Foraging dive frequency predicts body mass gain in the Adélie penguin

Study site and system

Data were collected at Cape Crozier (77°27′S, 169°12′E), Ross Island, one of the largest Adélie penguin breeding colonies (~ 275 000 pairs at the time of the study32), during austral summer 2018–2019. Individuals arrive at Cape Crozier in late October/early November, lay (usually two) eggs in mid-November, and feed their chicks between mid-December and early February. They are one of the few penguin species that can fledge two chicks. During the brood/guard stage, one parent remains with the chick(s) while the other forages at sea. Nest reliefs at Crozier occur every 1–2 days during early chick-rearing and chicks are fed relatively small meals (0.43–0.58 kg) by the attending parent33. After about two weeks, chick demands are too great for adequate provisioning by one parent, so chicks are left on their own (“crèche” stage) while both parents forage simultaneously. Our study period included most of chick-rearing, i.e., all of the guard stage and half the crèche stage, from December 21, 2018 to January 15, 2019.

Since 1997, every austral summer, the same subcolony of ~ 200 pairs (152 pairs in the year of study) was surrounded by a plastic fence, leaving only one opening as an access point, where the weighbridge was located30. The weighbridge consisted of an electronic scale, direction indicator, and radio frequency identification (RFID) reader34,35. In 2018–2019, it was installed on November 16 and removed on January 20. A subset of adult individuals were implanted with unique RFID tags beginning in 1997, with a few more added each year30,36. RFID code, date and time, direction, and weight were recorded automatically as the RFID-implanted birds crossed the weighbridge. Adults were captured on the nest during incubation, when they can be approached slowly and gently lifted off their nest. A warm hat was placed over the eggs or small chicks to avoid chilling, while the RFID tag was injected into the bird.

All penguin survey, capture and handling methods used for data collection were performed following all relevant guidelines and regulations under the approval and oversight of the Institutional Animal Care and Use Committees of Oregon State University and Point Blue Conservation Science. Additionally, all work was approved and conducted under Antarctic Conservation Act permits issued by the US National Science Foundation and the U.S. Antarctic Program. The study is reported in accordance with ARRIVE guidelines.

Diving parameters

Between November 2 and December 7, 2018, we equipped 32 RFID-implanted birds with geolocating dive recorders (“LUL” tags, 22 × 21 × 15 mm, weight = 4 g, from Atesys, Strasbourg, France, hereafter referred to as GDRs) that recorded light every minute, temperature (with a precision of ± 0.5 °C) every 30 s and pressure (with a precision of ± 0.3 m) every second for 12–15 months. Adults were captured using a hand net (2 m long handle) or on the nest during incubation (see above). The GDRs were encapsulated in flexible heat-shrink tubing shaped into a leg strap and attached to the tibio-fibula of each bird in the field using a polyester-coated stainless-steel zip tie to secure the ends of the strap together such that the tag could rotate freely around the leg but not slip over the tarsus joint. Tags were left in place for one year, with 21 recovered at the beginning of the 2019–2020 breeding season. Pressure data were processed in R (v. 3.6.0) with several processes modified from the diveMove package (v. 1.4.5)37. To correct for instrument drift, pressure data were zero offset corrected using the calibrateDepth function38. We used a depth threshold of 3 m to qualify as a dive. Following methods described in previous studies27,39,40, we computed a number of statistics about each dive including dive duration, maximum dive depth, post-dive interval duration, bottom time, the number of undulations (changes of any amplitude in underwater swimming duration from either ascent to descent, or descent to ascent—used for the purposes of categorizing dives) and the number of undulations > 1 m (changes in underwater swimming direction from ascent to descent > 1m39). The two undulation metrics are highly correlated (Pearson’s r = 0.92 in our data set). Bottom time was defined as the time spent at > 60% of maximum depth of dive with < 0.5 m/s change in depth. Based on these statistics, dives were classified into ‘foraging’, ‘exploratory’ and ‘other’39 (Fig. 1). Foraging and exploratory dives both were at least 10 m. To be classified as a foraging dive, a dive needed to have one of the following set of characteristics: (a) bottom time of at least 20 s, max depth of at least 15 m and more than 4 undulations, (b) bottom time of at least 15 s, max depth of at least 10 m, total dive time of at least 30 s, more than 4 undulations, at least 30% of the dive duration spent in slow depth change rate and 30% with fast depth change rate, (c) bottom time of at least 15 s, max depth of at least 10 m, total dive time of at least 30 s, more than 6 undulations and very fast (at least 1 m s−1) ascent/descent phases. To be classified as an exploratory dive, a dive needed to have one of the following set of characteristics: (d) max depth of at least 15 m and either less than 20 s of bottom time or less than 4 undulations, (e) max depth of at least 10 m, less than 15 s of bottom time, less than 6 undulations and fast (at least 0.8 m s−1) ascent/descent phases. All other dives were categorized as ‘other’ and are thought to be primarily shallow commuting dives41.

Figure 1

Adélie penguin dives were classified into ‘foraging’, ‘exploratory’ and ‘other’39. (a) foraging dives were at least 10 m deep, had significant bottom time and many undulations (cases a–c in the methods); (b) exploratory dives were at least 10 m deep but had relatively little bottom time and/or few undulations (cases d, e in the methods); (c) other dives, thought to be primarily commuting dives, were shallower than 10 m and/or not matching the requirements for foraging and exploratory dives.

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Foraging trip estimation and duration

A foraging trip was defined as the duration elapsed between the exit over the WB of a RFID-implanted bird (after having first been recorded as entering the subcolony) and its subsequent recrossing upon return to the subcolony (Fig. 2). In order to avoid including non-foraging trips (i.e. for nest maintenance purposes), we excluded trips that were shorter than 6 hrs36. To minimize the occurrence of resting periods while outside of the subcolony, we selected foraging trips performed between December 21, 2018 and January 15, 2019, while the birds were actively provisioning chicks. To further reduce the influence of digestion on body mass changes over the trip42, and after the visual examination of the distribution of trip durations, we also excluded trips that were > 60 h (trip duration during chick-rearing takes 1–2 days on average36,39 but their frequency distribution showed a tail from 60 to 100 h in our data).

Figure 2

Conceptual visualization of the study design. (a) chick-rearing Adélie penguins breeding in a semi-enclosed subcolony are implanted with a RFID tag and equipped with a leg-mounted time-depth recorder (GDR). (b) Bird ID, departure mass and direction of travel are recorded by the weighbridge as penguins leave the colony to forage at sea. (c) During the foraging trip, the GDR tag records depth every second, enabling the calculation of several dive behavior metrics. (d) Bird ID, return mass and direction of travel are recorded by the weighbridge as penguins return to the colony to feed their chicks.

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Body mass estimation

For each foraging trip, we calculated meal size and body mass change (see Supplementary Information for more details on the weight calculation). Meal size (in kg) is the difference between an individual’s out-mass (departing) and its most recent in-mass (returning from sea), i.e. this is a measure of how much food a parent left in the colony and includes both the food delivered to chicks and the food digested by the parent while attending the nest39. Body mass change (in kg) of individual birds over each foraging trip was calculated as the return mass (post-foraging trip at sea) minus the departure mass (pre-foraging trip at sea). Hence, body mass change measures the amount of food that was collected during the trip at sea (i.e. foraging success43), minus what could have been digested before returning to the colony at the end of this trip (Fig. 2). We further filtered trips based on these two variables, keeping only trips where meal size was > 0 and < 1.3 kg33 and body mass change was > − 0.8 and < 1.6 kg (confirmed to be in the range of mass changes directly measured in the field using Pesola scales44). Our final data set included 25 foraging trips and associated body mass changes (i.e. 2.3 trips per bird, ranging 1–4 trips per bird) from 11 GDR-equipped birds (including 11 trips from 6 females and 14 trips from 5 males). Birds were sexed by DNA from a feather sample (n = 6), or lacking that, by a combination of size, behavior, and timing of colony attendance45,46,47 (n = 5). Data collected during a previous study (from 2010 to 2013, n = 140) showed that we were able to assign the correct sex to 97.14% of the birds using this latter method when compared with DNA data on the same birds48.

Foraging success indexes

Based on the estimated dive parameters and on existing literature, we selected the following behavioral variables as potential indexes of food intake (all per hour metrics computed as the total over the entire foraging trip divided by the trip duration in hours): (1) number of undulations > 1 m per hour, as previous work indicated that undulations in the dive profile represent feeding and/or prey capture16,24,25, (2) dive (underwater) time per hour, (3) dive time per hour during foraging dives only, (4) bottom time per hour, (5) number of foraging dives per hour, (6) Attempts of Catch per Unit Effort (ACPUE, calculated as the number of undulations per trip divided by total bottom duration23,49). We also considered two variables calculated at the scale of dive bouts: (7) mean bout duration, thought to reflect the time spent within a prey patch50,51, (8) number of dives per bout, as an index of the size of the prey patch51,52,53. Dive bouts were defined as successive diving events interrupted by relatively longer surfacing periods. To separate post-dive intervals from inter-bout duration, we used a maximum likelihood approach54 using the diveMove package37 in R, which allowed us to determine a bout-ending-criterion (BEC). In this study, BEC = 47.6 s.

Statistical analyses

We first calculated a Pearson correlation matrix using the corrplot package in R and removed highly correlated (r > 0.7) behavioral covariates, keeping those that were the most correlated with body mass change. To test the hypothesis that some behavioral dive variables can be used to predict the amount of food collected while foraging at sea, we evaluated linear mixed models including body mass change as the dependent variable, each of the selected behavioral variables as independent variables and bird ID as a random effect, as well as a null model (intercept only) using the nlme package55 in R. Once we had determined the most competitive models, and as Adélie penguin’s foraging success can vary according to sex29,36 and chick needs39, and also be influenced by the trip duration56, we added sex, study day (day in the season as a Julian date with Dec 20 = 0) and trip duration (in hours) to the top intrinsic model(s) including potential interactions with the selected behavioral variable(s). A null model was also included in this second model set. Residuals were examined to verify normality, homogeneity of variances, and independence. To evaluate these models and determine the strength of evidence supporting specific effects, we used an information theoretic approach57. Models were ranked using the small-sample-size corrected version of Akaike Information Criterion (AICc), with the best model having the lowest AICc value. We calculated ΔAICc as the difference in AICc between each candidate model and the model with the lowest AICc value, and considered all models within 2 ΔAICc as competitive models57. We determined the strength of evidence supporting specific effects by examining the unstandardized effect sizes (slope coefficients and differences in means) and the associated 95% confidence intervals (CI). If the 95% CI for a parameter in a competitive model (ΔAICc < 2.0) included zero, it was considered uninformative. Models were fitted using maximum likelihood (ML) estimation during model selection (in order to choose the best fixed effect structure), then the best model was fitted using restricted ML (REML) estimation to obtain accurate parameter estimates58.

To test for the effect of GDRs on trip duration and mass change, we used all available WB records for the season (including RFID-implanted birds with and without GDRs) filtered on dates, breeding status, trip duration and weights as detailed above (resulting in a data set of 122 trips, ranging 1–13 trips per individual). We then fitted linear mixed models using REML with either trip duration or mass change as the dependent variable, the presence/absence of GDR, sex and study day as independent variables and individual as a random effect.

All statistics from this section were performed using R 4.0.3. Means ± SE are given unless indicated otherwise.

Source: Ecology -

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