Leave or stay? Video-logger revealed foraging efficiency of humpback whales under temporal change in prey density

Central place foraging theory (CPF) has been used to predict the optimal patch residence time for air-breathing marine predators in response to patch quality. Humpback whales (Megaptera novaeangliae) forage on densely aggregated prey, which may induce drastic change in prey density in a single feeding event. Thus, the decision whether to leave or stay after each feeding event in a single dive in response to this drastic change, should have a significant effect on prey exploitation efficiency. However, whether humpback whales show adaptive behavior in response to the diminishing prey density in a single dive has been technically difficult to test. Here, we studied the foraging behavior of humpback whales in response to change in prey density in a single dive and calculated the efficiency of each foraging dive using a model based on CPF approach. Using animal-borne accelerometers and video loggers attached to whales, foraging behavior and change in relative prey density in front of the whales were successfully quantified. Results showed diminishing rate of energy intake in consecutive feeding events, and humpback whales efficiently fed by bringing the rate of energy intake close to maximum in a single dive cycle. This video-based method also enabled us to detect the presence of other animals around the tagged whales, showing an interesting trend in behavioral changes where feeding duration was shorter when other animals were present. Our results have introduced a new potential to quantitatively investigate the effect of other animals on free-ranging top predators in the context of optimal foraging theory.

[1]  J. Krebs,et al.  Foraging Theory , 2019 .

[2]  A. Friedlaender,et al.  Kinematic Diversity in Rorqual Whale Feeding Mechanisms , 2016, Current Biology.

[3]  E. Hazen,et al.  Prey-mediated behavioral responses of feeding blue whales in controlled sound exposure experiments. , 2016, Ecological applications : a publication of the Ecological Society of America.

[4]  R. B. Tyson,et al.  Does optimal foraging theory predict the foraging performance of a large air-breathing marine predator? , 2016, Animal Behaviour.

[5]  M. Hindell,et al.  Testing optimal foraging theory models on benthic divers , 2016, Animal Behaviour.

[6]  R. B. Tyson,et al.  Multiple-stage decisions in a marine central-place forager , 2016, Royal Society Open Science.

[7]  E. Hazen,et al.  Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density , 2015, Science Advances.

[8]  Katsufumi Sato,et al.  Estimation of metabolic rate from activity measured by recorders deployed on Japanese sea bass Lateolabrax japonicus , 2015, Fisheries Science.

[9]  E. Hazen,et al.  Prey density and distribution drive the three‐dimensional foraging strategies of the largest filter feeder , 2015 .

[10]  T. Akamatsu,et al.  Acoustically invisible feeding blue whales in Northern Icelandic waters. , 2014, The Journal of the Acoustical Society of America.

[11]  Akinori Takahashi,et al.  Testing optimal foraging theory in a penguin–krill system , 2014, Proceedings of the Royal Society B: Biological Sciences.

[12]  Akinori Takahashi,et al.  Linking animal-borne video to accelerometers reveals prey capture variability , 2013, Proceedings of the National Academy of Sciences.

[13]  Peter T. Madsen,et al.  Keeping momentum with a mouthful of water: behavior and kinematics of humpback whale lunge feeding , 2012, Journal of Experimental Biology.

[14]  Yasuhiko Naito,et al.  Using a mandible accelerometer to study fine-scale foraging behavior of free-ranging Antarctic fur seals , 2012 .

[15]  Colin Ware,et al.  Shallow and deep lunge feeding of humpback whales in fjords of the West Antarctic Peninsula , 2011 .

[16]  V. Lesage,et al.  Optimal foraging theory predicts diving and feeding strategies of the largest marine predator , 2011 .

[17]  E. Hazen,et al.  Super-Aggregations of Krill and Humpback Whales in Wilhelmina Bay, Antarctic Peninsula , 2011, PloS one.

[18]  R. Shadwick,et al.  Scaling of lunge feeding in rorqual whales: an integrated model of engulfment duration. , 2010, Journal of theoretical biology.

[19]  Nobuaki Arai,et al.  Data-processing artefacts in three-dimensional dive path reconstruction from geomagnetic and acceleration data , 2010 .

[20]  Colin Ware,et al.  Fine-scale prey aggregations and foraging ecology of humpback whales Megaptera novaeangliae , 2009 .

[21]  S. Wanless,et al.  Can Ethograms Be Automatically Generated Using Body Acceleration Data from Free-Ranging Birds? , 2009, PloS one.

[22]  John Calambokidis,et al.  Foraging behavior of humpback whales: kinematic and respiratory patterns suggest a high cost for a lunge , 2008, Journal of Experimental Biology.

[23]  N. Lecomte.  Foraging: Behaviour and Ecology , 2008 .

[24]  A. Brierley,et al.  Regional variation in distribution pattern, population structure and growth rates of Meganyctiphanes norvegica and Thysanoessa longicaudata in the Irminger Sea, North Atlantic , 2007 .

[25]  Jeremy P. Winn,et al.  Morphological specializations of baleen whales associated with hydrodynamic performance and ecological niche , 2006, Journal of morphology.

[26]  John Calambokidis,et al.  Kinematics of foraging dives and lunge-feeding in fin whales , 2006, Journal of Experimental Biology.

[27]  Ian L. Boyd,et al.  THE BEHAVIORAL BASIS FOR NONLINEAR FUNCTIONAL RESPONSES AND OPTIMAL FORAGING IN ANTARCTIC FUR SEALS , 2004 .

[28]  Yasuhiko Naito,et al.  Factors affecting stroking patterns and body angle in diving Weddell seals under natural conditions , 2003, Journal of Experimental Biology.

[29]  Yasuhiko Naito,et al.  Dive depths of Weddell seals in relation to vertical prey distribution as estimated by image data , 2003 .

[30]  Peter L. Tyack,et al.  A digital acoustic recording tag for measuring the response of wild marine mammals to sound , 2003 .

[31]  Y. Watanuki,et al.  An application of optimal diving models to diving behaviour of Brünnich's guillemots , 2002, Animal Behaviour.

[32]  Y. Naito,et al.  Swimming speeds and buoyancy compensation of migrating adult chum salmon Oncorhynchus keta revealed by speed/depth/acceleration data logger. , 2001, The Journal of experimental biology.

[33]  M. Fedak,et al.  How long should a dive last? A simple model of foraging decisions by breath-hold divers in a patchy environment , 2001, Animal Behaviour.

[34]  J. Sumich,et al.  Marine Mammals: Evolutionary Biology , 1999 .

[35]  Alasdair I. Houston,et al.  The optimal allocation of time during the diving cycle , 1992 .

[36]  A. Houston,et al.  Optimal foraging and learning , 1985 .

[37]  J. Emlen.  The Role of Time and Energy in Food Preference , 1966, The American Naturalist.

[38]  R. Macarthur,et al.  On Optimal Use of a Patchy Environment , 1966, The American Naturalist.

[39]  P. Ponganis,et al.  The physiological basis of diving to depth: birds and mammals. , 1998, Annual review of physiology.