dugong habitat use in relation to seagrass nutrients, tides, and diel cycles

MARINE MAMMAL SCIENCE, 26(4): 855–879 (October 2010)C! 2010 by the Society for Marine MammalogyDOI: 10.1111/j.1748-7692.2010.00374.x

Dugong habitat use in relation to seagrass nutrients, tides,and diel cyclesJAMES K. SHEPPARD1

HELENE MARSH

School of Earth and Environmental Sciences,James Cook University,

Townsville 4811, AustraliaE-mail: [email protected]

RHONDDA E. JONES

School of Marine and Tropical Biology,James Cook University,

Townsville 4811, Australia

IVAN R. LAWLER

School of Earth and Environmental Sciences,James Cook University,

Townsville 4811, Australia

ABSTRACT

The dugong is the only herbivorous mammal that is strictly marine and a seagrasscommunity specialist. The pasture available to the dugong varies with the tidesbecause seagrass occurs in both intertidal and subtidal areas. We GPS-trackedseven dugongs within a 24 km2, intensively used seagrass habitat in subtropicalAustralia in winter. We modeled resource selection within the habitat by comparingthe dugongs’ use of space with the distribution of seagrass in an area definedusing the combined space-use of the tracked animals. Selection by dugongs forseagrass quantity (biomass) and quality (nutrients) was analyzed within six time/tidecombinations to examine the influences of tidal periodicity and the diel cycle onresource selection. Dugong habitat use was consistently centered over seagrasspatches with high nitrogen concentrations, except during the day at low tideswhen the animals had fewer habitat choices and their space use was centered overhigh seagrass biomass. The association of dugongs with seagrass high in starch waspositive during both day and night high tides when the animals could access theintertidal areas where seagrass biomass was generally low. Associations betweendugongs and seagrass species were less definite, reflecting the potential for dugongsto exploit several species. Our model of dugong resource selection suggests thatnitrogen is the primary limiting nutrient for dugong populations and also confirmsthe preference of dugongs for high-energy foods.

1Current address: San Diego Zoo’s Institute for Conservation Research, 15600 San Pasqual ValleyRoad, Escondido, California 92027-7000, U.S.A.

855

856 MARINE MAMMAL SCIENCE, VOL. 26, NO. 4, 2010

Key words: dugong, Dugong dugon, grazer, resource selection, seagrass, utilizationdistribution, GPS-telemetry.

The nutritive value of a grazing mammal’s food plants is a primary determinantof the quality and carrying capacity of its habitat. Grazers are faced with food thatvaries in quality (Kie 1999) and the effects of food quality on nutritional statuscan outweigh those of food quantity (Owen-Smith and Novellie 1982). Grazersmust balance their intake of various nutrients and nutrient content typically dictatesforage selectivity (e.g., van Wieren 1996). The relative value of the various foodresources in the habitats of mammalian grazers can be assessed via analysis of theselection of food resources by individual animals (e.g. Edwards et al. 1994, Nilsenet al. 2004), as long as the potentially confounding effects of predation, competitionand individual condition are also considered. Mammalian grazers generally selecttheir food and foraging habitats to maximize their rate of intake of nutrients and/orenergy. For example, McNaughton (1988, 1990) showed that large mammaliangrazers aggregated in areas of African rangeland where grass nutrient levels werehighest.

Terrestrial mammalian grazers forage in spatiotemporally complex habitats char-acterized by patchy distributions of food grasses (see reviews by Bailey et al. 1996,Wallis de Vries et al. 1999, Searle et al. 2005). Although seagrasses are monocotyle-dons and not true grasses, many seagrasses, including the families Posidoniaceae andZosteraceae, superficially resemble terrestrial grasses of the family Poaceae. In par-ticular the growth form of these seagrasses is comparable to that of those terrestrialgrasses that spread across the substrate via rhizomatous growth e.g., genera Imperataand Glyceria. Like communities of terrestrial grasses, seagrass communities exhibithigh spatial heterogeneity (patchiness) at a range of scales. Thus, like terrestrial pas-tures, seagrass meadows form both continuous and fragmented landscapes of varyingnutritional quality interspersed with nonvegetated substrate, e.g., Robbins and Bell(2000), Frederiksen et al. (2004). The dugong, Dugong dugon, is the only herbivorousmammal that is strictly marine and a seagrass community specialist. Like terrestrialgrazers, dugongs also appear to prefer some food patches and avoid others (Preen1995, Anderson 1998) and have presumably adapted their foraging strategies to copewith the spatial variability in their herbage, much like their terrestrial counterpartswhich face similar foraging challenges, e.g., African migratory ungulates (Seagle andMcNaughton 1992, Wilmshurst et al. 1999).

How dugongs choose their food is poorly understood. We do not know whether theenergy or nitrogen content of their seagrass food is the more important nutritionalcurrency. Researchers generally agree that dugongs prefer seagrass plants that arehigher in nitrogen (protein) and soluble carbohydrates (starch) and lower in fiber(Preen 1993, de Iongh et al. 1995, Yamamuro and Chirapart 2005). The influence ofherbage biomass on dugong food selection is also unknown (although see Wirsing2007a, b). Dugongs often graze low biomass seagrass species, a practice that enablesthem to access the entire plant, including the roots and rhizomes (Marsh et al. 1982,Preen 1995, Andre and Lawler 2003, but see Anderson 1998). The shoots and leavesof seagrass have a higher percentage nitrogen than the rhizomes, which have a higherpercentage starch (Aragones et al. 2006, Sheppard et al. 2007). At a local scale, dugongforaging is strongly influenced by tidal movements that restrict access to intertidalseagrass meadows at low tide (Anderson and Birtles 1978, Anderson 1982a, Sheppardet al. 2009). The influence of the diel cycle on dugong foraging behavior has not

SHEPPARD ET AL.: FOOD RESOURCE SELECTION BY DUGONGS 857

been studied. Analysis of dugong dive activity suggests that dugongs forage at allhours (Chilvers et al. 2004). Nonetheless, satellite tracking and acoustic recordingsindicate that dugongs access intertidal areas mainly at night (Marsh and Rathbun1990, Tsutsumi et al. 2006, Sheppard et al. 2009), confirming anecdotal reports, e.g.,Jarman (1966), Nietschmann (1984).

A detailed understanding of dugong grazing processes and the interactions be-tween dugongs and their seagrass food habitat is required to inform the conservationmanagement of dugongs, because dugongs depend almost exclusively on seagrassesfor food and seagrass habitats are facing increasing threats from anthropogenic dis-turbance (Orth et al. 2006). An animal’s use of space in the context of the availabilityof its food resources is frequently used to infer food selection which, once accuratelyquantified, can be incorporated into models for mapping, predicting and managingsuitable habitat (Boyce et al. 2002, Manly et al. 2002, Thomas and Taylor 2006).We used global positioning system (GPS) tags, a geographic information system(GIS), and an established spatial resource selection function (RSF) model to quantifyfine-scale use of food resources by dugongs in an intensively used seagrass habitat.We aimed to answer the following questions: (1) Do dugongs display patterns ofresource selection that are consistent across individuals? (2) Is dugong resource se-lection driven by: (a) spatial variation in seagrass biomass, (b) the concentrations ofnutrients, and (c) seagrass species? (3) How is dugong resource selection within ahabitat influenced by the tidal and diel cycles?

METHODS

Study Site, Telemetric Equipment, and Study Animals

We studied fine-scale habitat selection by dugongs in subtropical Hervey Bay,Queensland, Australia, in 2003 and 2004. Hervey Bay supports 2,300 km2 ofseagrass, one of the largest single areas of seagrass on the eastern Australian seaboard(McKenzie et al. 2000). Since 1988, aerial surveys have estimated that Hervey Baysupports a dugong population that has fluctuated between about 600 and 2,500animals due to migration and mortality as a result of the large-scale loss and recoveryof seagrass after extreme weather events in 1992 (Marsh and Lawler 2006). Althoughdugongs can occur throughout much of Hervey Bay, a significant concentrationconsistently occurs around the southwest coast, near Burrum Heads (25"11.4#S,152"36.6#E) (Fig. 1). The 23.8 km2 Burrum River estuary and adjacent coastal waterssupport extensive intertidal and subtidal seagrass beds, which are used intensivelyby foraging dugongs throughout the year (Marsh and Lawler 2001, 2006, Sheppardet al. 2009). Sheppard et al. (2007) describe the physiography and biogeographyof this site where they surveyed, analyzed, and mapped the species composition,nutrient profile and patch structure of the seagrass beds at high resolution (200 m)in July 2004 (winter); the month that most of the tracked dugongs considered herewere caught. The dugongs were then tracked between early July and late September.

We captured seven wild dugongs, $2 m long without an attendant calf, usingthe method of Lanyon et al. (2006) and Sheppard et al. (2009). All dugongs werefitted with Argos PTT/ GPS satellite tags (Telonics Inc, Mesa, AZ). The satellite tagand the harness assembly currently used to track wild dugongs was a modification ofthat previously described by Marsh and Rathbun (1990). The dugong telemetry unitcombined the PTT system of remote relocation fix delivery via the Argos network


Figure 1. Study site and survey location (circled) off the Burrum coast in Hervey Bay,Queensland, Australia.

with the accuracy of GPS, which was “piggybacked” on the PTT signal. A VHFtransmitter was also included for short-range (<5 km) ground truthing of locationsand recovery of tags. All GPS data were collected from the archival memory of unitsrecovered from the field. The whole system was incorporated into a slightly buoyant,streamlined, polycarbonate cylinder with an epoxy resin flotation collar that wasresistant to impact and shark attack. The unit measured 40 % 20 cm and weighed2 kg. Because the satellite tag could only acquire a location fix when it was at thesurface, a saltwater switch was incorporated into the unit to shut down transmissionattempts when the tag was submerged to conserve battery life.

The seven dugongs we tracked in winter were haphazardly selected from about30 animals that were using the Burrum habitat at the times of catching and area significant sample of that subpopulation (Table 1). Tracked dugongs were givennames for presentation. We sampled the positions of these animals at high temporaland spatial resolution (20 min sampling attempt interval, (±SE) mean total numberof location fixes = 1,377 ± 277, each fix correct to 10 m accuracy) for an average(±SE) of 48 ± 6 days (Table 1). The implications for the study of the relativelysmall number of dugongs sampled relative to the broader population are consideredin Table 2 along with the other assumptions inherent in our approach.

Dugong Space Use as an Indicator of Food Selection

We generated the home ranges of the seven dugongs using a kernel estimator todetermine the spatial extent of their fine-scale habitat use at the Burrum habitat and to


Table 1. Attributes of the seven dugongs that exhibited high site fidelity to the Burrumhabitat and were used in the RUF analysis.

Total # Ratio SizeTotal # Burrum Mean # day to Size 95%

Length Date days location fixes/day night MCP kernelAnimal ID Sex (m) deployed tracked fixes (±SE) fixes (km2) (km2)

Kalba M 2.7 3 July 2003 48.8 868 18.9 (2.0) 0.36 19.1 12.4Na’rawi M 2.2 5 July 2003 69.9 1012 33.7 (2.2) 0.78 12.1 10Bunda M 3 12 July 2004 41.6 838 44.1 (2.3) 0.5 11.7 5.5Yin’mai F 2.5 12 July 2004 35.4 539 24.3 (2.6) 1.46 9.8 7Bul’la M 2.9 13 July 2004 41.1 609 18.8 (2.7) 0.69 10.2 0.6Wurraman M 2.9 13 July 2004 70 489 31.6 (1.8) 0.65 9.7 3.7Ku’rui M 2.8 14 July 2004 31.3 823 34.7 (3.1) 0.58 9 1.2

define the boundaries of resources available for individual animals at the study site (i.e.the third-order of resource selection described by Johnson 1980, McClean et al. 1998,Kernohan et al. 2001). We used fixed kernel estimators with a least-squares crossvalidation (LSCV) smoothing function to measure dugong home range. Coverageswere calculated using the Animal Movement Analyst Extension (AMAE) (Hoogeand Eichenlaub 1997) for the ArcView v3.3 GIS program (Environmental SystemsResearch Institute, Inc., Redlands, CA). We overlaid the outer 95% boundaries ofthe kernel home ranges of the dugongs to create a single polygon (Fig. 2). Theresultant combined kernel boundary mirrored the extent of the Burrum habitat(Sheppard et al. 2007) and enabled us to quantify the available seagrass resources forthe dugongs based on the space-use of the tracked animals (similar to Miller et al.2000). We interpolated the spatial distribution of biomass for each of the four seagrassspecies found in the Burrum habitat via kriging (Nieuwland Automatisering krigingextension for ArcView, Wageningen, The Netherlands). For reviews of the krigingtechnique see Royle et al. (1981), Oliver and Webster (1990), and Atkinson (1996).The biomasses of the four species were combined to provide an estimate of totaldry-weight biomass (g/m2). Coverages of nitrogen and starch concentration obtainedusing near-infrared spectroscopy (NIRS; see Andre and Lawler 2003, Aragones et al.2006, Sheppard et al. 2007) were also interpolated into a 100 m2 resolution map ofdugong habitat quality over which the map of dugong space use was overlaid (Fig. 3,4). We measured dugong feeding habitat selectivity based on the spatial distributionsof (1) seagrass biomass, (2) the concentrations of seagrass nitrogen and starch, and(3) seagrass species composition (to investigate if the dugongs were selecting forparticular seagrass species).

We used the Resource Utilization Functions (RUF) technique of Marzluff et al.(2004) and Millspaugh et al. (2006) to compare each animal’s space use with themaps of available resources from Sheppard et al. (2007) (Fig. 5). The RUF techniquecompares the probabilistic measure of individual space use in a utilization distribu-tion (UD) to resource variables and uses multiple regression to relate resources (theindependent variables) to the height of the UD (the dependent variable). The RUFtechnique was chosen to analyze dugong food resource use because it (1) correctlytreats the individual animal as the primary sampling unit; (2) uses the entire dis-tribution of animal movements rather than individual sampling points avoiding theneed to place locations in specific habitat patches; and (3) uses the UD summary of


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Figure 2. Bathymetry of the Burrum seagrass habitat in 0.5 m intervals (at mean low watersprings, MLWS). Arrows indicate the locations of the three tidal zones used in our analysis.The extent and boundary of the habitat was determined using the overlaid home ranges ofthe tracked dugongs.

space-use as a continuous probabilistic metric, thus reducing the effects of telemetryerror and autocorrelation. Our application of the RUF technique was a Type IIIstudy design for resource selection, wherein both the availability and use of foodresources were measured concurrently for each animal (Boyce et al. 2002, Manly et al.2002, Thomas and Taylor et al. 2006). The RUF technique also allowed us to stratifydugong locations across tidal and diel cycles to incorporate environmental variabilityin food resource availability.

We analyzed the resource use of each dugong with two regression equations eachof which related dugong space use (the height of the UD) for each cell (Fig. 5) tothe following independent measures of habitat quantity and quality for that cell: (1)total seagrass biomass and two measures of nutritional quality (starch and nitrogenconcentration) and (2) the biomass of each of the four seagrass species at that cell.We divided the location data for each dugong into the following six time/tidecombinations (three tidal levels crossed with two diel periods). Diel periods: (1)day and (2) night treatments, based on the sunrise and sunset times within eachanimal’s location data set (acquired from Maritime Safety Queensland tables usinginformation from the National Mapping Division of Geoscience Australia). Tides:(1) low tide (<1.3 m MLWS),2 (2) intermediate tide (>1.3 and <1.9 m MLWS),and (3) high tide (>1.9 m MLWS). These tidal categories were based on the range

2MLWS = Mean Low Water Springs below datum.


Figure 3. Kriging interpolated landscapes of A: total combined dry weight seagrass biomassg/m2, B: mean starch concentration, and C: mean nitrogen concentration within the Burrumdugong habitat.% DW = % dry weight/m2 and represents the concentration of nutrient ina sample of dried seagrass averaged across 1 m2. The resolution of each nutrient landscapemetric is 100 m2. The black outer boundary of the nutrient landscapes demarcate availablehabitat resources to the dugongs used in the RUF procedure. The white double lines withinthe habitat indicate the boundary of the intertidal zone at mean low water springs and theapproximate area of the intermediate and high tide zones (INT = intermediate tide, HT =high tide). D: Histogram of the relative percent of depths (meters MLWS) of high biomassseagrass (>15 g/m2 dry weight) and low biomass seagrass (<15 g/m2 dry weight) throughoutthe Burrum habitat. The dashed lines indicate the extent of the low and intermediate tidalzones (refer Fig. 2A–C and Fig. S1).

of tidal heights occurring at the Burrum habitat during the tracking period and thefrequency of tidal heights experienced by each dugong (mean (±SE) experienced tideheight = 1.6 m ± 0.01, minimum = 0.3 m, maximum = 3.6 m). We developed theseregressions for each of the six time/tide combinations for the following biologicaland operational reasons: (1) the semidiurnal tidal periodicity at the Burrum habitatconstrains dugong foraging on intertidal seagrass meadows; (2) the design of thesatellite tag assembly meant that the tag was more often at the surface in shallow


Figure 4. Kriging interpolated landscapes of the four seagrass species present in the Burrumhabitat. Seagrass density is measured in dry weight biomass (g/m2). The black outer boundaryof the nutrient landscapes demarcate available habitat resources to the dugongs used inthe RUF procedure. The white double lines within the habitat indicate the boundary ofthe intertidal zone at mean low water springs and the approximate area of the intermediateand high tide zones (INT = intermediate tide, HT = high tide). The table indicates thedistribution and biomass of the component seagrasses across the study area in July 2004 forthe three tidal regimes.

water than in deep water and was thus able to transmit its location more frequently inshallow water (consequently, more locations were acquired from a dugong foragingover a shallow intertidal area than across a deep subtidal meadow); and (3) wehypothesized that the dugongs may forage differently during daylight hours than atnight because of the anecdotal information suggesting that animals were more likely


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asth

enm

atch

edw

ith

the

valu

esof

unde

rlyi

ngse

agra

ssbi

omas

s,st

arch

,and

nitr

ogen

conc

entr

atio

nla

ndsc

apes

.The

gray

laye

rin

C.r

epre

sent

sto

tals

eagr

ass

biom

ass

proj

ecte

din

to3-

Dus

ing

the

unde

rlyi

ngba

thym

etry

,wit

hda

rker

shad

esin

dica

ting

grea

ter

biom

ass;

the

cont

ours

repr

esen

tth

ehe

ight

ofth

eU

Dfo

rth

edu

gong

duri

ngth

eda

yat

low

tide

.


to forage inshore at night, perhaps in response to diurnal differences in the risk ofhuman disturbance or shark predation (Heithaus et al. 2002, Hodgson and Marsh2007, Sheppard et al. 2009).

We constructed UDs for each dugong for each time/tide combination using theAMAE and at least 50 location fixes (Fig. 5). This minimum sample size was basedon previous simulation studies that suggest that, at minimum, 30 (preferably >50)points are required for kernel methods to define an animal’s home range accurately(Seaman et al. 1999, Kernohan et al. 2001). Following Marzluff et al. (2004), wetested for a positive correlation between the number of location fixes and the size ofthe animal’s minimum convex polygon (MCP) home range; the estimator known tobe most sensitive to sample size (Manly et al. 2002). Tests for the effect of dugonglocations on home range size were conducted within each individual’s data set, withlocations added sequentially. Home range areas generated using >50 fixes were notcorrelated with location sample size (intertidal location fixes: r2 = 0.18, P = 0.30;subtidal location fixes: r2 = 0.16, P = 0.22). Thus, we considered a dugong tobe adequately sampled if more than 50 location fixes were acquired within eachtime/tide combination. Kernel home ranges and UDs that crossed over the land wereclipped to cover only the sea. To incorporate areas within an animal’s total space usethat were not utilized within the UD category, each UD was clipped to the extentof a MCP that was generated using the total animal’s movement data. The UD gridcells outside of the 99% kernel boundary (but within the MCP) were assigned a usevalue of zero in the RUF analysis.

Following Marzluff et al. (2004), we used standardized RUF ! j to compare therelative influence of nutrient resources on dugong space-use and to test for population-wide consistency in resource selection, where j = the nutrient landscape beneath therelevant animal’s UD. We averaged across the ! j for each animal to obtain an estimateof the standardized coefficient for the population assuming that each dugong wasindependent of the other sampled animals.

Determining the role that acoustic communication may have played in maintain-ing contact between foraging dugongs was beyond the scope of our study (Andersonand Barclay 1995, Tsutsumi et al. 2006). The assumption of independence was basedon the following analysis of the distances between pairs of dugongs within theBurrum habitat during the tracking period. This analysis combined estimates of in-teranimal contact distance and the maximum perceptual axis of the herd to comparethe distances between pairs of animals within the dugong herd for different stagesof the tidal cycle. We defined interanimal contact distance as the maximum distanceover which two dugongs at the Burrum habitat could maintain visual contact witheach other underwater and the perceptual axis of the herd as the combined contactdistances of all animals in that herd. Using direct observation, Hodgson (2004)estimated individual dugong contact distances to be <15 m, an estimate supportedby the assessments that dugong eyesight is comparable to that of a masked diver(Anderson 1982b). From a human perspective, water visibility at the Burrum habitatduring the tracking periods was <10 m. Our estimate of the maximum perceptualaxis of the Burrum dugong herd was conservatively based on a hypothetical oval-shaped herd of 24–30 animals, with a maximum axis equivalent to the combineddistance of six individuals arranged in a straight line spaced at their maximum per-ceptual distances apart (75 m) plus an arbitrary error margin of 45 m (three times theempirical contact distance). The longest consecutive period that two animals wererecorded within 120 m of each other was 3 h. An average of 4.4% of pairs couldplausibly have been members of the same herd at the time their position was recorded.


Because individual dugongs varied in their use of the food landscape, the varianceof each ! j was calculated to include the variation within and between dugongs usingEquation (3) in Marzluff et al. (2004) (see Equation 1 below) to allow conservative,population-level inferences to be made about dugong resource use:

Var! ˆ! j

"= 1

n & 1

n#

i=1

!!i j & ˆ! j

"2(1)

If the standardized coefficients were positive (+), then use of an area increasedwith the quantity of the available resource; if the coefficients were negative (&)then use decreased with increasing quantity of the resource (Millspaugh et al. 2006).Standardized coefficients that were significantly different from zero in a t-test deter-mined which nutrient resources were selected for or against by the study population.Standardized coefficients were also analyzed using factorial ANOVA using dugongas a random factor to determine the relative influence of the tide and time as fixedenvironmental factors on dugong resource use. Selection for each food resource wasalso indicated by the proportion of dugongs whose standardized RUF coefficientswere significantly correlated with the resource.

We also calculated ! j for simulated dugongs to compare the dugong resourceselection we observed with that of a simulated randomly moving animal. Correlatedrandom walks (CRW) were generated using the AMAE (Hooge and Eichenlaub1997). The AMAE uses Monte Carlo simulation and parameters from the observeddata (step lengths and turning angles) to generate the CRWs. The Monte Carlosimulation begins with an individual’s initial location and generates a random walkpath using the observed distance moved between each tracking location while ran-domizing the direction of movement from each point (Hooge and Eichenlaub 1997).CRWs were generated within the six tide/diel treatments based on the movementparameters of the seven dugongs used in the RUF analysis. Random walks werebounded within the extent of the Burrum habitat at each tidal category so thatthe simulated paths were constrained within the limit of resources available to theobserved dugongs. The resulting simulated paths were converted to “location fixes,”which were then used to generate UDs and calculate RUF coefficients. RUF coeffi-cients calculated from simulated CRWs were used as controls to validate estimatesof dugong resource selection. All values are given ± standard errors.

RESULTS

Two male dugongs were captured and tagged in the Burrum seagrass habitatduring July 2003; one female and four male dugongs were tagged in the habitatduring July 2004. Six of the seven captured animals were mature adults with bodylengths >2.5 m; the reproductive status of the 2.2 m long male cannot be reliablyinferred from his body length (Marsh et al. 1984). Each dugong was tracked for atleast a month; two animals were tracked for 70 d. The mean MCP home range sizecalculated using the entire location fix dataset was 11.3 km2 (±1.3); the mean 95%kernel home range size was 5.8 km2 (±1.7). The attributes of the tracked dugongsare summarized in Table 1.


Variability of Available Seagrass Food Resources across the Study Habitat

The area of seagrass available to the dugongs changed with the tides; the entire23.8 km2 seagrass meadow was available at high tide; 19.9 km2 (84%) at intermediatetide and 16.8 km2 (71%) at low tide (Fig. 2, 3, 6). The distribution and biomass ofthe component seagrasses across the study area in July 2004 for the three tidalregimes are quantified and illustrated in Figure 4. Four species of seagrass domi-nated the Burrum habitat: Halodule uninervis, Halophila ovalis, Zostera muelleri, andHalophila spinulosa. The mean concentrations of both starch (F33,1,255 = 2.3, P =<0.01) and nitrogen (F33,1255 = 3.8, P = <0.01) varied significantly with seagrassbiomass (Fig. 7). Post hoc comparisons indicated that both the mean nitrogen andstarch concentrations were lower for seagrass patches of higher biomass (±34 g/m2

N; $29 g/m2 starch) than for seagrass patches of lower biomass ('34 g/m2 N;'29 g/m2 starch, Tukey HSD: nitrogen MS = 0.03, df = 1,287, P = 0.01; starchMS = 2.4, df = 1,287, P < 0.01).

The mean starch concentration of the seagrass patches varied with tidal zone.The area accessible to dugongs at low tide had a mean starch concentration of3.5% ± 0.03%, maximum = 6.9%, compared with 5.2% ± 0.1%, maximum =7.4% in the intermediate tide zone and 4.1% ± 0.1%, maximum = 8.1% in thehigh tide zone (Fig. 7). The starch content of Z. muelleri was significantly higherin the intermediate tide zone (mean = 4.3% ± 1.2, maximum = 9.8) and hightide zone (mean = 8.8% ± 0.7%, maximum = 10.4%) than in the low tide zone(3.1% ± 0.4%, maximum = 8.1%) (F229 = 15.0, P = <0.01). The starch content ofH. uninervis, the dominant seagrass species in the Burrum habitat, may also have beensimilarly higher in the intermediate tide zone (mean = 7.7%, ± 1.3%, maximum =14.3%) and high tide zone (mean = 7.6% ± 1.1%, maximum = 10.9%), wherebiomass was relatively lower, than in the low tide zone (mean = 6.0%, ± 0.6%,maximum = 12.9%), where biomass was high. However, these differences were lowand not statistically significant (F2,46 = 1.18, P = 0.3). Total nitrogen concentrationof seagrass patches varied significantly across tidal zones (F2,3.3, P = 0.04), althoughthese differences were small (1.11% ± 0.005%, maximum = 1.42%) in the areaaccessible to dugongs at low tide and 1.11% ± 0.01%, maximum = 1.45% inthe high tide zone compared with 1.14% ± 0.008%, maximum = 1.39% in theintermediate tide zone (Fig. 3C). The nitrogen concentrations of individual seagrassspecies did not vary significantly across tidal zones (H. uninervis: F2,0.23, P = 0.8;H. ovalis: F2,0.7, P = 0.51; Z. muelleri: F2,1.9, P = 0.17).

Effects of Tide and Diel Cycle on Dugong Use of Food Resources

The home range sizes of individual dugongs varied greatly across the tidal and dieltreatments, from “Narawi” with a daytime, high tide kernel range of 833 hectaresto “Bulla” with a nighttime intermediate tide range of 10 hectares. The trackeddugongs appeared to move independently of each other (validating Assumption 2of Table 2). Nonetheless, their home ranges overlapped extensively (validating As-sumption 1). The percentage of overlap across the 95% kernel home ranges of pairsof individual dugongs ranged from 0% to 100% (mean day: 39.2% ± 4.3%, meannight: 22.0% ± 5.3%). The ranges of all five animals tagged in July 2004 andthe two animals tagged in July 2003 overlapped with the range of every otheravailable tagged dugong during the day. The ranges of all but one tagged dugong


Figu

re6.

An

exam

ple

ofth

ein

tra-

anim

alva

riat

ion

ofdu

gong

reso

urce

sele

ctio

nac

ross

tide

and

diel

cycl

esis

prov

ided

byth

ese

kern

elho

me

rang

esca

lcul

ated

from

the

loca

tion

fixes

from

the

indi

vidu

aldu

gong

“Wur

ram

an.”

The

anim

al’s

mov

emen

tda

taw

assu

bdiv

ided

into

tide

and

day-

nigh

ttr

eatm

ents

.The

blac

kri

ngs

repr

esen

tth

eou

ter

95%

boun

dary

ofea

chke

rnel

rang

e.T

hest

ripe

dpo

lygo

nsre

pres

ent

100

m2

cont

iguo

uspa

tche

sof

high

ests

tarc

hco

ncen

trat

ion

(>6%

)in

the

Bur

rum

habi

tat.

The

dark

grey

poly

gons

repr

esen

t100

m2

patc

heso

fhig

hest

tota

lsea

gras

sbio

mas

s(>

15g

DW

/m2 ).

The

dash

edlin

ede

mar

cate

sthe

boun

dary

ofth

ein

tert

idal

zone

atlo

wti

de(M

LWS)

and

the

appr

oxim

ate

area

ofth

ein

term

edia

teti

dezo

ne.

The

stip

pled

area

repr

esen

tsth

ehi

ghti

dezo

ne.N

ote

how

the

anim

al’s

spac

eus

eis

cent

ered

over

the

dens

estb

iom

assp

atch

inth

esu

btid

alzo

neat

low

tide

duri

ngbo

thda

yan

dni

ght.

As

the

tide

heig

htin

crea

ses

from

left

tori

ght

the

spac

eus

ece

nter

shri

nks

and

mov

esov

erth

epa

tche

sof

high

star

chco

ncen

trat

ion

inth

ein

tert

idal

zone

duri

ngbo

thda

yan

dni

ght.


Figure 7. Relationship between total seagrass biomass and seagrass starch and nitrogenconcentration across the Burrum habitat. Starch and nitrogen concentrations initially increasedwith seagrass biomass, and then decreased at high biomass densities greater than 30 g/m2

dry weight. Consequently, areas of the habitat that contained high total seagrass biomasscontained lower total nutrient concentrations than more sparse patches. Dugongs foraging inhigh biomass areas would have to consume greater amounts of herbage to acquire the samenutrients than they would when foraging in sparse patches of seagrass.

(“Bulla”) overlapped with the ranges of all available tagged dugongs during the night(Appendix Table S1).

GPS fixes were acquired from tagged dugongs only when the transmitter was atthe surface. Consequently, more locations were acquired when a dugong was in ashallow intertidal area than in a deeper subtidal meadow. More locations were alsoacquired from dugongs at night when the animals were in shallow intertidal waterthan during the day when the animals were more often offshore in deeper (>3 mMLWS) subtidal habitats (Sheppard et al. 2006, 2009). The average number of fixesfor the seven dugongs for each time/tide combination follows: (1) day: low tide93 ± 5.7, intermediate tide 79 ± 2.7, and high tide 83 ± 2.9; (2) night: low tide154 ± 10.3, intermediate tide 168 ± 6.4, and high tide 162 ± 10.4. Within thehabitat that was accessible to dugongs at low tide, patches of high-density seagrass(>15 g/m2 dry weight) were concentrated in shallower (0–4 m MLWS) rather thandeeper locations (4–8 m MLWS) (Fig. 3A), possibly resulting in a sampling bias atlow tide during the day as discussed below.

The absolute average value of the standardized ! j (Table 3, 4) was an index of therelative use of the food resources by the study population. The extent of associationwith particular resources varied between dugongs and between the time-tide treat-ments as reflected in the variation in the standardized RUF coefficients. Nevertheless,there were consistent foraging patterns. The tagged dugongs were consistently pos-itively associated with seagrass patches with high nitrogen concentrations, exceptduring the day at low tides when they were associated with seagrass of high biomass;the association with high biomass seagrass may have been a sampling artifact as


Tabl

e3.

Esti

mat

esof

stan

dard

ized

RU

Fco

effic

ient

s(ˆ !

)fo

rth

ese

ven

dugo

ngs

wit

hin

the

Bur

rum

habi

tat

from

the

mul

tipl

ere

gres

sion

sre

lati

ngre

sour

ceus

eat

each

grid

cell

(res

pons

e)to

seag

rass

biom

ass,

seag

rass

nitr

ogen

cont

ent

and

seag

rass

star

chco

nten

t(in

depe

nden

tva

riab

les)

.Sep

arat

ere

gres

sion

sw

ere

perf

orm

edfo

rea

chdu

gong

and

each

diel

/tid

alph

ase

com

bina

tion

.Pos

itiv

eco

effic

ient

sin

dica

teth

atus

ein

crea

ses

wit

hin

crea

sing

valu

esof

the

nutr

ient

reso

urce

s.R

elat

ive

impo

rtan

ceof

the

nutr

ient

reso

urce

sis

indi

cate

dby

the

mag

nitu

deof

ˆ !.C

onsi

sten

cyin

reso

urce

sele

ctio

nac

ross

indi

vidu

aldu

gong

sisi

ndic

ated

byth

esi

gnifi

canc

eof

ˆ !(s

igni

fican

t[P

<0.

05]d

iffer

ence

sfro

m0

are

bold

ed)a

ndth

enu

mbe

rofd

ugon

gsw

hose

spac

e-us

ew

asei

ther

posi

tive

lyor

nega

tive

lyas

soci

ated

wit

hea

chse

agra

ssla

ndsc

ape

met

ric

(the

+an

d&

sign

s).

!B

iom

95%

C.I

.aP(

ˆ !=

0)b

+&

!ni

tro

95%

C.I

.P(

ˆ !=

0)+

&!

starc

h95

%C

.I.

P(ˆ !

=0)

+&

Day

LTc

+1.0

30.

40–1

.66

<0.

017

0&

0.25

&0.

77–0

.27

0.28

34

!0.5

3!0

.78–

0.28

<0.

010

7D

ayIN

T+

0.02

&0.

56–0

.59

0.95

34

+0.9

60.

20–1

.71

0.02

61

&0.

02&

0.43

–0.4

00.

934

3D

ayH

T+

0.20

&0.

58–0

.99

0.55

34

+0.8

10.

49–1

.14

<0.

017

0+0

.88

0.37

–1.3

90.

017

0N

ight

LT+

0.50

&0.

26–1

.26

0.16

52

+0.

76&

0.11

–1.6

30.

086

1+

0.23

&0.

10–0

.55

0.14

43

Nig

htIN

T&

0.02

&0.

44–0

.39

0.90

25

+0.9

70.

54–1

.40

<0.

017

0+

0.52

&0.

17–1

.21

0.11

52

Nig

htH

T&

0.19

&0.

50–0

.12

0.19

34

+0.7

00.

21–1

.20

<0.

017

0+1

.07

0.42

–1.7

3<

0.01

61

C.I

.=

95%

confi

denc

ein

terv

al.

The

sew

ere

calc

ulat

edus

ing

both

intr

a-an

imal

and

inte

r-an

imal

vari

atio

nto

prov

ide

cons

erva

tive

esti

mat

esof

popu

lati

on-l

evel

coef

ficie

nts.

b Pva

lues

test

the

null

hypo

thes

isth

atth

eav

erag

e!

isze

ro,g

iven

n=

7du

gong

s.c LT

=lo

w-t

ide,

INT

=in

term

edia

te-t

ide,

HT

=hi

gh-t

ide.


Tabl

e4.

Mea

nst

anda

rdiz

edR

UF

coef

ficie

nts

(ˆ !)f

orth

ese

ven

dugo

ngs

wit

hin

the

Bur

rum

habi

tat

from

the

mul

tipl

ere

gres

sion

sre

lati

ngre

sour

ceus

eat

each

grid

cell

(res

pons

e)to

biom

ass

ofth

efo

urse

agra

sssp

ecie

sat

this

loca

tion

(inde

pend

ent

vari

able

s).S

epar

ate

regr

essi

ons

wer

epe

rfor

med

for

each

dugo

ngan

dea

chdi

el/t

idal

phas

eco

mbi

nati

on.P

osit

ive

coef

ficie

nts

indi

cate

that

use

incr

ease

sw

ith

incr

easi

ngva

lues

ofth

enu

trie

ntre

sour

ces.

Rel

ativ

eim

port

ance

ofth

esp

ecie

sis

indi

cate

dby

the

mag

nitu

deof

ˆ !.C

onsi

sten

cyin

reso

urce

sele

ctio

nac

ross

indi

vidu

aldu

gong

sis

indi

cate

dby

the

sign

ifica

nce

ofˆ !

(sig

nific

ant

[P<

0.05

]dif

fere

nces

from

0ar

ebo

lded

)and

the

num

ber

ofdu

gong

sw

hose

spac

eus

ew

asei

ther

posi

tive

lyor

nega

tive

lyas

soci

ated

wit

hea

chse

agra

ssla

ndsc

ape

met

ric.

ˆ !H

.uni

nerv

is95

%C

.I.

P(ˆ !

=0)

+&

ˆ !H

.ova

lis95

%C

.I.

P(ˆ !

=0)

+&

Day

LT+0

.62

0.09

–1.1

40.

036

1+

0.10

&0.

36–0

.55

0.62

25

Day

INT

&0.

11&

0.46

–0.2

30.

454

3+

0.40

&0.

24–1

.03

0.18

52

Day

HT

+0.

11&

0.24

–0.4

60.

483

4+

0.41

&0.

21–1

.04

0.15

61

Nig

htLT

+0.

29&

0.35

–0.9

30.

315

2+

0.17

&0.

02–0

.82

0.06

52

Nig

htIN

T+

0.04

&0.

39–0

.47

0.83

25

+0.3

50.

03–0

.68

0.04

61

Nig

htH

T+

0.10

&0.

21–0

.42

0.45

34

&0.

02&

0.15

–0.2

30.

633

4

ˆ !H

.spi

nulo

sa95

%C

.I.

P(

ˆ !=

0)+

&ˆ !

Z.m

uelle

ri95

%C

.I.

P(ˆ !

=0)

+!

Day

LT+0

.43

0.12

–0.7

30.

016

1&

0.16

&0.

36–0

.05

0.10

25

Day

INT

&0.

16&

0.48

–0.1

60.

273

4!0

.21

!0.3

9–0.

030.

031

6D

ayH

T&

0.38

&0.

82–0

.06

0.08

16

!0.3

5!0

.52–

0.19

<0.

010

7N

ight

LT+

0.00

2&

0.49

–0.4

90.

993

4&

0.11

&0.

50–0

.29

0.53

25

Nig

htIN

T!0

.34

!0.6

3!0.

060.

031

6+

0.01

&0.

45–0

.45

0.99

34

Nig

htH

T!0

.41

!0.8

0!0.

030.

041

6+

0.06

&0.

45–0

.34

0.73

16


discussed above. The tracked dugongs were positively associated with areas of sea-grass high in starch during both day and night high tides when the animals couldaccess intertidal areas where the seagrass biomass was generally low (Table 3). Thepatterns of association between the dugongs and the seagrass species were less definite(Table 4). Dugongs were positively associated with H. uninervis and H. spinulosa onlyon daytime low tides when they apparently had fewest choices as discussed below,presumably because these species occurred in the shallow high biomass areas, andwith H. ovalis only at intermediate tides at night. Dugongs appeared to be negativelyassociated with areas of dense H. spinulosa at intermediate and high tides at nightwhen they had most choice and dense Z. muelleri at intermediate and high tidesduring the day.

The proportion of the tracked dugongs positively or negatively associated witheach resource landscape was another index of resource selection (Table 3, 4). Alltracked dugongs were positively associated with high biomass areas during theday at low tide, seagrass patches with high nitrogen concentration at intermediateand high tides during the night and with high tides during the day. Six of theseven dugongs were positively associated with high nitrogen concentrations duringintermediate tides during the day. Conversely, all dugongs were negatively associatedwith patches with high starch concentrations during the day at low tide. All dugongswere positively associated with seagrass containing high starch concentration duringthe day at high tide and six dugongs were positively associated with high starchduring the night at high tide. Six dugongs were positively associated with highbiomass patches of H. uninervis during the day at low tide and with H. ovalis at nightduring intermediate tides. Six dugongs apparently avoided H. spinulosa during thenight at intermediate and high tides but were positively associated with H. spinulosaat low tide during the day. All animals were negatively associated with Z. muelleriduring the day at high tide; six during the day at intermediate tides.

Factorial ANOVAs using the standardized RUFs as dependent variables providedfurther confirmation that tide had a significant effect on the patterns of dugong spaceuse, although the influence of tide on the dugongs’ association with high nitrogenareas was limited. There was a significant interaction between time and tide in thedugongs’ association with areas of high nitrogen concentration (F2,36 = 3.2, P =0.048); dugongs were negatively associated with patches of high nitrogen seagrass atlow tide during the day (Fig. S1.B). The limited influence of tide and diel cycles onthe dugongs’ association with high nitrogen areas indicated by the RUF model wasconfirmed by averaging the nitrogen concentration in the 100 m2 grid cells beneatheach animal’s location fixes. The mean nitrogen values were then stratified acrosstide and diel cycles and used as dependent variables in a multifactorial general linearmodel ANOVA. The only significant effect was that of Animal (as a random factor;F6,4.2 = 24.7, P = 0.003) indicating that the tendency to associate with areas highin nitrogen varied significantly across individual dugongs. The mean percentage ofnitrogen in the grid cells containing location fixes for all animals was 1.20 per m2

(±0.01 SE, minimum = 0.69, maximum = 1.45), compared with the habitat averageof 1.12 (± 0.003 SE, minimum = 0.69, maximum = 1.45).

Dugongs were more often associated with seagrass patches with high starch con-centrations as the tide rose; this association was greatest at high tide (F2,36 = 15.6,P = <0.001, Fig. S1.C.). There was also a significant effect of the diel cycle on thedugongs’ association with patches of high starch concentration; the association wasstronger during the night (F1,36 = 8.8, P = 0.005). The diel cycle had no significanteffect on the dugongs’ association with areas of high seagrass biomass (F1,36 = 1.1,


P = 0.12) and nitrogen concentration (F1,36 = 2.3, P = 0.14). Dugongs were morelikely to be associated with patches of high seagrass biomass during low tide thanduring intermediate or high tides throughout the diel cycle (Fig. S1.A.). This resultmay be a sampling artifact as explained above. Diel cycle had no significant effect onthe spatial association of the dugongs with the biomass landscapes of any of the sea-grass species. Tide had a significant effect on dugong association with the biomass ofboth H. uninervis (F2,36 = 3.8, P = 0.03) and H. spinulosa (F2,36 = 8.5, P = 0.01); theassociation with both species was strongest at low tide. H. uninervis comprised 80%of the total seagrass biomass of the Burrum habitat. Thus, the positive association ofthe dugongs with H. uninervis mirrored their association with patches of high totalbiomass (Fig. 3, 4, 6). The tidal and diel cycles had no significant interactive effecton the spatial associations of tracked dugongs with any seagrass species. The RUFcoefficients of the correlated random walks (CRWs) displayed no population-leveltrends of space use in association with the food resource layers and all RUF outputshad extremely high variation. Factorial ANOVA using the individual standardizedcoefficients of the simulated CRWs showed no significant interactions between theday/night and tide and any of the seven food resource variables (P > 0.05). The nullresults of the CRW controls further support the evidence for food resource selectionin the tracked dugongs.

DISCUSSION

Despite large differences in the diurnal and nocturnal home ranges of individualanimals, our study population displayed consistent patterns of resource association.The tracked dugongs appeared to exploit the spatial variation in the nutrient concen-trations within the constraints on resource availability imposed by the tidal regime(see also Sheppard et al. 2009). However, the association of dugongs with subtidal/deep water (>3 m MLWS) seagrass must be interpreted in the context of the samplingbias associated with the acquisition of location fix discussed above. For example, thepositive association of dugongs with patches of high biomass seagrass during low tide(particularly during the day) and the lack of association of dugongs with patches ofH. spinulosa and Z. muelleri (which occurred mainly in the subtidal zone, see Sheppardet al. 2007) may have been influenced by the telemetric bias.

Like their terrestrial analogues, the dugongs demonstrated a fine-grained matchingresponse to the quality of their food plant resources. Matching occurs when herbivoresspend proportionally more time foraging in areas of greatest nutrient return (Senftet al. 1987, Gordon and Illius 1989). Dugongs exploited areas with higher starchand nitrogen concentrations than the surrounding habitat, the availability of whichchanged with the tides. The RUF analysis indicated that the dugongs foraging inthe Burrum habitat located their combined home range over seagrass patches highin starch and nitrogen concentration relative to the available resources (Table 3).The animals then selected areas within this region where starch and nitrogen con-centrations were especially high, as permitted by the tidal regime.

Our model of dugong resource selection suggests that nitrogen is the primarylimiting nutrient for dugong populations. The RUF model indicated that dugongsselected patches of high nitrogen concentration relative to available resources. Wildhorses and zebra also actively select habitats with herbage high in nitrogen (Duncan1983, BenShahar and Coe 1992). Interestingly, dugongs (Murray et al. 1977), horses,and zebras (Clauss et al. 2007) are all colon fermenters. The persistent association


of dugong space use with herbage high in nitrogen also suggests that dugongforaging strategies in the Burrum habitat enable them to optimize their intakeof nitrogen from a sparse and heterogeneous food resource (Schoener 1971, Krebset al. 1974, Pyke 1984). The mean total nitrogen levels recorded in the Burrumseagrass habitat varied by species: H. uninervis 1.28% ± 0.05%, H. ovalis 1.21% ±0.08%, H. spinulosa 0.88% ± 0.04%, and Z. muelleri 0.82% ± 0.04%. Mature horsesrequire a diet containing 1.28% nitrogen for maintenance and 1.6% nitrogen forwork (irrespective of intake rate as a result of constraints on gut capacity; NationalResearch Council 1989). Protein requirements for horses increase to 1.7% and 2.11%nitrogen during the last third of gestation and peak lactation, respectively. Foalsrequire 2.3% nitrogen for optima1 growth (National Research Council 1989). Basedon the equine model, the nitrogen content of seagrass would be barely adequate fordugong maintenance and inadequate for dugongs during reproduction and growth.If dugongs are nitrogen limited, their foraging behavior would be expected to reflecta need to maximize nitrogen intake as predicted by Lanyon (1991).

The influence of food resource quality on the nutritional status of herbivores isoften greater than food quantity per se. That is, while the total biomass of foodplant material may be high, if a proportion of it is below an adequate nutritionalthreshold then it cannot be considered to be part of the food resource. Terrestrialhindgut fermenters are able to survive on less nutritious forage than ruminants byincreasing food throughput as digestibility decreases and by selective excretion offibrous digesta (Iason and Van Wieren 1997). In contrast, the dugong occupies aspecialist niche within mammalian hindgut fermenters by having a 30 m long colon,which extends food retention times for up to a week to maximize nutrient returnfrom ingested forage (Marsh et al. 1977, Lanyon and Marsh 1995). Presumably,dugongs are able to counter the energetic costs of a large and heavy hindgut throughthe weightlessness of the marine environment. However, long food retention timesin conjunction with a poor masticatory ability likely obligates dugongs to foragefor and ingest large amounts of comparatively nitrogen-rich seagrass that is alsolow-fiber, morphologically small and typically sparse.

The significant association of dugongs with areas of high starch concentrationduring the part of the tidal cycle in which they had greatest choice confirms dugong’spreference for high-energy foods (see also de Iongh et al. 1995, Anderson 1998).However, dugongs foraging in the Burrum habitat may face a trade-off betweenmaximizing their intake of seagrass biomass at low tide and maximizing their intakeof starch at high tide. We cautiously infer that this behavior suggests that dugongadopt: (1) a time-minimizing strategy at low tide, i.e. minimizing foraging time byfeeding in areas of high biomass; and (2) an energy-maximizing strategy at high tide,i.e., maximizing energy intake by foraging for sparse foods high in starch content(Schoener 1971, Belovsky 1984, Stephens and Krebs 1986). However, consistencyamong the individuals tracked could simply have been an artifact of our smallsample of dugongs. Also, the sampling bias introduced to the location data foranimals in deep subtidal water may have affected our results by underestimatingdugong foraging behavior in such areas (Table 2). We also assumed that the trackeddugongs were moving while foraging across tidal and diel cycles and that they werenot using specific seagrass patches as resting areas. If these biases were not there, wewould expect our results to show an even stronger association between dugongs andseagrasses high in nitrogen and starch because our foraging model is conservative. Ofcourse, these patterns of dugong resource use could also have been modified by theinfluence of predators such as sharks or by human disturbance from boats, but such


responses were beyond the scope of our study (see also Sheppard et al. 2009). Ouranecdotal evidence (acquired from extended periods on the water and discussionswith professional fishermen) suggest that the densities of large sharks at the Burrumhabitat are low in contrast to the situation in Shark Bay, Western Australia wherethere is evidence that shark predation modifies dugong microhabitat use (Wirsinget al. 2007a, b). The high density of recreational boats that traverse the Burrumhabitat during the day may have caused the tracked dugongs to forage close to theshore only during the night (Maitland et al. 2006), which may be reflected in thestrong association of dugongs with intertidal seagrass patches that are high in starchconcentrations.

ACKNOWLEDGMENTS

Funding and in-kind support were generously provided from the following sources: CRCReef, Australian Research Council LIEF Scheme, James Cook University, Edith Cowan Uni-versity, Western Australia Department of Conservation and Land Management, Sea WorldResearch and Rescue Foundation, Great Barrier Reef Marine Park Authority, Ian Potter Foun-dation, Society for Marine Mammalogy, Norman Wettenhall Foundation, Rothwell WildlifeConservation Fund, Queensland Parks and Wildlife Service, Queensland Environmental Pro-tection Agency, the Wondunna Aboriginal Corporation, and an anonymous donor. We thankN. Gales, C. Limpus, and P. Harvey for their valued contributions and G. Rathbun andJ. Reid for help with the design of the transmitters and harness assembly. We are grateful tothe many field assistants who helped capture and tag wild dugongs and survey their habitats,in particular, T. Stevens, J. Kruger, G. Parra, A. Abdulla, A. Hurlbatt, P. Quayle, A. Hodgson,R. Groom, and B. Chainey from Hervey Bay Air Adventures. We thank D. Coomans,M. Steele, and S. Delean for help with analytical and programming procedures,J. Marzluff and M. Handcock for invaluable advice about using their RUF technique, and I.Gordon and M. Heithaus and three anonymous reviewers for their comments on an earlierdraft of this manuscript. Our research was approved by the James Cook University AnimalEthics Sub-Committee (Ethics Approval No.: A56900), the Permitting Committee of theDepartment of Environment and Water Resources under Section 266A of the EnvironmentProtection and Biodiversity Conservation Act 1999, the Queensland Environmental Protec-tion Agency (permits W4/002726/02/SAA and MP2002/005), and the Great Barrier ReefMarine Park Authority (permit G01/304).

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Received: 24 April 2009Accepted: 17 December 2009

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article:Table S1. Percentage overlap of the 95% kernel home range of the seven dugongs

tracked in the Burrum habitat, divided into day/night categories.Figure S1. Factorial ANOVA conducted on individual standardized RUF coef-

ficients for A: Total seagrass biomass; B: Nitrogen concentration, and; C: Starchconcentration.

Figure S2. Factorial ANOVA conducted on individual standardized RUF coeffi-cients for the biomass of the four seagrass species found in the Burrum habitat.

Figure S3. Semi-variograms of the circular kriging models used to interpolate theseagrass meadow surfaces.

dugong habitat use in relation to seagrass nutrients, tides, and diel cycles

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