MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 508: 247–260, 2014doi: 10.3354/meps10851

Published August 4

INTRODUCTION

Species habitat models are widely used for conser-vation issues, such as planning protected areas,investigating population trends, assessing animalabundance or evaluating the impact of climatechange and human activities. Together with thenumber of applications, the variety of modelingmethods is also increasing (Elith et al. 2006). Gener-ally, statistical methods can be divided into 2 groups,based on the quality of species data: presence/absence methods (i.e. generalised linear/additivemodels, classification−regression tree analyses, arti-

ficial neural networks) and presence-only methods(i.e. Ecological Niche Factor Analysis, Bioclim andDomain) (Guisan & Zimmermann 2000, Brotons etal. 2004).

Presence/absence methods are recognized to havemore explanatory and predictive power than pres-ence-only methods (Hirzel et al. 2001, Brotons et al.2004), but their performance is highly dependent onthe accuracy of absence data. The non-detection ofa species while present (false-absence) can occureither by a missed detection or by a temporaryabsence of the species. When dealing with mobile,rare, or difficult-to-detect species, the false-absence

© Inter-Research 2014 · www.int-res.com*Corresponding author: paola.tepsich@cimafoundation.org

Habitat preferences of two deep-diving cetaceanspecies in the northern Ligurian Sea

Paola Tepsich1,2,*, Massimiliano Rosso1, Patrick N. Halpin3,4, Aurélie Moulins1

1CIMA Research Foundation, via Magliotto 2, 17100 Savona, Italy2DIBRIS, Università degli studi di Genova, viale Causa 13, 16145 Genova, Italy

3Duke University Marine Laboratory, Beaufort, North Carolina 28516, USA4Nicholas School of the Environment, Duke University, Durham, North Carolina 27708-0328, USA

ABSTRACT: We used generalized additive models (GAMs) as exploratory habitat models fordescribing the distribution of 2 deep-diving species, Cuvier’s beaked whale Ziphius cavirostrisCuvier, 1823 and sperm whale Physeter catodon Linnaeus, 1758, in the Pelagos Sanctuary (north-western Mediterranean). We analyzed data collected from research surveys and whale-watchingactivities during summer months from 2004 to 2007. The dataset encompassed 147 Cuvier’sbeaked whale sightings and 52 sperm whale sightings. We defined and applied a post hoc work-flow to the data, to minimize false absence bias arising from the unique ecology of the species andthe lack of a dedicated sampling design. We calculated a novel topographic predictor, distancefrom the canyon axis, as a covariate for use in the habitat model. Given the complex topographyof the area, the analysis was performed on a high-resolution spatial grid (1 km). Our methodsallowed effective use of the non-dedicated sampling dataset for building habitat models of elusiveand cryptic species (Cuvier’s beaked whale final model sensitivity = 0.88 and specificity = 0.84;sperm whale final model sensitivity = 0.65 and specificity = 0.77). The GAM results confirmed thepreference for submarine canyons for both species and also highlighted the importance of thedeeper portion of the Ligurian basin, especially for Cuvier’s beaked whale. Habitat overlap nevertheless is resolved by a well-defined spatial partitioning of the area, with sperm whale occupying the western part and Cuvier’s beaked whale the central and eastern parts.

KEY WORDS: Cuvier’s beaked whale · Sperm whale · GAM · Habitat modeling · NorthwesternMediterranean Sea

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bias is very likely to affect the dataset. This a crucialissue in habitat modeling, as the false-absence biasstrongly affects model interpretation and accuracy(Gu & Swihart 2004).

Cuvier’s beaked whale Ziphius cavirostris Cuvier,1823 and sperm whale Physeter catodon Linnaeus,1758 are deep-diving cetaceans; both species per-form deep and long dives which make them difficultto sight at sea (Tyack et al. 2006). In addition to pre-forming long dives, the Cuvier’s beaked whale is alsoinconspicuous at the surface and is considered toavoid vessels (Heyning 1989). As a consequence, col-lecting accurate presence/absence data on thesespecies usually requires dedicated surveys involvingdifferent techniques (i.e. coupling acoustic and visualsurveys, tagging), as standard visual protocols mightnot be effective (Barlow & Taylor 2005). The con-straints on dedicated studies, particularly the amountof time or expense required to perform the surveys,often result in small sample sizes, with possible con-sequences on the accuracy of habitat models (Stock-well & Peterson 2002). Indeed, the use of non-dedi-cated platforms and, in particular, whale-watchingvessels for collecting cetacean data at sea is becom-ing broadly recognized as a valuable supplement todedicated surveys (Evans & Hammond 2004, Redfernet al. 2006). While the use of non-dedicated platformsmay result in wider datasets, a well thought-outmethodology is still essential for addressing false-absence bias in the habitat modeling of such crypticand elusive species.

Both species are regularly sighted in the Mediter-ranean Sea (Notarbartolo di Sciara 2002). Within theMediterranean basin, the area of the Pelagos Sanctu-ary, which covers 87 000 km2 in the northwesternMediterranean, encompassing the Ligurian Sea andparts of the Corsican and Tyrrhenian Seas (Notarbar-tolo di Sciara et al. 2008), plays a crucial role in theecology and natural history of these 2 species. Here,2 key areas for Cuvier’s beaked whale distributionare present: one in the NW Ligurian Sea (MacLeod &Mitchell 2006) and one in the northern TyrrhenianSea (Gannier & Epinat 2008). The Pelagos Sanctury isalso an important crossroad for intra-Mediterraneanmovements of male sperm whales (Frantzis et al.2011).

Cuvier’s beaked whale and sperm whale belong tothe same guild: they share a similar feeding ecologywith both being primarily teutophagous (Blanco &Raga 2000, Praca & Gannier 2007). In the PelagosSanctuary, Cuvier’s beaked whale exploits the upperand lower slopes along the Ligurian−Provençal coast,at depths ranging from 1000 to 2500 m (Azzellino et

al. 2008, Moulins et al. 2008), showing preference forareas with complex topography (Moulins et al. 2007,Gannier & Epinat 2008, Azzellino et al. 2012). Similarhabitat preferences have been found for the spermwhale, whose preferred habitat lies in the canyons inthe slope area (Gannier et al. 2002, Moulins et al.2008, Praca et al. 2009, Azzellino et al. 2012). Gan-nier & Praca (2007) also described a preferred habitatin oceanic waters for the sperm whale. Partial habitatoverlap thus occurs among the 2 species, especiallyin the canyon areas. Submarine canyons are cha -racterized by complicated patterns of hydrography,flow, and sediment transport and accumulation. Act-ing as a connecting corridor between continentalshelf areas and the deep-sea, they play a major rolein enhancing oceanographic processes and enrichingthe deep-sea food web (Canals et al. 2006, De Leo etal. 2010). As a consequence, submarine canyons arewidely recognized as hotspots in cetacean distribu-tions (Hooker et al. 1999, Mussi et al. 2001, Moulinset al. 2008). Previous interspecific comparisons sug-gest that a spatial (Moulins et al. 2008) or temporal(Azzellino et al. 2008) segregation may facilitate thecoexistence of the 2 species in canyons. Difficultiesin effectively modeling the habitat preferences of the2 species arise from false-absence bias and fromthe complexity of the topography that usually charac-terizes their preferred habitat.

In this study we followed a 3-step process in orderto analyze the habitat of deep divers in the PelagosSanctuary. We used a large dataset collected fromwhale-watching vessels and a research vessel oper-ating in the northern Ligurian Sea from 2004 to 2007.First, because the target species are rare and difficultto detect (when using only visual methods), we de -fined and applied a rigid post hoc workflow for thedetermination of absence units. Second, we defined afine resolution grid to characterize the topography ofthe area, building a specific topographic descriptorfor submarine canyons. In the third step, we usedpresence/absence methods and, in particular, gener-alized additive models (GAMs) (Hastie & Tibshirani1986) to investigate the habitat preferences of the2 species.

MATERIALS AND METHODS

Study area

In 2001 a protected area (the Pelagos Sanctuary)dedicated to Mediterranean marine mammals wasestablished, as the result of an international agree-

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ment between Italy, France and Monaco. Currentlythe Pelagos Sanctuary is the largest Special Pro-tected Area of Mediterranean Interest (SPAMI) andthe only pelagic SPAMI. The study area is located inthe northern Ligurian Sea, in the northern part of thePelagos Sanctuary, and extends from approximately7° 30’ to 9° 30’ E and from Genoa south to 43° 20’ N(Fig. 1), encompassing an area of approximately7000 km2. The study area is located in a region char-acterized by very complex bottom topo graphy. Thecontinental shelf is narrow and presents a steepslope, cut by several submarine canyons (De Leo etal. 2010). Among these is the large Genoa Canyon,

which is formed by a system of 2 submarine canyons,corresponding to the 2 rivers that flow into the seaat Genoa, the Polcevera and the Bisagno. Southeastof the mouth of the canyon is also a system of 2seamounts (Fig. 2).

Though the study area represents <10% of theoverall width of the Pelagos Sanctuary and, conse-quently, it cannot be considered representative of thewhole area, it lies in the most impacted area. Themain commercial ports of the Ligurian Sea are lo -cated here, and, as a consequence, all main com -mercial routes end or begin from this region. Marinetraffic as well as environmental and noise pollution in

this area are at some of the highest levelswithin the entire Mediterranean basin(LMIU 2008). Though this study is areaspecific, it is dedicated to an area requir-ing effective conservation measures.

Cetacean data

Cetacean observation data used in thispaper come from 2 different data sets.The first dataset was collected on boardwhale-watching vessels operating in theLigurian Sea from 2004 to 2007, from dif-ferent operators and from April to Octo-ber each year. These whale-watchingvessels had previously been used as non-dedicated plat forms for carrying outcetacean research (Moulins et al. 2007,2008). As a consequence, a strict protocolfor data collection was al ready in placeand was followed on each vessel.

The second dataset was collected onboard the University of Genoa’s re searchvessel from 2004 to 2007. Research sur-veys were carried out year round, withgreater re search effort in good weathermonths (spring and summer). Surveysperformed from the vessel did not followany pre-defined sampling design be -cause the aim of surveys was to maxi -mize the probability of encounteringcetaceans.

The same protocol for collecting datawas adopted on all the vessels used. Whenthe meteorological conditions allowedfor an effective survey (Beaufort sea state< 3), the on-effort monitoring activity wasconducted at a speed of approximately10 to 15 km h−1. At least 3 trained

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Fig. 1. Study area location inside the Pelagos Sanctuary is shown in theupper left-hand box (black lines are vessel track lines). The final surveyeffort grid is represented by the grey cells (potential-absence cells), whilethe white cells were surveyed for <3.5 km throughout the study period.Cuvier’s beaked whale (Ziphius cavirostris, d) and sperm whale (Physeter

catodon, +) sightings are also shown, locating presence cells

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observers were placed on the upper deck to scan360° around the vessel with and without binoculars.The elevation of the upper deck was variable accord-ing to the dimensions of the vessels, and ranged from4 to 7.5 m above sea level. On each vessel, observersrotated quadrants every 30 min to avoid fatigue.Effort data were recorded throughout the trip, usingLogger2000 software or a portable GPS. Weatherconditions were recorded every 30 min. Whencetaceans were spotted, they were approached bythe vessel to allow for exact identification of the spe-cies, a count of the number of individuals, and obser-vation of any peculiar behavior.

Whale-watching vessels departed from 4 harbors(Genoa, Savona, Imperia, Andora) along the west-ern Ligurian coast and, consequently, they hadslightly different search-effort areas: the central-eastern part of the study area when departingfrom Genoa or Savona and the western part whendeparting from Imperia or Andora). The researchvessel departed from Savona, and its effort wasmore concentrated in the central part of the studyarea (Fig. 1). In order to achieve a uniform cover-age of the study area, and given the similarities inthe survey strategy and temporal distributions ofresearch effort, the 2 datasets were analyzedtogether as a single dataset.

Environmental variables

The environmental variables used to model specieshabitat are the ones commonly used to describeCuvier’s beaked whale and sperm whale distribu-tions: depth, distance from the 200 m contour (corre-sponding to the shelf break) and slope (Cañadas etal. 2002, Azzellino et al. 2008, Praca et al. 2009). Allthese variables were derived using ArcGIS 9.2 from afine-resolution bathymetric grid (100 m) of the Lig-urian Sea kindly provided by IFREMER (Institutfrançais de recherche pour l’exploitation de la mer,France). The bathymetric grid was imported intoArcGIS and projected onto a universal transversemercator (Zone 32N) coordinate system before deriv-ing the different topographic rasters.

For the definition of submarine canyons, the can -yon axis was identified by computing a relative slopeposition index (RSP) (Parker 1982, Wilds 1996, Weiss2001, Dunn & Halpin 2009). The RSP for each cellexpresses the percent distance of the consideredpixel from the bottom of the slope; cells representingthe bottom of the slope then have the value 0%,while cells representing the ridge top have the value100%. The bottom and the top of the slope wereidentified using the hydrological tools in ArcGIS Spa-tial Analyst. In particular, first the flow-direction tool

Fig. 2. Canyon axes in thestudy area, obtained by com-putation of the relative slopeposition (RSP) index. Panelson the right show a closeup of the Genoa Canyonand the corresponding RSP values (upper panel) and ascheme illustrating the RSP

index (lower panel)

Tepsich et al.: Habitat preferences of deep-diving cetacean species

was used to identify the direction of steepest descentfrom each cell. Then using the flow-accumulationfunction, we computed the total number of neighbor-ing cells that flow to each grid cell. The bottom of theslope was identified by all those cells with at least3000 cells of accumulation. Finally, using the flow-length tool (with downstream option, FD), we calcu-lated for each cell the distance from the bottom of theslope. For the top of the slope, we identified cellsshowing a difference from the mean elevation ofneighboring cells >50 m within a rectangle window~1000 km2 wide. The flow-length tool (upstreamoption, FU) was then used to measure distance fromthese cells. The RSP was subsequently calculated fol-lowing the formula:

(1)

All cells with a RSP ≤ 20 were classified as thecanyon axis, while all other pixels were set as no data(Fig. 2). For the definition of the thresholds chosen inthe canyon axis identification process, we startedfrom default thresholds proposed by the software,then adjusted these in accordance with our knowl-edge of the sea bottom topography in the study area(Weiss 2001, Qin et al. 2006). Finally, a raster of dis-tances from the nearest canyon axis was calculatedusing the Euclidean Distance tool in ArcGIS.

Previous research carried out in the Pelagos Sanctu -ary revealed a longitudinal segregation be tween the 2species, with a higher presence of sperm whale in thenorthwestern part of the area, while Cuvier’s beakedwhale is more common in the Gulf of Genoa area(Gannier et al. 2002, Moulins et al. 2008). These oppo-site longitudinal gradients in the distribution of the 2species might allow the spatial differentiation of habi-tat. In order to model this gradient, we included longi-tude as a variable in the habitat model (Certain 2008).Spatial variables, such as longitude and latitude,though not directly related to the biology or ecologyof the species, nor to their prey, can be consideredproxies for variables that cannot be measured, such assub-regional differences in species distribution (Asheet al. 2010, Pirotta et al. 2011, Spyrakos et al. 2011,Bonizzoni et al. 2013). The variables used as predictorsare summarized in Table 1.

Oceanographic variables have not been includedin the habitat modeling for several reasons. First, asinter-annual differences in species distribution wasnot the aim of this study, cetacean distribution datafor the whole study period were pooled. Conse-quently, oceanographic descriptors should be used atthe same temporal scale, resulting in multiyear aver-

ages. The area is characterized by a strong inter-annual variability in oceanographic parameters (As -traldi et al. 1994, Fusco et al. 2003, Picco et al. 2010);the use of multiyear averages might confound therelationship between predictors and species distri -bution. Moreover, the fact that sperm whales andCuvier’s beaked whales are preferentially found insub-marine canyon regions may be more dependenton the deep circulation occurring in the canyons thanon the environmental varia bility of the thermal frontor of the upper layers (Azzellino et al. 2008).

Data processing

We set the analysis grid at a 1 km2 resolution. Thisfine resolution was chosen in order to better inspectthe role of the complex topography of the area. Toensure temporal consistency in the analysis, onlydata collected during the summer season (from Juneto September) were included.

All vessel track-lines, as well as all sighting data ofthe 2 target species, were plotted onto the 1 km2

square cell grid. We considered each cell containinga sighting to be a presence cell. Presence cells weredetermined separately for Cuvier’s beaked whalesand sperm whales.

The first step in defining absence cells required theexclusion of poorly surveyed cells. As a consequence,the total number of kilometers covered on effortwithin each cell was computed, and only cells con-taining at least 3.5 km of on-effort survey distance,but not containing sightings, were considered po -tential-absence cells (Fig. 1). The 3.5 km thresholdwas fixed using the Jenks natural breaks classifica-tion method (Jenks 1967) in ArcGIS, on the overallsum of kilometers surveyed in each cell.

The second step involved the assessment of thereliability of the potential-absence cells. Consideringthe feeding behavior of the 2 species and the fine res-

RSP FD/ FU+FD= ( )⎡⎣ ⎤⎦ × 100

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Topographic variable Unit Abbrev.

Depth Meter DEPTHDistance from Meter DIST_200

shelfbreakDistance from Meter DIST_CAN

canyon axisSlope Degree SLOPELongitude Meters easting LON

Table 1. Topographic variables used as predictors in habitatmodels, with units and abbreviations used in formulas and

graphs

Mar Ecol Prog Ser 508: 247–260, 2014

olution of the grid used, potential-absence cells inclose proximity to presence cells are more likely to befalse-absence cells. Thus, we decided to apply a cir-cular buffer to each sighting position; this occupancybuffer accounts for the assumption that once thewhale has been sighted it has occupied (or it willoccupy) a wider area around the actual sighting posi-tion. The size of the buffer was selected separatelyfor each species. Four different buffer sizes weretested: 2, 4, 6 and 8 km radii. Each cell of the bufferedpresence/absence grid was given a value for each ofthe environmental variables described above. Forlongitude, the longitude of the center of the cell waschosen.

Current knowledge of the 2 target species distribu-tions in the area indicates how both species exploithabitat described by different topographic features(slope and canyon areas, but also oceanic waters).The relationship between species presence andthese variables thus is not expected to be linear.Among the several different statistical approachesavailable for modeling species habitat, GAMs arean appropriate technique to model species whichare expected to have complex relationships withenvironmental variables (Segurado & Araújo 2004).GAMs allow a data-driven approach by fittingsmoothed non-linear functions of explanatory vari-ables without imposing parametric constraints (Has -tie & Tibshirani 1986). As a consequence, GAMs aremore flexible and are particularly good at identifyingnon-linear relationships between species presenceand predictor variables.

In order to assess an adequate size for the buffer, aGAM was built first using the entire dataset and thenprogressively eliminating cells within the differentbuffers. As the purpose of these GAMs was to assessthe ability of the model to discriminate between habi-tat and non-habitat rather than to inspect the eco -logical influence of variables, all variables were in -cluded and no spline-fitting knot limit was set.

Five GAMs were built for each species. The opti-mal buffer size was selected considering the increasein model ability to discriminate between used andnon-used habitat, evaluated by the deviance ex -plained by each model. The optimal buffer size wasselected when the deviance explained by the modelreached at least 50%.

Habitat modeling

The entire dataset was split in order to allow modelevaluation with a dataset different from the one used

for model fitting. We randomly selected two-thirds ofthe original dataset for model construction and usedone-third as the model-testing dataset. Then a bufferof the size selected in the first step was applied to allsightings, and all potential-absence cells within thebuffer were excluded from the analysis. This processwas done separately for each species and separatelyfor the model construction and for the model-testingdataset.

GAMs were fitted using the mgcv package in Rv.2.14.1 following a presence/absence approach. Thebinary approach was preferred to the density ap proach (modeling of encounter rates) as the aimwas to point out differences in habitat preferencesbetween the 2 species, rather than to predict densityand/or map distribution patterns of the 2 speciesthroughout the survey area. Moreover, given the finespatial resolution used in this study, each presencecell contained only a single sighting. Consequently,transforming presence into a density measure, suchas an encounter rate, would result in a biased signal,with cells with less effort having more power but notnecessarily reflecting a higher preference of the species towards the cell.

GAMs for each species were fitted using a quasi-binomial distribution family with a logit link functionin order to account for over-dispersion in the dataset.In order to avoid over-fitting, the spline-fitting pro-cess was restricted to 4 knots and a gamma functionof 1.4 was applied (Wood 2006). Model selection wasconducted using a basis smoothing function thatshrinks non-significant terms to 0 degrees of free-dom, i.e. thin-plate splines with shrinkage. Covari-ance between environmental variables was in vesti -gated before their inclusion in the habitat modelingprocesses. Variables showing covariance >0.8 werenot included in the GAMs. The amount of surveyeffort in each grid cell was used as a weighting factorin the model, in order to both determine whether allhabitat types had been adequately sampled and tofurther control for the risk of ‘false’ absences (Mac -Leod et al. 2008). Output of the final GAM was eval-uated following the GAMvelope approach (Torres etal. 2008). We used the zero line on each GAM plot todivide the range of the explanatory variable that hada positive effect (identified as habitat) from the rangethat had a negative effect (identified as non-habitat)on the response variable. Peaks in the GAM plotsindicate preferred habitat.

Model performance was evaluated with confusionmatrices that compare the binary predictions to theobserved values and report the true and false pres-ences and the true and false absences, thus summa-

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rizing the goodness-of-fit of the model (Fielding &Bell 1997). To select the appropriate cut-off probabil-ity value for building confusion matrices, we used thereceiver operating characteristic (or ROC curve)method (library ROCR in R; Sing et al. 2005). Sincethe goal of the modeling process was to correctlyselect species habitat, as well as to exclude non-habi-tat, model evaluation with the model-testing datasetwas conducted using the true skill statistic (TSS;Allouche et al. 2006). TSS is computed from 2 meas-ures of model accuracy: model sensitivity (or truepositive rate, i.e. the proportion of correctly classifiedpresences) and model specificity (or true negativerate, i.e. the proportion of incorrect presence classifi-cations). These 2 parameters are independent ofeach other and are also independent of prevalence(the proportion of sites in which the species has beenrecorded as present).

TSS is expressed by the following formula:

TSS = sensitivity + specificity − 1 (2)

TSS ranges from −1 to +1, with +1 indicating per-fect model performance and 0 indicating model per-formance is not better than a random model.

RESULTS

A total of 524 daily surveys were conducted in thestudy area from June to September from 2004 to2007. On the whole, vessel track lines accounted for47 188 km distributed in 6988 cells. Cuvier’s beakedwhale Ziphius cavirostris was sighted 147 times, for atotal of 376 individuals; and sperm whale Physetercatodon was sighted 52 times for a total of 68 indi -viduals during the study period. After eliminatingpoorly surveyed cells, the final grid used for theanalysis consisted of 3892 cells for Cuvier’s beakedwhale (145 presence cells, including 15 in poorly surveyed cells [research effort <3.5 km]) and of3884 cells for sperm whales (52 presence cells, in -cluding 6 in poorly surveyed cells). These datasetswere then used to assess the ‘occupancy buffer’ size.

GAMs built using the entire dataset exhibited verylittle power to explain species distribution (Table 2).This is likely caused by the overdispersion of datadue to false-absence inflation. The 2 km buffer didnot improve model performance. In the case ofCuvier’s beaked whale, strong improvement wasachieved applying the 4 km buffer, while, for thesperm whale, a 6 km buffer was required to reach thefixed threshold of at least 50% explained deviance(Table 2). Potential-absence cells found within each

species buffer were eliminated and not consideredfor the final GAMs.

Cuvier’s beaked whale

For Cuvier’s beaked whale, 95 presence cells and1141 absence cells were used for model construction.Analysis of covariance among environmental vari-ables showed high covariance between distancefrom the 200 m contour (DIST_200) and depth(DEPTH) (0.87). Since DIST_200 also showed strongcorrelation with the predictor distance from thecanyon axis (DIST_CAN) (0.79), the variable DIST_200 was excluded from the model. The final GAM re -tained all predictor variables and was able to explain60.5% of the deviance in data (Table 3).

For Cuvier’s beaked whale, the habitat highlightedby the GAM was characterized by depths exceeding600 m and with a single peak at around 1500 m.The role of canyons as hot spots for this speciesemerged with a favorable range of DIST_can of up to10 000 m and a peak at ~5000 m. A non-canyon habi-tat is also evidenced as a positive effect of this vari-able, seen at DIST_CAN >30 000 m. The speciesseemed to prefer flat areas, as a negative effect ofslope is seen for SLOPE >7°. Longitude also showeda clear role in describing species habitat, which isencompassed between 440 000 and 500 000 m east-ing, with a clear peak in the central part of the studyarea. (Fig. 3).

The ROC method was used to select habitat versusnon-habitat areas and the optimal cutoff was set to0.08. The model-testing dataset consisted of 50 pres-

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Buffer (km) N Deviance r2

explained (%)

Cuvier’s beaked whale (2004−2007)0 3892 15.2 0.05522 3346 19.9 0.08534 2038 53.8 0.4956 1357 90.1 0.9258 1031 100 1

Sperm whale (2004−2007)0 3884 24.7 0.08122 3690 28.1 0.1214 3003 42 0.3766 2294 57.9 0.4948 1666 87.4 0.89

Table 2. Results of a generic generalized additive model(GAM) fitted applying different buffer sizes to Cuvier’sbeaked whale Ziphius cavirostris and sperm whale Physeter

catodon datasets

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ence cells and 762 absence cells. When applied to themodel-testing dataset, the model correctly predicted44 of the 50 presence cells and 642 of the 762absence cells. This model has a sensitivity of 0.88 anda specificity of 0.84. The TSS value for this modelis 0.72.

Sperm whale

For sperm whale, 35 pre sence cells and 1427absence cells were used for the model construction.Similarly to the Cuvier’s beakedwhale model fitting process, DIST_200 was ex clu ded, being strongly cor-related with DEPTH and DIST_CAN.The final GAM for this species alsoretained all considered variables, butwith a lower power, as it was able toexplain only 25.7% of the deviancein the data (Table 4).

Sperm whale habitat is found atdepths between 450 and 1500 m,while its preferred habitat is des cri -bed by a peak at ~800 m depth. Therole of submarine canyons as keyhabitat areas for the species didnot emerge as expected, as specieshabitat was found at DIST_CAN>3000 m, with 2 peaks, the first at10 000 m distance and the secondat distances >30 000 m. There wasevidence of a clear preference forsteep-slope areas, even though aflat seafloor is also selected by thespecies. Again, we found a cleargeographic preference, with a sin-gle mode identifying a hot spot inthe western part of the study area(LON < 450 000), while the centraland eastern portions of the studyarea were classified as non-habitat(Fig. 4).

The dataset used for evaluatingmodel performance consisted of 17presence cells and 831 ab sence cells.The optimal cutoff evaluated with theROC method was 0.02, and the modelcorrectly predicted 11 out of the 17presence cells (sensitivity = 0.65) and636 out of the 831 absence cell (speci-ficity = 0.77). The TSS value for thismodel is 0.41.

DISCUSSION

Despite the establishment in 2002 of PelagosSanctuary (Notarbartolo di Sciara et al. 2008),information about species abundance and distribu-tion in the area is still scarce; the conservation sta-tus of a Mediterranean subpopulation of 4 out of 8ceta cean species is listed as ‘Data Deficient’. Whilein most regions the very first step towards theidentification of cetacean habitat, migration pat-terns and population structure has largely beenbased on whalers’ logbooks (Jaquet 1996, Gregr et

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Estimate edf SE t F p

Intercept −8.1608 0.4778 −17.08 <2×10–16***Smoother termsDEPTH 2.852 91.58 <2×10–16***DIST_CAN 2.948 11.56 1.08×10–7***SLOPE 2.512 16.12 1.36×10–11***LON 2.668 73.76 <2×10–16***Deviance explained 60.5%

Table 3. Final generalized additive model (GAM) results for Cuvier’s beakedwhale Ziphius cavirostris (see Table 1 for variable abbreviations). ***p < 0.001

2500 1500 500 0

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Fig. 3. Generalized additive model (GAM)−predicted smooth splines of the re-sponse variable presence/absence of Cuvier’s beaked whale Ziphius cavi-rostris as a function of the explanatory variables (see Table 1). The degrees offreedom for non-linear fits are in parentheses on the y-axis. Tick marks abovethe x-axis indicate the distribution of observations (with and without sight-ings). Shaded area represents the 95% confidence intervals of the smoothspline functions. Confidence bands include uncertainty about the overall

mean (Marra & Wood 2012). UTM: Universal Transverse Mercator

Tepsich et al.: Habitat preferences of deep-diving cetacean species

al. 2000, Gregr & Trites 2001), such datasets do notexist for the Mediterranean, where a whalingindustry never developed. As a consequence, ithas been difficult to assess baselines to develop adhoc research studies.

On the other hand, the development of a whale-watching industry offers the possibility of obtaininglarge observation datasets in a relatively short periodof time. Many studies demonstrate how these data -sets, when collected following an ad hoc protocoland/or analyzed taking into account potential sour -ces of bias, can be a valuable resource for under-

standing species distribution. Whale-watching data sets have previouslybeen used to characterize ceta ceanhabitats within the Pelagos Sanc tuary(Azzellino et al. 2001, 2008, Moulinset al. 2007).

In this work, we used data collectedfrom several vessels in order toinspect the habitat preferences of 2deep-diving species, Cuvier’s beakedwhale Ziphius cavirostris and thesperm whale Physeter catodon. Whenobserving elusive species, the over-inflation of false-absence cells, causedby non- detection of the species whilethe species is actually present, is acommon issue. This adds to the effortbias related to the use of datasets notobtained from dedicated surveys. Ourmethodology is based both on a criti-cal assessment of false-absence biasand an analysis of effort data.

The analysis of the effort data car-ried out before proceeding with thehabitat modeling process allowed foran effective minimization of effort-re -lated biases. Biases associated withthe lack of a dedicated survey design,and especially when using whale-watching datasets, may result in intra-annual coverage differences, multiplesurveys during the day, dif ferences invessel characteristics, duplicate sight-ings, or biased effort distribution dueto ‘whaler behavior’ (concentration ofeffort in specific areas and periods)(Koslovsky 2008).

Though the vessels considered inthis analysis operated from April toOctober, we selected only trips/sur-veys operated during the summer

season (June to September), because this is the sea-son during which both whale-watching and researchvessel effort is at a maximum and more regular. Thisminimizes the differences between whale-watchingcompanies’ effort during the year. In analyzing biasfrom whale-watching vessels, Koslovsky (2008) alsohighlighted that when multiple surveys are per-formed during the same day by the same vessel, areaand time spent searching for whales differ. In ourdatabase we only observed 2 days on which a surveywas performed by the same vessel, so we are confi-dent this issue did not affect our analysis.

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Estimate edf SE t F p

Intercept −4.8532 0.1371 −35.39 <2×10–16***Smoother termsDEPTH 2.998 53.785 <2×10–16***DIST_CAN 2.946 43.668 <2×10–16***SLOPE 2.812 9.471 1.39×10–6***LON 2.714 69.950 <2×10–16***Deviance explained 25.7%

Table 4. Final generalized additive model (GAM) results for sperm whale Physeter catodon (see Table 1 for variable abbreviations). ***p < 0.001

2500 1500 500 0

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Fig. 4. Generalized additive model (GAM)−predicted smooth splines of the re-sponse variable presence/absence of sperm whales Physeter catodon as a func-tion of the explanatory variables (see Table 1). The degrees of freedom for non-linear fits are in parentheses on the y-axis. Tick marks above the x-axis indicatethe distribution of observations (with and without sightings). The shaded arearepresents the 95% confidence intervals of the smooth spline functions. Confi-dence bands include uncertainty about the overall mean (Marra & Wood 2012).

UTM: Universal Transverse Mercator

Mar Ecol Prog Ser 508: 247–260, 2014

When different whale-watching companies oper-ate in the same area they usually co-operate andinform each other about cetacean positions. As a con-sequence, different datasets may report the samecetacean group sighting. Our cetacean database hasbeen compiled coherently throughout the operatingyears, allowing corrections, such as elimination ofsightings of the same cetacean group by differentvessels operating during the same day. Anotherpotential issue is that data were collected on boarddifferent vessel types. Vessel characteristics, such asobservation deck height and speed affect the sighta-bility of cetacean species. In order to avoid this bias acommon protocol was adopted during all trips.

Both the whale-watching vessels and research ves-sel attempted to maximize the probability of ceta ceanencounter during trips/surveys (haphazard samp -ling). As a result, effort is biased by the so-called‘whaler behavior’ search method, resulting in effortbeing more concentrated in areas where cetaceansare expected. The most common species in the areaare the striped dolphin and fin whale (Reeves & Notarbartolo Di Sciara 2006). Fin whale is the maintarget species for whale-watching activities in thearea. Thus, it is unlikely that the search patterns ofvessels are biased towards the habitat of deep divers.Moreover, deep-diving species are not considered asattractive by the whale-watching industry, as thesewhales spend little time at the surface. Sperm whaleand Cuvier’s beaked whale sightings then can beconsidered accidental sightings, occurring whilesear ching for other species. In the case of the re -search vessel, no acoustic device was used to detectsperm whale or Cuvier’s beaked whale presence;therefore, the encounter probability is ex pec ted to besimilar to that of whale-watching vessels.

Our final datasets consisted of 147 sightings of Cu-vier’s beaked whale. The large number of sightingsrecorded using only visual methods seems to indicatethat the species is not as elusive to vessels as usuallystated for other areas worldwide (Heyning 1989, Bar-low & Taylor 2005). Difficulties in collecting data onCuvier’s beaked whale arise mainly as a result of theshort time individuals spend at the surface. Specifi-cally trained observers, together with the adoption ofan ad hoc protocol for approaching the animals, max-imize the probability of sightings and, consequently,the success of data collection.

Together with improving the detection rate of thisspecies and increasing the number of presence cellsin order to effectively model species habitat, wealso focused significant attention on the definition ofabsences. While presence/absence habitat models

should be preferred to presence-only models (Mac -Leod et al. 2008, Praca et al. 2009), their interpretationcan be strongly affected by false-absence bias (Gu &Swihart 2004). We particularly addressed the sourceof false-absence bias that leads to erroneous classifi-cation of habitat cells as non-habitat cells. In the firstinstance, we excluded from the analysis all cells inwhich non-detection of the target species could arisefrom limited survey effort. Secondly, we excluded allcells in close proximity with presence cells. Given thediving duration of both species and the inconspicuousbehavior of Cuvier’s beaked whale at the surface,considering cells in close proximity with presencecells as absences would likely lead to a misclassifica-tion of habitat cells as non-habitat cells. The identifi-cation of an occupancy buffer allowed for more con-trolled fitting of habitat models for both species. Inorder to verify the appropriate size of the occupancybuffer, we looked at information about the species’horizontal movements. Falcone et al. (2009) measuredhorizontal movements of Cuvier’s beaked whale overthe course of a sighting as a straight-line distance be-tween the first and last observed surfacing whichranged from 0.08 to 6.65 km. The occupancy buffer of4 km fits within this observed range.

For sperm whales, displacement is usually nega-tively correlated with feeding success (Whitehead2003b). In the northwestern Mediterranean Sea,which is recognized as a feeding ground for this spe-cies, Drouot et al. (2004) measured a mean horizontalrange of 1.3 nautical miles (~2.4 km) between feedingdive cycles. In the same study, it was observed howforaging animals tend to remain within the same areaand follow isobaths, thus moving within the samehabitat. This could explain the need for a wider buffer(8 km) when studying this species.

Submarine canyons are the preferred habitat ofmany squid species and, consequently, they are asuitable feeding ground, especially for toothed deep-diving cetaceans (Gowans et al. 2000, Ciano & Huele2001, Waring et al. 2001, MacLeod & Mitchell 2006,Moulins et al. 2007, Azzellino et al. 2008). Despitebeing widely used as an environmental predictorfor cetacean presence, no objective identification ofcanyon spatial boundaries has ever been made. Incetacean habitat analysis, topographic de scriptorssuch as slope and depth (and their means and/orstandard deviations) are usually used as proxies forcanyon presence as model predictors (Azzel lino et al.2011). Previous attempts to identify canyon areasversus continental shelf areas have been made byenclosing canyons into arbitrary (Kenney & Winn1987, Hooker et al. 1999) or topographically defined

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(Waring et al. 2001) boxes. The GIS method imple-mented in this work identified the centerline axis ofthe 17 submarine canyons in the study area whencompared to official mapping (Migeon et al. 2011)(Fig. 2). Previous attempts to study the influence ofcanyons on cetacean distributions seldom consideredthe actual bottom topography, and sightings havebeen qualitatively assigned to canyon areas (Hookeret al. 1999, Waring et al. 2001, D’Amico et al. 2003).In the northern part of the Pelagos Sanctuary a num-ber of canyons cut through the shelf break; as a con-sequence, from a qualitative point of view, the entirearea might be considered a canyon basin. The GIS-based topographic analysis method used for the opti-mal identification of the canyon axis allowed for amore effective characterization of this kind of habitat.For both species, final GAMs retained all the topo-graphic variables chosen for the analysis, even thoughsome differences in species responses were noted.

For Cuvier’s beaked whale the model’s ability tocapture the variability in the distribution is high(60% deviance explained by the final model). Depthpreferences well reflect the results obtained both inthe same study area (Moulins et al. 2007, Azzellino etal. 2008) and elsewhere in the Pelagos Sanctuary(Gannier & Epinat 2008). A clear preference for thecanyon habitat is exhibited, and a core habitat area isindicated in the central part of the study area. Thiscentral habitat area corresponds to the Genoa Valley,where the Genoa Canyon, as well as the smallerwestern coast canyons, converge. The Cuvier’s bea -ked whale GAM also confirms the existence of a sec-ond habitat for this species, in deeper waters and notclosely related to canyon axes. It is also possible thatthe 2 habitats are exploited by the species at differenttimes of the year. Seasonal or even monthly analysesshould be done to address this question.

Final model performance was high even whencompared with the model-testing dataset. This resultmay be considered an indication of residency of thespecies in the area. Photo-identification studies con-ducted in the area confirm a high fidelity to theGenoa area of a population of about 100 individuals(Rosso et al. 2011). The sperm whale model generallyperformed less well than that for Cuvier’s beakedwhale, with the best model explaining only 25.7% ofthe deviance. Lower performance of the habitat models for sperm whale might have been due tothe smaller dataset size, which must be taken intoaccount as a study constraint.

Sperm whale habitat is generally located in thewestern part of the study area, while no preferenceseemed to occur for the central or eastern region.

Other studies identified an important feeding groundfor this species in the western part of the PelagosSanctuary, in an area that is rich in submarinecanyons; this area lies west of our study area (Gan-nier et al. 2012). Though our GAM results show thatspecies habitat is highly connected with the steepestpart of the continental slope, the role of submarinecanyons did not emerge as expected. When model-ing sperm whale habitat in the northwestern Medi-terranean, other authors have noted that the speciesseems to have 2 different distribution patterns: oneclosely related to the topography of the sea-bottom inshallower waters and one in the deeper pelagicdomain (Gordon et al. 2000, Gannier et al. 2002).These 2 different habitats do also emerge from ouranalysis, as evidenced by the 2 peaks in the smoothsplines for DIST_CAN (Fig. 4): one at ~10 000 m dis-tance and one at distances >30 000 m. In the deeperdomain the species is thought to feed on pelagiccephalopods and its distribution is thought to bemore influenced by oceanographic features than bytopographic ones (Gannier & Praca 2007). The soleuse of topographic features in our habitat model maynot properly characterize the additional oceano-graphic influence on the species distribution. Thiscould explain the weaker performance of the GAMwith this species.

In contrast, it is also interesting to note that thehabitat model for Cuvier’s beaked whale performswell even if sea-surface oceanographic variables arenot considered; these might then play a secondaryrole in shaping this species’ distribution. From a con-servation point of view, this highlights the possibilitythat it may be more efficient to map conservation areasfor Cuvier’s beaked whale than for sperm whale, asthe latter species may require dynamic mapping.

When evaluated with the model-testing dataset,the model showed lower sensitivity and specificity.Indeed, though the shape of the smooth spline showsan inflection point at around 2000 m from which thefunction starts tending towards positive values, ourmodel failed to detect species habitat in oceanicwaters. Moreover, lower TSS values for the spermwhale may also arise due to the free-ranging behav-ior of the species, in contrast with the more residentCuvier’s beaked whale.

Differences in habitat use among sperm whalesand beaked whales have been noted elsewhere(Whitehead et al. 1997, Waring et al. 2001). White-head (2003a) also assessed how sperm whale feedinghabits are more differentiated than those of beakedwhales. When considering feeding habits, spermwhales can be considered generalists, while Cuvier’s

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beaked whales should be considered specialists.Feeding habits also have an influence on movementpatterns (or vice versa), with sperm whales beingmuch more free ranging than Cuvier’s beakedwhales (Whitehead 2003a). These 2 different behav-iors might help explain the different powers of the 2GAMs in describing species distribution patterns.While the models performed well with the residentspecies, they lost power when dealing with themigratory species.

The 2 habitat models also suggested that habitatpartitioning occurred amongst these 2 species in thePelagos Sanctuary. In particular, peaks in the pres-ence of Cuvier’s beaked whale coincide with thehabitat limit of the sperm whale and vice versa. Thisis particularly evident with depth, distance from thecanyon axis and slope. While sperm whales andCuvier’s beaked whales may consume the same preyspecies, both whales are size-limited predators andonly take a narrow range of prey relative to bodysize. As a consequence, they do not necessarily con-sume the same sizes of prey (MacLeod et al. 2006).The different habitat preferences of these 2 predatorsmight then reflect differences in the distributions ofprey species, indicating a reduction in niche overlap.

Acknowledgements. We thank the whale-watching opera-tors for hosting students and researchers on board and mak-ing every possible effort to collect scientific data duringwhale-watching trips. Special thanks go to bluWest and, inparticular, to Albert Sturlese, Barbara Nani and Marco Bal-lardini whose collaboration was essential to this study. Thecollaboration of Consorzio Liguria via Mare (in particular,Roberta Trucchi and Simone Scalise) allowed us to enlargeour study area. Thanks to the University of Genoa and, inparticular, to Maurizio Würtz. This work would not havebeen possible without the enthusiasm of all the students andresearchers who participated in the surveys on board theresearch vessel of the University of Genoa and whoembarked as observers on whale-watching vessels. Manythanks go to Martin Desruisseaux and Michel Petit for thedevelopment of the database.

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Editorial responsibility: Yves Cherel, Villiers-en-Bois, France

Submitted: October 8, 2013; Accepted: May 7, 2014Proofs received from author(s): July 23, 2014