Fine-scale movement patterns and behavioral states of gray triggerfish Balistes capriscus determined from acoustic telemetry and hidden Markov models

Abstract Movement is a central feature of the ecology of fish, yet the study of fish movement has been inhibited due to its multidimensional nature and technological and analytical limitations. We used a relatively new fine-scale acoustic tracking system to quantify movements of an economically valuable, demersal marine fish species (gray triggerfish Balistes capriscus) on a natural hardbottom reef on the continental shelf of North Carolina, USA. Overall, 30 fish were tagged and released, and 104,170 highly precise (˜1–3 m) spatial positions were estimated during the 43-d study. To quantify gray triggerfish movements, we used a combination of exploratory data analyses and hidden Markov models (HMM), the latter of which can identify and elucidate normally hidden behavioral states. Both methods suggested gray triggerfish movements varied by diel period and among individuals, and that some of the variation among individuals could be explained by fish size. Depending on model specification, HMMs identified two or three behavioral states, one of which was likely resting that occurred mostly at night and another was likely foraging or transit that occurred mostly during the day. Moreover, resting at night occurred in small, discrete patches within the study area, whereas foraging or transit behaviors occurred broadly throughout the study area. We encourage a wider use of acoustic telemetry and HMMs to shed light on the normally hidden behaviors of demersal fishes.

[1]  Alastair Franke,et al.  Prediction of wolf (Canis lupus) kill-sites using hidden Markov models , 2006 .

[2]  E. Revilla,et al.  A movement ecology paradigm for unifying organismal movement research , 2008, Proceedings of the National Academy of Sciences.

[3]  Eliezer Gurarie,et al.  A novel method for identifying behavioural changes in animal movement data. , 2009, Ecology letters.

[4]  S. Szedlmayer,et al.  Movement patterns of gray triggerfish, Balistes capriscus, around artificial reefs in the northern Gulf of Mexico , 2016 .

[5]  Roland Langrock,et al.  Modelling group dynamic animal movement , 2013, 1308.5850.

[6]  J. F. Gilliam,et al.  Explaining Leptokurtic Movement Distributions: Intrapopulation Variation in Boldness and Exploration , 2001, The American Naturalist.

[7]  Toby A Patterson,et al.  Classifying movement behaviour in relation to environmental conditions using hidden Markov models. , 2009, The Journal of animal ecology.

[8]  Ronan Fablet,et al.  Coupling spectral analysis and hidden Markov models for the segmentation of behavioural patterns , 2017, Movement Ecology.

[9]  W. Zucchini,et al.  Hidden Markov Models for Time Series: An Introduction Using R , 2009 .

[10]  Y. Papastamatiou,et al.  Seasonal and diel movements of giant trevally Caranx ignobilis at remote Hawaiian atolls : implications for the design of Marine protected areas , 2007 .

[11]  Christopher G. Lowe,et al.  Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals , 2011 .

[12]  Nathan M. Bacheler,et al.  Tropical storms influence the movement behavior of a demersal oceanic fish species , 2019, Scientific Reports.

[13]  P. Rudershausen,et al.  Behavior of gray triggerfish Balistes capriscus around baited fish traps determined from fine-scale acoustic tracking , 2018, Marine Ecology Progress Series.

[14]  E. Hobson.  Diurnal-Nocturnal Activity of Some Inshore Fishes in the Gulf of California , 1965 .

[15]  P. Turchin.  Quantitative analysis of movement : measuring and modeling population redistribution in animals and plants , 1998 .

[16]  Andrew J. Read,et al.  Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales , 2017, Scientific Reports.

[17]  Nathan M. Bacheler,et al.  Movement of invasive adult lionfish Pterois volitans using telemetry: importance of controls to estimate and explain variable detection probabilities , 2015 .

[18]  David M. Kaplan,et al.  Consequences of adult and juvenile movement for marine protected areas , 2011 .

[19]  S. Szedlmayer,et al.  Fine‐Scale Movements and Home Ranges of Red Snapper around Artificial Reefs in the Northern Gulf of Mexico , 2014 .

[20]  Kevin C. Weng,et al.  Electronic tagging and population structure of Atlantic bluefin tuna , 2005, Nature.

[21]  Michael Li,et al.  Incorporating periodic variability in hidden Markov models for animal movement , 2017, Movement ecology.

[22]  Urška Demšar,et al.  Activity seascapes highlight central place foraging strategies in marine predators that never stop swimming , 2018, Movement ecology.

[23]  Brett T. McClintock,et al.  momentuHMM: R package for generalized hidden Markov models of animal movement , 2017, 1710.03786.

[24]  Nathan M. Bacheler,et al.  Spatial Distribution of Reef Fish Species along the Southeast US Atlantic Coast Inferred from Underwater Video Survey Data , 2016, PloS one.

[25]  Norman A. Slade,et al.  Relating Body Size to the Rate of Home Range Use in Mammals , 1988 .

[26]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[27]  C. Simpfendorfer,et al.  Improving conservation planning for an endangered sawfish using data from acoustic telemetry , 2010 .

[28]  D. Réale,et al.  Personalities influence spatial responses to environmental fluctuations in wild fish , 2018, The Journal of animal ecology.

[29]  R. Kays,et al.  Terrestrial animal tracking as an eye on life and planet , 2015, Science.

[30]  G. T. Kellison,et al.  No evidence of increased demersal fish abundance six years after creation of marine protected areas along the southeast United States Atlantic coast , 2016 .

[31]  M. Dance,et al.  Does transmitter placement or species affect detection efficiency of tagged animals in biotelemetry research , 2016 .

[32]  John Calambokidis,et al.  A multivariate mixed hidden Markov model for blue whale behaviour and responses to sound exposure , 2017 .

[33]  Roland Langrock,et al.  Analysis of animal accelerometer data using hidden Markov models , 2016, 1602.06466.

[34]  J. Olney,et al.  Ecological correlates of population density and behavior in the circumtropical black triggerfish Melichthys niger (Balistidae) , 2006, Environmental Biology of Fishes.

[35]  Kenneth H. Pollock,et al.  A combined telemetry – tag return approach to estimate fishing and natural mortality rates of an estuarine fish , 2009 .

[36]  Roland Langrock,et al.  Sex-specific and individual preferences for hunting strategies in white sharks , 2016 .

[37]  N. Furey,et al.  Fine-scale movements and habitat use of juvenile southern flounder Paralichthys lethostigma in an estuarine seascape. , 2013, Journal of fish biology.

[38]  Roland Langrock,et al.  moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models , 2016 .

[39]  Roland Langrock,et al.  Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. , 2012, Ecology.

[40]  Salvador Balle,et al.  Bayesian State-Space Modelling of Conventional Acoustic Tracking Provides Accurate Descriptors of Home Range Behavior in a Small-Bodied Coastal Fish Species , 2016, PloS one.

[41]  Toby A Patterson,et al.  Objective classification of latent behavioral states in bio-logging data using multivariate-normal hidden Markov models. , 2015, Ecological applications : a publication of the Ecological Society of America.

[42]  S. Szedlmayer,et al.  Depth preferences and three-dimensional movements of red snapper, Lutjanus campechanus, on an artificial reef in the northern Gulf of Mexico , 2017 .

[43]  D. M. Ware,et al.  Bioenergetics of Pelagic Fish: Theoretical Change in Swimming Speed and Ration with Body Size , 1978 .

[44]  J. F. Gilliam,et al.  MOVEMENT IN CORRIDORS: ENHANCEMENT BY PREDATION THREAT, DISTURBANCE, AND HABITAT STRUCTURE , 2001 .

[45]  Jennifer Pohle,et al.  Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement , 2017 .

[46]  Justin M. Calabrese,et al.  ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process , 2016 .

[47]  Gary C. White,et al.  Analysis of Wildlife Radio-Tracking Data , 1990 .

[48]  Eva B. Thorstad,et al.  The use of external electronic tags on fish: an evaluation of tag retention and tagging effects , 2015, Animal Biotelemetry.

[49]  Roland Langrock,et al.  Estimation and simulation of foraging trips in land-based marine predators. , 2016, Ecology.

[50]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[51]  Devin S Johnson,et al.  Continuous-time correlated random walk model for animal telemetry data. , 2008, Ecology.