Feeding ecology and niche segregation in oceanic top predators off eastern Australia

Transcription

1 Mar Biol (21) 157: DOI 1.17/s y ORIGINAL PAPER Feeding ecology and niche segregation in oceanic top predators off eastern Australia Jock W. Young Matt J. Lansdell Robert A. Campbell Scott P. Cooper Francis Juanes Michaela A. Guest Received: 21 May 29 / Accepted: 21 June 21 / Published online: 8 July 21 Ó Springer-Verlag 21 Abstract We examined the feeding ecology and niche segregation of the ten most abundant fish species caught by longline operations off eastern Australia between 1992 and 26. Diets of 3,562 individuals were examined. Hook timer data were collected from a further 328 fish to examine feeding behaviour in relation to depth and time of day. Prey biomass was significantly related to predator species, predator length and year and latitude of capture. Although the fish examined fed on a mix of fish, squid and crustacea, fish dominated the diet of all species except small albacore (Thunnus alalunga) which fed mainly on crustacea and large swordfish (Xiphias gladius) and albacore which fed mainly on squid. Cannibalism was observed in lancetfish (Alepisaurus spp.). Multidimensional scaling identified three species groups based on their diet composition. One group consisted of yellowfin tuna (T. albacares), striped marlin (Tetrapturus audax) and dolphinfish (Coryphaena hippurus); a second group consisted of bigeye tuna (T. obesus), swordfish and albacore; and a third consisted of southern bluefin tuna (T. maccoyii) and blue shark (Prionace glauca). Of note was the separation of Communicated by M. A. Peck. J. W. Young (&) M. J. Lansdell R. A. Campbell S. P. Cooper CSIRO Wealth from Oceans National Research Flagship, CSIRO Marine and Atmospheric Research, GPO Box 1538, Hobart, TAS 71, Australia Jock.Young@csiro.au F. Juanes University of Massachusetts, Amherst, MA 13, USA M. A. Guest Tasmanian Aquaculture and Fisheries Institute, Nubeena Crescent, Taroona, TAS 753, Australia mako shark (Isurus oxyrhynchus) and lancetfish from all other predators. Prey length generally increased with increasing predator length although even large predators fed on a wide range of prey lengths including very small prey. Overall, differences in prey type and size, feeding times and depths were noted across the range of species examined to the extent that predators with overlapping prey, either in type or size, fed at different times of the diel period or at different depths. Taken together these data provide evidence for feeding niche segregation across the range of oceanic top predators examined. Introduction With an increasing emphasis towards ecosystem management in many oceanic fisheries, one of the first priorities has been to establish the ecological relationships of the component species, particularly top predators such as tuna, billfish and sharks (Cox et al. 22). Understanding the trophic relationships of these predators underlies models that attempt to predict impacts of fishing and climate on other components of the ecosystem (e.g. Kitchell et al. 1999; Heithaus et al. 28; Griffiths et al. 21; Dambacher et al. in press). There are conflicting analyses on the relative importance of these predators in terms of their effect on ecosystem structure within pelagic systems. Some analyses suggest there is no dominant species (Kitchell et al. 1999; Griffiths et al. 21), whereas other studies have made cases for individual species producing substantial changes when removed (Cox et al. 22). The starting point for any of these analyses is a detailed account of the trophic relationships of these predators along with other species that share the same environment.

2 2348 Mar Biol (21) 157: Off eastern Australia fishing impacts on target, bycatch and threatened species has led to a series of studies to evaluate the trophic links between predators and their prey in the region (Young et al. 26; Lansdell and Young 27; Revill et al. 29). An ecological risk assessment of the Eastern Tuna and Billfish Fishery (ETBF) identified an overall lack of ecological data for target and bycatch species (Hobday et al. 24). Without these data, accurate assessment of the impacts of fishing and climate change on the wider ecosystem is not possible, or at best only feasible using literature values mainly from the northern hemisphere. Information on feeding ecology was only available for individual species (Young et al. 1997, 21), but more general information on diet interactions among species was missing. Also, estimates of prey consumption rates and daily rations needed for quantitative population models were lacking. This paper compares the feeding ecology of ten pelagic fish species from oceanic waters off eastern Australia. These species albacore (Thunnus alalunga), southern bluefin (T. maccoyii), bigeye (T. obesus) and yellowfin (T. albacares) tuna, dolphinfish (Coryphaena hippurus), swordfish (Xiphias gladius), striped marlin (Tetrapturus audax), lancetfish (Alepisaurus spp.), blue (Prionace glauca) and mako (Isurus oxyrhynchus) shark individually account for[1% of the abundance of the Eastern Tuna and Billfish Fishery (ETBF) catch. Collectively, they constitute *9% of the total catch and, one assumes, of the associated pelagic fish biomass found in the region (Dambacher 25). In general terms, these species occupy the same pelagic habitat, mainly within the upper 5 m of the water column and often overlap spatially, as evidenced by multispecies longline capture records (Campbell and Young 28). Each, however, has their own environmental preferences with individual species associated with a specific range of environmental conditions (e.g. Bigelow et al. 1999). For example, yellowfin tuna are usually associated with warmer surface waters (Block et al. 1997), southern bluefin tuna with colder waters (Farley and Davis 1997). Swordfish and bigeye tuna make extensive diel vertical migrations (Sedberry and Loefer 21; Evans et al. 28). Dolphinfish are found above the thermocline (Lansdell and Young 27), and adult albacore in the region spend most of their time below the thermocline (S. Hall, AFMA Observer program, pers. comm.). Generally, open ocean ecosystems have limited productivity (Pauly and Christensen 1995). The ETBF is dominated by oligotrophic Coral Sea water (Condie and Dunn 26) and as such is likely to provide limited prey resources for predators. With these limitations, how do the predators divide the available prey? Is it through differences in prey type or size, or at the depth or time at which they feed, or some other factor? In a recent review, Pusineri et al. (28) highlighted a number of mechanisms causing niche separation in pelagic predators but highlighted the importance of differences in behaviour. They identified the energetics of predation and depth preferences as the main structuring forces in feeding niche segregation for top predators in the Bay of Biscay. In this study, we aimed at distinguishing whether distinct trophic assemblages of species or species groupings existed using a variety of trophic and ecological measures to understand how these predators share the pelagic ecosystem in the ETBF. We also examined the vertical distributions and feeding cycles of a subset of these species in the region to see whether there were spatial or temporal differences in their feeding behaviour which could reveal possible feeding strategies. Finally, we estimated the daily ration for these species as such information is an important requirement for quantitative models. Methods Physical oceanography of the region The Eastern Tuna and Billfish fishery lies within a broad latitudinal range off eastern Australia from *2 S to 4 S and thus occupies a wide range of oceanographic conditions. In the north, the main feature is the oligotrophic tropical waters of the Coral Sea; in the south are the nutrient-rich waters of the Tasman Sea (Ridgway and Godfrey 1997). These two water masses are separated by the Tasman Front at *25 S. The East Australia Current (EAC) is derived from the Coral Sea and runs southward along the eastern seaboard of Australia generally in the form of a series of eddies and filaments (see Fig. 1). A semi-permanent warm core eddy is often found close to the coast at *32 S (Young et al. 21). The regional oceanography is characterised by strong seasonal and interannual cycles. In the Austral summer, and/or La Niña conditions (Young et al. 1993), the Tasman front and EAC extend further southward, the latter as far south as Tasmania. In the winter months, colder Tasman Sea waters are the dominant feature, particularly south of 25 S. Data collection Stomachs of target and bycatch fish species were collected at sea from fish caught on tuna longline sets deployed off eastern Australia between 1992 and 26 (Fig. 1, Table 1a and b). A typical set consisted of a single monofilament line supporting *1, baited hooks deployed over *3 km to a maximum depth of *4 m (Fig. 2). The line was supported by a series of floats which also provided, through the length and sag of the line (and number of hooks) between floats, a variety of fishing depths. Longlines were set night

3 Mar Biol (21) 157: Laboratory analysis EAC Coral sea Tasman front The contents of each stomach were weighed en masse (g) and photographed. Individual prey items were then identified to the lowest possible taxon, weighed and counted (Young et al. 1997). For each prey, depending on digestion stage, we recorded standard length for fish and mantle length for cephalopods (estimated length for digested individuals). Fish were identified from the taxonomic keys detailed in Young et al. (26). Hard parts (beaks and otoliths), once identified, were enumerated, and their reconstituted mass included in later analyses (Jobling and Breiby 1986; Christensen et al. 25). Data analysis Tasman sea Fig. 1 Positions of sample collection from fishing vessels operating off eastern Australia. EAC, East Australian Current; Light shaded dots refer to stomach samples; dark shaded diamonds represent locations for fish caught on a hook timer and day, depending on the target species. For the sets on which gear monitors were deployed (see below), the total duration of sets generally ranged between 15 and 24 h, with setting and hauling the hooks accounting for, on average, 18 and 39% of this total time. Each stomach collected was labelled with the fish species, fish length, position and date of capture and frozen. To examine diel feeding cycles and to support the estimation of daily ration and feeding depth, *1 hook timers and 1 temperature-depth recorders (TDRs) were attached to commercial longline sets deployed north of 32 S between 24 and 26 (Campbell and Young 28) (Table 1a). TDRs were set along the longline to determine the range of depths fished by the longline gear and recorded depths up to 4 m, while hook timers allowed the time when a fish struck a monitored bait to be determined. A total of 1,929 temperature-depth profiles from 248 sets and 682 hook-timer recordings (385 with the fish still hooked upon retrieval) from 21 sets was collected. Although extensive collections were made for this study, all were made from longline sets. Thus, fish feeding outside the depth range of these sets, particularly in surface waters, would not be captured (e.g. Ménard et al. 2). We initially investigated diet among the ten predator species in terms of wet weight prey biomass (g), assumed as an index of prey availability, in relation to a suite of variables. These were predator species, predator length, year, season (Warm season, mean SST [ 18 C, September to April; Cool season, mean SST \ 18 C, May to August), sea surface fluorescence (SSF, lg l -1 ), sea surface temperature (SST, C) and position of capture, using linear regression models from the statistical package S-Plus (see Young et al. 26). Prey biomass was log transformed to stabilise the residuals. Diet was determined from the overall contribution of each prey type in terms of percentage wet weight (% WW) and percentage frequency of occurrence (% FO). To identify broad size-based differences in prey composition for each predator, predator diets were summarised as \/[1 cm in length. Predator overlap was compared in two ways (1) among predators using nonmetric multidimensional scaling (nmds) ordinations generated from a Bray Curtis similarity matrix on percentage wet weight of prey (Primer software) and (2) betweenpredator comparisons were made using Horn s index (Krebs 1989). For each predator species, Costello diagrams (Costello 199; Grubbs et al. 26; Chipps and Garvey 27), which plot per cent occurrence against per cent abundance (in this case per cent biomass), were used to examine differences in prey by predator length class (\/[1 cm) and area (\/[32 S). Significant differences were tested using Wilcoxon signed rank tests (Siegel 1956). The relationship between the lengths of predator and their prey was also compared using Komolgorov Smirnov tests on cumulative per cent frequencies of prey lengths. To assess predator length prey length relationships, we used quantile regression techniques (see Scharf et al. 1998, 2; Juanes et al. 22). Quantile regression is a nonparametric technique where, in contrast to least squares regression, the sum of the absolute values of the residuals is minimised. Quantile regression is a robust method with

4 235 Mar Biol (21) 157: Table 1 (a) Fish species examined for stomach contents off eastern Australia between 1992 and 26 (N = total number of specimens analysed; HT = number for which hook timer data were recorded off eastern Australia between 24 and 26; mean length ± standard error (SE); length range; number of stomachs containing prey, number empty) and (b) Total number of predator samples collected by year Species Common Name Code N HT Mean length (mm) a SE (mm) Length range (mm) Non-empty stomachs Empty stomachs Min. Max. (a) Thunnus alalunga Albacore ALB , Alepisaurus spp. Lancetfish ALX , Thunnus obesus Bigeye tuna BET , , Prionace glauca Blue shark BSH , , Coryphaena Dolphinfish DOL , , hippurus Tetrapturus audax Striped marlin MLS , ,77 2, Thunnus maccoyii Southern bluefin tuna SBF 1,363 1, ,84 1, Isurus oxyrinchus Shortfin Mako SMA 26 1, , shark Xiphias gladius Swordfish SWO , , Thunnus albacares Yellowfin tuna YFT , , Total 3, , Code Year Total by Predator species (b) ALB ALX BET BSH DOL MLS SBF ,363 SMA SWO YFT Total by year ,562 a Length measurements: billfish (OFL, orbital fork length); others (LCF, length to caudal fork) which to quantify the edges of scatter plots (Scharf et al. 1998) and is particularly useful for characterising prey length predator length relationships (Scharf et al. 2; Ménard et al. 26; Chipps and Garvey 27). We first quantified lower bound, median and upper bound relationships of prey length predator length scatters for each species. The choice of which quantiles to use to represent upper and lower bounds was based on sample size (Scharf et al. 1998). We compared slopes using a modified t-test (Scharf et al. 2; Juanes 23). We then quantified ontogenetic shifts in relative prey sizes (prey length/predator length) by estimating the lower and upper bounds of relative prey length versus predator length scatters. Differences in the slopes of upper and lower edges indicate ontogenetic increases or decreases (depending on whether significant upper and lower bounds were diverging or converging) in trophic niche breadth (Scharf et al. 2; Juanes 23; Bethea et al. 24). To compare across species, we calculated average trophic niche breadths as the average difference between upper and lower edges for each predator size and regressed average trophic niche breadth versus average predator length (Scharf et al. 2). Depth and time of feeding was examined using the TDRs and hook timers (Campbell and Young 28).

5 Mar Biol (21) 157: (a) Observed Soak Time (%) (b) Percent 24 Hour Period (%) Depth Bin (m) All Sets Hook-Timer Sets Hour of Day Fig. 2 Profiles of a the monitored depths of longlines by soak times for all sets and those sets on which at least one hook timer was triggered, and b the deployment of hook timers in relation to time of day P dg, obtained from the depth information collected by the temperature-depth recorders, and C dg obtained from the catch-by-hook position data collected by the observers. A mean index for each gear configuration was calculated from the mean of P dg and C dg across all corresponding sets, and these were then combined to provide a single index for each species. This was achieved by scaling each index by the associated total nominal catch rate for each gear configuration. Finally, the resulting index was scaled by dividing the index for each depth strata by the mean index. Daily ration (DR) was estimated following Olson and Mullen (1986), where feeding rate (^r, grams per hour) was determined by dividing the mean wet weight of stomach contents per predator (W i, grams) by the average time required to evacuate the average proportion of prey type i (A i ): ^r ¼ XI i¼ W i A i where i refers to each of I prey types consumed by the predator. This represents the prey consumption per hour, so that ^r is multiplied by the number of hours per day in which the predator feeds to estimate the daily consumption (DC, g day -1 ). Hook timer data indicated that feeding was restricted for individual species to either daytime or nighttime but not both, indicating an *12 h feeding period (Fig. 3). Based on these data, fish were assumed to feed for Actual time spent feeding is an essential component in the estimation of daily ration. However, for longline-caught fish in particular, this is a difficult parameter to estimate as time of fish landing will often bare no relationship to fish capture so the use of digestion rates or capture time is likely to be misleading. Hook timer data (Boggs 1992) offer the most direct record of when a fish is likely to feed in longline-caught fish. Although the distribution of hours sampled by the hook timers was reasonably uniform (Fig. 2), the profile of observed feeding times for each species was adjusted to account for differences in the times that the hook timers were deployed. An index of availability-by-depth for five of the ten species was derived from the catch equation. For each longline set, the observed catch (or catch rate), C dg, within each depth stratum d was related to an assumed distribution-by-depth for each species, S d, as follows: Number of fish 6 All Fish (a) Blue shark (c) Albacore (e) Bigeye tuna 4 2 (g) 6 Dolphinfish (b) Striped marlin (d) Yellowfin tuna (f) Swordfish 6 3 (h) C dg ¼ q P dg S d where P dg was the proportion of the time spent within depth strata d by all hooks within the set having a gear configuration of g hooks-per-float, and q was a measure of the catchability of the gear. An index of the availability (qs d ) was then defined and calculated from knowledge of Hour of day Fig. 3 Adjusted profiles of the number of fish caught versus capture time for fish caught on longlines with a hook timer for a all species; b dolphinfish; c blue shark; d striped marlin; e albacore; f yellowfin; g bigeye; h swordfish

6 2352 Mar Biol (21) 157: h per day. Evacuation times (A i ) for different prey taxa were sourced from Olson and Boggs (1986) values for the prey of yellowfin tuna. They assigned values of A i to squid (4.48), mackerel (Scomber japonicus) (5.29), smelt (Hypomesus pretiosus) (4.12) and nehu (Stolephorus purpureus) (2.24), and the mean for four experimental food types (3.77). Our estimates for each prey type were based on the similarity of digestibility by taking into account the size and softness of the prey type (Olson and Galvan- Magana 22; Griffiths et al. 27). Empty stomachs were included in the analyses on the assumption their inclusion represented the true proportion of the population that had not fed before time of capture. Results A total of 3,562 stomachs from the ten most abundant large pelagic fish species off eastern Australia was examined from fish sampled between 1992 and 26 (Table 1a and b). Data from 1,929 TDR recordings and hook timer data from a further 328 fish were collected between 24 and 26 to establish availability profiles and the associated feeding times for the main predator species over the diel period (Table 1a). Prey biomass We found that predator type and predator length were the major contributors to differences in prey biomass in stomachs (Table 2). Year and latitude were also significant but the interaction between them indicated some confounding influence (Table 2). No significant difference was found in relation to season, longitude and sea surface temperature. There was significant difference in relative prey biomass between predators (KW, v 2 = 82.5, df = 9, P \.1; Fig. 4a). Table 2 Analysis of variance table showing variables of the final linear regression model for the response Log (prey mass?1) for top predators from eastern Australian waters (Multiple r 2 :.544; F = 7.9; 25 and 3,79 df, P \.1) df Sum of Sq Mean Sq F value Pr(F) Species \.1 Length \.1 Year \.1 Latitude \.1 Season Longitude SST \ Latitude: Year \.1 Length: Species Residuals 3, Bonferroni contrasts showed that lancetfish and blue shark had significantly higher relative prey biomass than most of the predators, whereas albacore prey biomass was significantly lower. Predator length had a significant effect on relative prey biomass (KW, v 2 = 91.3, df = 9, P \.1). Smaller fish (\1,1 mm) had significantly higher relative prey biomass than larger predators (Fig. 4b). Prey biomass was significantly different among years (Kruskal Wallis [KW], v 2 = 9.3, df = 13, P \.1) with peaks in biomass in 1997 and 24. There was some evidence for an increase in squid as prey during the period (Fig. 4c). Prey biomass in stomachs was also significantly affected by latitude of capture (KW, v 2 = 35.6, df = 7, P \.1) with peaks in biomass at 2 S and32 S (Fig.4d). Prey composition Predators examined in this study consumed a diverse range of micronekton composed of fish, squid and occasionally crustacea (Fig. 5, Tables 3 and 4). Predators could be separated into two main groups, those consuming mainly fish and those consuming mainly squid, but predators did not feed solely on one particular prey group. Surprisingly, the bulk of the predators were mainly piscivorous. In predators \ 1 cm, only albacore and blue shark ate more squid than fish (Table 3). In predators[ 1 cm, albacore and swordfish were the only two predators with a mainly squid diet. Although not comprising a large proportion by weight, crustacean prey consistently occurred in a number of predator species indicating they were also an important dietary component, particularly for albacore and yellowfin tuna. Costello diagrams revealed that prey from the families Carangidae (particularly Trachurus declivis), Scombridae (including Katsuwonus pelamis) and Ommastrephidae (particularly Ommastrephes bartramii and Nototodarus sloanii) were the dominant prey families in all predators except lancetfish, albacore and dolphinfish (Fig. 6, Table 4). Significant cannibalism was noted in lancetfish (Alepisaurus spp.) but rarely in other species. Myctophid fishes were important prey in both small and large bigeye tuna. Nomeids (particularly Cubiceps caeruleus) were important in small swordfish and large bigeye tuna (Fig. 6). Diet overlap Three general groupings of predators were identified using multi-dimensional scaling one consisting of striped marlin, dolphinfish and yellowfin tuna, a second consisting of blue shark and southern bluefin tuna and a third consisting of swordfish, bigeye and albacore tuna (Fig. 7). Outlier species included lancetfish and mako shark. Direct pair-wise comparisons of prey type were also made between fish predators (Table 5). In fish\ 1 cm, there were only two statistically

7 Mar Biol (21) 157: (a) Prey Proportion (%) (c) Prey Proportion (%) ALB ALX BET BSH DOL MLS Species SBF SMA SWO Prey Proportion: YFT Year (b) Prey Biomass (g Prey.kg Pred -1 ) Prey Proportion (%) Prey Biomass (g Prey.kg Pred -1 ) (d) Prey Proportion (%) Teleostei Mollusca Crustacea Other <7 > -16S <9 > -2S <11 > -24S <13 <15 <17 Length (mm) > -28S > -32S Latitude > -36S <19 > -4S < S Prey Biomass (g Prey.kg Pred -1 ) Prey Biomass (g Prey.kg Pred -1 ) Fig. 4 Mean prey biomass (standardised as g prey kg predator -1 ) plotted against significant variables identified from regression analysis. a Predator species, b Predator length, c Year and d Latitude; See Table 1 for species name abbreviations Fig. 5 Relative proportions of prey families contributing [ 1% wet weight to prey biomass for fish predators off eastern Australia. ALX Lancetfish, DOL Dolphinfish, SMA Mako shark, YFT Yellowfin tuna, MLS Striped marlin, SBF Southern bluefin tuna, BET Bigeye tuna, BSH Blue shark, ALB Albacore, SWO Swordfish, See Table 1 for numbers of fish sampled with non-empty stomachs significant overlaps ([.6) in diet; between albacore and bigeye tuna, and yellowfin and southern bluefin tuna. However, another 13 species pairs (46%) had moderate but non-significant diet overlap with values between.4 and.6. In fish [ 1 cm, six significant overlaps were found between bigeye tuna and swordfish, blue shark and southern bluefin tuna, dolphinfish with striped marlin and yellowfin tuna, and striped marlin with mako shark and yellowfin tuna and a further 24 (53%) likely overlaps (Table 5). Latitudinal differences Prey biomass was significantly different within predator species found north and south of 32 S. Lancetfish (t-test,

8 2354 Mar Biol (21) 157: Table 3 Relative proportions of the major prey taxa by weight (%Wt) and occurrence (%O) for the major predators off eastern Australia Predator species (a) Predators \ 1 cm a Teleostei Mollusca Crustacea Other %Wt %O %Wt %O %Wt %O %Wt %O ALB ALX BET BSH DOL SBF SWO YFT (b) Predators 1? cm ALB ALX BET BSH DOL MLS SBF SMA SWO YFT See Table 1 for species name abbreviations a MLS and SMA \ 1 cm excluded due to nil/minimal numbers t = 3.14, P =.2), yellowfin tuna (t = 3.57, P =.4) and striped marlin (t = 3.43, P =.1) had significantly higher prey biomass to the north. In contrast, sharks (blue shark, t =-2.19, P =.3) and swordfish (t =-2.38, P =.2) had higher prey biomass in southern waters (Fig. 8). Significant differences were also detected in prey composition, but only for some species. Prey composition differed between northern and southern populations of dolphinfish (Wilcoxon signed rank test, WT, P =.36), albacore (WT, P =.1) and swordfish (WT, P =.5). Noteworthy was the presence of flying fishes (family Exocoetidae) and juvenile puffer fish (Tetraodontidae) as prey in northern populations of dolphinfish. Nomeids were a major contributor to the diet of southern populations of swordfish. No latitudinal difference in prey type was found for bigeye and yellowfin tuna. Prey-to-predator length relationships When grouped according to family, prey taxa were not generally restricted to specific predator length classes (Fig. 9). Prey species from the families Ommastrephidae, Scombridae and Carangidae were found in the stomachs of predators in all size classes, although species within Carangidae were more prevalent in smaller predators, and the proportion of Scombridae within stomachs tended to increase with increasing predator size. Bramid fishes were found only in the stomachs of predators that were [ 13 cm length, whereas Exocoetids were mainly present in predators up to 15 cm length. Significant differences were noted in the size of prey consumed among the various predators (Fig. 1, Table 6). Prey length frequency distributions calculated for albacore, lancetfish, dolphinfish and yellowfin tuna were similar and significantly different from other predator species (Table 6). Prey-to-predator length ratios ranged from *5% in albacore and lancetfish to [15% in swordfish and blue shark and were significantly different across the predator species examined (ANOVA, df = 7, F = 24.53, P \.1) (Fig. 11). Bonferroni contrasts on these ratios indicated three groupings with albacore and lancetfish eating relatively small prey, a middle group consisting of dolphinfish, striped marlin, yellowfin, bigeye and southern bluefin tuna that ate medium-sized prey and blue shark and swordfish eating the largest prey (Fig. 11). Quantile regression analysis consistently showed wedge- or triangular-shaped scatters illustrating asymmetric predator prey size distributions. With increasing predator size, maximum prey length significantly increased, whereas minimum prey length did not (or increased very little) (Table 7, Fig. 12). Slope values for linear regressions describing changes in upper percentiles of prey size with increasing predator size varied approximately sixfold and were statistically different among species (F = 2.63, P \.5). Upper bound slope values (describing changes in 75th to 99th percentiles depending upon species/number of measurements) were largest ( ) for blue shark, bluefin, bigeye, lancetfish, dolphinfish and yellowfin. In contrast, lower bound slopes were either zero or very small (the only two statistically significant ones were.31 and.38 for lancetfish and swordfish, respectively). Median slopes were mostly small and not significantly different from zero, with no apparent similarities to each other or upper bound slopes. Significant median slopes ranged from.3 (for yellowfin) to.29 (for bluefin). Median prey length predator length slopes were not correlated with upper bound (P =.62) or lower bound (P =.74) slopes, indicating that any ontogenetic changes in median prey lengths were not driven by changes in the maximum prey length consumed. Similarly, upper and lower bound slopes were not correlated (P =.72), suggesting that maximum prey length increased independently from the minimum prey length. We only detected significant differences between upper and lower bounds of the relative prey length versus predator length relationships for three species, bigeye tuna (F = 4.33), southern bluefin

9 Mar Biol (21) 157: Table 4 Percentage weight for prey species by type, family and taxa for predator species less than 1 cm in length and 1? cm in length Type/ Family /Genus-species ALB ALX BET BSH DOL MLS b SBF SMA b SWO YFT \1 1? \1 1? \1 1? \1 1? \1 1? 1? \1 1? 1? \1 1? \1 1? Teleostei (26.69) (35.46) (69.51) (62.8) (55.53) (51.57) (24.32) (73.68) (7.96) (81.26) (84.85) (76.88) (67.38) (99.73) (85.67) (27.69) (87.22) (89.18) Alepisauridae Alepisaurus brevirostris Alepisaurus ferox Alepisaurus spp Bramidae Brama brama Brama orcini Pterycombus petersii Bramidae nd Carangidae Decapterus koheru 3.59 Decapterus spp Trachurus declivis Carangidae nd Clupeidae Sardinops neopilchardus Sardinops spp Emmelichthyidae Emmelichthys nitidus nitidusnitidus Emmelichthyidae nd.3.94 Gempylidae Gempylus serpens Thyrsites atun Gempylidae nd Myctophidae Diaphus spp Myctophidae nd Nomeidae Cubiceps baxteri Cubiceps caeruleus Cubiceps pauciradiatus Cubiceps spp Nomeidae nd

10 2356 Mar Biol (21) 157: Table 4 continued Type/ Family /Genus-species ALB ALX BET BSH DOL MLS b SBF SMA b SWO YFT \1 1? \1 1? \1 1? \1 1? \1 1? 1? \1 1? 1? \1 1? \1 1? Phosichthyidae Phosichthys argenteus Phosichthyidae nd.17 Scombridae Auxis rochei rochei 1.38 Euthynnus spp Katsuwonus pelamis Scomber australasicus Scomber spp Scomberomorus spp Thunnus albacares 18. Thunnus spp Scombridae nd Sternoptychidae Sternoptyx spp Sternoptychidae nd Other Teleostei a Apogonops anomalus 1.26 Balistidae nd Chaetodontidae nd Chiasmodontidae nd Dactyloptena spp Diodontidae nd Exocoetidae nd Gonostoma elongatum 1.64 Kyphosidae nd 1.39 M. scolopax Molidae nd Monacanthidae nd Ostraciidae nd Paralepididae nd Scomberesox saurus scomberoides Tetragonurus spp Tetraodontidae nd

11 Mar Biol (21) 157: Table 4 continued Type/ Family /Genus-species ALB ALX BET BSH DOL MLS b SBF SMA b SWO YFT \1 1? \1 1? \1 1? \1 1? \1 1? 1? \1 1? 1? \1 1? \1 1? Trachichthyidae nd Trichiuridae nd Xiphias gladius Teleostei nd Mollusca (52.17) (6.52) (2.16) (33.95) (39.65) (47.75) (75.13) (14.12) (23.98) (17.21) (14.93) (22.33) (3.3) (.25) (11.3) (67.32) (1.95) (7.75) Argonautidae Argonauta nodosa Argonauta spp Carinariidae Carinaria spp Cavoliniidae Cavolinia spp Cavolinia tridentata Cranchiidae Cranchia scabra Cranchiidae nd Octopodidae Eledone palari 1.9 Octopus spp Octopodidae nd Ommastrephidae Eucleoteuthis luminosa Nototodarus gouldi Nototodarus sloanii 8.28 Ommastrephes bartramii Ornithoteuthis volatilis Sthenoteuthis oualaniensis Todarodes filippovae Todaropsis eblanae Ommastrephidae nd Onychoteuthidae Moroteuthis spp Onychoteuthidae nd Other Cephalopoda a Architeuthis spp

12 2358 Mar Biol (21) 157: Table 4 continued Type/ Family /Genus-species ALB ALX BET BSH DOL MLS b SBF SMA b SWO YFT \1 1? \1 1? \1 1? \1 1? \1 1? 1? \1 1? 1? \1 1? \1 1? Enoploteuthis spp Histioteuthis spp Lycoteuthis lorigera Octopoteuthis spp Ocythoe tuberculata Thysanoteuthis rhombus 1.7 Cephalopoda nd Crustacea (11.73) (3.16) (7.41) (1.37) (4.62) (.55) (.4) (.) (5.6) (.28) (.9) (.66) (1.45) (.2) (1.88) (.4) (1.77) (2.85) Decapoda Brachyura megalopa Caridea nd Penaeidae nd Hyperiidea Brachyscelus crusculum Hyperiidea nd Phronima sedentaria Phrosina semilunata Other Crustacea a Gnathophausia ingens.5.2 Scyllarid larvae Stomatopoda nd Crustacea nd Other (9.41) (.86) (2.92) (1.88) (.2) (.13) (.51) (12.2) (.) (1.25) (.13) (.13) (1.14) (.) (1.42) (4.95) (.6) (.22) Algae.7.2 Seaweed.7.2 Cheloniidae.85 Chelonia mydas.85 Delphinidae 9.86 Delphinidae nd 9.86 Other Gelatinous zooplankton Misc/Inorganic Polychaeta nd Unidentified remains No. of prey taxa consumed

13 Mar Biol (21) 157: Table 4 continued Type/ Family /Genus-species ALB ALX BET BSH DOL MLS b SBF SMA b SWO YFT \1 1? \1 1? \1 1? \1 1? \1 1? 1? \1 1? 1? \1 1? \1 1? No. of stomachs examined No. of empty stomachs Mean prey biomass per fish (g Prey. kg Predator -1 ) ± SE Mean no. prey taxa per fish (excl. empty stomachs) ± SE N Family groupings omitted for families with single taxon and listed alphabetically MLS and SMA \1 cm excluded due to nil/minimal numbers a b tuna (F = 4.36) and swordfish (F = 8.33), with no consistent ontogenetic changes in trophic niche breadth (one species divergent slopes, two species convergent slopes). Finally, we found no relationship across species when correlating average trophic niche breadth and mean predator length across species (F =.15, P =.7). Vertical distribution and feeding times Indices of availability-by-depth were determined for the five principal target species in the ETBF (Fig. 13). Striped marlin was found to be available to the longlines mainly in the upper 5 m, with swordfish mainly available above 8 m. Both species were rarely caught below 15 m depth. Yellowfin and bigeye tuna were distributed across the range of fishing depths, and albacore was available mainly below 15 m depth (Fig. 13). Although hook timer data indicated that feeding took place at al times of day when all species were combined, some species fed mainly at night and some fed mainly during the day (Fig. 3). Hook timer data showed that of the 138 albacore caught, 83.1% fed during the day (Table 8). Day feeding was also more likely for yellowfin tuna, dolphinfish and striped marlin. Swordfish fed mainly at night, whereas bigeye tuna and blue shark appeared to feed day and night. We concluded that feeding period should be restricted to 12 h to calculate estimates of daily ration for all predator species, except for bigeye tuna and blue shark which had a feeding period of 24 h. Prey consumption and daily ration With the exception of dolphinfish, estimates of daily ration were generally less in fish [1 cm than in their smaller (\1 cm) counterparts across the species examined (Table 9). Daily ration ranged from \1% for albacore \1 cm to *9% for small blue and large mako shark, although the estimates for the sharks should be viewed with caution given the small sample sizes and associated large error standard errors (Table 9). Of note were the relatively high daily ration estimates for small and large lancetfish. Discussion Prey biomass Four variables were significantly related to prey biomass found in the stomachs of pelagic predators within oceanic waters off eastern Australia year, latitude, predator species and predator length. The first of these variables underlined the inherent temporal variability of the region. Interannual variability, particularly in sea surface

14 236 Mar Biol (21) 157: Fig. 6 Costello diagrams showing prey biomass (% wet weight) plotted against frequency of occurrence (% Occurrence). Points represent prey type for predators \ 1 cm (circles; normal type) and 1? cm (triangles; bold type). Figures in brackets are x,y coordinates of outliers for values ranging outside the standardised x and y scales; prey abbreviations are Alep Alepisauridae, Argo Argonautidae, Bram Bramidae, Car Carangidae, Dec Decapoda, Emm Emmelichthyidae, Gem Gempylidae, Hist Histioteuthidae, Hyp Hyperiidea, Myct Myctophidae, Nom Nomeidae, Oct Octopodidae, Ocy Ocythoidae, Omm Ommastrephidae, Scom Scombridae, Scosid Scomberesocidae, Tetra Tetraodontidae % Weight Alep Omm 4 (21.43, 92.85) Scom Alep 4 Striped marlin Scom Scom Car Scom Car Car Omm Scosid Omm Scosid Dec Lancetfish Hyp Mako shark Yellowfin tuna Dolphinfish Scom Scocid Ocy Tetra Argo 4 Blue shark (68.75, 6.63) Omm Bram (1., 5.58) Hist Omm Car Oct Hist Hist Albacore Dec 4 Sthn bluefin tuna (39.5, 47.95) Car Bram Emm Omm Car Omm Hyp Dec 4 3 Omm Bigeye tuna 4 3 Swordfish (33.33, 49.87) Nom 2 Gem 2 Omm 1 Bram Nom Bram Myct 1 Omm Myct % Occurrence Fig. 7 nmds plot of predators based on their diet composition (% wet weight) off eastern Australia. Axis scales are arbitrary in nmds and are therefore omitted; See Table 1 for species name abbreviations temperature, is an important component of the regional oceanography of the area and is apparently reflected in the prey available. Years of high prey biomass were associated with cooler surface waters and El Niño years, whereas lower biomass years were associated with warmer surface waters and La Niña years. This wider pattern reflects similar patterns found for swordfish (Young et al. 26), jack mackerel (Trachurus declivis) (Young et al. 1993) and cephalopods (Lansdell and Young 27) in the region. That latitude was also a significant factor reflected the wide range of ocean waters sampled between *15 S and 35 S along the eastern Australian seaboard (see Fig. 1). Most of the fish predators had higher prey biomass north of the Tasman front; only swordfish and shark species had greater values to the south. This is surprising as northern waters are considered nutrient poor (Ridgway and Godfrey 1997) and therefore potentially prey limited relative to more southerly waters. However, a number of seamounts occur on the

15 Mar Biol (21) 157: Table 5 Diet overlap for 1 predator species over two size categories, (a)\1 cm (MLS and SMA omitted due to low numbers) and (b) 1? cm, using Horn s index of overlap (R o ; Krebs 1989) and based upon % weight for family groups of prey taxa ALB ALX BET BSH DOL SBF SWO (a) YFT *.4 SWO SBF DOL BSH BET.63*.5 ALX.54 ALB ALX BET BSH DOL MLS SBF SMA SWO (b) YFT *.82* SWO * SMA *.13 SBF * MLS * DOL BSH BET ALX.25 * = statistically significant, R o [.6 g Prey.kg Pred North South * = significant (35.85 ± 2.93) with predator species and predator length, not surprisingly given the size and range of predators examined. Lancetfish and the sharks ate significantly more in relation to their body weight than the other predators. Lancetfish in particular is known to be a voracious predator (Romanov et al. 28). Prey composition ALX* YFT* DOL MLS* BET ALB SBF SWO* BSH* SMA North dominated South dominated Fig. 8 Mean (± SE) prey biomass of northern (\32 S) and southern populations ([32 S) of predator species off eastern Australia (*t-test, P \.1). No data were available for SBF in the north. Southern SMA value is shown in brackets to improve resolution on the y-axis; See Table 1 for species name abbreviations northern side of the front and seamounts are often associated with sub-surface prey aggregations (Young et al. in press). This pattern is likely to be repeated at other seamounts in the region, particularly as fishers target these formations frequently (Campbell and Hobday 23). This fact, together with observations of inshore upwelling and frontal activity north of the front, indicates a more productive habitat than previously thought (Young et al. 29, in press). Finally, prey biomass was significantly correlated Overall, carangid and scombrid fishes, and ommastrephid squid (particularly Ommastrephes bartramii) were the main prey species found in this study for most of the predators. No one prey species dominated as a major food source, a result similar to that found in other subtropical and temperate pelagic ecosystems (Potier et al. 27), and contrasting to more tropical systems where often an individual micronekton prey species was dominant (e.g. Vinciguerria nimbaria, Ménard et al. 2; Cubiceps pauciradiatus, Bard et al. 22; Potier et al. 28). This difference, however, may be due, in part, to different sampling methods as some of these studies focused on the diets of fish sampled in surface waters by purse seines. Cannibalism was noted in lancetfish, a trait previously observed in this species (Romanov et al. 28). With the exception of the bigeye tuna, myctophids were not a major contributor to top predator diets but were likely to be,

16 2362 Mar Biol (21) 157: Fig. 9 Relative proportions of main prey families (% wet weight) in relation to predator length off eastern Australia. Values in brackets represent number of samples per size class Fig. 1 Percentage frequency distributions of prey lengths for each predator sampled off eastern Australia (N number of prey measured) Lancetfish N = 338 Dolphinfish N = Southern bluefin tuna N = 139 % Frequency Albacore N = 45 Yellowfin tuna N = Striped marlin N = 119 Swordfish N = Bigeye tuna N = Blue shark N = Prey length classes (mm) indirectly, very important (Young et al. 21). Myctophids are a major prey source to many of the prey species, particularly the squid, which are known to be major predators of myctophids (Parry 26). In a study of micronekton prey in the region we identified a large biomass of the myctophids Ceratoscopelus warmingii and Diaphus spp. in midwater net catches, particularly in the more productive offshore waters (Young et al. 29). Three loose predator groupings were identified based on their prey. One group consisted of striped marlin, dolphinfish and yellowfin tuna; a second that included swordfish, albacore and bigeye tuna; and a grouping of blue shark and southern bluefin tuna. The first two groupings appear to be divided largely on their depth preferences and subsequently their prey. Although all these fishes have wide depth distributions (see Bernal et al. 29), the former group was restricted mainly to above the thermocline, the second to waters below the thermocline. Potier et al. (27) found that prey species from epipelagic and mesopelagic depths could separate predators into those that were primarily shallow or deep feeders. Based on their prey, we found that yellowfin tuna, dolphinfish and striped marlin were predominantly epipelagic feeders preying on tetraodontid, scombrid and balistid fishes and surface crustacea (Lansdell and Young 27). Mesopelagic fishes such as those from the Paralepididae and Nomeidae were present in the diet of deeper-living fishes such as swordfish and bigeye tuna. Evidence from hook timers suggested that these

17 Mar Biol (21) 157: Table 6 Between-predator comparisons of prey length using Komolgorov Smirnov tests of cumulative length frequency distributions Mean length ratio BSH SWO SBF MLS BET YFT DOL ALX ALB ALX DOL YFT BET MLS SBF SWO.23 Numbers represent D-values, bold values significant at a =.1. See Table 1 for species name abbreviations ALB ALX DOL YFT MLS BET SBF SWO BSH Predator species Fig. 11 Mean prey-to-predator length ratios (± 1SE) for predator fishes off eastern Australia. See Table 1 for species name abbreviations fish also reduced direct competition for prey by feeding at different times of the day. Mako sharks grouped separately, although small sample size suggests this result be treated with caution. Nevertheless, a companion study of predator isotopes in the region found that mako shark had the highest stable isotope value of all fish predators in the region, placing them at the top of the regions food web (Revill et al. 29). Prey type also differed in relation to latitude. In particular, dolphinfish, albacore and swordfish had significantly different diets depending upon where they were caught. Dolphinfish caught in north of 3 S were distinguished by the presence of flying fish (Exocoetidae) in their diets; albacore and swordfish by the presence of a range of squid species. Latitudinal differences in feeding ecology are known for a range of pelagic fish predators (e.g. Ménard et al. 27, Romanov et al. 28). Off eastern Australia, we previously found a break in stable isotope concentrations, a measure of prey type, in swordfish, yellowfin and albacore tuna muscle tissue at approximately 3 S (Revill et al. 29). Prey-to-predator length relationships Prey-to-predator length ratios generally increased with mean predator length. Relatively smaller predators (e.g. albacore and lancetfish) had lower ratios than larger predators such as swordfish and shark species. These ratios were, in general, lower than those reported in similar studies. For example, Juanes (1994) reported that prey-topredator ratios of piscivores were mostly between 2 and 3%. Olson and Galvan-Magana (22) reported ratios of 17% for dolphinfish collected from purse seine nets in the eastern Pacific Ocean. The results of those studies and the present study suggest that prey were relatively small for the same predators in the waters off eastern Australia. This is supported in the case of dolphinfish, which fed on a range of larval and smaller fishes, whereas in the eastern Pacific, small dolphinfish were feeding on flying fish (Olson and Galvan-Magana 22). The more detailed quantile regression analysis showed that, in general, prey size increased with predator size. However, with the Table 7 Regression equations relating the upper and lower bounds and median prey lengths to predator length for predator species Species Upper bounds Median Lower bounds n Quantiles ALB PY =.55PD? 1 NS PY = -.54PD? NS PY = PD? 22 NS 6 75/25 ALX PY =.25PD PY =.11PD? NS PY =.12PD? /1 BET PY =.257PD PY =.119PD PY =.14PD? NS 233 9/1 BSH PY =.216PD PY =.9PD? NS PY =.53PD? NS 53 75/25 DOL PY =.25PD NS PY = -.29PD? NS PY = PD? /5 MLS PY =.69PD? NS PY =.119PD PY = -.187PD? NS 14 9/1 SBF PY =.277PD PY =.287PD PY = -.39*PD? NS 183 9/1 SWO PY =.15PD? 232 PY =.168*PD? PY =.38*PD? /5 YFT PY =.263PD? PY = -.312PD? 15 PY = PD? 8 1,179 99/1 Estimates of all relationships were generated using quantile regression. Median relationships were represented by the 5% quantile. Quantiles representing upper and lower bounds were chosen based on samples sizes (PY maximum, median, or minimum prey length in mm; PD predator total length in mm; NS regression coefficient not significant, P [.5. Data for Shortfin Mako Shark were unavailable. See Table 1 for species name abbreviations)

18 2364 Mar Biol (21) 157: (a) BSH Table 8 Number of fish captured on hook timers in relation to daytime (6 18 h) and night-time off eastern Australia 2 Hours ALB ALX BET BSH DOL MLS SWO YFT Prey length (mm) (b) (ALB; DOL; ALX) SBF BET SWO SWO YFT (MLS; BET; ALB; DOL; ALX; SBF; YFT) MLS BSH Total N %N day 83.3%.% 4.% 44.4% 83.3% 85.7% 15.7% 73.5% %N 16.7% 1.% 6.% 55.6% 16.7% 14.3% 84.3% 26.5% night For predator abbreviations see Table 1 exception of the sharks, there was widespread feeding on very small prey. Our study agreed with Ménard et al. (26) indicating that the median prey size for yellowfin tuna is smaller than that for bigeye tuna. Daily ration (c) DOL BET YFT SWO SBF MLS BSH 1 ALB ALX Predator length (mm) Fig. 12 Quantile regression slopes for a minimum, b median and c maximum predator to prey length relationships for predator species in the ETBF. See Table 1 for species name abbreviations Fig. 13 Depth distributions of target species determined from hook timers deployed on longlines in the ETBF. Depth distributions are expressed as an index of availability. See Table 1 for species name abbreviations Daily ration estimates for pelagic fishes vary between species (e.g. Kirby 25; Ménard et al. 2), size (Olson and Galvan-Magana 22; Griffiths et al. 27), region (Young et al. 1997; Olson and Galvan-Magana 22), sampling gear, whether longline, purse seine or Fish Aggregating Devices (FADs) (Ménard et al. 2; Romanov et al. 28) and methodology (Kirby 25). For example, Ménard et al. (2) found daily rations ranging from 1.27 to 4.82% body weight for bigeye tuna;.89 to as much as 16% for yellowfin tuna, depending on whether fish were sampled at or away from FADs. Southern bluefin tuna daily rations ranged from 3% in inshore waters off south-eastern Australia to 1% in offshore waters where prey was relatively scarce (Young et al. 1997). The variability in daily ration of pelagic predators is further underlined by the recent findings of Bestley et al. (28) who reported individual southern bluefin tuna not feeding for up to 24 days at a time. Excluding the shark data, which may have been biased by small sample sizes, our estimates of daily ration were generally less than results elsewhere. For example, Kirby (25) modelled a mean daily ration of 3% body weight per day for albacore tuna in the western central Pacific which was significantly higher than we report here (\1%). Similarly, we reported a ration estimate of *2% for dolphinfish which was less than the average of 5% reported for dolphinfish in the eastern Pacific Ocean (Olson and Galvan-Magana 22). That study, however, used a feeding period of 24 h indicating only small differences between the ration estimates. In this study, there may have been some downward bias introduced by possible inclusion of fish that had thrown their stomach contents on capture (Ménard et al. 2). However, the waters of the ETBF are generally considered oligotrophic so low ration estimates could be an accurate reflection of prevailing conditions (Condie and Dunn 26). As many of the species examined here are at the edge of their distributions, limited prey

19 Mar Biol (21) 157: Table 9 Daily consumption and ration estimates for predator fish (a) \1 cm (MLS and SMA \1 cm excluded due to nil/minimal numbers) and (b) [1 cm length off eastern Australia (H, time (in hours) required to evacuate the relative proportions of all prey taxa present in the diet of each predator Predator \1 cm H PP N r (g/h) PW a DC DR (a) ALB (4.38) (.55) ALX (3.52) (.4) BET (28.97) (1.8) BSH (21.69) (.22) DOL (9.25) (.57) SBF (7.85) (.13) SWO (16.97) (1.13) YFT (16.59) (.36) (b) ALB (19.1) (.88) ALX (17.22) (.14) BET (2.6) (1.77) BSH (58.99) (2.88) DOL (1.16) (.39) MLS (7.84) (3.38) SBF (1.) (1.21) SMA (1398.6) (8.24) SWO (43.6) (2.13) YFT (9.57) (.68) Feeding time was restricted to 12 h. See Table 1 for species name abbreviations PP prey weight in g (SE), N number of fish, r gastric evacuation rate in g h -1, PW predator weight in kg (SE), DC daily consumption in g day -1, DR Daily ration, % PW day -1 a Weights derived from length measurements using published and CSIRO L-W regressions where actual weight data were not collected resources and hence rations may be a reality for predators in the region. Of particular importance in estimating daily ration is an understanding of whether fish predators feed over the diel cycle, or part thereof. The limitations imposed by not knowing when longline-caught fish actually fed in relation to when they were captured make identifying feeding times almost intractable. Nevertheless, our analysis of hook timer data provided evidence for species-specific feeding times with some species feeding at night (swordfish, bigeye tuna) and others during the day (albacore, yellowfin tuna, dolphinfish and striped marlin). We used this rationale to apportion feeding time to our daily ration estimates. Our approach agreed with some studies (e.g. Ménard et al. 2; Griffiths et al. 27) but not with others (e.g. Olson and Galvan-Magana 22). If future research supports a 24-h feeding period, then the results here should be doubled where 12-h periods were used. However, diel visceral warming data (an indication of feeding events) of southern bluefin tuna suggested that 58% of fish fed at dawn, whereas only 7% fed at night (Bestley et al. 28). Thus, restricted diel feeding appears to be a likely foraging strategy for at least some pelagic predators. Niche segregation Identifying consistent similarities or differences between the predator species was equivocal with different analyses identifying different traits or commonalities. However, the attributes of prey composition, prey length, average predator depth distributions and feeding times, when factored together, indicated that the feeding habit of each predator could, in some way, be distinguished from that of the others (Fig. 14). For example, although striped marlin and dolphinfish fed mainly during the day, the latter occupied a shallower depth distribution and fed on smaller prey. Bigeye tuna had a similar depth distribution to albacore but fed day and night, whereas albacore fed mainly in the daytime. Diel overlaps between the two tuna congeners (yellowfin and bigeye) were reduced by different feeding times and depth distributions. Dolphinfish, like albacore, fed mainly on small prey. However, the former was mainly caught in surface waters unlike albacore which were caught in deeper waters. In fact, all possible comparisons indicated at least one way in which each predator separated itself, if not trophically then behaviourally. Whether these patterns are consistent across different ocean habitats is not yet

20 2366 Mar Biol (21) 157: Fig. 14 Schematic representation illustrating how the major fish predators off eastern Australia divide their pelagic habitat over the diel period with respect to depth, prey type and prey size. Their positions reflect a generalised view of their vertical distribution in the water column at times and depth when they are likely to be caught by the fishery (Prey lengths are compared over a 5 cm range; relative proportions of prey taxa refer to per cent biomass). Southern bluefin tuna, lancetfish and the sharks were not included because of insufficient data on one or more of the contributing variables clear. For example, albacore in the Bay of Biscay are epipelagic nocturnal feeders (Pusineri et al. 28), whereas we found them feeding deeper and in the daytime. That study concluded that for top predators in the Bay of Biscay, the energetics of predation and constraints relative to the sea surface were the main structuring forces. Our results for a suite of species off eastern Australia suggest that pelagic predators can occupy a wider range of niches due to subtle differences in foraging attributes including differences in prey size and composition and spatio-temporal aspects (e.g. water depths, times of day). Where diet overlaps did occur between species, differences in one or more foraging attributes ensured some degree of niche segregation. Acknowledgments This work was carried out as part of wider research programme on the trophodynamics of the Eastern Australian Tuna and Billfish fishery and was funded by a grant from the Fisheries Research and Development Corporation (FRDC Grant 63/24). The hook timer data were funded separately by a grant to R. A. Campbell and J. W. Young, also by FRDC (FRDC Grant 4/25). 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