DOI: 10.5061/dryad.15dv41p1f
title: Extremely low seasonal prey capture efficiency in a deep-diving whale, the narwhal

description: Successful foraging is essential for individuals to maintain the positive
 energy balance required for survival and reproduction. Yet, prey capture
 efficiency is poorly documented in marine apex predators, especially
 deep-diving mammals. We deployed acoustic tags and stomach temperature
 pills in summer to collect concurrent information on presumed foraging
 activity (through buzz detection) and successful prey captures (through
 drops in stomach temperature), providing estimates of feeding efficiency
 in narwhals. Compared to the daily number of buzzes (706.9 ± 368), the
 daily rate of feeding events was particularly low in summer (19.8 ± 8.9),
 and only 8–14% of the foraging dives were successful (i.e., with a
 detectable prey capture). This extremely low success rate resulted in a
 very low daily food consumption rate (&lt; 0.5% of body mass),
 suggesting that narwhals rely on body reserves accumulated in winter to
 sustain year-round activities. The expected changes or disappearance of
 their wintering habitats in response to climate change may therefore have
 severe fitness consequences for narwhal populations.
description: Data
 collected Between summer 2012 and summer 2016,
 live-capture operations of narwhals were conducted in collaboration with
 Inuit hunters in Scoresby Sound fjord, South-eastern Greenland. Following
 Heide-Jørgensen et al.’s method (2014), 14 narwhals were instrumented with stomach
 temperature pills (STPs), while three of these individuals were
 simultaneously equipped with acoustic tags
 (Acousonde<sup>TM</sup>) as described in Blackwell et al.
 (2018). From the
 Acousondes, two variables from that analysis are of interest here: animal
 depth (every second), and the start time of terminal buzzes, which are
 believed to indicate prey capture attempts (i.e. foraging dives)
 (Blackwell et al 2018). Data analysis
 Estimation of prey capture
 success The buzz database used in this study is the one
 generated in Blackwell et al. (2018) and includes the depth every second, as well as
 the start time of each buzz for three females (Frida, Thora and Eistla).
 Statistical analyses were conducted on R software version 4.1.2. For the
 ST values derived from the satellite transmitter (low-resolution dataset,
 n=4 individuals), histograms of the depth at feeding start were generated
 for each whale individually. For the ST values obtained from the Acousonde
 (high-resolution dataset, n=3 individuals), the following metrics were
 derived for each dive: 
 Total number of
 buzzes, Depth at buzz (mean, standard deviation, and
 range), Total number of feeding events (ST
 drops), Depth at feeding start,
 Recovery time in the case of
 feeding event. A dive was defined whenever depth exceeded 2 m (after zero offset correction) and
 duration exceeded 30 sec. Dives were then classified as non-foraging dives
 (without buzzes) and foraging dives (with buzzes). Dives associated with a
 ST drop but no buzzes (assuming narwhals are able to catch some prey in
 shallower waters using visual cues) were discarded from the analysis (n=29
 dives) as they prevented the comparison between prey capture attempts
 (buzz with no ST drop) and successful prey captures (ST drop). Within
 foraging dives, dives were then classified as successful (buzz and ST
 drop) or unsuccessful (buzz but no ST drop). After instrumentation,
 narwhals remain silent for several hours without
 echolocating. To exclude
 this atypical behaviour, only the dives occurring after onset of
 echolocation and buzzing were retained in the
 analysis. Using the <em>mgcv</em> package, a
 Generalized Additive Model (GAM) was performed to relate the probability
 of a ST drop (feeding probability) to the number of buzzes per dive. The
 presence of a drop [1] (vs. absence of a drop [0]) was modelled as a
 binary response variable using a binomial distribution (link: logit). To
 account for the inter-individual variability, whale ID was added as a
 random factor on both the slope and the intercept. Individual response
 curves were then generated for each whale. To avoid overfitting and large
 confidence intervals due to extreme values, dives containing more than 40
 buzzes were removed from the modelling analysis.
 Correction of the daily
 feeding rate Due to different sampling rates between both
 tags (every 1–2 min for STP tags vs. every second for Acousondes), daily
 feeding rates were corrected. A correction factor was calculated based on
 the ratio between the total number of drops recorded by Acousondes and the
 one derived from the STP tags for the three whales equipped with both
 tags. The average correction factor estimated was then used to re-estimate
 the number of drops for the whales equipped with STP tags only. Ndrops
 corrected = Ndrops from STP tag x correction factor. The associated daily feeding rates were then
 re-estimated from the corrected number of drops and the entire tracking
 duration of the STP tags. Daily food consumption
 estimate Previous stomach content analyses showed that
 the diet of narwhals is mainly composed of the squid
 <em>Gonatus</em> <em>fabricii</em> (Garde et al
 2022). Based on an average
 mantel length of 20 cm in Scoresby Sound (Garde, personal communication),
 the average mass of <em>Gonatus</em>
 <em>fabricii</em> was estimated using Golikov et al’s
 equations (2018). Assuming an average mass of 150 g per prey item
 ingested, we then estimated the daily food consumption of each narwhal in
 summer: kg food per day = Ndrops x average prey mass.
 This daily food
 consumption was then expressed as a percentage of each whale’s body
 mass.