A new approach to study of seabird-fishery overlap: Connecting chick feeding with parental foraging and overlap with fishing vessels

Global Ecology and Conservation 4 (2015) 632–644

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Short communication

A new approach to study of seabird-fishery overlap: Connecting chick feeding with parental foraging and overlap with fishing vessels Junichi Sugishita a,∗ , Leigh G. Torres b , Philip J. Seddon a a

Department of Zoology, University of Otago, Dunedin, 9016, New Zealand

b

Oregon State University, Department of Fisheries and Wildlife, Marine Mammal Institute, OR, 97331, United States

article

info

Article history: Received 30 September 2015 Received in revised form 1 November 2015 Accepted 1 November 2015 Available online 1 December 2015 Keywords: Fishery overlap Chick provisioning Meal size Foraging behaviour GPS tracking Vessel monitoring systems

abstract Incidental fisheries bycatch is recognised as a major threat to albatross populations worldwide. However, fishery discards and offal produced in large quantities might benefit some scavenging seabirds. Here, we demonstrate an integrated approach to better understand the ecological ramifications of fine-scale overlap between seabirds and fisheries. As a case study, we examined whether foraging in association with a fishing vessel is advantageous for chick provisioning in terms of quantity of food delivered to chicks, in northern royal albatross (Diomedea sanfordi) at Taiaroa Head, New Zealand. Fine-scale overlap between albatrosses and vessels was quantified by integrating GPS tracking and Vessel Monitoring Systems (VMS). Meal size delivered to chicks was measured using custom-designed nest balances, and monitoring of attendance of adults fitted with radio transmitters was used in conjunction with time-lapse photography at the nest allowed us to allocate each feeding event to a specific parent. The combination of these techniques enabled comparison of meal sizes delivered to chicks with parental foraging trip durations with or without fishing vessels association. A total of 45 foraging trips and associated chick feeding events were monitored during the chick-rearing period in 2012. Differences in the meal size and foraging trip duration relative to foraging overlap with fisheries were examined using a linear mixed-effect model, adjusted for chick age. Our results, based on three birds, suggest that foraging in association with vessels does not confer an advantage for chick feeding for this population that demonstrated low rates of overlap while foraging. The integrated research design presented can be applied to other seabird species that are susceptible to bycatch, and offers a valuable approach to evaluate habitat quality by linking habitat use and foraging success in terms of total amount of food delivered to offspring. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Commercial fisheries are prevalent in most oceans globally with substantial impacts on marine ecosystems through alterations of the trophic structure and abundance of both fish stocks and their predators in marine ecosystems (Goñi, 1998; Lewison et al., 2004; Halpern et al., 2008). Seabirds and other predators at the apex of marine food webs are particularly

∗ Correspondence to: Department of Zoology, University of Otago, 340 Great King Street, Dunedin, 9016, New Zealand. Tel.: +64 03 479 7986; fax: +64 03 479 7584. E-mail address: [email protected] (J. Sugishita). http://dx.doi.org/10.1016/j.gecco.2015.11.001 2351-9894/© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

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vulnerable to such ecosystem perturbations as a result of bottom-up control in plankton-based marine food chains (Frederiksen et al., 2006; Heithaus et al., 2008). Fisheries can influence the dynamics of seabird populations and communities in complex ways, and the effects can be negative or positive (Tasker et al., 2000; Furness, 2003). Incidental mortality of seabirds as bycatch in commercial fisheries is an important global conservation issue (Croxall et al., 2012). Large numbers of seabirds are killed each year by drowning in longline fisheries after becoming caught on baited hooks (Anderson et al., 2011), and in trawl fisheries after getting entangled in nets or colliding with trawl warps (Baker et al., 2007). High levels of such mortality are unlikely to be sustainable and are believed to have led to global population declines in many seabird populations (Lewison et al., 2004; Sullivan et al., 2006), particularly members of the order Procellariiformes (albatrosses, shearwaters and petrels) (Robertson and Gales, 1998; Lewison et al., 2004; Sullivan et al., 2006; Anderson et al., 2011). Currently, 17 of the 22 albatross species are listed as threatened with extinction, making them the most threatened family of birds in the world (IUCN, 2014). Additionally, some fisheries may adversely influence seabird populations through direct resource competition by reducing or depleting the availability of the fish prey on which seabirds depend (Furness, 1982; Hamer et al., 1991; Furness and Tasker, 2000; Tasker et al., 2000; Furness, 2003; Cury et al., 2011). However, because seabirds are both predators and facultative scavengers, some species might benefit from fishing activities as many commercial fisheries generate considerable quantities of biomass available to scavenging seabirds in the form of bait (Pierre and Norden, 2006), discarded undersized and unwanted catch (Xavier et al., 2004; Louzao et al., 2011), and offal produced during on-board processing (Thompson and Riddy, 1995). An average of 7.3 million tonnes of fishery waste is estimated to have been dumped into the world’s oceans each year between 1992 and 2001 (Kelleher, 2005). The utilisation of this abundant food source by seabirds has been shown to have effects on breeding success (Oro, 1996) and population dynamics (Oro et al., 2004), and can even modify individual movement patterns (Bartumeus et al., 2010; Bodey et al., 2014; Collet et al., 2015) and foraging behaviour (Granadeiro et al., 2011; Torres et al., 2011). The importance of fishery waste for seabirds has been demonstrated through assessments of the relative contribution of fishery waste to the overall diet, either by stomach contents or stable isotope analysis (Freeman and Wilson, 2002; Votier et al., 2004; Bugoni et al., 2010). The beneficial effects of feeding on fishery waste have been documented as reduced foraging costs in wide-ranging seabirds. For example, breeding northern gannets (Morus bassanus) with a higher proportion of fishery wastes in their diet performed shorter foraging trips (Votier et al., 2010). Similarly, non-breeding cape gannets (Morus capensis) that extensively fed on fishery wastes spent less time flying and exhibited reduced diving effort, and despite the lower calorific value of fishery wastes, compared to natural prey, they showed high survival (Grémillet et al., 2008). These examples indicate that fishery wastes may serve as a predictable and easy-to-access food source, allowing birds to satisfy energy requirements with reduced foraging costs (but see Oro et al. (2013) for potential adverse effect of such predicable food source from human). In contrast, breeding cape gannets showed increased diving effort in an attempt to provide their young with higher-quality natural prey, rather than abundant but lower-quality fishery waste, in the face of scarcity of natural prey (Grémillet et al., 2008). A few studies have shown that parents of some species of seabirds collect higher-quality food for provisioning offspring than for feeding themselves (Davoren and Burger, 1999; Hodum and Hobson, 2000; Cherel, 2008). Taken together, it might be suggested that, for some populations, the quality of fishery wastes is good enough for non-breeders that must meet only their own energetic needs to survive, but not for breeders faced with the higher energetic demands associated with raising chicks (but see, e.g. Garthe et al., 1996, Furness, 2003, Yorio and Caille, 2004). It is widely accepted that both the quality and quantity of food fed to seabird chicks has a strong influence on growth patterns and survival rates (Barrett et al., 1987; Croxall et al., 1988; Becker and Specht, 1991; Weimerskirch and Lys, 2000). A large quantity of food alone can enhance growth rate and development of vital organs, as well as accumulation of fat deposit in seabird chicks, resulting in greater fledging mass (Takenaka et al., 2005). Most studies on seabirds have shown that heavier or larger chicks have a higher likelihood of fledging success and post-fledging survival (Perrins et al., 1973; Phillips and Furness, 1998; Sagar and Horning, 1998). Therefore, it would be advantageous for parent birds to increase the quantity of food delivered to chicks so long as this can be achieved with limited extra energetic costs. We are unaware of any previous studies that have addressed the potential ecological advantages of foraging with fishing vessels for seabirds raising chicks, in terms of quantity of food delivered to chicks. As shown in previous studies, wide-ranging seabirds can easily locate fishing vessels, up to 30 km away and target them for feeding opportunities (Bodey et al., 2014; Collet et al., 2015), but might only associate with vessels 50% of the time if they have the opportunity to do so (Torres et al., 2013). This indicates that parent birds might choose to forage in association with fishing vessels that produce an easy-to-find and -capture food source in order to deliver more food to chicks. Even if chicks are already well-fed, additional food would be beneficial because chicks may be able to build up additional fat reserves and this could increase post-fledging survival (Phillips and Hamer, 1999). Northern royal albatross (Diomedea sanfordi) is endemic to New Zealand and is classified as Endangered according to the IUCN Red List of Threatened Species (IUCN, 2014). This species has been observed attending fishing vessels for fishery wastes at relatively moderate rates compared to other Procellariiformes (Richard et al., 2011a). Analysis of regurgitations from nestlings of northern royal albatross suggested that some of the main food items were likely obtained by scavenging on discards (Imber, 1999). Like other albatrosses, this species has been reported killed as bycatch in commercial fisheries that use trawl and longline gear (Richard et al., 2011b; Richard and Abraham, 2013; Jiménez et al., 2014). In recent risk assessments of fishery bycatch, northern royal albatross was identified as a species at risk from fisheries in New Zealand waters (Richard and Abraham, 2013) and also in the Western and Central Pacific Ocean (Waugh et al., 2012). Previous tracking studies of northern royal albatrosses clearly show that albatrosses coincide in time and space with commercial fisheries within a window of 25 km and 30 min of fishing events in the New Zealand EEZ during the incubation period

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(Waugh et al., 2005). For the other part of the breeding season, very little is known about at-sea distribution patterns, let alone their associations with fisheries activities. Given the endangered status of the species, together with the identified bycatch risk, there is a need for further investigation to examine the relationship between foraging birds and fisheries at finer scales, and to better understand the extent and ecological consequences of such overlaps. In this study, we present an integrated approach incorporating GPS data loggers, VMS-derived fishing vessel locations, automated nest balances, radio-transmitters, and time-lapse photography to explore the ecological ramification of parental foraging overlap with commercial fisheries in relation to chick provisioning. We address two specific questions: (1) Are there measurable differences in meal sizes delivered by adults that did and did not associate with fishing vessels? and (2) Does the length of foraging trips relate to association with fishing vessels? We hypothesise that any benefits to chick-rearing northern royal albatrosses that associate with fishing vessels for relatively easy-to-access feeding opportunities would be evident either as larger meal sizes and/or shorter foraging trip durations. 2. Materials and methods 2.1. Study species and site We studied breeding northern royal albatross during the 2011/12 breeding season (hereafter 2012) at Taiaroa ′ ′ Head/Pukekura near the city of Dunedin on the mainland of southern New Zealand (45°46 S, 170°44 E). This species takes approximately one year to fledge a chick and breeds biennially if successful. Although the current population trend is unknown, the main breeding population in the Chatham Islands (over 99% of the total) is estimated at approximately 6500–7000 pairs, with an estimated 5200–5800 annual breeding pairs (Croxall and Gales, 1998; Robertson et al., 2003; BirdLife International, 2013). A small fraction of the total population breeds at Taiaroa Head/Pukekura. This colony was naturally established in 1937 with only one breeding pair and since then has slowly increased to 30 breeding pairs in 2014 (L. Perriman, unpublished data). This is the only mainland breeding colony of any albatross species in the Southern Hemisphere and is managed by New Zealand’s Department of Conservation as a Nature Reserve (5.3 ha). 2.2. External attachment devices We deployed three different external attachment devices on breeding birds for telemetry and data-logging: GPS loggers, coded VHF transmitters, and temperature loggers. During early brooding (February/March) in 2012, 16 breeders of known sex were fitted with each of the three devices. The sample size represented 26% of breeding adults (63 breeders). Whenever possible, we studied both individuals in a breeding pair, which resulted in 7 pairs and 1 male and 1 female. All breeding birds had successfully fledged more than one chick over previous breeding seasons. The GPS logger and the radio transmitter were R R simultaneously attached to back feathers between the wings using Tesa⃝ tape, heavy duty Velcro⃝ plastic plates, and cable ties. We also attached temperature loggers to the tarsus of the 16 birds with GPS tags using reusable rubber cable ties. Each deployment was carried out without physically restraining the bird while it was on the nests either brooding or incubating and was completed within 10 min. The combined weight of all these devices and attachment materials was approximately 50 g (less than 1% of adult body mass), well below the suggested threshold of 3% where detrimental behavioural effects might be observed (Phillips et al., 2003). 2.3. Foraging movement and activity patterns To obtain data on fine-scale foraging movements and distribution of birds at sea, we deployed ‘Fast-Fix’ GPS loggers (35 × 90 × 15 mm, 27 g) that were developed and custom-designed for this study (T. Molteno, Department of Physics, University of Otago, New Zealand). The GPS loggers were programmed to acquire a GPS fix every 10 min, and location accuracy was estimated to be ±30 m in a clear sky view environment. Birds were tracked for 56–154 days. We used iButton temperature loggers (DS1922, Maxim Integrated Products, CA, USA; 3.3 g) to investigate whether the bird was in flight or on the sea surface at the time of each GPS location. The loggers were programmed to record the surrounding temperature every 60 s (±0.5 °C accuracy) with time stamps that were synchronised with the GPS tags. Changes in temperature enabled the activity patterns of foraging birds to be inferred because while in flight the leg with the logger is tucked in and insulated by feathers, and landing on the water is characterised by an abrupt drop in temperature (Jakubas et al., 2012). A subsequent stable low temperature recording can be inferred as a period on the sea surface, and a steep increase in temperature indicates take-off. The memory capacity of the temperature loggers allowed 5.7 days of continuous recording so each logger were set with a delayed activation according to the anticipated time of departure to sea to maximise data recording at-sea. Each logger was waterproofed in heat-shrink tubing with both ends sealed together to form a small loop shape that was attached to the tarsus of each bird by putting a reusable rubber cable tie through the loop and wrapping it around the tarsus. 2.4. Nest attendance patterns and foraging trip duration Parental feeding visits and foraging trip durations were remotely monitored by coded VHF radio transmitters (NTQB-6-2, Lotek Wireless Inc., Canada; 24 × 9 × 7 mm, 4.7 g). The presence of radio-tagged birds in the colony was scanned and was

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logged using an automatic recording station, comprising data-logging VHF receiver (DataSika C, Biotrack, Ltd., UK) and an omni-directional antenna. The transmitters were individually, uniquely coded. Before deployment on birds, we tested signal detectability and range of the transmitters at each of the study nests. We incorporated time-lapse photography to monitor feeding visits at the study nests after several radio transmitters constantly failed at an early stage of the data collection. All the study nests were subsequently monitored by time-lapse cameras (Timelapse PlantCam, Wingscapes, Inc. AL, USA). Cameras, powered externally by a 12V 7Ah sealed lead–acid battery, were set to continuously capture one still image per minute. Images were recorded onto a SD (Secure Digital) memory card and were downloaded to a laptop computer every 3–4 days. The images were reviewed to determine the time of feeding visits and identify colour bands on parent birds. 2.5. Meal size The size of meals delivered to chicks was remotely measured by automatic nest balances to the nearest 20 g every 1.25 s, allowing us to eliminate any confounding gradual weight changes due to added nesting material, or drying or rainfall. The automatic nest balances consisted of a heavy duty shipping scale (HD-150; My Weigh, Arizona, USA, capacity 60 kg) connected to a data logger (DT-CF-02-XC and DT-MC02-XC; DATA TECNO, Kyoto, Japan), with the whole system powered by a 12V lead acid battery. Meal sizes were calculated from weight increments, and each meal was allocated to a particular parent visiting the nest at the time as determined by VHF and camera detections (see above). During the early brooding period, we installed 9 automatic nest balances underneath natural nests for 35–145 days. To minimise potential effects of nest manipulation on chick provisioning, installation was carried out when the chick weight reached 4–4.5 kg, 48–60 days of age. 2.6. Characterisation of foraging behaviours To identify foraging locations of albatrosses, we quantified and characterised changes in flight patterns for each GPS point along all tracks by calculating speed, path straightness, and residence, following the methods established by Torres et al. (2011). Path straightness for each point was determined over a 40-min window (±2 GPS points) and was calculated by dividing the straight-line distance between the initial and final points within the window by the along-path distance. Using a Pascal algorithm provided by Barraquand and Benhamou (2008), we calculated residence values for each point along every track using spatial segments of 650 m, with the radius of the virtual circle set to 5 segments (3.25 km) and the maximum GPS points allowed outside the circle set to 5 points. These values were chosen to represent changes in foraging movements accurately and reliably, relative to the temporal resolution of the GPS track data. Based on these movement parameters, all albatross GPS points were assigned one of three behaviour states: ‘drift’, ‘transit’, and ‘forage’. We determined drift points over a 20-min window (±1 GPS point) as having slow mean speed (≤5.00 km h−1 ), high mean path straightness values (≥0.9), and short mean distance travelled (≤0.65 km). We are confident that these cut-off values correctly identified drift behaviour in this study because the temperature loggers recorded low temperature (<15 °C), likely indicative of sea surface temperature in the study region, for 95% of GPS points identified as drift (n = 210). In this study, we assumed drift and forage are mutually exclusive behavioural states and removed drifts points from further analyses to avoid confusion with slow-speed foraging. Although a ‘‘sit and wait’’ foraging tactic (i.e. the prey is captured when the bird is drifting and sitting on the water) is known for albatrosses (Weimerskirch et al., 2007b), our analysis focused on more active foraging strategies, including vessel attendance. It was important to exclude drift points with elevated residence values from the dataset as these points would have overshadowed other points with high residence values that denoted active foraging behaviour. Additionally, we removed from the data set all points within 5 km of the colony because albatrosses would form a raft on the ocean near the colony (Weimerskirch et al., 1997; Awkerman et al., 2005). In this way, we reduced the chances of falsely characterising resting points as foraging points. We then classified all remaining points either as transit or forage on the basis of residence, speed and path straightness values over a 20-min window (±1 GPS point). Following the approach of Torres et al. (2011), we used percentiles of residence and path straightness for each track to determine behavioural states. We defined transit points as points with low residence (in the lower 50th percentile), high path straightness (in the upper 50th percentile), and high speed (>30 km h−1 ). Forage points were defined as having high residence (in the upper 50th percentile) and low speed (<30 km h−1 ), which infer area-restricted search (ARS) behaviour (Kareiva and Odell, 1987). While this approach may fail to identify other albatross foraging strategies such as ‘directed flight’ (Catry et al., 2004; Weimerskirch et al., 2007a), our focus was to capture foraging events related to ARS, which has been shown to be induced in wandering albatrosses (Diomedea exulans) by the detection of cues from conspecifics or other predators (Weimerskirch et al., 2007a). We did not use path straightness as an index to define foraging points because it has been shown that birds tend to travel in a relatively straight line when foraging behind R vessels (Waugh et al., 2005; Torres et al., 2011). The software programme Matlab⃝ R2012a (The MathWorks, Natick, USA) was used for all data processing. 2.7. Identification of fine-scale overlap between albatrosses and fishing vessels To determine albatross forage points that overlapped spatially and temporally with a fishing vessel at a fine scale, we applied the methods developed in Torres et al. (2011). Fine-scale data on the spatial and temporal distributions of commercial fishing vessels, recorded by Vessel Monitoring System (VMS), were provided by Ministry for Primary Industries, New

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Zealand. Vessel monitoring systems on each vessel accurately recorded location every 1–2 h. Therefore, while VMS points were spatially highly accurate, their temporal resolution was lower than the albatross GPS points (i.e. 10-min intervals), and the exact locations of a fishing vessel in between two consecutive VMS points were unknown. Thus, we first interpolated vessel locations at 10-min intervals. We considered ‘overlap’ as any albatross forage points that fell within a buffer radius of 11 km from a vessel location and within a 3-min window of each vessel location. A buffer radius of 11 km was used because it has been shown that foraging behaviours of wide-ranging seabirds can be influenced by fishing vessels and their activities at distances up to 11 km (Bodey et al., 2014). Any albatross foraging points outside of a buffer radius of 11 km from a vessel location were considered to be ‘non-overlap’ although this might be conservative because Collet et al. (2015) has recently shown that foraging albatrosses change flight direction towards fishing vessels at distances up to 30 km. The association of foraging albatrosses with fishing vessels was treated as binary event (‘overlap’ or ‘non-overlap’), regardless of the number of ‘overlap’ points during a single foraging trip. We then cross-referenced albatross GPS points identified as ‘overlap’ to a catch effort database provided by Ministry for Primary Industries in New Zealand. The catch effort database includes all start locations and times of fishing events (sets) and the target species, fishing method and catch weight of each fishing event, as recorded by fishermen on all commercial fishing vessels operating in New Zealand Exclusive Economic Zone (EEZ), and the R target species, fishing method and catch weight of each fishing event. Matlab⃝ was used to iteratively perform these spatial and temporal comparisons between albatross foraging points and vessel locations for all foraging tracks recorded. Additionally, as a complement to the fine-scale overlap analysis, a utilisation distribution (UD) (Van Winkle, 1975) was estimated to quantify the large-scale overlap in distributions between albatross GPS points identified as ‘forage’ and the start locations of all fishing events within the same spatio-temporal area. We used the catch effort database to determine all start locations of fishing events. Kernel estimates were calculated for a 1 × 1 km cell using the ‘kernelUD’ function in the ‘adehabitatHR’ package (Calenge, 2006) in R (R Core Team, 2014). The appropriate smoothing parameter (h) was determined by the default, ad hoc method, which assumes a bivariate normal distribution (Worton, 1995). This produced realistic contours that did not overestimate space use. To describe the degree of overlap quantitatively, we calculated the UD overlap index (UDOI) (Fieberg and Kochanny, 2005). The UDOI statistic quantifies overlap as a function of the product of the UDs of the albatross and the vessels and measures the extent to which shared area is used (Fieberg and Kochanny, 2005). This index generally ranges between 0 and 1, where a value of 0 indicates no overlap, whereas 1 indicates complete overlap. We calculated the overlap indices for the 50% and 95% contours for locations in a Lambert Equal-Area Azimuthal projection. The analysis was performed in R using the ‘kerneloverlaphr’ function in the ‘adehabitatHR’ package (Calenge, 2006). Map figures were created in ArcGIS 10 (ESRI, Redlands, CA, USA). 2.8. Data analysis We used a linear mixed-effects model to test the effects of foraging-fishery overlap on meal size and foraging trip duration using the ‘lmer’ function of the ‘lme4’ package (Bates et al., 2012) in R. We fitted two models using restricted maximum likelihood with meal size and foraging trip duration as response variable, respectively. In both models, fishery overlap (binomial) was included as fixed effects; chick age was additionally included as a fixed effect to control for changes in meal size and foraging trip duration that might be influenced by chick age. We included bird ID as a random intercept to account for repeated measures of the same individuals and chick age as a random slope for bird ID, allowing the change in meal size by chick age to be different for each individual bird. The p-values for the fixed effects were calculated using conditional F -tests based on Kenward–Roger approximation for degrees of freedom (Kenward and Roger, 1997) derived from the ‘KRmodcomp’ function from ‘pbkrtest’ package (Halekoh and Højsgaard, 2014). To improve the interpretability of regression coefficients, we centred trip duration and chick age predictor variables (Schielzeth, 2010). We used a kernel density plot and SiZer plot (Chaudhuri and Marron, 1999) to examine the modality of distributions in foraging trip duration by identifying significant gradient in kernel density estimates (Phillips et al., 2009). Results are expressed as mean ± standard error of the mean (SEM), with significance level set at α = 0.05. 3. Results 3.1. Data available We recovered GPS tags from 11 birds (68.8% recovery rate), and 5 tags were lost (birds returned without tags), Of the 11 tags, we obtained usable data from 5 tags which recorded 90 complete foraging trips. Meal size was successfully measured at 9 nests for 367 chick-feeding visits. Chicks were between 45 and 183 days of age. There were several feeding visits that we could not allocate unequivocally to a particular parent because of simultaneous failure of the radio transmitters and the time-lapse cameras. Adverse effects associated with handling and the additional weight of tracking devices may be difficult to completely avoid (Barron et al., 2010). However, there were no instances of nest desertion as a result of the device attachment and the nest manipulation. In addition, all chicks successfully fledged from the nest equipped with the weighing system. Considering only the complete data sets, a total of 45 foraging trips and subsequent chick-feeding events were successfully monitored from 3 breeding adults. This included 21 foraging-chick feeding events performed by a 17 year-old male (hereafter Bird 1), 7 events by a 23 year-old male (hereafter Bird 2), and 17 events by a 16 year-old female

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(hereafter Bird 3). Within this data set (n = 45) (Table 1), on average, northern royal albatross delivered 573 ± 36 g of meal after travelling a distance of 854 ± 129 km on a foraging trip to a mean maximum range from the colony of 158 ± 26 km. The mean duration of foraging trip was 3.0 ± 0.4 days. Each bird showed considerable variation in foraging parameters and distributions (Table 1; Fig. 1). Although visual examination of the density plot of trip duration indicated the possibility of bimodality (Fig. 2), data in the right tail of the distribution were too sparse for the SiZer plot to identify significant gradient in kernel density estimates for reliable estimation of modality. There was a significant correlation between duration of foraging trip and both distance travelled on a trip (Spearman’s rank correlation rs = 0.92, P < 0.0001, n = 44) and maximum range from the colony (Spearman’s rank correlation rs = 0.78, P < 0.0001, n = 44).

Table 1 Meal size and foraging characteristics of chick-rearing northern royal albatross from Taiaroa Head, New Zealand. Values are means ± SEM with range in parentheses. Bird ID

Sex

Age (year)

Complete trips

Meal size (g)

Trip duration (day)

Maximum range (km)

Distance travelled (km)

2.6 ± 0.5 (0.2–9.7)

156 ± 28 (11–595)

812 ± 171 (28–3426)

Bird 1

M

17

21

544 ± 53 (120–1000)

Bird 2

M

23

7

663 ± 66 (400–900)

4.1 ± 1.7 (0.7–12.2)

247 ± 126 (24–774)

1107 ± 506 (148–3093)

Bird 3

F

17

17

571 ± 64 (100–1040)

3.0 ± 0.7 (0.2–9.1)

124 ± 31 (29–421)

800 ± 187 (81–2454)

All

–

–

45

573 ± 36 (100–1040)

3.0 ± 0.4 (0.2–12.2)

158 ± 26 (11–774)

854 ± 129 (28–3426)

170ºE

175ºE

180ºE

170ºE

40ºS

175ºE

180ºE

40ºS

50º 50ºS

50º 50ºS

165ºE

170ºE

175ºE

180º

175ºW 170ºE

165ºE 175ºE

170ºE

175ºE

180º

175ºW

180ºE

40ºS

50º 50ºS 165ºE

170ºE

175ºE

180º

175ºW

Fig. 1. Movements of chick-rearing northern royal albatross from Taiaroa Head/Pukekura, New Zealand during February to May 2012.

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Fig. 2. Density histogram and kernel estimates of foraging trip durations by chick-rearing northern royal albatross on Taiaroa Head/Pukekura, New Zealand.

3.2. Albatross-fishery overlap and its effects on meal size and foraging trip duration During the albatross-tracking period, 58 fishing vessels were active in the same spatio-temporal areas as foraging albatrosses. The fine-scale analysis of overlap indicated all 3 birds listed above overlapped a fishing vessel while foraging. Of the 45 foraging trips, 11 trips (24% of the total) included foraging points that were identified as overlapped with fishing vessels (i.e. overlap points) (Fig. 3). Although transit and forage states are mutually exclusive, our classification produced points that were identified as neither transit nor forage. Due to a relatively low sampling interval of the GPS loggers, 48% of the GPS points (after having removed drift points) were classified ‘neither’. For this reason, this method of classifying transit and forage points was conservative. Nonetheless, our method was still adequate to identify foraging points along each track where the individual searched for prey (Fig. 4). We used only those points that were identified as forage to investigate spatial and temporal associations between albatrosses and fishing vessels.

Fig. 3. Locations of overlap points derived from albatross GPS tracking and vessel monitoring system (VMS) data. Tracks are overlaid on the 1000 m isobath and kernel density contours of start locations of fishing events during the same temporal period as when northern royal albatrosses were tracked from Taiaroa Head/Pukekura, New Zealand.

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a

639

b

c

Fig. 4. Visual examples of characterisation of behavioural state in northern royal albatross using GPS data sampled at 10-min intervals. (a) Overview of track destinations with black boxes demarcating area of each example enlarged. (b) and (c) These examples illustrate typical results of classification of behavioural state. Each GPS point is labelled by forage, transit and neither state, which is designated based on high residence (in the upper 50th percentile) and low speed (<30 km h−1 ).

Of all 3044 albatross GPS points classified as ‘forage’, only 31 points (1.0%) occurred while overlapping a fishing vessel. At a large scale, kernel analysis showed that the foraging zones of the study birds had little overlap with fishing effort during the chick-rearing period (Fig. 5). While the core foraging area of the albatrosses (50% contour) was in waters mostly within 100 km of the colony, the distribution of fishing effort was concentrated in two different core areas: (1) over the shelf and shelf-break waters around the subantarctic Auckland Islands, and (2) in the middle part of the Chatham Rise near 177°E. The overlap identified was along the southeast coast of New Zealand’s South Island, encompassing the waters near the colony, and in the middle part of the Chatham Rise, as well as along the offshore coast of the northern half of the South Island. Quantification of overlap showed that, at the 95% contour level (overall range), the study birds exhibited low overlap with fishing effort (0.03 UDOI), but almost no overlap at the 50% contour level (core area) (0.00 UDOI). These results concur with the results from the fine-scale analysis of overlap that indicated the low rate of overlap. For birds that had overlapped with fishing vessels, the birds had travelled, on average, a distance of 1466 ± 320 km to a mean maximum range from the colony of 262 ± 86 km for the duration of 5.1 ± 1.1 days. There was a significant increase in foraging trip duration as a function of overlap (mixed model: F(1, 40.0) = 10.1, P = 0.003) (Fig. 6(a)). The mean meal size delivered after foraging trips with overlap with fishing vessels was 580 ± 99 g, whereas that without association with vessels was 571 ± 38 g (Fig. 6(b)). Our results showed no significant difference in meal size between foraging with and without overlap with fishing vessels (mixed model: F(1, 41.2) = 0.03, P = 0.86). For both models, visual inspection of a residual plot for the model did not indicate any obvious violation of homoscedasticity or normality (Zuur et al., 2010).

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Fig. 5. Kernel density contours for GPS data derived from northern royal albatross at Taiaroa Head/Pukekura (red) and start locations of fishing events (blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion The case study findings presented here demonstrate the applicability of our new integrated approach to better understanding ecological ramifications of seabird–fishery interactions. Although the interpretation of our findings in this study is hampered by the small sample size and the low overlap rate while foraging, we believe that our approach will be useful for seabird populations with known high levels of overlap with fisheries. Within the limitation of the small sample size, we found no clear evidence for any benefits, expressed as larger meal size and/or shorter trip duration, for chick-rearing northern royal albatrosses to forage in association with a fishing vessel. Contrary to our prediction, parents delivered a similar amount of food to their chicks regardless of overlap states with a fishing vessel and foraged at sea for longer duration during trips with association with a vessel. However, all conclusions are tentative and will need to be substantiated by further research. It should also be noted that the small sample size necessitated the use of a crude analytical approach (i.e. treating

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Fig. 6. Boxplot of the foraging trip duration (a) and meal size (b) in relation to foraging-fishery overlap.

foraging overlap between albatrosses and vessels as a binary event regardless of the number of ‘overlap’ points during a single trip) to derive these conclusions. As for meal size, one possible explanation could be, as Weimerskirch et al. (1997) suggested, that meal sizes could be influenced by the assimilatory capacity of the chick’s digestive tract. When recently fed or overfed at their maximum of food intake, chicks were not able to swallow all the food delivered by the parent. Similarly, younger, smaller chicks may not be able to assimilate food as much as older, bigger chicks. Thus, the meal size measured as chicks gain weight does not necessarily reflect the size of the whole meal brought back by the parent. However, we believe that this is already partly accounted for by including a random slope for chick age, allowing meal size to differ according to chick age, and that the meal size was not hugely influenced by the swallowing capacity of the chick. Another possibility could be that northern royal albatross may be out-competed or out-numbered behind fishing vessels by other albatross and seabird species. While northern royal albatrosses may be able to locate fishing vessels, competition for fishery waste near vessels may be fierce with large numbers of other seabirds around vessels (Richard et al., 2011a). Seabird counts around fishing vessels in New Zealand EEZ recorded between January 2007 and June 2009 showed that counts of northern royal albatross within 100 m of fishing vessels was far fewer than most of other albatross species on the record (Richard et al., 2011a). For example, during the survey period, while a total of over 1700 New Zealand white-capped albatrosses (Thalassarche cauta steadi) were counted within 100 km of vessels, 30 individuals were counted for northern royal albatross. Large numbers of Thalassarche albatrosses, together with other seabird species such as petrels and shearwaters attend fishing vessels (Richard et al., 2011a) and compete for access to fishery wastes. Although northern royal albatross are larger than Thalassarche albatrosses and all other species of seabird, it has been shown that birds outnumbered by another species often fail to win aggressive encounters with the other species regardless of the size difference (Burger and Gochfeld, 1984). Ultimately, it may be that energetic benefits from scavenging on fishery wastes do not outweigh energetic costs of aggressive interactions with other seabirds that congregate in large numbers. This hypothesis could help explain the low level of overlap with fishing vessels found in our fine-scale analysis (i.e. 1.0% of GPS points identified ‘forage’ occurred when overlapping with vessels). The tracked birds in this study rarely overlapped with fishing vessels during their foraging trips during our study period. As we did not measure energy expenditure during foraging trips, we cannot address the relationship between energetic cost of foraging with vessels and level of overlap with vessels. However, instead we suggest that the low level of overlap can be reasonably explained by the minimal congruence in overall distributions between foraging albatrosses and fishing effort, as depicted by the kernel density plot on Fig. 4. Our tracked birds did not frequent oceanic areas where fishing efforts were concentrated, and thus had minimal opportunities for interaction with fishing vessels during the study period. Lack of data from more than one breeding season did not allow us to examine inter-annual variation in overlap rate, as demonstrated in Torres et al. (2013) where they found that inter-annual variability in overlap rates were due to distributional shifts of both albatrosses and fisheries. Similarly, as demonstrated by Granadeiro et al. (2011), individual variation relative to vessel association rates could contribute to the low overlap rate detected in this study. A previous study on gannets demonstrated marked individual differences in behavioural response to fishing vessels and also in consumption of fishery discards (Votier et al., 2010; Tew Kai et al., 2013). Considering that the complete dataset is based on only 3 individuals, it is possible that our sample does not reflect the overall population’s true overlap rate. A larger sample size of albatross GPS tracks is required to further evaluate potential variations. One other possibility for the low overlap rate is that northern royal albatross might be better adapted to foraging on natural prey. It has been shown that northern royal albatross at Taiaroa Head/Pukekura consistently exploit adult octopuses and feed them to chicks, to the extent that no other procellariiform seabirds do, though it is not known how they obtain octopuses in such quantities (Imber, 1999). Such prey specialisation would be advantageous to minimise competition with other seabirds.

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Our results also demonstrated that, contrary to prediction, the duration of foraging trips was longer when the tracked birds foraged in association with a fishing vessel. Given the significant correlations noted between foraging trip duration, distance travelled, and maximum range from the colony, longer foraging trips are, in this case, associated with a greater foraging range. Thus, the higher rate of overlap between foraging albatrosses and vessels may be attributed to a higher probability of encountering a fishing vessel by covering a larger area. However, another possibility might be related to foraging strategies of northern royal albatross that include undertaking short (in duration and distance) and long foraging trips during the chick-rearing period, although the sample size was too small to statistically identify bimodality in trip duration. Many procellariiform seabirds employ a dual foraging strategy by alternating or mixing short and long foraging trips to balance the demands of chick provisioning with the maintenance of their own body condition (Chaurand and Weimerskirch, 1994; Weimerskirch et al., 1994; Granadeiro et al., 1998; Magalhães et al., 2008). Typically, during short trips, parents forage close to their breeding sites to maximise food delivery rate to chicks, whereas long trips are mainly used for self-feeding to replenish body reserves (Weimerskirch et al., 1994; Weimerskirch, 1998; Stahl and Sagar, 2000; Terauds and Gales, 2006). Mixing of short and long trips observed in this study may represent a parental mechanism to balance chick provisioning with self-feeding. If parents are indeed mostly self-feeding in distant waters where high fishing efforts were observed, it may provide additional explanation why there was no difference in meal size between foraging trips with or without fishing vessel association. Based on the findings of Davoren and Burger (1999) that showed parent rhinoceros auklets (Cerorhinca monocerata) collected a better quality food for feeding their chicks than when self-feeding, we speculate that, during longer trips, our tracked birds may have been more prone to target vessels for feeding opportunities where potentially poorer quality, but easier prey, are accessible. Such a foraging strategy could be advantageous for parents if efficient self-feeding long trips enable parents to increase the frequency of chick provisioning. This possibility can be evaluated by detailed diet analysis comparing food items collected between short and long foraging trips. 5. Conclusion This paper contributes to the growing body of literature on the study of the complex nature of fine-scale overlap between seabirds and fisheries by presenting a new approach that connects seabird distribution, fishery association, and meal size. We did not find any obvious ecological advantage, at least for our study birds during the study period, to foraging in association with fishing vessels in terms of meal size delivered to chicks or foraging trip duration. The quantity of food delivered to chicks was similar regardless of overlap with a fishing vessel, whereas chick-rearing northern royal albatross overlapped with a vessel more frequently when they were travelling for a longer time. To the best of our knowledge, this study is the first to address the potential ecological advantages of foraging with vessels for seabirds raising chicks, in terms of quantify of food delivered to chicks as a proxy for benefit. However, this indirect measurement of energetic gain from fishery overlap poses challenges when assessing if foraging with vessels is an energetically advantageous tactic due to the lack of information on the quality of food delivered to the chick and quantification of the foraging energetics of fishery interactions. Nevertheless, our integrated approach provides new insight into better understanding ecological ramifications of seabird–fishery interactions. The integrated methods developed here can be extended further using diet analysis and quantification of energy expenditure during foraging trips to provide a more comprehensive picture of seabird–fishery interactions. Furthermore, our approach offers a valuable approach to quantifying the functional relationships between marine predators and their environment. Acknowledgments Funding for this study was provided by the following sources to JS: the University of Otago (Ph.D. Scholarship); jointly the Otago Peninsula Trust and Department of Conservation Otago Conservancy; the Royal Forest and Bird Protection Society of New Zealand Dunedin branch. Additional funding was provided as part of the Ministry of Business, Innovation & Employment Grant (UOOX0904) to PJS and Tim Molteno. We especially thank Lyndon Perriman for his help with fieldwork and for sharing his experience and knowledge of the albatross, and T. Molteno for the funding and for designing and providing the GPS loggers used in this work. We are grateful to the Ministry for Primary Industries, New Zealand for supplying fishing vessel and catch effort data. We wish to thank David Thompson for his help with obtaining the fishery data. We are especially grateful to Murray McKenzie for his technical support for the nest balances, Daisuke Ochi for lending data loggers used with the nest balances, and Richard Phillips for invaluable information on nest balances. Special thanks to Keith Payne and Phil Brown for their technical help on the GPS loggers, Ed King for his help on building a prototype nest balance, and Colin Facer and Sharyn Broni for their help in the field. We are indebted to the Korako Karetai Trust and Otakou Runanga for consents to carry out the field work in a culturally significant site and to Port Otago Limited for allowing us to use their facility at Taiaroa Head. We would like to thank two anonymous reviewers for their constructive comments. References Anderson, O.R.J., Small, C.J., Croxall, J.P., Dunn, E.K., Sullivan, B.J., Yates, O., Black, A., 2011. Global seabird bycatch in longline fisheries. Endanger. Species Res. 14, 91–106. Awkerman, J.A., Fukuda, A., Higuchi, H., Anderson, D.J., 2005. 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