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Estuarine, Coastal and Shelf Science 80 (2008) 529–537
Contents lists avai
Estuarine, Coastal and Shelf Science
journal homepage: www.elsevier .com/locate/ecss
Random walk, zonation and the food searching strategy of Terebralia palustris(Mollusca, Potamididae) in Kenya
Marco Vannini a,*, Stefano Cannicci a, Elisha Mrabu b, Rocco Rorandelli a, Sara Fratini a
a Dipartimento di Biologia Evoluzionistica ‘‘L. Pardi’’, dell’Universita di Firenze, Italyb Kenyan Marine Fisheries Research Institute (KMFRI), Mombasa, Kenya
a r t i c l e i n f o
Article history:Received 24 May 2008Accepted 9 September 2008Available online 11 October 2008
Keywords:mangroveMolluscsTerebralia palustrisrandom walk strategyfood searchingKenya
* Corresponding author.E-mail address: vannini_m@unifi.it (M. Vannini).
0272-7714/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.ecss.2008.09.020
a b s t r a c t
Terebralia palustris is a common mud-whelk present at a particularly high density in all Indo-West Pacificmangroves. Young snails feed on nothing but mud while larger specimens are able to feed on fallenleaves too. In Kenya (Mida Creek) under the canopy, competition for mangrove leaves can be very highdue to the high density of Sesarmidae crabs. On open exposed muddy platforms, no Sesarmidae occurbut the leaf density is very low because the leaves are only randomly present as they are deposited andremoved twice a day by the tide. However, the snail density is always very high, raising the question as towhether the snails use a special searching strategy to optimize their resource finding rather than a purelyrandom movement. By analyzing the snails’ movements on a uniform area at different levels andcomparing them with simulated random paths, we could show that the snails’ movements are not purelyrandom. The distribution of different size classes of T. palustris in Mida Creek was known to be quite odd:the same simulation approach suggests that the zonation asymmetry could reasonably be due to thestochastic recruitment of juveniles in space and time and maintained by a substantial long-lasting spatialinertia.
� 2008 Elsevier Ltd. All rights reserved.
1. Introduction
Terebralia palustris is a common Indo-Pacific gastropod livingon muddy intertidal platforms, especially among mangroves.Within the mangroves it occupies almost the whole mangrovebelt from just below the average High Water Spring level tosomewhere between the Neap and Spring Low Water levels. InKenya, within such a wide zone, it can be found under the Avi-cennia marina trees (on the upper levels), under the Rhizophoramucronata or R. mucronata-Ceriops tagal trees (on the relativelylower zones) or in open areas, and on more or less extendedmuddy platforms bordering the seaward edge of the mangrovebelt (Fratini et al., 2004).
The young T. palustris are known to feed on mud alone becausetheir radula is insufficiently developed for them to feed on the thickmangrove leaves. However, starting from about 50 mm in length,they become active leaf eaters (Houbrick, 1991; Slim et al., 1997;Fratini et al., 2004).
Leaf density should be much higher in the planted zones than onthe open muddy platforms where scattered leaves are only found
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when carried and dropped by the outgoing tide. T. palustris can onlydetect leaves when crawling on it. Obviously, the probability ofcreeping on a fallen leaf is relatively higher under the canopy thanon the open areas. However, in the planted zones there is a largepopulation of Sesarmidae crabs that also feed on mangrove leaves,leading to a high level of inter-specific competition (Dahdouh-Guebas et al., 1998; Fratini et al. 2000). In the study area (MidaCreek, Kenya), T. palustris appears to be equally densely distributedin both environments: areas under the canopy with their higherdensity of leaves and of competitors can host an average density of30–40 animals m�2 (Fratini et al., 2004) with peaks of over 200animals/m2 whereas areas with no competitors and rare leaveshave recorded peaks of 160 animals m�2 (personal observations).
Different areas of Mida Creek appear to be dominated bydifferent densities and different size classes of animals (Fratiniet al., 2004) a finding that is reminiscent of the observations ofother authors (Plaziat, 1977; Wells and Lalli, 2003).
The non-uniform zonation of T. palustris in Mida Creek wasattributed to the possibility that the animals tended to remain, maybe for years, within the area where they had landed as a smallswarm of veligers. In this way the non-uniform spatial distributioncould be interpreted as the effect of differential recruitment, i.e.years of local intense recruitment separated by years with norecruitment at all, combined with a substantial spatial inertia.
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Together these two phenomena could explain the peculiar distri-bution of these snails. Hence the first aim of the present study wasto test whether the hypothesis of non-uniform larval recruitmentcoupled with random juveniles and adults’ movement could beresponsible for the final T. palustris spatial distribution.
Under the canopy even short random movements could lead ananimal into contact with a freshly fallen leaf; furthermore, such anapproach would be widely facilitated by the leaves themselveswhen damaged by other leaf consumers, releasing chemical cuesboth in the air (Fratini et al., 2001) and in the water (Fratini et al., inpress). In the open, where leaves are scarce, the situation isdifferent because intact leaves cannot be chemically perceived(Fratini et al., 2001) and the problem of finding a leaf becomesrelevant. Do the animals wander at random or are they able toadopt some searching strategy that increases the probability offinding the few scattered leaves deposited by the water? Hence, oursecond aim was to test if the movement of T. palustris on the wide,uniform platform could be considered random, or else if it includessome non-random element, i.e. an active food searching strategy.
To achieve the above aims, direct observations and a Monte-Carlo simulation were used based on observed angles and steps.Three possible hypotheses were made: a), the distributions ofsimulated distances overlap the observed ones, b), the distributionsof the simulated distances are shorter than the observed ones, andc), the distributions of the simulated distances are longer than theobserved ones. Consequently, three possible conclusions can besuggested: a), animals are walking at random, b), random move-ments are periodically combined with centripetal movements,thereby keeping the animals within a ‘‘home-area’’, or c), randommovements are periodically combined with centrifugal movements,thereby probably increasing the exploration of the substratum.
2. Material and methods
2.1. The site
Mida Creek is a roughly circular mangrove swamp 25 km S ofMalindi (Kenya), bordered by an almost continuous belt ofmangroves, dominated by A. marina, on the upper level, and byR. mucronata together with Ceriops tagal, on the lower level. Alongthe north-western margin of the creek, the site selected for thisstudy, the forest belt is about 1 km wide and ranges between theHigh Water Spring Tide (HWST) and the Low Water Neap Tide(LWNT) levels while the area between LWNT and Low Water SprintTide (LWST) is a muddy, bare platform, nearly 500 m wide, witha slope of about 0.4–0.5%, that is covered twice a day by the tide,even Neap Tides.
Observations were made during February and March 2005,when the NE monsoon was regularly and constantly blowing.
2.2. The species
T. palustris (Potamididae) is the only representative of its familyin East Africa; it is a large sized snail (up to 16 cm length; Fratiniet al., 2004) common among most of Indo-Pacific mangroves, fromEast Africa to New Caledonia (Plaziat, 1977; Houbrick, 1991), whereit can be found to reach the astonishing density of 150 adultanimals m�2 and up to 475 juvenile animals m�2 (Plaziat, 1977).Due to the lack of external sexual dimorphism, no distinctionbetween males and females was possible.
2.3. Methods
2.3.1. Animal densityA wide open area (165�180 m) of apparently uniform, bare
muddy sand was chosen. In the course of 10 recording sessions,
pictures were taken of randomly chosen surfaces (cm 110� 80), atlow tide. Sites where two or more animals appeared to be feedingon a leaf were excluded. The frequency distribution of the animalswas measured by applying a grid of 5� 4 cells to the picture andcounting the animals included in each of the 20 cells.
The null hypothesis is that animals are distributed randomly,thus following a Poisson distribution; rejection of the abovehypothesis implies that animals are spaced from each other(uniform distribution) or else clustered (‘‘contagious’’ distribution)more than one would expect by chance alone.
2.3.2. Animal movementsWe compared the recorded movement patterns (frequency of
rotation and frequency of rectilinear ‘‘steps’’) with Monte-Carlosimulations based on the above distribution pattern frequencies.Movement pattern frequencies were obtained by following singleindividuals in the field for short (2 min), medium (6 h) and long (24days) periods of time.
Short movements were studied by simply taking 100 pictures,2 min apart, of a 140� 95 cm surface area. The 10 recordingsessions were always conducted at low water, at both Spring andNeap Tides, during the day and night (with a flash), for 6 sessions (3at ST and 3 at NT). The camera position was always different andchosen at random.
For each of the recording sessions, up to 20 randomly chosenanimals were followed through the series of photographs. If ananimal was found to be ‘‘leaving’’ the recorded area, i.e. dis-appearing from the pictures, another animal was randomly chosenand followed from that moment forward. Despite some missedrecords and short interruptions, we made a total of 4784 fixes forthe ST sessions and 5809 for the NT sessions. The angles betweentwo adjacent fixes were also measured but they were taken intoaccount only when, in both photographs, the animal was actuallydetected to move for at least 1.0 mm. This filter was applied to avoidinclusions of small movements due to the irregularity of the mudsurface or smaller scale animal movements. In this way we wereable to measure 1106 angles at ST and 656 angles at NT.
Medium and long-term displacements were studied by tagging 28animals with super-glue and plastic numbered tags; 26 of them werefollowed for the whole study period, i.e. for 24 days. The entire markingprocedure took place without removing the animals from substratum.
Medium-term movements were studied by recording the posi-tion of the tagged snails both at the beginning and at the end of theHigh and Low tides (i.e. about six hours apart) for 2-3 days aroundtwo different ST for a total of 22 records for each animal. Theserecords were only made in order to appreciate the activity differ-ence between high and low water.
Long-term movements were studied by recording the taggedsnails’ positions every day, for a total of 25 days.
During medium and long-term movements, the positions of theanimals were recorded by measuring the distance and orientationof each animal relative to a tagged wooden stick (1 m long, 1 cmdiameter) that had been placed close to the animal during theprevious recording session, Thus the technique was similar to theone applied to river crabs (Barbaresi et al., 1997) and to T. palustrisby Wells and Lalli (2003). After each measurement, the stick(labelled as the animal) was removed and firmly put in the mud,20 cm behind the shell apex of the snail so as to become thereference point for the next record.
A metric tape and a common compass were used to make thesemeasurements.
2.4. Data analysis
We assumed that the path followed by the animals between twosuccessive fixes (a ‘‘step’’) could be considered as a straight vector
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characterized by a length and two angles: the angle of the vectorwith respect to N (geographical angle) and the angle between twoadjacent steps (vectorial angle).
Monte-Carlo simulations were performed based on theobserved steps and vectorial angle distributions (both for short andlong-term displacements) producing a series of ‘‘artificial paths’’,made by the same steps and angles as those of the observed paths,with the same frequency as the observed paths, but randomlychosen. Then, the final distance from the last point to the startingpoint of the simulated paths were compared with those actuallyobserved. In each case, 3*105 simulated replications (artificialpaths) were used although after about 3*103 replications, theresults were already noticeably stable. The software (in BASIC) wasdeveloped by the authors.
The distributions were compared using G-test, with Bonferronicorrection in case of multiple comparisons. The test was applied tocompare measured angles and distances in the different conditions(observed vs. observed), or observed distributions with simulatedones (observed vs. expected). ANOVA was used to test thehypothesis that the observed data sets from different
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Fig. 1. Examples of path of T. palustris at NT (A, C) and at ST
environmental conditions were independent. Non-parametricANOVA (Friedman-test) was applied in case of repeated measures.
The following abbreviations have been used: ST, Spring Tide; NTNeap Tide; HW, High Water; LW, Low Water, SE, standard error.
3. Results
3.1. Animal distribution
Average animal density was 101.0 snails m �2 (SE¼ 4.5). In 8out of 10 cases the animal distribution overlapped with a random(Poissonian) one. In two cases animals appear significantlyconcentrated, i.e. they were forming clusters significantly largerthan expected by chance (G-test with Bonferroni correction: 13.98and 9.29, with df¼ 4 and 2, respectively; P< 0.01 in both cases).The differences in animal density observed during the 10recording sessions should be considered stochastic and notrelated to the tidal phase nor to the period of the day (ANOVA-test: ST vs. NT, F¼ 0.49; Day vs. Night, F¼ 0.28; df¼ 1, 6 andP¼NS, in both cases).
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Fig. 2. Frequency distribution of (A) the geographical angles maintained during the 2 min steps and (B) the vectorial angles between adjacent 2 min steps (animals¼ 60; totalangles¼ 1762).
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Fig. 3. Examples of path of T. palustris from 24 day observations. One fix per day (scale in cm). A: path with short, apparently random, movements. B: maintenance the samedirection for several days (note the scale difference). C and D: alternation of apparently random activity with straight displacements.
M. Vannini et al. / Estuarine, Coastal and Shelf Science 80 (2008) 529–537532
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Fig. 4. Mean step length recorded from 26 animals 24 days during February 2005. Horizontal bars, SE.
M. Vannini et al. / Estuarine, Coastal and Shelf Science 80 (2008) 529–537 533
3.2. Short-term movements – pattern distributions
The path followed by T. palustris for 200 min (Fig. 1)appears as a series of steps (2 min each) showing roughly thesame direction for several minutes and then slowly loopingaround.
The distribution frequencies of the length of the 2 min stepsrecorded in ST and NT showed a somewhat similar pattern, buttheir distributions are significantly different (G¼ 113.6, df¼ 14,P< 0.001), their average step lengths being 0.63 cm (SE¼ 0.02) and0.44 cm (SE¼ 0.02), respectively.
The geographical angle distributions of ST and NT are indistin-guishable (G¼ 16.65; df¼ 15; P¼NS) and, once pooled (Fig. 2A),they reveal a dispersed distribution (G¼ 17.94, df¼ 15; P¼NS). TheST and NT distribution frequencies of the vectorial angles alsooverlapped with each other (G¼ 20.14, df¼ 15, P¼NS) whilepooling their distributions (Fig. 2B) gives a non-random distribu-tion (G¼ 547.7; df¼ 15; P< 0.0001) with an average direction of6.2� (r¼ 0.455, n¼ 1762).
The distance reached by the snails in 200 min is longer during ST(27.0 cm; ES¼ 2.94) than at NT (11.6 cm, ES¼ 1.86) with no differ-ence between day and night (ANOVA-test: ST vs. NT, F¼ 20.07,df¼ 1,116; P< 0.01; Day vs. Night, F¼ 0.11, df¼ 1,116, P¼NS).
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Fig. 5. Frequency (%) distribution of 566 angles, geographical (A) and vector
3.3. Medium term movements – pattern distribution
Medium term (6 h) movements were investigated to comparethe effect of day and night and especially the effect of high and lowwater since the pictures could only be taken during low water.Animal positions with a 6 hr frequency were only recorded at ST, i.e.during a period when the snails were known to be more active(Fratini et al, 2000), within which possible differences due to thediurnal or tidal cycles may have been more readily detected.Friedman ANOVA-test proved that animals where reachingdifferent distances under different conditions (Fr¼ 35.73; df¼ 3;P< 0.001). In particular, at HW (average distance 50.8 cm, ES¼ 3.5)snails were more active than at LW (average distance 36.4 cm,ES¼ 2.6), while a difference between day and night failed to beproved.
3.4. Long-term movements – pattern distribution
Long-term movements (one fix every 24 h) as recorded fornearly a synodic month (24 days) show different patterns (Fig. 3).Some T. palustris appear to move at random, other snails maintainthe same direction for long time, while others appear to mix thetwo strategies.
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Fig. 6. Frequency distribution of distance from starting point reached after 200 min of recording during NT (A) and ST (B), both of observed and simulated distributions.
M. Vannini et al. / Estuarine, Coastal and Shelf Science 80 (2008) 529–537534
The positions recorded for 26 animals, every day for 24 days,were used to calculate both the angle and step length distributions.A regular pattern appears during the course of a month, showingthat at ST the distances covered by the snails in one day can be 4–6times longer than at NT (Fig. 4). The average distance between twosuccessive fixes, 24 h apart (the daily step length), is 0.80 m(SE¼ 0.06) with a few steps between 3.5 and 7.5 m in length. Theaverage distance reached at the end of the 25 days period is 7.1 m(SE¼ 1.11).
The angle distributions are represented geographical (Fig. 5A)and vectorial (Fig. 5B) showing a mean orientation of 77.1�
(r¼ 0.314) and 21.1� (r¼ 0.216), respectively. Both distributions arestatistically non-uniform (G-test¼ 151.72 and 63.03, respectively,df¼ 15, P� 0.001 in both cases).
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Fig. 7. Frequency distribution of distance from starting point reached after 2
3.5. Simulation
A total of 3*105 series of 100 artificial steps, whose length andangle were randomly chosen starting from the original distribu-tions, were then created and the distance reached by such artificialpaths were compared with the ones actually observed at ST and NTFigs. 6A and B compare the observed and simulated distributions ofthe distances attained after 200 min. For NT (Fig. 6A) there areapparently more short displacement than expected but the differ-ence was not significant (G¼ 5.41, df¼ 2, P¼NS). On the contrary,during ST (Fig. 6B), there are more long distances than one wouldexpect by chance, the differences between the two distributionsbeing significant (G¼ 28.80, df¼ 3, P< 0.001). Notice that, at ST,10% of the snails (6 of 60) reached a distance equal to or longer than
4 16-18 20-22 24-26nce (m)
observed simulated
4 days by 26 T. palustris, both of observed and simulated distributions.
Table 1Final distances reached by snails and by simulated points after different timeperiods
average distances (cm) ratio
time (min) observed simulated obs/sim
2 0.63 at ST2 0.44 at NT200 27.0 16.5 1.64 at ST200 11.6 12.8 0.91 at NT360 43.6 22.3 1.95 6 h (only at ST)1440 79.8 43.8 1.82 1 day36000 717.2 607.9 1.18 25 days
M. Vannini et al. / Estuarine, Coastal and Shelf Science 80 (2008) 529–537 535
5 cm, a performance that has been observed in only 200 cases overthe 3*105 simulated runs (P¼ 0.00066).
In conclusion, at ST animals appear to move longer distancesthan at NT but, more importantly, they achieve longer distancesthan they would do if they were randomly following the observedvector distributions.
An analogous process has been undertaken to simulate a 25 daypath from the 24 h steps and angles distribution data. Fig. 7 showsthat at least 3 (11.5%) of the 26 animals, having reached a distance ofmore than 14 m from the releasing site, do not fall within the right-hand tail of the simulated distribution, under which only 7747cases of distances>14 m were recorded over 3*105 simulated paths(P¼ 0.0258). We should note that the probabilities that the twoextreme observed cases, 21.1 m and 24.6 m, occurred by chance areP¼ 1.7$10�3 and P¼ 2.2$10�5, respectively.
Simulation using the step and angle frequencies during the200 min observation sessions, as seen for ST above, can be per-formed to compare the distances actually achieved during the 6 h(medium term) sessions at ST. Similarly the same frequencies, butaveraging ST and NT sessions, can be use to simulate a one-daypath, to be compared with the observed one-day paths (Table 1).
Fig. 8 shows the mean distance covered as a function of time forboth the observed and simulated paths. The two regression linesare remarkably similar (Table 2, bobs vs. bsim: t¼ 0.64, df¼ 8, P¼NS)with the observed consistently indicating slightly greater distancesthan the simulated line (F¼ 5.76; df¼ 1,9; P< 0.05).
3.6. Extrapolation
Since the relationship between mean distance and time (Fig. 8)seems quite robust and strictly linear an attempt at extrapolationwas made to predict the distance animals would have achieved in
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Fig. 8. Relation between time period and mean distance covered d
longer periods of time. Following the simulated distributions, 95%of the generated results fall within approximately double the meanof each distance (Table 3).
The simulations so far attempted, assumed that the animalswere able to move freely on a two-dimensional uniform surface, anapparently unrealistic condition. Paths were thus simulated to takeplace within a more realistic ‘‘corridor’’ of variable width. Theapplied algorithm caused the ‘‘animal’’ to rebound within thecorridor edges while maintaining the randomly chosen step lengthand randomly searching for another angle. Simulating 1-year longruns, showed that for corridors wider than about 100 m (a realisticcondition in the study site) the distance overlaps with the resultsobtained without corridor.
4. Discussion
Following the movements of T. palustris for longer periods, andfor different time intervals, allowed us to gather detailed infor-mation on the spatial behaviour of these molluscs.
Stochastic processes surely play a dominant role in thewandering behaviour of T. palustris: in an apparently uniformenvironment their distribution in the space appears largely randomand unaffected by the daily or monthly lunar cycle. At low water,small-scale movements (one fix every 2 min) followed smoothpaths, irregularly looping apparently at random, leading theanimals to cover, in 200 min, an average distance of about 27 cm atST and about 12 cm at NT, with no difference between day andnight. Medium term movements (one fix every 6 h) indicate thatanimals reach a distance about 50% longer at HW (about 51 cm)than at LW (36 cm) and, again, with no difference between day andnight. Long-term movements (one fix every 24 h, covering anaverage distance of 80 cm) indicate a clear difference between STand NT activity in terms of the distance covered and a tendency tofollow randomly chosen vectorial angles but with a significantpreference for ENE angles.
The simulated paths, based on the frequency of step length andvectorial angle as recorded during small-scale and large-scale, arequite similar to the observed paths. At NT the two kinds of paths areindistinguishable while at ST the observed paths lead the snails ata greater distance than the simulated ones, especially because longstraight paths from 6 to 10 cm occasionally take place while theprobability of their happening by chance is negligible.
The same can be said for long-term displacements: the proba-bility of the simulated paths leading the snails more than 15 maway from the starting points are very low and the animals’
in
rs
1 day
105104103
uring that time period for both observed and simulated data.
Table 2Parameter and statistics of the regression lines (y¼ b.xa) shown in Fig. 8
observed simulated
b¼ 0.342 0.318a¼ 0.749 0.989r¼ 0.992 0.994df¼ 5 3P< 0.01 0.01
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behaviour can better be explained by accepting that periodically, atleast some snails, can follow straight paths, maintaining the samedirection for days and days.
In conclusion, animals seem to display occasional transectingbehaviour and a capability of keeping the same direction not onlyfor several minutes but occasionally for several days independentlyfrom a relatively higher activity at ST than at NT. This findingsuggests that a compass, the wind and/or a surface water current,are probably used. T. palustris behaviour can thus be described as analternation of random wandering with occasional straightdisplacements probably increasing their probability of meeting theleaves on which they feed. Future research with a simulation modelshould be able to tell us whether, and to what extent, this searchingstrategy may actually increase the probability of finding a leaf ina uniform or non-uniform (forested) environment.
The non-random component apparently increases the distancean animal can reach by a factor ranging from 0.91 to 1.95 (Table 1),i.e., in one day they can reach about 0.8 m instead of 0.5 or inapproximately one month, 72 m instead of 61. The linearity of thesimulated relation between time and distance is so strict and sosimilar to the observed one (Table 2) that an extrapolation wasattempted and it showed that within 5 years most of the animalswould still keep themselves within an area with a radius of 153 m(Table 3).
To the value of 153 m we should also apply both an increasingfactor (from 0.91 to 1.95) but some reducing factor too. Within thevegetated areas, which, in the study site, are more common thanbare muddy areas, the existence of a thick mesh of pneumato-phores (A. marina zone) or aerial roots (R. mucronata zone) wouldreduce the snails diffusion by a consistent factor (some preliminarysimulations gave a further 40–60% reduction). In the only previousstudy of T. palustris movements, Wells and Lalli (2003) found that inWestern Australia, it moved an average of 39.9 cm day �1 within themangroves and up to 120.2 cm day �1 on open sandflats. Similarobservations were made by Crowe and McMahon (1997) studyingT. palustris in Australia (Darwin Bay). Hence, the hypothesis thatafter 5 years, 95% of the snails should be found somewhere withinan area with a radius of 150 m seems like an overestimation.
Mida Creek has a perimeter of about 45 km and a forestedsurface of about 15 Km2. To simplify, we can assimilate the wholesuitable environment to a 0.3� 45 km circular belt of space avail-able to T. palustris. We have no idea of the longevity of this speciesbut the idea that the patchy zonation of T. palustris in such a wideenvironment, as observed during previous work (Fratini et al.,
Table 3Distance reached by simulated paths within different periods of time and thedistances that include 95% of the simulated paths
period of time average distance (m) distance (m) including95% of replications
1 day 0.44 8.51 month 6.1 13.71 year 23.4 47.12 years 49.7 98.95 " " 78.9 153.510 " " 111.5 222.6
2004), may depend on irregular (both in time and space) recruit-ment process followed by substantial inertia on the part of theanimals themselves, now seems reasonable.
Differential mortality could also be involved: the main possiblepredators, such as Scylla serrata, Eurycarcinus natalensis, Thalamitacrenata (Dahdouh-Guebas et al., 1999) and Epixanthus dentatus,(Vannini et al., 2001), could for instance be present at differentdensities in different areas. In any cases, in the study zone, theabove predators appear commonly scattered and more or lessuniformly distributed (personal observations).
The movements of T. palustris had only been previously studied(for 12 days, one fix per day) by Wells and Lalli (2003) in WesternAustralia which suggested that, in their study locality, the snailswere more active at ST, than at NT, when they remain immersed forlong periods. The same authors recovered 3 T. palustris 21, 44 and57 m from the marking point, after 9 months. These values are quitecompatible with our findings and correspond to a probability of66.7%, 19.1% and 8.1% under our diffusion simulated diffusionmodel. In a similar environment, a related species (Telescopiumtelescopium) can cover 4 m day�1 with peaks of 10 m day�1 (Lasiakand Dye, 1986).
Detailed studies of intertidal gastropods movements exist(Underwood, 1977; Underwood and Chapman, 1985) but usuallyaimed at investigating the influence of substratum structure(Underwood and Chapman, 1989) or substratum selection andinter-specific interaction (Chapman, 2000). In particular, randommovements have been suggested for some species by Underwood(1977), on the bases of angle and distance distributions. The studywas anyway conducted only on very short periods (1–3 days, andonly at day time) on animals known to be often good homers.
Some studies exist on the movement patterns of Ilyanassaobsolete, an intertidal gastropod living in conditions comparablewith that of T. palustris (Borowsky, 1979; Curtis, 2005). Althoughnot based on long-term tracking of individuals, they both indicatedthat, within a uniform area, the snails’ movements are nomadicrandom walks.
From the present study it seems that, when foraging on fallenleaves, T. palustris is able to add a simple non-random strategy to itsbasic random movements. However, the movements are so limitedand winding, encumbered by physical obstacles (pneumatophores,aerial roots, trunks, dead trunks across the mud), that an initialspatial asymmetry in veliger recruitment can probably be main-tained for years, resulting in a permanent non-uniform zonation ofdifferent size classes, widely unrelated to a possible asymmetry inthe local environment, resource availability or micro-climaticfactors.
Acknowledgements
The work could not have been done without the collaboration ofour students Gianni Brescacin, Nadja Frodella, Outi Lahteenoja andAngela Sacchi. Funding from MIUR (FIRB project), University ofFlorence (‘‘Fondi d’Ateneo’’) and the EC project ‘‘PUMPSEA’’ isgratefully acknowledged.
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