D.M. Bailey Æ I.G. Priede

Predicting fish behaviour in response to abyssal food falls

Received: 21 September 2001 /Accepted: 18 May 2002 / Published online: 17 September 2002� Springer-Verlag 2002

Abstract Estimates of fish abundance based on arrivalrates and numbers present at baited cameras allowmultiple, replicate assessments where data are notavailable by other means. Unfortunately such estimatesare strongly affected by the assumed behaviour of thefish species concerned. Three of the possible foragingstrategies of deep-sea fish were modelled and the likelypatterns of fish arrival calculated for the same fishdensity, swimming and current velocities and odourplume properties. Cross-current foraging resulted in thehighest numbers of fish at bait, with arrival rates thatfitted well to field data. The sit-and-wait strategy pro-duced lower arrival rates with passive drifting animalsarriving slowest. Each model produces a distinctivepattern of animal arrivals that may be diagnostic of eachforaging strategy. The advantages, disadvantages andlikely metabolic and sensory demands of each strategyare discussed.

Introduction

The use of baited camera systems has provided valuableinsights into the behaviour and ecology of scavengingdeep-sea animals allowing determination of the speciesattracted to carrion and their rates of arrival at recentfood falls (Isaacs and Schwartzlose 1975; Jones et al.1998; Collins et al. 1999b; Witte 1999). These data allowmultiple, replicated, estimates of scavenger abundancenot otherwise available due to the difficulty and time

required to make quantitative collections of deep-seaanimals by trawling or large-scale surveys by submers-ible or camera sled (Priede and Merrett 1996, 1998;Cailliet et al. 1999).

In conjunction with the use of ingestible acoustic tagsautonomous lander platforms have measured fishswimming speeds and allowed the description of beha-viour and direction of travel after feeding (Bagley 1992;Collins et al. 1999a; Priede and Bagley 2000). Howeverthe biologically important processes by which deep-seascavengers detect food falls and locate the potential mealcannot be observed in this way. Inferences must there-fore be derived from estimates of the sensory abilities ofdeep-sea animals (Wagner et al. 1998; Warrant 2000),tracking and other studies in shallow water relatives (He1991; Arnold et al. 1994; Løkkeborg 1998; Løkkeborget al. 2000) and the behaviour of other scavenginganimals (Morton and Yuen 2000).

A variety of potential food acquisition strategiesexist, including active foraging across currents (Priedeet al. 1991), drifting on the ambient current, sit-and-waitambush or passive plume detection (Wilson and Smith1984; Sainte-Marie and Hargrave 1987). Optimal searchdirections for plumes of different shapes have been cal-culated by Dusenbury (1989) and show that as plumesbecome more elongated cross-current foraging should befavoured. Compromises such as active foraging arounda ‘‘core’’ area or periodic foraging and torpor havealso been documented in Gadids (Løkkeborg 1998;Løkkeborg et al. 2000).

Several factors might affect the number of fish at-tracted to bait and their rates of arrival and dispersal(Priede et al. 1994b). Current velocity affects the lengthof the odour plume emanating from the bait; its lateraldispersal and the across-ground swimming speed of fish.The intrinsic, or through-water, swimming speed offoraging fish affects the rate at which animals come intocontact with the bait, as does the abundance (density) ofthe fish. Changes in fish speed in response to odourplumes have been documented in cod, Gadus morhua(Løkkeborg 1998), altering their potential rates of

Marine Biology (2002) 141: 831–840DOI 10.1007/s00227-002-0891-9

Communicated by J.P.Thorpe, Port Erin

D.M. Bailey (&) Æ I.G. PriedeOcean Laboratory, University of Aberdeen,Newburgh, Aberdeenshire, AB41 6AA, UK

E-mail: d.bailey@abdn.ac.ukTel.: +44-1224-274413Fax: +44-1224-274402

arrival. The effective length of the plume depends on thebait characteristics and persistence, the olfactory per-formance and behaviour of the fish. Even the mostpersistent exposed baits such as marine mammal car-casses do not endlessly accumulate fish with a plateau innumbers being attained (Jones 1999; Witte 1999). Thenumbers present depend on the staying time of animalsas well as their arrival rates (Sainte-Marie and Hargrave1987).

The complexity of these interacting variables is re-flected in the range of relationships between time andfish abundance presented for deep-sea fish (Lampittet al. 1983; Wilson and Smith 1984; Jones et al. 1998;Collins et al. 1999a; Witte 1999). Unfortunately muchof the empirical data required to develop a morecomplex model to explain these observations does notexist. In the present study, three simple models of fishbehaviour around both transient and long-lasting baitsare presented. The models demonstrate the importanceof our assumptions about the behaviour of foragingfish and which data are required to quantitatively im-prove our understanding of the ecology of deep-seafish.

The main model describes the ‘‘cross-current’’ for-aging strategy suggested for Coryphaenoides armatus(Priede et al. 1991) with ‘‘sit-and-wait’’ and ‘‘passivedrifting’’ strategies (Wilson and Smith 1984) consideredseparately (Fig. 1). The models allow the manipulationof several parameters likely to affect the behaviourof fish around bait or natural food falls in order todevelop a priori hypotheses that may be tested byobservation.

Materials and methods

Model development – cross-current foraging

Outline

The plume represents a barrier to fish foraging across the current.On contact with the plume fish turn upstream and swim to the baitthen, after a period at the bait, the fish leaves. With small, transient,baits such as single mackerel carcasses, the plume becomes lessattractive as it dilutes and eventually falls below the threshold atwhich the fish can detect the odour. Fish swimming close to the baitcontinue to be attracted by the sounds, movements, and lightemissions of other animals feeding.

Assumptions

Fish swam at an average of 90� to the direction of current move-ment. This was based on the findings of Priede et al (1991) whichshowed that the exit direction of C. armatus ingesting transponderswas biased towards cross-current movement. All fish contacting theplume reacted by turning upstream (rheotaxis) and followed theplume to its source. Approach direction of grenadiers arriving atbait was most often from a downstream direction (Wilson andSmith 1984). The ranges at which fish can detect plumes and reactby turning upstream were based on those for cod (Løkkeborg1998). Fish were evenly distributed across the seafloor. No shoalingbehaviour has been demonstrated in tracked fish (Collins et al.1998) with animals dispersing independently after feeding. Eachspecies or size class was also treated independently with no densitydependence affecting fish behaviour. There is some evidence ofinterspecific competitive exclusion (Armstrong et al. 1992; Jones1999) but not enough data are available to include this in themodels.

Water velocity and direction were constant. While this is rea-sonable over the first 1–2 h post-deployment during longer-termexperiments this may be a significant over-simplification.

Input parameters

The variables that might be manipulated within the model werefish swimming speed (through water as opposed to across groundspeed) during foraging. Unless stated the fish swimming speedused was 0.1 m s–1, based on values for C. armatus (Priede andMerrett 1996). A different swimming speed could be set for ap-proaching the bait. Cod (G. morhua) swim up to 3 times as fastfollowing olfactory contact with bait than during foraging(Løkkeborg 1998).

Staying time, the length of time from arriving at the bait todeparting, can be determined from tracking of ingested tran-sponders (Collins et al. 1998) and is set at a fixed value in eachmodel run. Staying times and the time taken to consume the baitused were 60 and 120 min respectively and are based on observa-tions of mackerel bait using photographic or video lander experi-ments. Observations with marine mammal carcasses demonstrate aturnover of animals with fish leaving before the bait is fully con-sumed (Jones et al. 1998). Fish densities in individuals per km2 andcurrent velocity were based on previous trawl and lander data re-spectively (Priede and Merrett 1998). Fish densities of 1–800fish km–2 were modelled, with a fish density of 100 fish km–2 and acurrent velocity fixed at 0.05 m s–1 unless otherwise stated. Theinadequacy of trawling as a method of determining absolute fishabundances are acknowledged. The radius around the landerwithin which fish can detect the bait by the sights and sounds ofother animals is not known empirically but some estimates basedon morphological studies are available (Warrant 2000). The valueof 10 m used here is conservative and likely to require adjustmentgiven the high levels of bioluminescence observed by low-lightcameras at bait in some deep-sea systems (Herring et al. 2000).

Fig. 1 The types of foraging behaviour proposed for deep-sea fish.The shape of the odour plume with respect to the prevailing currentis shown, as are the predicted tracks of fish responding to the bait

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Rate of arrival at the plume

Rate of arrival at the plume (Rc, fish s–1) depended on the densityof the fish species (nf, fish m–2), their velocity normal to the current(Vfc, m s–1) and the length of the plume (Lpl, m). In long-term baitexperiments the plume reached a maximum width (Lpwmax, m)beyond which the plume is too dilute to be detected by the fishspecies. Within a fixed radius (Lrd, m) around the bait the fish wereable to detect the food source audio-visually as a result of otheranimals feeding. Fish density (nf) was a fixed parameter based uponexisting trawl data, plume length (Lpl) depended on time (t, s) andthe velocity of water flow (Vw, m s–1) across the bait (based onexisting current logger data). Fish ‘‘over-ground’’ swimming ve-locity while moving across the current depended on the fishswimming speed through the water (Vfs, m s–1) and the currentvelocity (Vw).

Lpl ¼ t � Vw

Vfc ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

V 2fs � V 2

w

q

Rc ¼ nf � Vfc � ðLpl þ LrdÞ

An effect of this is that the fish is orientated at an angle to itsdirection of movement, a fact of potential significance to any use ofsight and hearing during foraging. The angle, /, depends on therelative velocities of the current and fish.

/ ¼ ArcSinVw

Vfs

Time to arrive at the bait

A delay existed between the fish contacting the plume and arrivingat the bait. This delay depended on the distance between the fishand the bait, its swimming speed and the velocity of the current.Assuming uniform fish distribution the mean distance between thebait and the fish was taken as half the plume length. Mean time tobait (tmb, s) was then mean distance between the fish and the baitdivided by the approach speed of the fish (Vfa, m s–1) where Vfsa isthe through-water speed of the fish during the approach up theplume. Mean time to bait was added to elapsed time to create anelongated timescale over which fish arrive (tarr, s).

tmb ¼Lpl

2 Vfsa � Vwð Þ

The rate of fish contact with the plume depended on plumelength and therefore increased with time. Cumulative fish arrival attime t (narr) was calculated from the sum of the individual second-by-second arrival rates in fish s–1 until t.

narr ¼X

Rc

Staying time

In all the models, fish left at an identical rate to that at which theyarrived with a delay corresponding to the staying time (tstay). Un-less stated a staying time of 60 min is used throughout. Deductionof leaving rate from arrival rate gave the net rate of change innumbers from which the cumulative number of fish present wascalculated. At time t, numbers present (npres) depended onthe number accumulated by time t and the number accumulated byt-tstay, as these fish leave by time t (Fig. 2).

Numbers of fish present continued to rise while the plumelengthened. As the plume diluted, a threshold dilution after whichfish were not attracted to the plume was crossed. This fixed theplume at a maximum length. Olfactory foraging deep-sea fish wereassumed to be able to detect plumes at up to 700 m, the maximumrange from bait at which cod were observed turning to followodour plumes (Løkkeborg 1998). When the plume was at a fixedlength, time to arrive (tmb) was also fixed. Arrival and departurerates were equal so fish numbers stabilised.

Modelling transient baits Fish were attracted to, and left from,transient baits such as single fish carcasses, according to the same‘‘rules’’ as for persistent baits. With such small baits the odourplume did not increase up to a fixed width as described above. Asthe plume area increased the proportion of the plume which wasconcentrated enough to attract fish, or coherent enough to providea useful cue, was likely to reduce.

Odour plumes are not evenly diluted and consist of ‘‘strands’’(Finelli et al. 2000) of more concentrated odour interwoven withodour-free water. Chemosensory rules were assumed to operateallowing the animal to determine whether the energetic costs ofmoving upstream to the bait will be outweighed by the value of themeal (Zimmer-Faust 1989).

Plume spread was calculated according to the equations of(Stanley et al. 1985; Sainte-Marie and Hargrave 1987) and givesthe plume width (Lpw, m). The distance versus concentrationrelationship for a diluting plume with a weakening source is notsimple and given our poor knowledge of the thresholds of de-tection this level of complexity is not justified at this point. Ata fixed time the plume stopped due to bait consumption andonly the radius of direct detection (Lrd) remained (Warrant2000).

Lpw ¼ 2a � Lbpl

ðbþ dÞb

� �0:5

�e0:5

a ¼ By

Vw

Ap ¼ pLpl � Lpw

4

� �

Values of all constants are as for Sainte-Marie and Hargrave(1987). By=diffusivity constant, or spreading velocity, estimated at10–3m s–1 in deep waters. The constants b and d were assumed toequal 1. Vertical spread was estimated at 10–5m s–1 using the aboveformula.

As no data for the odour detection thresholds of deep-sea fishexist data for other modid species was used as a model. The odourdetection threshold of wild cod was estimated here from Løkke-borg’s (1990, 1998) data for alanine release from mackerel andresponse range of tagged cod together with Sainte-Marie and

Fig. 2 Cross-current foraging model, persistent bait. Predicted fisharrivals, departures, and number of fish present with time from thearrival of the bait on the seafloor. The bait does not deteriorate andis not consumed during the model run. A continuous plume isproduced. See text for model parameters

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Hargrave’s (1987) model of plume dispersal. The calculated valueof 1.96·10–10 mol l–1 is similar to experimentally obtained(Løkkeborg et al. 1995) values for sablefish starved for 1 day(1.83·10–10 mol l–1).

Løkkeborg et al’s (1995) data for the effects of satiation on thethreshold concentration of amino acids required to elicit a feedingresponse in sablefish allows the calculation of the power law rela-tionship between time since feeding (just fed to 4 days) andthreshold level (F10=60.21, P<0.001, R2=0.86). If we assume thatwild fish vary between these values of satiation according to abinomial distribution, the cumulative distributions of thresholdconcentrations of individuals within the fish population can becalculated. From Løkkeborg et al.’s (1995) data for sablefish amean odour threshold (ı) of 1.57·10–8 mol l–1 with a standarddeviation (o) of 2.51·10–8 mol l–1 was calculated.

For any value of plume length the mean plume concentation (x)can be estimated from the amino acid emission rate of mackereland the plume volume. The proportion of fish for which this is theodour plume threshold (p) can be determined using the formulabelow. The cumulative probability of response was computed nu-merically in Mathematica (Wolfram) and gives the proportion offish responding to odour plume strengths up to, and including, thecalculated mean value.

p ¼ 1

rffiffiffiffiffiffi

2pp � e

�x�l22�r2

Animal arrival rates, and thus their rates of accumulation anddeparture, were then the product of the number of animals en-countering the plume and the proportion which were responsive toa plume of that strength.

Modelling of other foraging strategies – sit and wait

Animals remained stationary on the sea floor until the bait plumereached them. On contact with the plume all animals were assumedto move upstream to the source, remain there for a set staying time,then depart. Number contacted (n) was calculated from the densityof the fish (nf) and plume area (Ap) assuming an elliptical plumeshape from the equations of Sainte-Marie and Hargrave (1987) asabove. A time delay was added between contact with the plume andarrival of the animal at the bait according to the length of theplume, the approach swimming speed of the animal and the watervelocity.

n ¼ nf � Ap

tmb ¼Lpl

Vfsa

Passive drifting

The final modelled strategy was passive drifting. The neutrallybuoyant animal floated down-current. Rate of arrival of passivedrifters (Rpass) depended on the current velocity (Vw), fish density(nf) and the range at which the foragers detect the bait (Lrd).Cumulative arrivals were calculated from arrival rate.

Rpass ¼ nf � Vw � Lrd

tmb ¼Lrd

Vfsa

Mean time to bait is also a fixed value as the range at which fishdetect the bait is fixed. This resulted in a simple linear increase infish numbers with time until fish began to leave, after whichnumbers remained stable.

Statistical analyses

Where possible, model predictions were compared with existingdata. First arrival times predicted by the models for a range of fishdensity and swimming velocity parameters were compared to

arrival times for C. armatus observed by Priede and Merrett (1996)where arrival times were correlated to fish densities estimated bytrawling. The proportion of the variance in observational dataaccounted for by the model was calculated and R2 values generated(Zar 1996).

Results

Arrival, departure and numbers of fish present

Cross-current foraging

Rate of fish attraction increased with time as the lengthof the plume increased and then remained stable oncethe maximum plume width was reached. After a delayfish began to leave at the same rate as they arrived. Rateof arrival decreased and numbers of fish stabilised withthe same number arriving as leaving (Fig. 2).

Small transient baits such as fish carcasses present themost complex scenario modelled here (Fig. 3). Theplume became less attractive due to dilution and dete-rioration in the bait’s odour emissions (Løkkeborg1990). Upon bait consumption plume emission ceasedand only animals which passed within the range in whichthey could detect other fish by sight or sound appearedat the bait site.

Sit and wait

Rate of fish attraction increased as the plume area in-creased but fell to zero when the maximum plume widthhad been reached and all the animals enclosed in the

Fig. 3 Cross-current foraging model, transient bait. Predicted fisharrivals, departures, and number of fish present with time from thearrival of the bait on the seafloor. Plume elongates then reduces ineffective length due to bait deterioration. Rate of attraction rises,falls and eventually drops to zero upon eventual bait consumption.A small residual population is maintained by animals that comewithin 10 m of the bait by chance and are attracted by non-olfactory means. See text for full model parameters

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plume had arrived at the bait. No further animals wereattracted and after animals began to leave, numbers atthe bait decreased rapidly. Each change in currentdirection may result in repeated attraction events untilthe entire area surrounding the bait has been ‘‘swept’’ bythe plume (Fig. 4).

Drift

Rate of fish attraction was proportional to the range atwhich the fish could detect the bait. This remained thesame throughout and was not affected by the plumecharacteristics. Fish numbers increased linearly until fishbegan to leave, after which fish numbers stabilised. Foreach fish species and given density the rate of arrivalwould on average be proportional to current velocityaround the bait (Fig. 5).

The short-term relationship between current velocityand arrival rate distinguishes drifting from the other twomodel strategies. Once a plume has been established areduction in current will decrease arrival rate of driftersbut result in a short-term increase in arrival rate ofcross-current and sit-and-wait foragers as their cross-ground swimming speed as they move across and up thecurrent would be increased.

Effect of foraging strategy and current velocityon initial fish arrival rate

The strategy adopted by the foraging fish greatly affectsthe numbers arriving at the bait. The alternativeodour plume models give very different results withcross-current foraging animals appearing in greater

numbers than sit-and-wait animals. With a currentvelocity of 0.05 m s–1 and fish swimming speed of0.1 m s–1, predicted first arrival times differ substantiallywith 108 min for sit and wait and 35 min for cross-current foraging (Fig. 6 A). Drifting animals arrive inthe lowest numbers as their arrival rate does not con-tinue to rise with time.

For the same through-water fish swimming speed,higher current velocities affect the relative arrival ratesof foragers with different strategies (Fig. 6B, C). At acurrent velocity of 0.07 m s–1 drifting animals arrive ingreater numbers than sit-and-wait foragers (Fig. 6B). At0.09 m s–1 the order of arrival rates is reversed com-pared to the 0.05 m s–1 current situation, with driftinganimals arriving in the greatest numbers, followed by sit-and-wait animals, with active, cross-current, foragersarriving in the lowest numbers (Fig. 6B).

Evaluation of the cross-current model of behaviour

Time to first arrival

Predicted mean first fish arrival was calculated for fishdensities from 10 to 800 fish km–2. The effect of foragingswimming speeds (through water) of 0.1, 0.2 and0.3 m s–1 were modelled (Fig. 7A) as were increases inswimming speed from 0.1 to 0.2 and 0.3 m s–1 uponcontact with the bait plume (Fig. 7B).

The importance of the assumptions made aboutfish swimming speed is demonstrated by Fig. 6A. A fisharrival time of 2,000 s (33 min) would predict fishdensities of 330, 120 or 80 fish per km–2 depending on

Fig. 4 Sit-and-wait model, persistent bait. Predicted fish arrivals,departures, and number of fish present with time from the arrival ofthe bait on the seafloor. Rate of fish attraction rises as the plumearea increases until it reaches the maximum width at which it ceasesto be attractive to waiting foragers. Once plume area is frozen nomore animals are attracted and numbers begin to fall

Fig. 5 Passive drifting model, persistent bait. Predicted fisharrivals, departures, and number of fish present with time fromthe arrival of the bait on the seafloor. Fish arrivals are linearlyrelated to the time the bait spends on the bottom as the range atwhich foragers detect the bait is fixed throughout. As fish begin toleave arrival and leaving rates equalise and a plateau is reached. Inthis case an average density of less than one fish results. See text formodel parameters

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assumed fish swimming speeds of 0.1, 0.2 or 0.3 m s–1.Changes in fish swimming speed on contact with theplume were less important in changing arrival time.With the same fish arrival time of 2,000 s predicted fishdensities were 330, 190 or 170 fish per km–2 dependingon whether the fish remained at 0.1 m s–1 or acceleratedto 0.2 or 0.3 m s–1 respectively (Fig. 7B).

These data are presented with field observational dataobtained by Priede and Merrett (1996) on the same axis.As these experiments used mackerel to attract scaveng-ers the data obtained are comparable to those of themodels presented here, where the odour plume producedby this bait is modelled. The field data are C. armatusfrom three N. Atlantic locations, at depths of 2,500–5,000 m. A high proportion of the variance in the realdata could be accounted for by the model results, es-pecially where approach speed was elevated above for-aging speed (Table 1). At low fish density, predictedarrival times were high, with small absolute changes inestimated fish density causing large differences in arrivaltime.

At present, swimming speed in deep-sea fish outsidethe field of view of cameras is determined using ingestedacoustic transponders (Priede et al. 1994b; Bagley andPriede 1997a, b). These transponders are attached tosmall amounts of bait in order to avoid satiating theanimal. They allow the accurate measurement of for-aging activity but are not able to measure speed on ap-proach to bait. New applications of technology such as

Fig. 6A–C Effect of current velocity on the predicted build-up infish numbers at bait for three differing foraging strategies. In allcases fish density was 300 fish km–2, and fish swimming speed(through water) was 0.1 m s–1. Fish remained at the bait for 60 minbefore departing and drifting animals detected the bait from 10 m.As current velocity is increased from 0.05 m s–1 (A), to 0.07 m s–1

(B) or 0.09 m s–1 (C) the relative rates of arrival change, so that theorder of increasing arrival rate is reversed. As current increasesdrifters are increasingly favoured as their movement speed, andtherefore rate of arrival at the bait is increased. Rates of arrival ofcross-current foragers fall as, although the plume is longer, theirmovement across the current and up the plume is increasinglyslowed. This reduces rates of encounter with the plume and thetime taken to move to the bait

Fig. 7A, B Cross-current model, predicted first arrival times. Firstarrivals were calculated for a range of fish densities and through-water swimming speeds. These predicted data are compared toarrival times for stations where the fish abundance was determinedby trawling (Priede and Merrett 1998). A The effect of differentthrough-water swimming speeds is presented. B The effect of thefish increasing its through-water swimming speed upon contactwith the plume. In each case the proportion of the variance in thereal data accounted for by the model was determined and the R2

values calculated. See Table 1 for details

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active sonar is required in order to determine fish be-haviour prior to feeding. Foraging swim speed and, to alesser extent, any change in swimming speed on contactwith the plume, make a big difference to estimates of fishabundance.

Initial arrival rates

Fish arrival rate during deployment is a potentially morereliable measure of fish abundance as it usually uses thetime to arrive of several fish. Assuming uniform fishdistribution bait might by chance land closer than av-erage to one fish but would therefore land further awaythan average from another. First hour arrival rate wasused in order to minimise the assumptions necessaryabout the properties of the plume and current direction,fish odour thresholds etc. It is assumed that no fish leavewithin the first hour so cumulative numbers equal thenumber present.

An abundance of at least 100 fish km–2 was requiredin order for first arrival to occur, on average, within anhour (Fig. 7A) at all the swim velocities modelled (attainfish arrival rates of >1 fish hr–1 or 0.00028 fish s–1). At0.1 m s–1 swim speed a density of at least 300 fish km–2

was required in order for 2 fish to arrive at the bait, theminimum requirement in order for arrival rate to be abetter measure of abundance than first arrival alone.

Relatively high abundances are required before theadvantages of first hour arrival can be utilised at theswim speeds estimated for deep-sea fish. In sparselyspread animals the options are (1) many replicate shortdeployments in order to determine first arrival time, or(2) estimates derived from longer deployments acceptingthat plume characteristics may differ from thosepredicted.

Discussion

Alternative foraging strategies

The models presented allow for a matrix of animalsmoving down and across currents with stationary animalson, or above, the seabed. The differences in arrival ratesand the form of the build up in numbers may be diag-nostic of the different foraging strategies and allow pre-dictions of the characteristics of the fish to be made.

Actively searching for odour plumes dramati-cally increases the rate of arrival of animals at bait.

Calculations of fish density based on arrival rates andmodels that assume that animals are stationary on thebottom until contacted by the plume (the sit-and-waitstrategy) will seriously overestimate the numbers of fishpresent. Drifting on ambient currents and detecting baitby the sounds of other animals feeding may result insurprisingly large numbers of fish at the source of thesound. A range of detection of at least 19.7 m is requiredin order for one animal to arrive on average within 1 h.Experiments to determine the volume of sounds emittedand the hearing thresholds of deep-sea fish will berequired to evaluate this further.

The different strategies carry different risks to theforager. Sit-and-wait minimises any acoustic or visual‘‘signature’’ but the animal emits its own odour plume.Drifting releases a reduced odour plume, also withminimal acoustic signature. Cross-current swimming, aswell as being energetically expensive is likely to be themost noisy and visually obvious strategy, covering themaximum area in the forager’s odour. This may be aconstraint on this foraging type. Catfish (Silurus glanis)can detect and follow the wake of guppies (Poecilia re-ticulata) over a minute after the guppy has left the po-sition where the catfish contacts the trail (Pohlmann et al.2001). If wake and sensory characteristics were similar indeep-sea fish cross-current foragers would leave an in-stantaneous wake length of 1800 m km–2 at fish densitiesof 300 km–2 and swimming speed of 0.05 m s–1. Theslow swimming and apparently streamlined body ofCoryphaenoides may be important in reducing turbu-lence around the body in order to prevent a noisy andluminescent wake from forming. Differential vulnera-bility to predation as well as energetic constraints mayinfluence the observed ontogenetic changes in foragingstrategy (McLellan 1977; Mauchline and Gordon 1984;Cronin et al. 1996; Drazen et al. 2001). Even in largeanimals such as C. armatus the sub-lethal cropping oftheir tails by Bathysaurus mollis (Campbell et al. 1980)may be a significant additional cost of this overt forag-ing strategy.

None of the models presented completely describesthe behaviour of C. armatus around bait as demon-strated by baited camera experiments (Bagley 1993;Priede and Merrett 1998). The cross-current foragingstrategy model produces arrival time data which matchwell to trawl abundances, and accurately describes thebuildup in fish numbers which occurs around large baits,such as marine mammal carcasses (Jones 1999). How-ever, the peak number of fish predicted is much greaterthan observed in the field. The main alternative model,that fish sit and wait for the plume to reach them, thenmove upstream, predicts peak fish numbers more accu-rately but the form of the relationship between fishnumbers and time is not consistent with the observedpatterns.

Various possible explanations exist for these dis-crepancies, the identification of which is the majorobjective of these models. Low feeding motivation oranimals remaining at previously encountered food

Table 1 Fits of fish density–first arrival time relationships pre-dicted by the cross-current foraging model to those observed in thefield

Swimming speed (m s–1) 0.1 0.2 0.3

R2 0.48 0.93 0.74Increase in swimming speed ·1 ·2 ·3R2 0.48 0.92 0.95

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sources could explain reduced numbers of arrivals. Asecond possibility is that at any time a mixture of activeforaging and sit and wait strategies may be employed byindividuals within the C. armatus population. The pro-portion foraging actively rather than remaining on thebottom, might be related to satiation. Ontogeneticvariations in foraging strategy would also explain thedifferences in size frequency distribution in C. armatusobserved from trawls and baited cameras (Priede et al.1994a). However, at 4,000 m C. armatus individualsanimals smaller than those seen at baits make up lessthan 15% of the population of this species as determinedby trawling (D. Bailey et al., unpublished trawl data) sothis alone is not sufficient to reduce fish arrival numbersin the way observed.

A mixed strategy could explain the lower than ex-pected number of fish arriving at the bait and the stablefish numbers over a period of day observed at large foodfalls (Jones et al. 1998). This pattern can also be ex-plained fully by a drifting foraging pattern but only ifdrifting animals detect the bait source by auditory meansfrom a range of around 120 m. These models assume astaying time of 60 min. Greater staying times requiremuch lower rates of arrival in order for numbers to buildup to the numbers of fish observed.

Very long staying times of greater than 24 h havebeen recorded for zoarcids at large baits with numbers ofthese species continuing to rise over a period of days(Jones et al. 1998; Witte 1999). Benthic zoarcid eelpoutsare thought to be negatively buoyant sit-and-wait pre-dators so their numbers should only continue to risewhile there is a continued increase in the area of seabedexposed to the odour plume at sufficient concentrationto attract the fish. Either zoarcids are very sensitive toodour plumes and can follow them to the source forseveral hours or these animals employ a differentmethod of foraging. Low but continuous rates of at-traction are most consistent with a drifting strategy, asobserved in midwater eelpouts (Robison 1999). Theobservation that the predominant mode of life of two-line eelpouts (Bothrocara brunneum) was to hover abovethe bottom (Ferry 1997) is consistent with this.

The bias in fish arrivals from a downstream directionis typically considered to be evidence of plume following(Wilson and Smith 1984). Terrestrial hunters may alsoapproach targets from down-current in order to surprisethem, even if the prey was detected by other means. Therelatively small fields of view available typically does notdetermine direction of approach until the animal is veryclose to the bait so long- and close-range bait detectionmethods may differ and therefore give false impressionsof foraging strategy (Wilson and Smith 1984).

Other complexities of fish behaviour such as thepossibility of inter- and intra-specific interactions be-tween animals have also not been included in the mod-els. Density-dependency in fish staying time may explainthe abrupt drop-offs in fish numbers in the modelswhere, in reality, numbers of fish often fall rapidly atfirst and then more slowly (Priede et al. 1994b; Smith

et al. 1997; Jones et al. 1998). Direct inter-specific in-teractions at bait have been observed in shallow marinefish and invertebrate scavengers (Godo et al. 1997;Tamburri and Barry 1999; Morton and Yuen 2000) andsuggested for deep-sea fish species (Armstrong et al.1992). Further deviation from the model predictionsmay exist where staying time (Priede et al. 1994b), odourdetection thresholds and the proportion of fish foragingvary between populations of fish with differing levels ofsatiation (Løkkeborg et al. 1995).

Conclusions

The numbers of fish attracted to bait and their rates ofarrival and departure may give valuable clues as to theforaging behaviour of the animals. This is particularlyvaluable in the deep-sea where behaviour prior to feed-ing is difficult to study. As the metabolic and sensorycapabilities of animals are fundamentally linked to theirforaging strategies laboratory data may give valuableinsights into the animals’ behaviours. Much new fielddata will be required, including swimming speeds beforefeeding and the metabolic costs of swimming in order toconstruct a full energy budget for foraging as has beenachieved for shallow-water species (Ware 1975). Theeffects of hydrography and hydrology on deep-sea fishforaging behaviour are as yet poorly understood as arethe olfactory and visual performance of foragers and thestrength and type of cues relevant to these animals. Evencomplex patterns of fish attraction such as those in re-sponse to small, rapidly consumed, baits can be mod-elled and the theoretical effects of fish satiation level,swimming speed, and staying time determined.

A single small bait item is the most complex situationto understand as the characteristics of the attractant areconstantly changing. The process of bait consumptionresults in a poorly understood feedback between theanimals present and the attractiveness of the bait. Themodels presented can only make predictions of the mostlikely, average, arrival rates of animals; only pooled datafrom a high number of lander deployments is likely toclosely match predictions of even the most completemodel. Experimentation isolating odour, sound, andlight as attractants is a potentially useful approach as thestrength of each can then be controlled and standardisedbetween experiments.

Acknowledgements The manuscript benefited greatly from discus-sions within the Oceanlab group and other colleagues, and wassupported by NERC Grant GR3/12789. The comments of MartinCollins, Jeff Drazen, and the anonymous referees were extremelyuseful.

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