is necessary to determine how movementmodes are related to environmental conditions(Johnston et al. 2005, Bestley et al. 2013, Trudelle etal. 2016).

© The authors 2020. Open Access under Creative Commons byAttribution Licence. Use, distribution and reproduction are un -restricted. Authors and original publication must be credited.

Publisher: Inter-Research · www.int-res.com

*Corresponding author: jnn@bios.au.dk

Influence of environmental variability on harbourporpoise movement

Dominique Stalder1,2, Floris M. van Beest1, Signe Sveegaard1, Rune Dietz1, Jonas Teilmann1, Jacob Nabe-Nielsen1,*

1Section for Marine Mammal Research, Department of Bioscience, Aarhus University, DK-4000 Roskilde, Denmark

2Present address: Department of Fish Ecology and Evolution, EAWAG Swiss Federal Institute of Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland

ABSTRACT: The harbour porpoise Phocoena phocoena is a small marine predator with a highconservation status in Europe and the USA. To protect the species effectively, it is crucial to under-stand its movement patterns and how the distribution of intensively used foraging areas can bepredicted from environmental conditions. Here, we investigated the influence of both static anddynamic environmental conditions on large-scale harbour porpoise movements in the North Sea.We used long-term movement data from 57 individuals tracked during 1999−2017 in a state-spacemodel to estimate the underlying behavioural states, i.e. whether animals used area-restricted ordirected movements. Subsequently, we assessed whether the probability of using area-restrictedmovements was related to environmental conditions using a generalized linear mixed model. Har-bour porpoises were more likely to use area-restricted movements in areas with low salinity lev-els, relatively high chlorophyll a concentrations and low current velocity, and in areas with steepbottom slopes, suggesting that such areas are important foraging grounds for porpoises. Our studyidentifies environmental parameters of relevance for predicting harbour porpoise foraging hotspots over space and time in a dynamic system. The study illustrates how movement patterns anddata on environmental conditions can be combined, which is valuable to the conservation of marine mammals.

KEY WORDS: Switching state-space model · Harbour porpoise · Argos satellite tracking · Area-restricted search · Movement behaviour · Phocoena phocoena · Cetacean conservation · North Sea

OPENPEN ACCESSCCESS

Mar Ecol Prog Ser 648: 207–219, 2020

Environmental conditions may induce changes inanimal behaviour directly by influencing the ani-mals’ physiology or indirectly by affecting the distri-bution of their prey (Block et al. 2011, Hays et al.2016). The importance of environmental variables onanimal movement and the distribution of their preytypically varies among species and ecosystems. Forexample, chlorophyll a (chl a) concentration, waterdepth, salinity and temperature have often beenused as proxies for resource availability in marinesystems (Steingrund & Gaard 2005, Elith & Leath-wick 2009, Scales et al. 2014, IJsseldijk et al. 2015). Incoastal systems, tidal current strength is the mostimportant driver of ecosystem dynamics due to itsassociation with high primary productivity (Sharpleset al. 2007, Embling et al. 2012), and in estuaries withstrong tides, prey densities appear to be related tosalinity (IJsseldijk et al. 2015). Both current strengthand the location of highly productive upwelling zonesare in turn influenced by seabed slope (Genin 2004,Embling et al. 2012, Amorim et al. 2017). Althoughthese environmental variables are not independent,their relative importance can yield information aboutthe underlying drivers of changes in animal move-ment behaviour.

Recent advances in biotelemetry and analyticalframeworks such as state-space modelling (Moraleset al. 2004, Jonsen et al. 2005) offer a way to charac-terize spatial and temporal patterns in animal move-ment data. State-space models (SSMs) are time-series models that can be used to infer an individual’sbehaviour (unobserved state) along its movementtrack (observed data), thereby offering the possibilityto objectively estimate behaviour from satellite track-ing data (Jonsen et al. 2013). The underlying as -sumption of this approach is that differences in be -haviour are reflected by changes in the animal’smovement path (Morales et al. 2004, Jonsen et al.2005). For example, an animal that moves in a fastand directed manner is expected to move to anotherpart of its range or between distinct foraging areas,while relatively slow movements with frequentchanges in direction likely indicate that the animal issearching for food using AR movements (Turchin1991). An advantage of SSMs is that error distribu-tions from the observed data can be included andthat the most probable movement track can be esti-mated at regular time intervals (Jonsen et al. 2005,2013). This makes SSMs suitable for error-prone andirregular data such as those originating from Argossatellite transmitters (Vincent et al. 2002), whichhave often been used to track movements of marinespecies. Indeed, SSMs have been used to address a

variety of ecological questions, e.g. identification ofimportant breeding grounds of humpback whalesMegaptera novaeangliae (Trudelle et al. 2016, Dulauet al. 2017), description of intermittent foraging bymigrating blue whales Balaenoptera musculus andfin whales B. physalus (Silva et al. 2013) and identifi-cation of offshore foraging grounds for killer whalesOrcinus orca (Reisinger et al. 2015).

The harbour porpoise Phocoena phocoena occursin cold waters of the Northern Hemisphere and is themost abundant cetacean species in the North Sea(Hammond et al. 2013). Nonetheless, there are con-cerns that the species is negatively affected bybycatch (Bjørge et al. 2013), pollutants (Siebert et al.1999) and underwater noise from pile-driving duringthe construction of offshore wind-farms (Tougaard etal. 2009), seismic surveys (Pirotta et al. 2014, Sarno -cińska et al. 2020) and shipping (Dyndo et al. 2015,Wisniewska et al. 2016). Accordingly, harbour por-poises are protected in the European Union (EU)through the EU Habitats Directive (EU 1992) and inthe USA through the Marine Mammal Protection Actof 1972. It is therefore a priority to identify areas ofparticular importance for harbour porpoises, whichcan be done based on their movement patterns.

Movement data of harbour porpoises have beencollected from the east coast of the northern USA(Read & Westgate 1997, Johnston et al. 2005), Green-land (Nielsen et al. 2018) and Denmark (e.g. Teil-mann et al. 2007, Sveegaard et al. 2011). In both thewestern Atlantic and the North Sea/Baltic Sea re -gions, porpoises are almost exclusively found on thecontinental shelf with water depth less than 200 mand little seasonal variation, while in Greenland, por-poises move towards deep, offshore habitats duringwinter and dive to >400 m. Thus far, only 1 otherstudy has explored the effect of dynamic and staticenvironmental conditions on movement patterns ofharbour porpoises (van Beest et al. 2018a). This studyused high-resolution GPS data, but was based on arelatively small sample size and movement data col-lected over small spatial and short temporal scales(i.e. several days).

Here, we used a long-term (1999−2017) movementdataset consisting of harbour porpoise Argos loca-tions collected in Skagerrak and the North Sea toidentify behavioural states using SSMs, and to assesshow the probability that animals used AR move-ments was related to different environmental vari-ables. We expected animals to be more likely to useAR movements in shallow coastal areas with strongtidal currents and steep seabed slopes, as such areasare often associated with productive upwelling zones.

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Further, we expected the likelihood to be high inareas where inflow of fresh water caused productivefrontal zones to occur and in productive areas withhigh chl a concentration. This is one of the first stud-ies to assess how movements of a small marine pred-ator are related to environmental conditions in theNorth Sea, and it provides important insight into thecharacteristics of areas of high conservation value forporpoises and other species with similar biology.

2. MATERIALS AND METHODS

2.1. Study area

The study area covered a major part of the NorthSea from 53 to 62° N and from 3.0° W to 11.8° E. Forestimation of behavioural states, we also includedmovement data from the northern part of the Katte-gat (Fig. 1). However, since we suspected the influ-ence of environmental conditions on behaviouralstates to be different in the Kattegat than in the NorthSea due to the prevalence of frontal systems associ-ated with inflow of brackish Baltic Sea water into theKattegat, we excluded the Kattegat from the studyarea (see Section 2.3). Most of the North Sea is shal-low, with water depths 600 m (Fig. 1).

2.2. Tagging of harbour porpoises

The data used in this study are part of an ongoing,long-term tagging project on harbour porpoises inDanish waters (Teilmann et al. 2007). Most individu-als were tagged after being incidentally caught inpound nets in the Skagerrak and Kattegat (Fig. 1),but 6 individuals were caught actively in the DanishWadden Sea. All porpoises were fitted with an Argossatellite tag (from Wildlife Computers, Sirtrack orTelonics), which was attached to the dorsal fin withsilicone-coated Delrin pins and iron nuts afterdrilling a hole through the fin using a 6 mm corkborer. The dissolvable iron nuts allowed the transmit-ter to detach from the individual after approximately1 yr. See Teilmann et al. (2007) and van Beest et al.(2018a,b) for further details about capture and tag-ging procedures, and Table S1 in the Supplement atwww. int-res. com/ articles/ suppl/ m648 p207 _ supp .pdffor details about individual animals.

The Argos tags were programmed to make a lim-ited number of satellite uplinks at pre-defined times(duty cycles) to increase battery life. Duty cycles var-ied between 1 and 4 d. Animal tracks were only in -cluded in this study if: (1) the duty cycle was ≤3 d and(2) the individuals stayed ≥30 consecutive days in thestudy area. In this study, 48% of the tags used exclu-sively 1 or 2 d cycles, whereas the rest used a mixtureof 1, 2 and 3 d cycles. To avoid splitting tracks, we

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Fig. 1. North Sea and adjacent waters, with (a) daily position estimates for 57 harbour porpoises based on the switching state-space model (red dots: area-restricted movements; blue: directed; grey: uncertain). The solid line shows the delimitation of thestudy area and the dashed line shows the area where predictions on the use of area-restricted movement were made (see alsoFig. 4). (b) Bathymetry and tagging sites (red dots); 1: Skagen, 2: Fjellerup Strand, 3: Knebel, 4: Begtrup Vig, 5: Køge Bugt,

6: Fænø, 7: Kerteminde, 8: Korsør, 9: Mandø, 10: Rømø

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allowed for movements outside the study area. Thesedepartures from the study area usually lasted 1−4 d.However, these positions were not considered forfurther analyses due to lack of environmental data.The track of 1 individual was split into 2 sub-tracksbecause of an extended stay (>30 d) in the Baltic Sea.Finally, we removed locations from the first 24 h ofeach track to reduce behavioural bias caused by cap-ture and tagging (following van Beest et al. 2018b).

2.3. Position and behavioural state estimation

To regularize the movement tracks and to infer theunderlying movement mode at each position, weimplemented a switching SSM (Morales et al. 2004,Jonsen et al. 2005, 2013). We fitted a Bayesian SSMusing the JAGS software (Plummer & Denwood 2018)through the package ‘bsam’ (Jonsen et al. 2013)within R 3.6.1 (R Development Core Team 2018). Theswitching SSM is designed to discriminate between 2movement modes: AR movements (with low autocor-relation in turning angles and distance travelled) anddirected movements (strong autocorrelation). Thepackage was developed specifically for Argos data,and Argos location error structure is incorporatedinto the model (Jonsen et al. 2005). Model parame-ters were estimated using Markov chain Monte Carlomethods. We used a hierarchical SSM allowing forjoint movement parameter estimation across all indi-viduals, potentially providing improved variable esti-mation (Brooks & Gelman 1998, Jonsen 2016). To runthe model, all locations with the lowest quality classZ were removed (271 positions, 1.0% of the full data-set; see www.argos-system.org for definition of qual-ity classes). The time interval for position and behav-ioural state estimation was set to 24 h. Two parallel

Markov chains were run, with 90 000 samples perchain. We used diagnostic plots to verify that (1) theMarkov chains had converged (i.e. posterior sampleswere stationary and the chains were well mixed), (2)within-chain sample autocorrelation was low and (3)the Brooks-Gelman-Rubin potential scale reductionfactors were

Stalder et al.: Variations in harbour porpoise movements

of approximately 300 m (GEBCO 30 arc-secondglobal grid, Weatherall et al. 2015). The dynamic en -vironmental data included daily estimates on salinity,temperature, current velocity and chl a concentrationand were obtained from the Copernicus MarineEnvironmental Monitoring Service (http://marine.copernicus.eu/; sea surface values used throughout;Table 1). Data were derived from forecast (1 July2014 − 31 April 2017) and from reanalysis (1 January1984 − 30 June 2014) of the Forecasting Ocean As -similation Model Atlantic Margin model (von Schuck -mann et al. 2016). We expected porpoises to use ARmovements in areas that were relatively productivecompared to other areas at a given point in time, i.e.with a relatively high chl a concentration. Since over-all chl a concentration fluctuates throughout the year,we calculated the relative chl a concentration (hence -forth called ‘chlorophyll anomaly’) as the deviationfrom the mean concentration for a given day. Thismean concentration was calculated from a loesscurve fitted to the daily chl a values (Fig. S2).

Before assessing the effect of environmental condi-tions on behavioural state, we omitted position esti-mates that were either (1) outside the study area andon land; (2) associated with aberrant environmentaldata (1 position with salinity

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transforming the predictor variables using principalcomponents analysis (PCA). In this analysis, we usedthe same random grouping variable as in the best ofthe models above. The PCA transformation results inuncorrelated linear combinations of predictor vari-ables (principal component axes) and is thereforeoften used for avoiding collinearity (e.g. Jolliffe 2002,Johnston et al. 2018). All mixed models were fittedusing the ‘glmer’ function and maximum likelihoodestimation in the ‘lme4’ package in R (Bates et al.2015).

3. RESULTS

We used a total of 26 649 Argos locations from 58tracks (from 57 individuals) to run the SSM, whichexcluded 271 locations of the lowest Argos qualityclass Z (1.0% of the full dataset). The SSM was ableto discriminate between 2 distinct movements modesfor North Sea harbour porpoises: AR movements withvariable turning angles and low autocorrelation be -tween consecutive daily moves, and directed move-ments with turning angles close to 0 and high auto-correlation (Fig. 2). Movement state was categorizedas uncertain for some of the daily position estimates,which is to be expected for animals that use a rangeof movement modes that do not fit into the 2 rathercoarse behavioural modes quantified here. Gamma,which indicates the directional persistence, was0.994 ± 0.0048 and 0.00156 ± 0.0016 (mean ± SD) fordirected and AR movements, respectively. A total of7762 positions and their underlayingbehavioural state (80.9% AR, 6.0%directed [D] and 13.1% uncertain [U])were estimated by the SSM. Meannumber of position estimates per indi-vidual was 124 (SD: 73.4, min: 48, max:340; see details in Table S1) and meannumber of position estimates per yearwas 597 (SD: 379, min: 65, max: 1192;see details in Table 2). Porpoise indi-viduals used AR movements 50.5−99.5% of the time (Table S1; mean:80.9%), but all individuals tagged inthe Wadden Sea on the Danish westcoast (Mandø and Rømø sites) used ARmovements >96% of the time, whichwas more than any of the animalstagged in the Skagerrak or Kattegat.The Wadden Sea animals remainedclose to the tagging site during thewhole tracking period, whereas some

of the other animals ventured across the North Sea tothe British coast and Shetland, while occasionallyshifting to AR movements (Figs. S1 & S6). Porpoisestended to avoid the deepest part of the study area,along the Norwegian coast (Fig. 1).

Before analysing how behavioural state was re -lated to environmental variation, we removed datafrom outside the study area and retained only 5753position estimates from 54 tracks (79.9% AR, 6.0% Dand 14.0% U; Fig. 1). Of these, we excluded 3294positions with uncertain behavioural state, largeerror estimates and outliers and retained 2460 posi-tion estimates (94.2% AR, 5.8% D), corresponding to42.8% of the position estimates within the studyarea, or 31.7% of all position estimates from the SSM.

The probability that animals used AR movementswas best explained by a model with variation amongindividuals nested within sex, but almost equallywell explained by a model which did not considervariation among sexes (ΔAICc = 0.54). The modelsthat included either tagging year (ΔAICc = 2.53) ortagging location (ΔAICc = 1.70) were also stronglysupported by the data, but the analyses of the impor-tance of environmental variability were based on themodel that included variation among individualsnested within sex.

The probability of using AR movements was re -lated to salinity, chlorophyll anomaly (variable impor-tance ≥0.95; Table 3), current velocity and slope(importance ≥0.84), whereas temperature and waterdepth were less important as predictors. We had ex -pected the likelihood of AR movements to be high in

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Year No. of No. of Argos Position Days per track %AR animals positions est. (mean ± SD)

1999 1 313 65 65 73.82000 4 994 648 162.0 ± 94.9 85.52001 8 3915 1192 149.0 ± 102.3 76.12002 2 149 156 78.0 ± 5.7 74.82003 5 808 589 117.8 ± 62 71.92006 2 1477 326 163.0 ± 31.1 85.72008 2 490 160 80.0 ± 4.2 73.72009 5 1583 473 94.6 ± 33.8 79.22010 2 1013 368 184.0 ± 101.8 81.62011 5 4148 785 157.0 ± 137.3 76.42014 11 4702 1038 94.4 ± 38.7 86.02015 6 3856 1107 184.5 ± 58.9 76.12016 5 3201 855 171.0 ± 53.9 98.5Total 58 26649 7762

Table 2. Number of tagged harbour porpoises and total number of positionsper year. Position est. indicates the mean number of position estimates peryear, estimated by the state-space model (SSM), and %AR indicates the pro-portion of the daily position estimates from the SSM that were characterized

as area-restricted movements

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shallow areas with steep slopes, butthis was only weakly supported bydata. Porpoises used AR movementsmore often in areas with relatively highchlorophyll concentrations and inareas that were influenced by inflow offresh water, as we had anticipated(Fig. 3). Predictions based on the aver-aged model indicate that animals areparticularly likely to use AR move-ments along the coasts of Denmark,Germany and the Netherlands and inthe Skagerrak area (Fig. 4), althoughthe estimates are associated with high

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Salinity Temperature Log(depth) Log(chl.anomaly) Log(current) Log(slope) R2m R2c df logLik ΔAICc w

−0.42 −0.27 − 0.31 −0.29 0.24 0.11 0.28 8 −505.35 0.00 0.39−0.43 −0.27 0.02 0.31 −0.29 0.24 0.11 0.29 9 −505.35 2.00 0.14−0.38 − − 0.30 −0.30 0.23 0.09 0.24 7 −507.53 2.34 0.12−0.49 −0.25 − 0.30 −0.24 − 0.10 0.27 7 −508.30 3.88 0.06−0.38 −0.29 − 0.29 − 0.20 0.09 0.29 7 −508.35 3.98 0.05

Variable importanceSalinity Temperature Log(depth) Log(chl.anomaly) Log(current) Log(slope)

0.99 0.76 0.28 0.95 0.87 0.84

Model-averaged coefficients Estimate SE Adjusted SE z p

(Intercept) 2.99 0.33 0.33 8.93

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uncertainty, particularly in the German Bight area.These areas are characterized by a high chlorophyllanomaly and high current velocity (Fig. S5). The fullmodel explained 28.5% of the variation in probabilityof using AR movements (conditional R2, model in -cluding environmental variables and random varia-tion among individuals and sexes), and the environ-mental variables alone explained 11.1%.

The random variation among individuals (reflectedin the confidence bands in Fig. 3) is related to differ-ences in condition, sex, state and stochastic varia-tions among animals. Overall, male porpoises had aslightly higher probability of using AR movementsthan females (95 vs. 87% of all daily positions; χ2 =94.9, p < 0.001, chi-squared test), which is also evi-dent from Fig. 3, but the proportion of time spentusing AR was unrelated to animal length (F1,53 = 0.12,p = 0.73).

After transforming the predictor variables using aPCA to ensure that analyses were based on uncorre-lated variables, the probability of using AR move-ments was best explained by the second principalcomponent, which was strongly correlated withsalinity and chlorophyll anomaly (variable impor-tance = 1.0; Table S2 and plot of variable loadings,Fig. S7). It was also well explained by the fifth princi-pal component, which was most strongly correlatedwith current velocity and slope (importance = 0.89).

The third-most important principal component wasstrongly correlated with temperature and weaklywith other variables (importance = 0.64). The resultsare in line with those based on untransformed vari-ables, suggesting that the results of the first analysiswere not confounded by collinearity issues.

4. DISCUSSION

We used a unique long-term movement datasetof harbour porpoises in the North Sea to estimateswitches in movement modes using a Bayesian SSM.The model enabled us to distinguish between directedmovements and AR movements, the latter of whichare typically associated with foraging activities (Fryx-ell et al. 2008, Owen-Smith et al. 2010, Gurarie et al.2016). The number of positions that could be assignedto either mode was high (86.9%), although it was evenhigher in some comparable studies (e.g. Jonsen et al.2007, Reisinger et al. 2015). By relating behaviouralstates to environmental parameters, we showed thatAR movements were associated with a range of staticand dynamic oceanographic conditions, which canbe used to identify spatially and temporally dynamicareas of particular importance for porpoises.

Chl a anomaly was one of the variables that bestexplained variations in animal movements. Porpoises

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Fig. 4. (a) Probability of harbour porpoises using area-restricted (AR) movements in different areas, on average for all individ-uals. Predictions are only calculated for the part of the North Sea with available environmental data and high numbers of posi-tion estimates. (b) Standard error (SE) of predictions. Predictions were based on dynamic environmental data for 1 randomlyselected day (1 April 2015) from the Copernicus Marine Environmental Monitoring Service (CMEMS) and water depth and

sea bottom slope data from the General Bathymetric Chart of the Oceans (GEBCO)

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were more likely to use AR movements in areas withrelatively high chl a concentrations. Such areas areoften found in frontal regions and upwelling zones(Polovina et al. 2001), oceanographic features thattend to aggregate primary consumers, which makesthem important foraging grounds for marine preda-tors (Embling et al. 2012, Gilles et al. 2016).

Harbour porpoises were less likely to use ARmovements in areas with strong currents, which wasnot what we had expected. Tidal current strength isusually the main driver of ecosystem dynamics incoastal systems due to its association with high pri-mary productivity (Sharples et al. 2007, Embling etal. 2012), and porpoises have been reported to aggre-gate and feed in locations with high tidal currentvelocities (Johnston et al. 2005, Pierpoint 2008, IJs-seldijk et al. 2015). A possible reason for this discrep-ancy could be that average daily surface currentstrength, which is the variable we used in our model,does not reflect the fine-scale variations in currentstrength found in tidal areas. In such areas, currentsalternate several times per day, but this generallydoes not result in a net daily movement of water, andmean current strength is therefore 0. To betterunderstand the importance of current strength, itwould be valuable to use high-resolution tidal mod-els for regions where these are available.

The porpoises’ tendency to use AR movementsmore often in less saline areas is also in accordancewith our expectations. This trend is driven by theirincreased likelihood of using AR movements in thesouth-eastern part of the North Sea, which is influ-enced by inflow of freshwater from the Elbe River,and in the northern Kattegat, which is influenced byinflow of water from rivers along the Baltic Sea(Fig. S5). The northern Kattegat is characterized bypelagic fronts and high benthic biomass (Josefson &Conley 1997), which is likely to cause fish to aggre-gate. Such areas where saltwater and freshwaterinteract may generally be more productive, causinganimals to forage more often. These findings are theopposite of those previously reported for porpoisefine-scale movements, based on GPS locations span-ning a few days (van Beest et al. 2018a). The reasonmay be that the study by van Beest et al. 2018a) onlyincluded porpoises in the eastern North Sea and,consequently, included few high-salinity positionsfrom the central North Sea. Also, the Argos datasetused in the present study allowed us to track move-ments over longer periods. The directed long-dis-tance movements across the highly saline centralpart of the North Sea that we observed for some ani-mals would not have time to develop in short-term

tracking datasets. Salinity was negatively correlatedwith bottom slope (Fig. S4), so the influence of salin-ity could also be partly mediated by high prey densi-ties in productive coastal areas with relatively steepbottom slopes. The effects of salinity and chlorophyllanomaly on porpoise movements persisted aftertransforming the variables using a PCA to avoidcollinearity, as revealed by their high contribution tothe most important predictor variable in this analysis(Table S2).

Our study also demonstrates a great deal of vari-ability in porpoise movements, with some animalsstaying in the same region using AR movements formany months (e.g. all animals tagged in 2016; Fig. S6)whereas others roamed over large distances andshifted between AR and directed movements (e.g.individual 2003-026642; Fig. S1). This variabilitycould, to some extent, be attributed to regional differ-ences in the animals’ probability of using AR move-ments (the model including individual nested withintagging site was strongly supported by the data).

Previous studies have shown that harbour por-poise movements also vary greatly among habitats.At one extreme, porpoises in inner Danish waterssometimes stay year round in particular areas withstrong currents where food is presumably abun-dant (Sveegaard et al. 2011). In contrast, porpoisestagged in Greenland show seasonal variability,with movements towards the shore in early sum-mer and towards the central Atlantic in the fall(Nielsen et al. 2018). There is little evidence thatporpoises in our study change movement behaviourin re sponse to changing temperatures, i.e. amongseasons, and the directed movements occasionallyob served for some animals should probably be con -sidered exploratory moves into less known areas, abehaviour that has been observed in a wide rangeof species (Mueller & Fagan 2008). This tendencyto use directed, ex ploratory movements is higherfor females than for males, possibly because theyneed to find additional foraging grounds due totheir larger body size and during periods with in -creased energetic requirements during pregnancyand lactation. Females might therefore be dispro-portionally ex posed to disturbance from humanactivities (e.g. shipping) when moving between for-aging grounds. Negative effects of higher exposureto disturbances might be amplified when a femaleis accompanied by a calf. The variability amongindividuals seen in this study supports the pictureof the porpoise as a species that is able to adapt itsbehaviour depending on local environmental con-ditions (Nabe-Nielsen et al. 2013).

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We found that porpoise movements were related toseveral environmental factors and variations amongindividuals, but the environmental variables only ex -plained 11.1% of the variation in data. The full modelincluding random variation among individuals ex -plained 28.5% of the variation. One obvious caveatwith the environmental variables we used is that theirspatial and temporal resolution may be too coarse tobe suitable as proxies for porpoise prey distributionand availability. Resource availability is often high inthe vicinity of ephemeral fronts and eddies (Biuw etal. 2007, Godø et al. 2012), tidal currents (Scott et al.2010) or island and headland wakes (Johnston et al.2005). Such features are weakly reflected in coarse-scale environmental variables like the ones usedhere, and future studies should aim at including fine-scale data on gradients in salinity and temperaturethat are likely to capture the presence of fronts andeddies. Further, even if high-resolution environmen-tal data had been available, it would presumably nothave been possible to detect the fine-scale variationsin animal movements related to such fine-scale envi-ronmental features using daily Argos positions.

Another caveat in our analyses is that we did notexplicitly incorporate or account for behavioural stateestimation uncertainty. Although methods that try toaccount for such uncertainty exist (Andrews-Goff etal. 2018), this was not done here, as we preferred todelete all positions with high estimation error to min-imize the risk that positions were incorrectly matchedwith environmental data. Another possible reasonwhy the models only explained a small proportion ofthe variation in data is that animal foraging is pre-dominately related to variations in prey availabilitythat are not associated with environmental conditions.Prey may, for example, be aggregated at certaintimes of the year, and such small-scale prey patchcharacteristics can play a larger role in how preda-tors perceive prey availability than the large-scaleprey availability (Benoit-Bird et al. 2013). Prey move-ments may also be one of the explanations for theseasonal variations in porpoise behaviour previouslyobserved in the Skagerrak (Sveegaard et al. 2011).Such variations in prey availability could result intemporal variation in the animals’ probability ofusing AR movements that is not explicitly accountedfor in our study due to a relatively small number ofposition estimates from the winter and early spring.Finally, our ability to predict porpoise foragingbehaviour based on environmental conditions maybe limited by variations among the individualsrelated to differences in their sex, reproductive stateand memory of previously visited foraging patches,

and such differences among individuals were evidentfrom the different behaviours of animals tracked inthe same areas over the same period of time.

Despite the variation in how individuals move andthe lack of fine-scale environmental data, our studyillustrates several great advantages of using move-ment data to identify areas of particular importancefor marine species. First, the use of movement datamakes it possible to provide a measure of the long-term use of different habitats, which is not availablefrom the aerial surveys often used for counting mar-ine species and modelling their distribution (e.g.Hammond et al. 2013, Gilles et al. 2016). Second,movement data allow for more objective proceduresto identify foraging hot spots of importance for long-term population survival. Since the North Sea is avery dynamic system, it can be challenging to pin-point well-defined areas of particular importance toharbour porpoises. Knowledge on how environmen-tal variables influence the availability of food in sucha dynamic system therefore helps to predict whereforaging hot spots are located when they cannot bepredicted from density data of the species alone. Ourstudy is a first step towards identifying importantvariables that can potentially be used for this case.Such information is becoming increasingly importantin individual-based models where population effectsof disturbances emerge from local competition forfood (Nabe-Nielsen et al. 2014, 2018).

5. CONCLUSIONS

In this study, we have demonstrated how animalmovement modes can be linked directly to environ-mental variables to assess the proximate cause ofvariations in animal movements. The study wasbased on a unique long-term data set includingArgos movement data for 57 harbour porpoises fromthe North Sea region. Differences in large-scalemovement behaviour were predominately related tovariations in salinity, chl a concentration, currentvelocity and slope, suggesting that the most impor-tant foraging grounds were located in relatively pro-ductive areas with low current velocities. Several ofthese variables have previously been identified asimportant for fine-scale movements in porpoises andfor the spatial distribution of other marine species. Tofurther improve the predictive power of these mod-els, fine-scale environmental data and telemetry datawith a finer temporal resolution should be used,which will make the models more precise and usefulfor management and conservation of marine species.

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Acknowledgements. We thank the team at the Section forMarine Mammal Research at Aarhus University for assis-tance with fieldwork and Patrick Möhl for statistical advice.Harbour porpoises were tagged as part of a joint projectbetween the Danish Institute for Fisheries Research, theFjord and Belt Centre, Aarhus University and University ofSouthern Denmark in the years 1999−2002 and in collabora-tion with the University of Kiel Research and TechnologyCentre (FTZ) in 2003−2007. Tagging was carried out underpermissions from the Danish Forest and Nature Agency (SN343/SN-0008 and SNS-342-00042) and the Animal WelfareDivision (Ministry of Justice, 1995-101-62 and 2010/561-1801) between 1999 and 2014; and under permissions fromthe Environmental Protection Agency (Ministry of Environ-ment and Food of Denmark, NST-3446-0016) and the Ani-mal Experiments Inspectorate (Ministry of Environment andFood of Denmark, 2015-15-0201-00549) between 2015 and2016. The study was conducted using E.U. Copernicus Mar-ine Service Information. This study is part of the DEPONSproject (www.depons.au.dk) funded by the offshore winddevelopers Vattenfall, Dogger Bank Wind Farms (SSE,Equinor), ENECO Luchterduinen, Ørsted and ScottishPower Renewables.

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Editorial responsibility: Elliott Hazen, Pacific Grove, California, USA

Submitted: August 16, 2019; Accepted: July 2, 2020Proofs received from author(s): August 10, 2020

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