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Extremely low seasonal prey capture efficiency in a deep-diving whale, the narwhal

Cite this dataset

Chambault, Philippine; Blackwell, Susanna B.; Heide-Jørgensen, Mads Peter (2022). Extremely low seasonal prey capture efficiency in a deep-diving whale, the narwhal [Dataset]. Dryad.


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 (< 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.


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 (AcousondeTM) 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 mgcv 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 Gonatus fabricii (Garde et al 2022). Based on an average mantel length of 20 cm in Scoresby Sound (Garde, personal communication), the average mass of Gonatus fabricii 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. 


European Commission, Award: 48068

Danish Cooperation for the Environment in the Arctic

Carlsberg Foundation

Grønlands Naturinstitut