Oecologia

, Volume 178, Issue 4, pp 967–979 | Cite as

Environmental variability drives shifts in the foraging behaviour and reproductive success of an inshore seabird

  • Nicole D. Kowalczyk
  • Richard D. Reina
  • Tiana J. Preston
  • André Chiaradia
HIGHLIGHTED STUDENT RESEARCH

Abstract

Marine animals forage in areas that aggregate prey to maximize their energy intake. However, these foraging ‘hot spots’ experience environmental variability, which can substantially alter prey availability. To survive and reproduce animals need to modify their foraging in response to these prey shifts. By monitoring their inter-annual foraging behaviours, we can understand which environmental variables affect their foraging efficiency, and can assess how they respond to environmental variability. Here, we monitored the foraging behaviour and isotopic niche of little penguins (Eudyptula minor), over 3 years (2008, 2011, and 2012) of climatic and prey variability within Port Phillip Bay, Australia. During drought (2008), penguins foraged in close proximity to the Yarra River outlet on a predominantly anchovy-based diet. In periods of heavy rainfall, when water depth in the largest tributary into the bay (Yarra River) was high, the total distance travelled, maximum distance travelled, distance to core-range, and size of core- and home-ranges of penguins increased significantly. This larger foraging range was associated with broad dietary diversity and high reproductive success. These results suggest the increased foraging range and dietary diversity of penguins were a means to maximize resource acquisition rather than a strategy to overcome local depletions in prey. Our results demonstrate the significance of the Yarra River in structuring predator–prey interactions in this enclosed bay, as well as the flexible foraging strategies of penguins in response to environmental variability. This plasticity is central to the survival of this small-ranging, resident seabird species.

Keywords

River plumes GPS Salinity Penguin Stable isotopes 

Introduction

The rate of energy acquisition determines how energy can be allocated to the processes of growth and reproduction (Levins 1968). Optimal foraging theory predicts that animals within heterogeneous environments should forage in ways that maximise net energy gain, to increase their survival and reproductive success (MacArthur and Pianka 1966; Stephens and Krebs 1986). The ability to forage efficiently should be especially apparent in central place foragers, such as seabirds, which are constrained in foraging duration and range due to their need to return frequently to their colony and feed their young (Weimerskirch et al. 2005). Strategies to forage efficiently are perhaps most pronounced in small-ranging seabirds, as these species are more limited in seeking out suitable foraging areas compared to wide-ranging volant species (Weimerskirch 2007). Indeed, many studies on small-ranging seabirds have identified that their foraging ranges occur within and around oceanographic features with enhanced productivity that aggregate prey and facilitate prey capture (Ballance et al. 2006; Ballard et al. 2010; Mattern et al. 2013). For instance, rivers entering coastal systems contain high quantities of nutrients known to aggregate planktonic organisms that in turn attract and sustain large schools of planktivorous fish (Grimes and Kingsford 1996; Kudela et al. 2010). Consequently, elevated densities of piscivorous seabirds are found in these feeding ‘hotspots’ (Skov and Prins 2001; Zamon et al. 2014).

Although certain physical or environmental features offer predictable prey resources to seabirds, these features can be subjected to climatic shifts or environmental variability (Ballance et al. 2006; Saether 2000). Recent advancements in bio-logging technologies have enabled remote monitoring of individual animal distribution and behaviour in response to environmental fluctuations. Some bio-logging studies have found that seabirds can adjust their foraging behaviour and distribution in response to climatically induced changes in resource availability (Pettex et al. 2012; Pinaud et al. 2005), while others have noted that seabirds use consistent foraging routes despite environmental variability (Kotzerka et al. 2011; Mattern et al. 2007). Similarly, dietary analyses can provide information on spatial and temporal patterns of habitat use and prey assimilation (Bearhop et al. 2006; Hobson et al. 1994). Stable isotope ecology is particularly useful in quantifying how seabirds respond to fluctuations in prey availability through monitoring shifts in the trophic position and isotopic niche of individuals or populations (Ceia et al. 2014; Jackson et al. 2011; Jaeger et al. 2010). Some species are capable of modifying their diet in response to resource variability (Suryan et al. 2000). Others cannot switch between prey types due to the absence of alternative prey taxa or their specialist foraging strategy, which can have implications for their body condition and reproductive success (Rindorf et al. 2000). These studies highlight that by monitoring the inter-annual foraging ecology of seabirds, we can not only gain information into which environmental features, foraging locations and prey types are important in their survival but can also investigate how they respond to environmental fluctuations in these marine systems. As the effects of global climate change on marine species become more apparent (Burrows et al. 2014; Edwards and Richardson 2004; Harley et al. 2006), it is vital that we continue to investigate how current patterns of climate variability impact these species in order to build more robust predictive models for the future.

In this study, we monitored the foraging behaviour and diet (isotopic niche) of little penguins (Eudyptula minor) in Port Phillip Bay, Victoria, Australia, over 3 years (2008, 2011, and 2012), a period of intense environmental variability. Penguins from the St Kilda colony forage exclusively within Port Phillip Bay, generally remain within 20 km of their colony during the breeding season, and have one of the shortest foraging ranges among seabirds (Collins et al. 1999; Preston et al. 2008). They use both mid-water and demersal diving strategies to search for and catch their prey, which is predominantly comprised of clupeoids, such as anchovy (Engraulis australis), pilchard (Sardinops sagax) and sandy sprat (Hypherlophus vittatus) (Chiaradia et al. 2012; Preston 2010; Ropert-Coudert et al. 2006). The short foraging range and narrow dietary breadth of this colony make them particularly vulnerable to local shifts in the abundance and distribution of their prey (Chiaradia et al. 2012; Kowalczyk et al. 2013). Identifying the environmental variables that are important in their resource acquisition is critical to their ongoing management and conservation.

The south-east marine system of Australia has been identified as one of the five fastest changing climates in the planet, with contrasting periods of drought and heavy storms (Voice et al. 2006). In 2010/2011, persistently high rainfall in south-eastern Australia broke the drought conditions which had occurred in the region from years 1997 to 2009 (Lee et al. 2012). Freshwater input into Port Phillip Bay, predominantly from the Yarra River, led to a drop in salinity across the bay, while dissolved inorganic nitrogen loads from key point sources increased from 60 tonnes in 2008 to 572 tonnes in 2011, substantially altering productivity in the bay (EPA Environmental Protection Agency Victoria 2012; Lee et al. 2012). Fluctuations in salinity and nutrients have substantial effects on the growth and distribution of marine organisms (Gillson et al. 2012) and may have been responsible for the observed increases in clupeoid [anchovy, pilchards, sandy sprat, and blue sprat (Spatelloides robustus)] abundance in Port Phillip Bay (Hirst et al. 2011; Parry and Stokie 2008). Under these conditions, little penguins are a good model species to investigate how small-ranging resident seabirds respond to changes in environmental conditions combined with changes in local prey availability.

Given the constrained foraging duration and range of breeding little penguins, we hypothesized the St Kilda colony can improve their foraging efficiency by (1) foraging in close proximity to a productive region, specifically the Yarra River, and (2) modifying their foraging behaviours and diet (isotopic niche) in response to environmental variability. Further, we expected that modifications in penguin foraging behaviour would have consequences on their breeding performance.

Materials and methods

Bird instrumentation and tracking

Single foraging trips of 44 individual penguins were tracked in the austral spring and summer of 2008 (n = 15), 2011 (n = 10) and 2012 (n = 19) from a small breeding colony (c. 400 breeding pairs), on St Kilda breakwater, Victoria, Australia (−37.51°S, 144.57°E). We tracked penguins during the guard breeding stage (when chicks are between 1 and 19 days of age) (Chiaradia and Kerry 1999). During this stage, adults typically undertake a 1-day foraging trip within a 20-km radius of their breeding site (Collins et al. 1999; Preston et al. 2008). In 2008, birds were weighed (±10 g) and equipped with mini-GPS loggers (46.5 × 16 mm, minimum cross-sectional area 496 mm2, mass in air 29 g; Earth and Ocean Technologies). In 2011 and 2012, penguins were weighed (±10 g) and equipped with CatTraq GT-120 GPS devices (44.5 × 28.5, minimum cross-sectional area 371 mm2, mass in air 17 g; Perthold Engineering) that were sealed in a heat-shrink rubber tube for waterproofing. Loggers were attached to the posterior dorsal region of the bird, with Tesa® tape (Beiersdorf, Hamburg, Germany) as per Wilson (1997). Devices weighed a maximum of 3.6 % of the bird’s mass in air, and were therefore under the upper limit of logger/body mass ratios recommended for penguins (Ropert-Coudert et al. 2007). Loggers recorded positions every 15 s from 0400 to 2100 hours to coincide with the daily foraging activities of penguins. After a single foraging trip, penguins were captured in their nests and their loggers were removed. In 2011 and 2012, a blood sample was collected (see “Penguin tissue collection and preparation”) following logger removal, after which penguins were released.

We calculated five foraging characteristics for each penguin foraging track: (1) total trip distance (km), (2) maximum distance from colony (km), (3) distance to core-range (km) (4) core-range area (km2) and (5) home-range area (km2). The total trip distance of the foraging trip was calculated as a series of straight movements between detections using the Haversine algorithm, and maximum distance was calculated as the distance between the colony and the furthest detection from this point. Distance to core-range was calculated as the distance (straight-line movement) from the nest of the penguins to the start of the core-range. We used the Adehabitat package in R (Calenge 2006), which uses a kernel utilisation distribution (KUD) to estimate the probability that an individual will be found at a specific location to calculate the home-range (95 % KUD, smoothing factor = 7, grid = 100 m) and core foraging area (50 % KUD smoothing factor = 7, grid = 100 m) of the penguins.

Environmental data

We obtained environmental data from a mooring site positioned near the surface (3 m) waters in Hobson Bay (−37.866°S, 144.929°E), Port Phillip Bay, managed by the Environment Protection Agency, Victoria, Australia (www.epa.vic.gov.au). This mooring site occurs within the home-range of all sampled penguins. We employed averaged daily measurements of salinity (psu), temperature (°C), and chlorophyll-a fluorescence (µg L−1) in our analyses. We obtained mean daily river flow rates (ML day−1) in the Yarra River (−37.7870°S, 145.0258°E), mean daily rainfall (mm) at St Kilda Marina (−37.8720°S, 144.9747°E), and mean daily water depth (m) in the lower Yarra River (−37.8231°S, 144.9567°E) from Melbourne Water (www.melbournewater.com.au) (Table 1).
Table 1

Averaged (± SD) daily measurements of salinity (psu), temperature (°C), chlorophyll-a fluorescence (µg L−1), river flow rates (ML day−1) in the lower Yarra River, mean daily rainfall (mm) at St Kilda Marina, and mean daily water depth (m) in the lower Yarra River, on days when little penguins (Eudyptula minor) were GPS-tracked in 2008 (n = 15), 2011 (n = 10) and 2012 (n = 19)

Environmental parameter

Year

2008

2011

2012

Mean ± SD

Range

Mean ± SD

Range

Mean ± SD

Range

Salinity (psu)

36.7 ± 0.2

36.4–36.9

32.8 ± 0.5

31.4–33.1

33.6 ± 0.2

33.2–34.0

Temperature (°C)

16.5 ± 1

14.2–18.3

19.8 ± 1.5

18.4–21.9

15.7 ± 2.7

12–19.9

Chl-a (µg L−1)

1.6 ± 0.1

1.4–1.7

6.3 ± 3.0

3.1–10.1

2.3 ± 0.9

1.5–3.6

Mean daily river flow rates (ML day−1)

120.6 ± 58.2

78.9–231.8

1811.6 ± 1959

283.1–4556.6

644.2 ± 478

204.6–1422.7

Mean daily rainfall (mm)

1.2 ± 0.9

0.3–2.5

3.0 ± 1.5

0.9–4.1

1.2 ± 0.1

1–1.3

Mean daily water depth (m)

0.12 ± 0.1

0.001–0.35

0.15 ± 0.1

0–0.28

0.17 ± 0.1

0.1–0.35

Penguin tissue collection, preparation and stable isotope analysis

For animals of similar mass as little penguins (~1 kg), the half-life of δ13C and δ15N stable isotopes in whole blood are 10–23 days (Hobson and Clark 1993). Accordingly, in 2008, blood samples represented the dietary intake of birds during the months of September and October of that year. We collected approximately 80 μL of blood from the tarsal vein of adults using venipuncture and capillarity, and samples were stored in 70 % ethanol at room temperature until analysis. In 2011 and 2012, we collected blood samples from GPS-tracked penguins following their foraging trip. Approximately 150 μL of blood was collected from the tarsal vein using venipuncture and capillarity, and was then transferred onto a microscope slide and dried at ambient air temperature (Bugoni et al. 2008). Blood samples were then powdered, transferred into tin capsules (8 × 5 mm), weighed (0.4–0.6 mg) and sealed. Blood lipids were not extracted prior to analysis, given that the lipid component of blood is less than 1 % of the total wet mass of whole blood (Bearhop et al. 2000). Although the use of different blood preservation techniques can result in significant differences in δ13C or δ15N values, Hobson et al. (1997) found that δ13C or δ15N values from samples preserved in 70 % ethanol at room temperature and samples air-dried on glass fiber filter paper did not differ significantly from the control.

The 2008 samples were processed at the Stable Isotopes in Nature Laboratory (SINLAB), Canada, and were combusted in an AS128 autosampler and analysed by a Delta XP isotope-ratio mass spectrometer (Bremen, Germany) using a continuous flow system with every 20 unknowns separated by laboratory standards. The 2011 and 2012 samples were analysed at the Monash University Water Studies Centre, Australia, using an ANCA-GSL 2 elemental analyser. The resultant CO2 and N2 gases were analysed using a coupled Hydra 20:22 isotope ratio mass-spectrometer (Sercon, UK) with every five unknowns separated by laboratory standards. Sample precision was 0.1 ‰ for both δ13C and δ15N. Stable isotope abundances are expressed in δ notation in per mille units (‰) following the equation:
$$\delta ^{{{\text{13}}}} {\text{C or }}\delta ^{{{\text{15}}}} {\text{N }} = {\text{ }}\left[ {\left( {{\text{R}}_{{{\text{sample}}}} /{\text{ R}}_{{{\text{standard}}}} } \right){\text{ }}{-}{\text{ 1}}} \right]~ \times1000$$
where \(R = \left( {^{ 1 3} {\text{C/}}^{ 1 2} {\text{C or}}^{ 1 5} {\text{N/}}^{ 1 4} {\text{N}}} \right)\) of the sample and standards or where R is the ratio of the heavy (rare) isotope to the light (common) isotope in the sample and standard (Fry 2006). The international standards for carbon and nitrogen stable isotope rations were Pee Dee Belemnite and atmospheric N2, respectively.

Reproductive success

In 2008, we monitored a subset of 20 penguin nests twice a week during the breeding season. In 2011 and 2012, we monitored subsets of 45 unique nests three times a week during breeding seasons. We identified penguins within these nests by scanning passive integrated transponders (Trovan, Australia) and determined sex through measurement of bill morphometrics (Arnould et al. 2004). To assess laying date, hatching date, and hatching and fledging success, we monitored nest contents. Chicks were weighed (±10 g) twice weekly until they fledged (chicks were considered to have fledged when their plumage reached an adult appearance and when they were older than 40 days when last encountered). We determined peak chick mass as the highest mass recorded for a chick (Chiaradia and Nisbet 2006).

We defined measures of breeding performance as follows: (1) egg success: the proportion of eggs that produced chicks; (2) nest success: the proportion of clutches that produced chicks; (3) number of fledglings per nest: the total number of fledglings divided by the number of nests where at least a single chick fledged, (4) annual reproductive success: mean number of chicks reared, following methods outlined in Murray (2000).

Statistical analysis

We tested all measures of foraging tracks for normality and homogeneity of variance. We found no sex-related differences in foraging measures and consequently pooled male and female data. To compare inter-annual differences in peak chick mass and the δ13C and δ15N stable isotope composition of penguins, we used a single factor ANOVA. To calculate annual differences in measures of breeding performance, we used Kruskal–Wallis and Mann–Whitney U tests.

We used conditional inference regression trees (CIRT) to (1) characterize environmental conditions in Port Phillip Bay across years, and (2) to provide descriptive models of the influence of environmental variables (see “Environmental data”) on penguin foraging characteristics; (1) total trip distance, (2) maximum distance, (3) distance to core-range (4) core foraging area, and (5) home-range area. CIRTs examine the relationship between multiple explanatory variables and a single response variable using a ‘recursive binary-partitioning process’ (Quinn and Keough 2002). Model outputs produce an ‘inverted tree’, in which the root at the top contains all observations, which is divided into two branches at the node (Quinn and Keough 2002). The node provides information about the explanatory variable’s name and associated p value. Branches are further split into two subsequent nodes and so on (Quinn and Keough 2002). For each predictor variable, all possible binary splits of the observations are assessed to determine groups with a between-variation as large, and within-variation as small, as possible. That is, the first split is based on the predictor variable that results in two groups with the smallest within-variation sums of squares for the response variable (Quinn and Keough 2002). The advantages of using this approach over general linear modelling are that an unlimited number of explanatory variables can be included in models, and that CIRTs are not invalidated by multicollinearity. Furthermore, there is no requirement for linearity and normality in explanatory variables (Johnstone et al. 2014). However, a major limitation of traditional tree branched models is that splitting is biased in favour of explanatory variables in which the most splitting is possible (Hothorn et al. 2006). Consequently, models can be over fitted. To overcome these drawbacks we used the more recently developed Conditional Inference Tree (CIT) in the R ‘party’ package (Hothorn et al. 2011). This function uses a machine learning algorithm embedded in a conditional inference framework to determine when splitting is no longer valid. A second limitation of using CITs is that even though CITs are not invalidated by multicollinearity, the models select only the best predictor variables. The selected predictor variables may therefore act as a proxy for other variables that are influencing the response variable (Johnstone et al. 2014). To overcome this limitation, we removed ‘Year’ as an explanatory variable from CITs as ‘Year’ is likely to have masked the importance of environmental factors driving the foraging characteristics of penguins.

To calculate the isotopic niche widths of breeding penguins in years 2008, 2011, and 2012, we employed the SIBER function (Jackson et al. 2011) within the Stable Isotope Analysis in R package (SIAR, v.4.1.3) (Parnell et al. 2008). Standard ellipses represent the isotopic niche width of 40 % of typical individuals within the groups based on bivariate normal distributions. We used the corrected version of the standard ellipse area (SEAc) to account for the loss of an extra degree of freedom when calculating bivariate data and to control for small sample sizes (Jackson et al. 2011). This elliptical area represents an estimate of the core isotopic niche width of penguins, and we hereafter use this metric as a proxy to measure the ecological isotopic niche of penguins. We calculated isotopic niche ellipse overlap between years 2008, 2011 and 2012 by dividing the area of overlap by the total ellipse area for a given year and multiplying the result by 100. We used the R statistical package (v.3.1.1; R Development Core Team 2009) to conduct all statistical analyses.

Results

Annual differences in environmental conditions

To characterise environmental conditions in Port Phillip Bay, daily mean measurements of salinity (psu), temperature (°C), chlorophyll-a fluorescence (µg L−1), river flow rate (ML day−1), rainfall (tidal mm) and water depth (m) in the lower Yarra River were used as explanatory variables (Table 1). Conditional inference tree results indicate that annual differences in salinity best characterized environmental conditions in the bay. CIT results indicate 2011 was best characterized as a year with low salinity (mean 32.79 ± 0.52 psu), with 100 % of tracked penguins in 2011 foraging in waters with salinity ≤33.176 psu (Fig. 1; Node 1, Fig. 2). Years 2008 and 2012 were characterized as periods with significantly higher salinity than 2011 (Fig. 2) with 100 % of 2008 and 2012 tracked penguins foraging in waters with salinity >33.176 psu. Conditions in 2012 (mean 33.61 ± 0.22 psu) were significantly less saline than conditions in 2008 (mean 36.73 ± 0.16 psu) (Fig. 2).
Fig. 1

GPS foraging trajectory of little penguins (Eudyptula minor) in Port Phillip Bay, Victoria, Australia, during periods of high water depth (i.e., >0.08 m in the Yarra River) (black, n = 23), on days with low water depth, and low salinity (i.e., ≤0.08 m in the lower Yarra River) (red, n = 10), and on days with low water depth and high salinity (blue, n = 11)

Fig. 2

Conditional inference tree characterising the environmental conditions in Port Phillip Bay in years 2008, 2011 and 2012. Daily mean measurements of salinity (psu), temperature (°C), chlorophyll-a fluorescence (µg L−1), river flow rate (ML/day), rainfall (tidal mm) and water depth (m) in the lower Yarra River were used as initial explanatory variables. Salinity (encircled variable) has the strongest association to the response variable (Year) and best characterizes annual environmental differences. The p values listed at each encircled node represent the test of independence between the listed variable (salinity) and the response variable (year). Terminal nodes indicate which salinity levels penguins foraged within and n indicates the number of penguins from each year corresponding to specific salinity levels

Effects of environmental variability on foraging

Across years, salinity influenced the total distances travelled by penguins in their foraging zone. When salinity was low, the total distance penguins travelled was significantly greater than when salinity was high (Fig. 3a). Maximum distance travelled from colony and distance to core-range were significantly shorter, and size of core- and home-ranges were significantly smaller when water depth in the lower Yarra River was low compared to when water depth was high (Fig. 3c–e). Additionally, when water depth was low, penguin maximum distance travelled from colony and distance to core-range were significantly shorter and size of core-range and size of home-range were significantly smaller in periods of high salinity compared to periods of low salinity (Fig. 3c–e).
Fig. 3

a Conditional inference tree indicating the influence of salinity on the total distance travelled by penguins. Penguins in low salinity (≤33.784 psu) travelled significantly further than penguins in high salinity (>33.784 psu). Node 2 represents 10/10 (i.e. 100 %) penguins from 2011 and 17/19 of penguins from 2012. Node 3 represents 15/15 penguins from 2008 and 2/19 penguins from 2012. Boxplots depict medians, ranges and upper and lower quartiles for penguins for which no further splitting was possible. b Conditional inference tree reflecting the influence of environmental variables on the maximum distance (km) penguins travelled from their colony. Mean daily water depth in the lower Yarra River was split into penguins tracked in periods of high water depth (>0.08 m) and those tracked on days with low water depth (≤0.08 m) in the lower Yarra River. Node 5 represents 5/15 penguins from 2008, 7/10 penguins from 2011, 11/19 penguins from 2012. Penguins tracked on days with low water depth were split by salinity; on days with low salinity, penguins travelled further from their colony than on days with high salinity. Node 3 represents 3/10 penguins from 2011 and 7/19 penguins from 2012. Node 4 represents 10/15 penguins from 2008, and 1/19 penguins from 2012. Boxplots depict medians, ranges and upper and lower quartiles for penguins for which no further splitting was possible. c Conditional inference tree reflecting the influence of environmental variables on the average distance penguins travelled (km) to their core-range. Mean daily water depth in the lower Yarra River was split into penguins tracked in periods of high water depth (>0.08 m) and those tracked on days with low water depth (≤0.08 m) in the lower Yarra River. Node 5 represents 5/15 penguins from 2008, 7/10 penguins from 2011, 11/19 penguins from 2012. Penguins tracked on days with low water depth were split by salinity; on days with low salinity, penguins travelled further from their colony than on days with high salinity. Node 3 represents 3/10 penguins from 2011 and 8/19 penguins from 2012, while Node 4 represents 10/15 penguins from 2008. Boxplots depict medians, ranges and upper and lower quartiles for penguins for which no further splitting was possible. d Conditional inference tree reflecting the influence of environmental variables on the size of the core-range area (km2) of penguins. Mean daily water depth in the lower Yarra River was split into penguins tracked in periods of high water depth (>0.08 m) and those tracked on days with low water depth (≤0.08 m) in the lower Yarra River. Node 5 represents 5/15 penguins from 2008, 7/10 penguins from 2011, 11/19 penguins from 2012. Penguins tracked on days with low water depth were split by salinity; on days with low salinity, penguins travelled further from their colony than on days with high salinity. Node 3 represents 3/10 penguins from 2011 and 8/19 penguins from 2012, while Node 4 represents 10/15 penguins from 2008. Boxplots depict medians, ranges and upper and lower quartiles for penguins for which no further splitting was possible. e Conditional inference tree indicating the influence of environmental variables on the size of penguin home-range (km2). Mean daily water depth in the lower Yarra River was split into penguins tracked in periods of high water depth (>0.08 m) and those tracked on days with low water depth (≤0.08 m) in the lower Yarra River. Node 5 represents 5/15 penguins from 2008, 7/10 penguins from 2011, 11/19 penguins from 2012. Penguins tracked on days with low water depth were split by salinity; on days with low salinity, the home-range of penguins was greater than on days with high salinity. Node 3 represents 3/10 penguins from 2011 and 8/19 penguins from 2012, while Node 4 represents 10/15 penguins from 2008. Boxplots depict medians, ranges and upper and lower quartiles for penguins for which no further splitting was possible

Inter-annual isotopic niche variation

We found no significant inter-annual differences in the δ13C stable isotope composition of penguins (F2,38 = 2.38, p > 0.05), but did find a significant difference for δ15N ratios (F2,38 = 2.38, p < 0.001; Fig. 4). In 2011, penguins had higher δ15N values compared to penguins in 2008 (t = 5.02, p < 0.01) and 2012 (t = −6.925, p < 0.01). No significant difference in δ15N values between penguins in 2008 and 2012 were found (t = −1.545, p > 0.05).
Fig. 4

Biplot depicting the δ13C and δ15N isotope ratios of breeding and GPS tracked little penguins in yeas 2008, 2011 and 2012. Ellipses represent the isotopic niche width of 40 % of typical individuals within the group based on bivariate normal distributions

We observed isotopic niche overlap between years 2008 and 2012. In 2008, 100 % of penguin isotopic niche area overlapped with penguins in 2012. By contrast, in 2012, only 11 % of the isotopic niche area overlapped with penguins in 2008 indicating penguins occupied a substantially different isotopic position and significantly wider isotopic niche width.

We found no isotopic niche overlap between years 2008–2011 and 2011–2012 (Fig. 4a).The isotopic niche width of penguins in 2011 (SEAc: 1.24) and 2012 (SEAc: 2.13) were substantially larger than that of penguins in 2008 (SEAc: 0.24).

Reproductive success

We observed significant variation in measures of reproductive success between years, including significant differences in the number of eggs laid per female (χ22 = 11.4, p < 0.01). In 2011, individual females laid a greater number of eggs (mean 2.6) compared to females in 2012 (mean 2.1) (W = 2375.5, p < 0.01). No difference in the numbers of eggs laid per female was found between years 2008 (mean 2.3) and 2011 (W = 563.5, p > 0.05) and between 2008 and 2012 (W = 727, p > 0.05). Egg success (the proportion of chicks fledged of eggs laid) was lowest in 2008 where only 32 % of eggs produced fledglings compared to 62 % in 2011 and 73 % in 2012 (Fig. 5a). We observed significant variation in the number of fledglings per successful nest between years 2008 and 2011–2012 (χ22 = 11.5, p < 0.01). Approximately twice as many chicks per nest were produced by parents in 2011 (1.6 fledglings per nest) and 2012 (1.7 fledglings per nest) compared to parents in 2008 (0.83 fledglings per nest) (2008:2011 W = 236.5, p < 0.01, 2008:2012 W = 250.5, p < 0.01). We observed no significant difference in the numbers of fledglings per nest between years 2011 and 2012 (W = 1060, p > 0.05). In 2011, annual reproductive success was boosted by a high proportion of females (42 %) laying a second clutch, and rearing chicks to fledging (Fig. 5b). In 2008, 15 % of females laid a second clutch, and in 2012, 20 % of females laid a second clutch, but most second clutches failed, leading to lower overall annual reproductive success than observed in 2011 (Fig. 5b).
Fig. 5

aBarplot depicts eggs success (the proportion of eggs that produced chicks) for years 2008, 2011, and 2012. bBarplot depicts annual reproductive success (counted as mean number of young reared per female) for years 2008, 2011, and 2012. In 2011, annual reproductive success was boosted by a high proportion of females laying early and successfully brooding a double clutch. cBarplot depicts mean (± SD) chick peak mass for years 2008, 2011, and 2012. Letters indicate significant differences in chick peak mass between years

We observed inter-annual variation in peak chick mass (F2,94 = 12.65, p < 0.01, Fig. 5c). In 2008, the peak mass of chicks was no different compared to chicks in 2011 or to chicks in 2012, while chicks in 2011 were significantly lighter than chicks in 2012 (t = 5.02, p < 0.01).

Discussion

Obtaining concurrent information on seabird foraging behaviour, diet and measures of breeding performance over periods of intense environmental variability is rare. As such, for many seabirds, their responses to environmental/climatic fluctuations are unknown. In order to predict how seabirds will respond to environmental variability, monitoring their foraging ecology is crucial to their management. This is particularly important for resident, small-ranging seabirds that rely on a small foraging area year round with contrasting years of drought and rainy periods. In this study, we monitored the foraging ecology of little penguins over 3 years of intense environmental variability. In line with our hypothesis, we found that the river plume (Yarra River) strongly influenced the foraging behaviours of little penguins. Specifically, the rise and fall of water depth in the Yarra River and fluctuations in salinity play an important role in distributing prey and little penguins in Port Phillip Bay. We found penguins can modify their foraging behaviours and diet in response to environmental variability demonstrating their ability to accommodate fluctuations in resource availability and distribution. However, despite the ability of little penguins to modify their foraging behaviour and diet, they displayed wide variations in their reproductive success. These results indicate that, even though this small-ranging resident seabird is highly adaptable to varying environmental conditions, local prey abundance determines their ability to survive and reproduce.

Foraging behaviour

The input of freshwater from the Yarra River had a strong influence on the foraging behaviours of little penguins. When water depth in the lower Yarra River was low, the maximum distance penguins travelled from their colony and distance to core-range were significantly shorter, and size of core- and home-ranges were significantly smaller compared to when water depth was high. Moreover, on days when water depth was low and when salinity was high, the above foraging characteristics were reduced and penguins were constrained to the northern regions of the bay. This suggests that in periods of low river discharge penguins remain in close proximity to river outlets presumably due to the high concentration of nutrients, productivity, and predictable concentration of prey within such areas (Grimes and Finucane 1991; Richards et al. 1989). Our results align with other studies that have reported the presence of foraging seabirds in close proximity to river plumes, as a means to access predictable resources (Certain et al. 2007; Skov and Prins 2001; Zamon et al. 2014). For example, in a year of poor prey abundance around the Phillip Island little penguin colony, Collins et al. (1999) found that penguins travelled long distances from their usual breeding foraging grounds to the south-western coast of Victoria, where they eventually clustered around five river outlets. The mechanisms by which seabirds locate these environmental features are not entirely understood, although a combination of memory effects and the presence of conspecific and/or different seabird species within these areas strongly influence observed foraging distribution in seabirds (Morales et al. 2010; Tremblay et al. 2014). Proximate factors enabling the detection of river plumes and plume fronts comprise multiple visual cues including changes in turbidity, and water flow patterns (Kingsford and Suthers 1994; Le Fèvre 1987). Ultimately, the presence of little penguins around river outlets in St Kilda and elsewhere provide evidence that these seabirds use rivers as environmental features to improve their foraging efficiency.

By contrast, in periods of high water inflow into the bay, we found that the maximum distance penguins travelled from the colony, distance to core-range, core-range, and home-range were greater compared to drier periods. We think that this shift in penguin distribution was in response to the increased dispersion of their prey following periods of heavy rainfall. Indeed, several studies have found that, during flood events or in periods of heavy rainfall, the size of river plumes increase, thereby dispersing nutrients and affecting the distribution of planktivores and their predators (Grimes and Kingsford 1996; Le Fèvre 1987). Within Port Phillip Bay, anchovies, the dominant prey species of St Kilda penguins (Preston 2010), displayed shifts in their distribution and abundance between years 2008 and 2011 (Hirst et al. 2011; Parry and Stokie 2008). In 2011, when rainfall was high, fish surveys recorded that the area inhabited by anchovies was 15 % larger than had been documented in 2008, a mostly dry year (Hirst et al. 2011; Parry and Stokie 2008). Additionally, the estimated total biomass of anchovy in the bay in 2011 was approximately four times greater (524 tonnes) than had been estimated in 2008 (159 tonnes) (Hirst et al. 2011; Parry and Stokie 2008). These findings suggest that anchovies had access to abundant resources and their population thrived when wet conditions returned, which potentially had flow on effects on the distribution of penguins.

Additionally, the increased foraging parameters in periods of high water inflow may have been representative of changes in the abundance and distribution of other clupeoid species that occurred between years 2008 and 2011. In 2011, pilchard, sandy sprat, and blue sprat, important prey types for little penguins at St Kilda and elsewhere (Chiaradia et al. 2010; Preston 2010), were significantly more abundant than in 2008 (Hirst et al. 2011; Parry and Stokie 2008). The combined biomass of these clupeids was approximately 170 times greater than observed in 2008 (Hirst et al. 2011; Parry and Stokie 2008). Some of these species, particularly pilchard, prefer saline, low turbid environments and may have migrated away from freshwater sources in periods of high water input, driving the distribution of penguins into central areas of the bay (Bakun 2014; Litz et al. 2014). Therefore, our results suggest that the composition, abundance and distribution of penguin prey modulate the strength of penguin associations with river outlets. This behaviour is in accordance with the “marginal value theorem”, which predicts that, as the overall productivity of a habitat increases, less time should be spent in a single patch, potentially leading to larger feeding areas for predators (Pyke et al. 1977).

Isotopic niche shifts

In 2008, when water depth in the Yarra River was predominantly low, and when the foraging activities of penguins occurred in close proximity to the Yarra River outlet, the isotopic niche width of penguins was narrow. Although a narrow isotopic niche is not necessarily indicative of narrow dietary diversity (i.e., different prey taxa can possess similar isotopic compositions; Martínez del Rio et al. 2009), the narrow isotopic niche in 2008 was likely in response to the consumption of small variety of prey. Stomach content analysis during the 2008 breeding season indicated the total wet mass of anchovies comprised 84 % of penguin diet (Preston 2010). Further, stable isotope mixing models confirmed anchovy dominated (mean dietary contribution: 61 %) the breeding diet of penguins in 2008 (Kowalczyk et al. 2015). These results suggest that anchovies are the dominant fish species interacting with freshwater plumes in the bay, in accordance with multiple studies that have found a strong association between anchovy distribution and freshwater sources (Bakun 2014; Litz et al. 2014). Considering the importance of anchovy to seabird diet worldwide (Brooke 2004), attempts to understand how their population biomass and distribution vary with river plume characteristics is a key to preserving the trophic links between this prey species and seabirds within their marine systems.

In 2011 and 2012, the isotopic niche of penguins was both wider and occupied a different position compared to penguins in 2008. These isotopic shifts suggest that penguins consumed a partially different subset of prey taxa and increased their dietary breadth. Given that little penguins are dietary generalists (Chiaradia et al. 2003), we propose the St Kilda colony diversified their diet in response to the increased abundance of pilchard, sandy sprat and blue sprat in Port Phillip Bay (Hirst et al. 2011; Parry and Stokie 2008). Stomach content data from years 2011–2012 are unavailable and therefore we cannot confirm an increase in dietary breadth. However, these prey species have been previously found in St Kilda penguin stomach contents (Preston 2010) and we would expect that penguins would opportunistically forage on these energetically rich species as they became locally abundant. Further, these species occupy a distinct isotopic position to anchovy (Kowalczyk et al. 2013), and their presence in penguin diet was likely responsible for the observed isotopic shifts between years 2008 and 2011–2012. Therefore, in 2011, penguins had access to both more abundant and more diverse prey compared to 2008 (Hirst et al. 2011; Parry and Stokie 2008) and appear to have improved their foraging efficiency by increasing their foraging range and dietary breadth. The observed foraging flexibility is critical to the survival and viability of little penguins as the species rely on a small foraging area throughout the year. Their access to resources during the breeding season shape their breeding events (e.g. lay date) and determines their breeding success (Kowalczyk et al. 2013). During the non-breeding season, penguins require access to local resources to successfully complete their annual moult and to survive the environmental constraints imposed by winter (Gales and Green 1990). Moreover, resources obtained during the non-breeding season can have carry-over effects that influence their subsequent breeding performance (Salton et al. 2015). As such, in order to survive and reproduce, little penguins need to be highly adaptable to changes in local prey conditions.

Reproductive success

Multiple studies have found that seabirds regulate the frequency and duration of feeding trips depending on environmental factors (Pelletier et al. 2014; Ropert-Coudert et al. 2004; Weimerskirch et al. 2003). Increases in total trip distance and/or maximum distance from the colony can be indicative of seabirds increasing their foraging effort to compensate for prey depletion close to the colony (Ballard et al. 2010; Elliott et al. 2009; Gaston et al. 2007). Under certain conditions, increased foraging effort has been found to have consequences for adult condition and chick survival (Ballard et al. 2010; Barrett and Erikstad 2013). Thus, we expected that short foraging trips would be associated with high reproductive success, while long foraging trips would be associated with low reproductive success(Chiaradia and Nisbet 2006). In contrast with our predictions, in 2008, when the maximum distance penguins travelled from their colony and distance to core-range were relatively short, and when the size of core- and home-ranges were comparatively small, the fledging success of penguins was relatively low. In 2011 and 2012, when penguins expanded their foraging range, potentially increasing their foraging effort, their egg success, nest success and fledgling success were significantly higher than observed in 2008. These results suggest that increased total trip distance and/or maximum distance from the colony are not necessarily indicative of penguins increasing their foraging effort to compensate for low prey availability close to the colony, as observed for the same species elsewhere (Chiaradia and Nisbet 2006). Rather, the observed increases in penguin foraging parameters indicate that birds were responding to environmentally induced shifts in the distribution and abundance of their prey. A number of studies have shown that seabirds can increase their rate of energy expenditure when resources are abundant (Jodice et al. 2006; Welcker et al. 2009). For example, female kittiwakes (Rissa tridactyla) increase their daily energy expenditure in periods of abundant prey, which has a positive effect on their reproductive success (Jodice et al. 2006). Moreover, the increased energetic costs associated with rearing multiple chicks in 2011 and 2012 (compared to a single chick in 2008) may have further forced penguins to increase their foraging effort (Chiaradia and Nisbet 2006). Interestingly, despite significant differences in fledging success between years 2008 and 2011–2012, we observed no difference in chick peak mass between these years. Our findings suggest that parents may favour rearing a chick of good condition over rearing multiple chicks of poor condition. This is expected, considering that peak and fledging body mass are critical determinants of first-year survival (Chiaradia and Nisbet 2006; Dann 1988; Sidhu et al. 2007), and by investing in a single healthy chick over two chicks of poor condition adults ultimately increase their fitness.

Conclusion

Our study demonstrates that the input of freshwater into the feeding zone of a seabird foraging in a large enclosed bay is an important physical feature that structures predator–prey interactions in this inshore ecosystem. By inhabiting a marine area within close proximity to a major river plume (Yarra River), penguins benefit from the high concentration of nutrients, productivity, and predictable concentration of prey within such areas (Zamon et al. 2014). Furthermore, using little penguins as a model species, we demonstrate the capacity for inshore resident seabirds to modify their foraging behaviours and diet in response to environmental variability. Such foraging flexibility is critical to the survival and viability of little penguins as the species rely on a small foraging area throughout the year (Preston et al. 2008). However, we found that, despite their ability to modify their foraging ranges and diet, they displayed high variation in their reproductive success, signalling that the continued monitoring of their foraging ecology is central to their management. Future directions in the study of the foraging ecology of little penguins and other coastal seabirds in relation to river outlets should address how the strength, size and spatial variability of river plumes influence productivity and the suitability of fronts for foraging. This is especially important given the anticipated effects of climate change where, in many regions, including south-eastern Australia, climate change scenarios predict decreases in rainfall and enhanced evaporation, which will have significant effects on salinity, productivity and ultimately on predator–prey interactions (Gillson et al. 2012; Lee et al. 2012).

Author contribution statement

N.D.K., R.D.R., A.C. conceived and designed the experiments. N.D.K. and T.J.P. performed the experiments. N.D.K. analysed the data. N.D.K. wrote the manuscript; all other authors provided editorial advice.

Notes

Acknowledgments

Parks Victoria granted permission to work along the breakwater. We thank Holsworth Wildlife Research Trust, Penguin Foundation, and Coastcare Australia for financial support. We acknowledge Earthcare St Kilda and Phillip Island Nature Parks for their in-kind support. We thank the Environment Protection Agency and Melbourne Water for environmental data. We thank all research volunteers especially Lisa Mandeltort for their tireless efforts in the field and to Christopher Johnstone and Bronwyn Isaac who provided statistical and mapping advice, respectively. Research was conducted under scientific permits issued by the Victorian Department of Sustainability and the Environment (10003848, 10005601), and approved by the Animal Ethics Committee of Monash University (BSCI/2010/22, BSCI/2011/33).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Nicole D. Kowalczyk
    • 1
  • Richard D. Reina
    • 1
  • Tiana J. Preston
    • 1
  • André Chiaradia
    • 1
    • 2
  1. 1.School of Biological SciencesMonash UniversityClaytonAustralia
  2. 2.Research DepartmentPhillip Island Nature ParksCowesAustralia

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