modelling predicted sperm whale habitat in the central mediterranean sea: requirement for protection...

Modelling predicted sperm whale habitat in the centralMediterranean Sea: requirement for protection beyond the Pelagos

Sanctuary boundaries

MEHDI AÏSSIa,b,*, AHMED OUAMMIc, CRISTINA FIORIb and JESSICA ALESSIdaFaculty of Sciences of Bizerte, Department of Life Sciences, Zarzouna, Tunisia

bMENKAB:il respiro del mare, Savona, ItalycUniversity of Genoa, Department of Communication, Computer and System Sciences (DIST), Genoa, ItalydUniversity of Genoa, Department for the Study of Territory and its Resources (DIPTERIS), Genoa, Italy

ABSTRACT

1. Climate change and human activities impacts are considered to be the main causes of sperm whale habitatalteration.2. Despite the creation of several marine protected areas in the Mediterranean Sea, the sperm whale status

remains ‘endangered’. Its spatial distribution has been reported in different areas of the Mediterranean, amongthem the Pelagos Sanctuary.3. Various biophysical parameters have been recognized to influence sperm whale distribution depending on the

modelling scale. Hence, this study investigates and predicts sperm whale relative occurrence, taking into accountthe parameters that affect their habitats in the central Mediterranean Sea, inside and beyond the boundaries ofthe Pelagos Sanctuary.4. An artificial neural network (ANN) model was used to predict the probability of sperm whale occurrence in

the central Mediterranean Sea, for each cell of a 3× 3minute grid using a Visual Basic script to interface with GISsoftware. The algorithm was trained using species presence/absence data and a set of physiographic variables suchas depth, slope, distance to shore and magnetic field.5. Some geographic areas exhibit a consistently high probability of occurrence and may be identified as highly

used areas for special management concern. Thus, this work represents a preliminary evaluation of managementand conservation effort outside the Pelagos Sanctuary. The map of sperm whale predicted relative presence canbe used to mitigate potentially harmful human activities and to support the design and management of marineprotected areas, including the delineation of ecologically meaningful boundaries.Copyright # 2013 John Wiley & Sons, Ltd.

Received 1 April 2012; Revised 12 September 2013; Accepted 16 September 2013

KEY WORDS: sperm whale habitat; central Mediterranean Sea; artificial neural network model; spatial prediction; relativepresence probability; managing the conservation status; protection measurements

*Correspondence to: M. Aïssi, Faculty of Sciences of Bizerte, Department of Life Sciences, Zarzouna, 7021, Tunisia. Email: [email protected]

Copyright # 2013 John Wiley & Sons, Ltd.

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. 24(Suppl. 1): 50–58 (2014)

Published online 4 November 2013 in Wiley Online Library(wileyonlinelibrary.com). DOI: 10.1002/aqc.2411

INTRODUCTION

The Mediterranean is a land-locked sea recognized asone of the world’s global biodiversity hotspots (Myerset al., 2000). Its unique connection with thesurrounding seas and ocean is through narrow straitsand channels, i.e. the artificial Suez Canal and theStrait of Gibraltar. These two narrow passages aredescribed respectively as a route for the introductionof invasive alien species (Zibrowius, 1983, 1991;Galil, 2000) and a gene-flow barrier to NorthAtlantic species. This natural obstacle has led to thecreation of genetically separate populations, such asstriped dolphin (Archer, 1996; Garcia-Martinezet al., 1999), fin whale (Bérubé et al., 1998) andsperm whale (Drouot et al., 2004a; Engelhaupt,2004). This genetic isolation, as well as acousticstudies consolidate the hypothesis of a distinctiveMediterranean sperm whale sub-population(Frantzis and Alexiadou, 2008).

Mediterranean biodiversity is undergoing rapidalteration under the combined pressure of climatechange and human impacts; but protectionmeasures, either for species or ecosystems, are stillscarce. Indeed, the most likely continuing cause ofrecent decline of sperm whales in the Mediterraneanis collisions with vessels and entanglement inpelagic driftnets, which have caused considerablemortality since the mid-1980s. Reeves andNotarbartolo di Sciara (2006) estimated the spermwhale Mediterranean sub-population had beenreduced to hundreds of individuals. Accordingly,this species is assessed as ‘Endangered’ underthe IUCN Red List criteria EN C2a(ii) (Tayloret al., 2008).

The development and implementation of effectiveconservation measures requires a detailedknowledge of the geographic occurrence of thespecies. Thus, several studies have attempted tocorrelate sperm whale distribution with thesurrounding environment. Sperm whales are notevenly distributed throughout the Mediterranean(Gannier et al., 2002). Habitat selection for foragingor reproduction in the north-western Mediterraneanhas been related to different parameters, amongthem physiographic features and hydrographiccharacteristics (Gannier and Praca, 2007; Azzellinoet al., 2008, 2012; Moulins et al., 2008; Di Fulvioet al., 2010). Focusing on topographic features in

this part of the Mediterranean Sea, habitat selectionwas associated mainly with the presence ofsubmarine canyons (David, 2000; Aïssi et al., 2012;David and Di-Méglio, 2012). This preference forcanyon areas and ‘complex’ bathymetries has beenhighlighted by other studies (Croll et al., 1998;Gannier and Praca, 2007; Moulins et al., 2008).

Modelling habitat is a powerful tool for theprediction of sperm whale distributions and forunderstanding the ecological processes determiningthese distributions (Redfern et al., 2006). Thistechnique has been used to inform cetaceanmanagement by attempting to understand therelationships between cetaceans and theirenvironment, from which inference is then drawnon space usage (Johnston et al., 2005; Cañadasand Hammond, 2008; Redfern et al., 2008;Stafford et al., 2009; Corkeron et al., 2011).

Habitat suitability models have been conducted(Kaschner et al., 2006), but habitat maps variedaccording to the modelling technique used. Pracaet al. (2009) used four different methods in theirmodelling study in the north-western Mediterranean:principal component analysis (PCA), ecologicalniche factor analysis (ENFA), generalized linearmodel (GLM) and multivariate adaptive regressionspline (MARS), but the resulting habitat suitabilitymaps differed. Moulins et al. (2008) attempted tosimulate sperm whale distribution using MonteCarlo methods and identified a canyon area as apossible hotspot, however, the small number ofsightings meant the results were not significant.

In this present study, a new modelling techniquebased on an artificial neural network model(hereafter ANN) has been used to attempt tooptimize mapping of sperm whale relative presencein the central Mediterranean Sea, beyond the bordersof the international sanctuary for marine mammals,the ‘Pelagos Sanctuary’. This study was carried outby analysing temporal acoustic data collected overthree consecutive years, inside the Pelagos Sanctuary.The model has been applied to a case study within alarge pelagic area. Outside the Pelagos Sanctuarydistribution maps were produced to help focusconservation actions that may help to mitigate theimpact of threats to sperm whales. In this way, itwould be possible to ensure better protection of moresensitive areas for this endangered species.

PREDICTION OF SPERM WHALE OCCURRENCE BEYOND THE PELAGOS SANCTUARY 51

Copyright # 2013 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 24(Suppl. 1): 50–58 (2014)

MATERIALS AND METHODS

Model development area

The case study covers the pelagic waters within thePelagos Sanctuary (Figure 1). This large marineprotected area (MPA) of 90 000km2 was establishedin 2002 by a tripartite agreement between thegovernments of France, Italy and Monaco. Itextends from the coastline of the Ligurian Sea to thewaters of northern Sardinia, and surroundingCorsica to the Tuscan Archipelago. It is considered adistinct bio-geographical region based on uniqueoceanographic and ecological characteristics, such asits heterogenic topography of canyons andseamounts, distinct water masses and patterns ofprimary production (Notarbartolo di Sciara et al.,2008). Indeed, this MPA is known to support thehighest densities of marine mammals in theMediterranean Sea (Notarbartolo di Sciara, 2002).

The area of interest is located in the centralMediterranean Sea outwith the borders of thePelagos Sanctuary, where considerable uncertaintyexists regarding sperm whale occurrence. Thislarge area of 305 500 km2 extends from thecoastline of the Ligurian Sea to the waters southof Sardinia (38°N), and from 6°E to 12°E. Thisarea is characterized by a continental slopedissected by numerous canyons (south of Sardinia)and several large seamounts in the central part ofthe Tyrrhenian Sea.

Data acquisition method

The study area was subdivided into a grid of 3× 3nautical miles (n.m.). The listening station designwas based on previous long-time visual monitoringthat had been established since 2004 in thenorth-western Ligurian Sea (Moulins et al., 2005,2008; Lamoni, 2008). Sperm whale spatialdistribution in this area was associated mainly withspecial topographic features such as submarinecanyons and seamounts (Aïssi et al., 2012). Thus,listening stations were located where specialtopographic features occur inside the PelagosSanctuary.

The acoustic survey extended over a period of3 years from March 2009 to November 2011, usinga motorboat managed by the association‘MENKAB: il respiro del mare’. This semi-rigidvessel was equipped with a simple omni-directionalhydrophone covering a range of 5 n.m. Thesampling protocol consisted of cruising from onestation to another to perform a regular listeningfor at least 5min.

Topographic indicators

Listening points were randomly monitored over theyear. The goals of the acoustic study includeddetection, localization, and tracking of spermwhales. The environmental variables (oceanographicand remote sensing data) were used as descriptors ofsperm whale habitat. These parameters includedthree topographic variables: depth, seafloor slope,and distance from the coast.

These variables were extracted from differentopen sources: (1) the bathymetry was extractedfrom the US Navy data set with a grid unit of

Figure 1. Map of the study area in the central Mediterranean Sea thatencompasses the Pelagos Sanctuary (between the black diagonallines), on topographical background of isobaths each 500m depth(dotted lines), acoustic listening stations (grey dots) and the grid cell

used for the analyses.

M. AÏSSI ET AL.52


1 × 1 n.m.; (2) the seafloor slope and distancefrom the coast were derived from the depthgrid, and are displayed in m km–1 and kmrespectively; and (3) the spatial informationabout the magnetic field was taken from thedata set available online (http://models.geomag.us/wdmam.html).

Artificial neural network modelling (ANN) anddata treatment

The data set consisted of a time series of sperm whaleacoustic listening over 3 years (2009–2011), date andlocation pooled for each 3min grid cell. This modelwas based on the analysis of the six non-dynamicvariables: latitude, longitude, bathymetry, slope,nearest distance from coast and anomaly of themagnetic field. These parameters were chosenbecause of their influence on sperm whaledistribution. Strong correlations between bathymetryand patterns of sperm whale occurrence have beenreported in different regions of the MediterraneanSea (David, 2000; Gannier et al., 2002; Pracaet al., 2009; Di Fulvio et al., 2010), makingseafloor elevation an ideal candidate as anenvironmental proxy for a generic habitat suitabilitymodel.

Kriging techniques were adopted for the spermwhale occurrence predictions (Bentamy et al.,1999). In this study, the prediction data weregenerated by an ANN algorithm to establish acorrelation coefficient between longitude, latitude,depth, slope, distance from coast, magnetic field,and the presence of sperm whales (Rumelhartet al., 1986; Cellura et al., 2008).

Specifically, for the ANN model, a three-layeredback-propagation standard ANN classifier has beenused consisting of input, hidden, and output layers(Rumelhart and McClelland, 1986). This means thatthe artificial neurons are organized in layers, andsend their signals ‘forward’, and then the errors arepropagated backwards. The network receives inputsby neurons in the ‘input layer’, and the output of thenetwork is given by the neurons on an ‘outputlayer’. There may be one or more intermediate‘hidden layers’. The back-propagation algorithmuses supervised learning, which means that thealgorithm is provided with examples of the

inputs and outputs the network is to compute,and then the error (difference between actualand expected results) is calculated. The object ofthe back-propagation algorithm is to reduce thiserror until the ANN learns the training data. Inorder to adjust the error to a minimum, thetraining should begin with random weights (Hillet al., 1994; Zhang et al., 1998).

The ANN input layer consists of six units whichare associated with longitude, latitude, depth,slope, distance from coast, and magnetic field(linearly normalized between 0 and 1, taking intoaccount, respectively, the minimum and themaximum) of a specific location.

The ANN output layer consists of one unit whichis associated with the listening results for the spermwhales (1 for the presence and 0 for the absence ofsperm whales). Regarding the hidden layer, thechoice of the number of units may be acceptedwith a strongly minimum relative error. In thisstudy, after some testing, a reasonable choice forthe hidden layer was 20 units.

In a back-propagation standard ANN learningphase, the characterizing ‘weights’ are defined as agiven set of patterns. Specifically, the output yi ofeach unit i in the network is determined by:

yi ¼1

1þ e�xi(1)

This output has a real value between 0 and 1; xi isthe total input to unit i given by:

xi ¼ ∑jwi; jyj þ bi (2)

where wi,j is a real number, called weight,representing the strength of the connection fromunit j to unit i; the weight sum of the inputs isadjusted by the bias characteristic of the unit i, bi.Network weights are initially assigned as randomvalues and uniformly distributed in [�0.3, 0.3]; ineach back-propagation cycle, the weights areadjusted in the total output error. The learningends either after a user-defined number of steps orwhen the total output error becomes asymptotic,where this error is defined as:

E ¼ ∑p∑j

Op;j �Dp;j

� �2(3)



http://models.geomag.us/wdmam.html

http://models.geomag.us/wdmam.html

where Op,j is the observed output on unit j forlearning pattern p and Dp,j is the desired output.

The ANN learning procedure was performed onlearning sets of patterns, where each learningpattern p is represented by six parameters in theinput layer (longitude, latitude, depth, slope,distance from coast, magnetic field) and oneoutput parameter in the output layer (one for thepresence and zero for the absence of sperm whales).

Model validation

To provide informative predictions, it is necessaryfor a model to predict a high proportion of testlocalities successfully (Anderson et al., 2002). Inthis study, a jackknife (or ‘leave-one-out’)procedure was implemented.

Each observed locality was removed once fromthe set of data and a model built using theremaining n – 1 localities. Hence, for a specieswith n observed localities, n separate models werebuilt for testing. Predictive performance was thenassessed on the ability of each model to predict thesingle locality excluded from the training data set(Pearson et al., 2007).

The test of significance implemented wasdeveloped to assess stability and bias estimates. Asubset of the original scores was randomlyreplaced through sampling without replacement. Itis considered to be an empirical samplingdistribution generated by deleting a single datapoint. In general, sampling subsets that leave outone, two, or a whole group of observations, thendefining a distribution across such deletions,provides an empirical distribution based on thejackknife approach (Lee Rodgers, 1999).

RESULTS

During the survey inside the Pelagos Sanctuaryover the 3 years, 570 stations were sampled, ofwhich 68 indicated sperm whale presence. Visualexamination of quadratic errors revealed that theANN model predictions may be consideredreliable as the value was around 5% (Figure 2).The model predicted large areas of suitablehabitat for sperm whale outside of the PelagosSanctuary (Figure 3).

In this study, the learning set consisted of thenormalized longitude, latitude, depth, slope,distance from coast, magnetic field and results of thelistening for the presence of sperm whales at 570stations. On the other hand, the testing set consistedof patterns represented by the input component(normalized longitude, latitude, depth, slope,distance from coast, magnetic field), while, accordingto a classic Jackknife procedure, the output

Figure 2. Quadratic error of the ANN simulation for the prediction ofsuitable habitat for sperm whale in the central Mediterranean Sea.

Figure 3. Predicted map of suitable areas for sperm whale occurrence inthe central Mediterranean Sea obtained by the ANN kriging technique

with indication of submarine canyons cited in the text.

M. AÏSSI ET AL.54


component is left unknown and its value results fromthe ANN algorithm for that specific input (Table 1).

The present simulation reveals the existence ofseparate areas of high probability of sperm whaleoccurrence, such as the north-western Ligurian Seaalong the Italian and French coasts, and waterssurrounding Corsica.

Within the Ligurian Sea, the areas of maximumprobability of sperm whale presence were theGenoa Canyon, the Imperia Canyon and areas offthe French coast characterized by successive smallcanyons. Waters surrounding Corsica were alsocharacterized by the presence of numeroussubmarine canyons (e.g. Saint Florent, Porto,Sagone, and Ajaccio).

Beyond the boundary of the Pelagos Sanctuary,the model predicts a large suitable area for spermwhale, covering almost the whole Tyrrhenian Sea.This area is characterized by extensive submarinecanyons and seamounts.

DISCUSSION

The correlation between sperm whale distributionpatterns and environmental data has been the

subject of several studies, with different analyticaltechniques applied (Praca et al., 2009; Di Fulvioet al., 2010; Pirotta et al., 2011). However, veryfew examples exist of ANN being used to predictthe spatial distribution of species or communitiesusing biophysical descriptors (Lees and Ritman,1991; Lees, 1996). Lek et al. (1996) illustrated thatneural network models are more powerfulthan multiple regression models when modellingnon-linear relationships.

Owing to the small number of patterns on whichthe training could be performed and also due to therough description of each station (longitude,latitude, depth, slope, distance from coast, magneticfield) several tests have been performed in order toproduce maps of presence/absence of sperm whale.When the ANN was tested on the 20 stations(Table 1), predictions portrayed an average absoluteerror of 0.092. The lowest error was 0.0002. Thehighest error was 0.3, which is, however, quite lowtaking into account that the interpolation has beenperformed only on a small number of stations.From these tests, it seemed worthwhile producingthe map of presence/absence of sperm whale.

In order to develop a sperm whale–physiographicparameter model to predict probable suitablehabitat, data were processed for a wide range ofphysiographic variables some of which wereconsidered a priori to be ecologically meaningfulto sperm whales (e.g. water depth) and others wereof unknown importance (e.g. distance to shore). TheANN model was used to calculate the probability ofoccurrence of sperm whale for each cell of a 3′ × 3′grid using a Visual Basic (VB) script to interfacewith GIS software. Some geographic areasconsistently exhibit a high-occurrence probability,and may be identified as highly-used areas and thusof special management concern.

Highest probability of sperm whale occurrencewas spatially distributed, in particular the upperslope zone of the Ligurian Sea, waters surroundingWestern Corsica and Eastern Sardinia. Thepredicted suitable areas inside the PelagosSanctuary are similar to those identified inprevious studies conducted in this area of theMediterranean (e.g. Drouot et al., 2004b;Azzellino et al., 2008, 2012; Moulins et al., 2008;Aïssi et al., 2012; David and Di-Méglio, 2012).

Table 1. Jackknife test of species presence/absence prediction of spermwhale as obtained by the ANN kriging method

Longitude Latitude

Data Presence/absence ofsperm whale

PredictionsPresence/absence ofsperm whale Error

8.967 43.550 0 0.0002 0.00028.967 43.483 0 0.0008 0.00088.967 43.417 0 0.0056 0.00568.967 43.350 1 0.9000 0.10008.967 43.283 1 0.8700 0.13009.033 43.283 1 0.9900 0.01009.100 43.283 0 0.0200 0.02009.100 43.217 1 0.7500 0.25009.167 43.217 0 0.0030 0.00309.300 43.150 0 0.0500 0.05008.736 44.191 1 0.8900 0.11008.698 44.215 1 0.7500 0.25008.330 43.705 1 0.7800 0.22008.329 43.725 1 0.8100 0.19008.287 43.736 1 0.9200 0.08007.867 43.417 0 0.0200 0.02007.800 43.417 0 0.0100 0.01007.733 43.417 0 0.0220 0.02208.267 43.762 1 0.9400 0.06008.075 43.733 1 0.7000 0.3000

Average 0.092



Beyond the Pelagos Sanctuary boundaries, themodel predicts a large area of sperm whalepresence. This area of the Tyrrhenian Sea has notbeen studied extensively, unlike the Ligurian andCorsican seas. However, taking the occasionalsurveys carried out in this area into account thepredicted occurrence may be considered asconsistent with these comparative studies (Drouotet al., 2004b).

This relatively high predicted occurrence in theTyrrhenian Sea (eastern Sardinia) is thought to berelated to the special topographical bottom profilecharacterized by the presence of submarine canyons.Sperm whale sightings have been sporadicallydetected in this area during systematic surveysundertaken from platforms of opportunity such aspassenger ferries (e.g. route between Civitavecchiaand Golfo Aranci) (Arcangeli et al., 2009).

Furthermore, this large area adjacent to thePelagos Sanctuary has been described as an areaof special interest owing to the regular occurrenceof various species of large cetacean (Bittau et al.,2012). This area includes a particularlyproductive canyon (Canyon of Caprera) locatedaway from the north-eastern coast of Sardiniaand has been monitored during summer 2012when sperm whales were sighted (Würtz, pers.Comm.). Moreover, Drouot et al. (2004b)estimated that the sperm whale encounter rate isslightly less frequent than that in the north-westernMediterranean based on visual and acoustic surveysover 5 years.

Models can be of two types: explanatory andpredictive. Explanatory models aim at highlightingthe variables mostly associated with the observeddata. Predictive models take these relationshipsand attempt to use the variables to predictpatterns of encounter (Guisan and Zimmermann,2000). In this study, the predictive modeldeveloped was based on the association betweenobserved habitat use, detected from acousticlistening stations, and measures of physiographicvariables within the modelled area. Areas knownas unsuitable such as shallow and coastal waterswere not eliminated from the data, allowing themodelling process not only to focus on previouslyknown suitable area, but also on others that mayintroduce noise. It is possible that a closer focus

on areas of interest enables the model to makerelatively more precise and accurate predictions.

These results indicate that the model was able todiscriminate suitable from unsuitable areas withreasonable accuracy. The relative contribution ofdifferent physiographic parameters or attributesand the relationship of each one with habitatoccupancy could not be estimated. However,habitat was divided into classes based on limitedcombinations of categorical predictors. Inaddition, sperm whale detections and parameterswere taken as statistically independent. This canlead to spurious significant relationships betweenhabitat predictors and occupancy.

This study shows that a model with relativelysimple inputs can help in understanding the spatialdistribution of sperm whales. This model wasdeveloped with the aim of identifying areas wheremost interactions might occur, and hence whererisks of mortalities would be likely to be greatest,with the view of using it as a dynamic, rather thanstatic approach to short-term area closures.Further improvements could be made byidentifying the rates of change of the explanatoryvariables.

The area predicted for sperm whale presencebeyond the limits of the Pelagos Sanctuary seemsto be a good reason to develop a managementplan that establishes achievable conservationtargets for sperm whales. The simulation resultsmay be considered as justification for the creationof a Specially Protected Area of MediterraneanImportance (SPAMI), carried out by theRegional Activity Centre for Special ProtectedAreas (RAC/SPA) under the patronage of the UnitedNation Environmental Programme – MediterraneanAction Plan (UNEP-MAP).

ACKNOWLEDGEMENTS

We are particularly grateful to the ‘FondationTOTAL’ for providing resources and support forthe ISHMAEL project. Surveys conducted by the‘University of Genoa’ and the NGO ‘MENKAB:il respire del mare’ were sponsored by Yanmarand SACS Boatyard. We are very thankful to thestudents and research volunteers who contributed

M. AÏSSI ET AL.56


to the data collection. Special thanks are given toresearchers of CIMA Research Foundation, AurélieMoulins, Massimiliano Rosso and Paola Tepsich,for their fundamental assistance in bringing thisproject to fruition. We are also grateful toanonymous referees for their useful comments on anearlier version of this manuscript.

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