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Eisaguirre JM, Booms TL, Barger CP, Lewis SB, Breed GA. Novel step selection analyses on energy landscapes reveal how linear features alter migrations of soaring birds. J Anim Ecol 2020; 89:2567-2583. [PMID: 32926415 DOI: 10.1111/1365-2656.13335] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 08/07/2020] [Indexed: 11/27/2022]
Abstract
Human modification of landscapes includes extensive addition of linear features, such as roads and transmission lines. These can alter animal movement and space use and affect the intensity of interactions among species, including predation and competition. Effects of linear features on animal movement have seen relatively little research in avian systems, despite ample evidence of their effects in mammalian systems and that some types of linear features, including both roads and transmission lines, are substantial sources of mortality. Here, we used satellite telemetry combined with step selection functions designed to explicitly incorporate the energy landscape (el-SSFs) to investigate the effects of linear features and habitat on movements and space use of a large soaring bird, the golden eagle Aquila chrysaetos, during migration. Our sample consisted of 32 adult eagles tracked for 45 spring and 39 fall migrations from 2014 to 2017. Fitted el-SSFs indicated eagles had a strong general preference for south-facing slopes, where thermal uplift develops predictably, and that these areas are likely important aspects of migratory pathways. el-SSFs also provided evidence that roads and railroads affected movement during both spring and fall migrations, but eagles selected areas near roads to a greater degree in spring compared to fall and at higher latitudes compared to lower latitudes. During spring, time spent near linear features often occurred during slower-paced or stopover movements, perhaps in part to access carrion produced by vehicle collisions. Regardless of the behavioural mechanism of selection, use of these features could expose eagles and other soaring species to elevated risk via collision with vehicles and/or transmission lines. Linear features have previously been documented to affect the ecology of terrestrial species (e.g. large mammals) by modifying individuals' movement patterns; our work shows that these effects on movement extend to avian taxa.
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Affiliation(s)
- Joseph M Eisaguirre
- Department of Biology & Wildlife, University of Alaska Fairbanks, Fairbanks, AK, USA.,Department of Mathematics & Statistics, University of Alaska Fairbanks, Fairbanks, AK, USA
| | | | | | | | - Greg A Breed
- Department of Biology & Wildlife, University of Alaska Fairbanks, Fairbanks, AK, USA.,Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
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53
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Chimienti M, Blasi MF, Hochscheid S. Movement patterns of large juvenile loggerhead turtles in the Mediterranean Sea: Ontogenetic space use in a small ocean basin. Ecol Evol 2020; 10:6978-6992. [PMID: 32760506 PMCID: PMC7391346 DOI: 10.1002/ece3.6370] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 01/07/2023] Open
Abstract
Mechanisms that determine how, where, and when ontogenetic habitat shifts occur are mostly unknown in wild populations. Differences in size and environmental characteristics of ontogenetic habitats can lead to differences in movement patterns, behavior, habitat use, and spatial distributions across individuals of the same species. Knowledge of juvenile loggerhead turtles' dispersal, movements, and habitat use is largely unknown, especially in the Mediterranean Sea. Satellite relay data loggers were used to monitor movements, diving behavior, and water temperature of eleven large juvenile loggerhead turtles (Caretta caretta) deliberately caught in an oceanic habitat in the Mediterranean Sea. Hidden Markov models were used over 4,430 spatial locations to quantify the different activities performed by each individual: transit, low-, and high-intensity diving. Model results were then analyzed in relation to water temperature, bathymetry, and distance to the coast. The hidden Markov model differentiated between bouts of area-restricted search as low- and high-intensity diving, and transit movements. The turtles foraged in deep oceanic waters within 60 km from the coast as well as above 140 km from the coast. They used an average area of 194,802 km2, where most individuals used the deepest part of the Southern Tyrrhenian Sea with the highest seamounts, while only two switched to neritic foraging showing plasticity in foraging strategies among turtles of similar age classes. The foraging distribution of large juvenile loggerhead turtles, including some which were of the minimum size of adults, in the Tyrrhenian Sea is mainly concentrated in a relatively small oceanic area with predictable mesoscale oceanographic features, despite the proximity of suitable neritic foraging habitats. Our study highlights the importance of collecting high-resolution data about species distribution and behavior across different spatio-temporal scales and life stages for implementing conservation and dynamic ocean management actions.
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Affiliation(s)
- Marianna Chimienti
- Department of Bioscience - Arctic Ecosystem EcologyAarhus UniversityRoskildeDenmark
| | - Monica F. Blasi
- Filicudi WildLife ConservationStimpagnato FilicudiLipariItaliaItaly
| | - Sandra Hochscheid
- Stazione Zoologica Anton DohrnMarine Turtle Research CenterPorticiItaly
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54
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Clay TA, Joo R, Weimerskirch H, Phillips RA, den Ouden O, Basille M, Clusella-Trullas S, Assink JD, Patrick SC. Sex-specific effects of wind on the flight decisions of a sexually dimorphic soaring bird. J Anim Ecol 2020; 89:1811-1823. [PMID: 32557603 DOI: 10.1111/1365-2656.13267] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Accepted: 04/07/2020] [Indexed: 11/30/2022]
Abstract
In a highly dynamic airspace, flying animals are predicted to adjust foraging behaviour to variable wind conditions to minimize movement costs. Sexual size dimorphism is widespread in wild animal populations, and for large soaring birds which rely on favourable winds for energy-efficient flight, differences in morphology, wing loading and associated flight capabilities may lead males and females to respond differently to wind. However, the interaction between wind and sex has not been comprehensively tested. We investigated, in a large sexually dimorphic seabird which predominantly uses dynamic soaring flight, whether flight decisions are modulated to variation in winds over extended foraging trips, and whether males and females differ. Using GPS loggers we tracked 385 incubation foraging trips of wandering albatrosses Diomedea exulans, for which males are c. 20% larger than females, from two major populations (Crozet and South Georgia). Hidden Markov models were used to characterize behavioural states-directed flight, area-restricted search (ARS) and resting-and model the probability of transitioning between states in response to wind speed and relative direction, and sex. Wind speed and relative direction were important predictors of state transitioning. Birds were much more likely to take off (i.e. switch from rest to flight) in stronger headwinds, and as wind speeds increased, to be in directed flight rather than ARS. Males from Crozet but not South Georgia experienced stronger winds than females, and males from both populations were more likely to take-off in windier conditions. Albatrosses appear to deploy an energy-saving strategy by modulating taking-off, their most energetically expensive behaviour, to favourable wind conditions. The behaviour of males, which have higher wing loading requiring faster speeds for gliding flight, was influenced to a greater degree by wind than females. As such, our results indicate that variation in flight performance drives sex differences in time-activity budgets and may lead the sexes to exploit regions with different wind regimes.
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Affiliation(s)
- Thomas A Clay
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Rocío Joo
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, Davie, FL, USA
| | - Henri Weimerskirch
- Centre d'Étude Biologique de Chizé, CNRS UMR 7273, Villiers-en-Bois, France
| | - Richard A Phillips
- British Antarctic Survey, Natural Environment Research Council, Cambridge, UK
| | - Olivier den Ouden
- R&D Seismology and Acoustics, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands.,Faculty of Civil Engineering and Geosciences, Department of Geoscience and Engineering, Delft University of Technology, Delft, The Netherlands
| | - Mathieu Basille
- Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, Davie, FL, USA
| | - Susana Clusella-Trullas
- Department of Botany and Zoology and Centre for Invasion Biology, Stellenbosch University, Stellenbosch, South Africa
| | - Jelle D Assink
- R&D Seismology and Acoustics, Royal Netherlands Meteorological Institute, De Bilt, The Netherlands
| | - Samantha C Patrick
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
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55
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Schafer TLJ, Wikle CK, VonBank JA, Ballard BM, Weegman MD. A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2020. [DOI: 10.1007/s13253-020-00399-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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56
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Beumer LT, Pohle J, Schmidt NM, Chimienti M, Desforges JP, Hansen LH, Langrock R, Pedersen SH, Stelvig M, van Beest FM. An application of upscaled optimal foraging theory using hidden Markov modelling: year-round behavioural variation in a large arctic herbivore. MOVEMENT ECOLOGY 2020; 8:25. [PMID: 32518653 PMCID: PMC7275509 DOI: 10.1186/s40462-020-00213-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/18/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND In highly seasonal environments, animals face critical decisions regarding time allocation, diet optimisation, and habitat use. In the Arctic, the short summers are crucial for replenishing body reserves, while low food availability and increased energetic demands characterise the long winters (9-10 months). Under such extreme seasonal variability, even small deviations from optimal time allocation can markedly impact individuals' condition, reproductive success and survival. We investigated which environmental conditions influenced daily, seasonal, and interannual variation in time allocation in high-arctic muskoxen (Ovibos moschatus) and evaluated whether results support qualitative predictions derived from upscaled optimal foraging theory. METHODS Using hidden Markov models (HMMs), we inferred behavioural states (foraging, resting, relocating) from hourly positions of GPS-collared females tracked in northeast Greenland (28 muskox-years). To relate behavioural variation to environmental conditions, we considered a wide range of spatially and/or temporally explicit covariates in the HMMs. RESULTS While we found little interannual variation, daily and seasonal time allocation varied markedly. Scheduling of daily activities was distinct throughout the year except for the period of continuous daylight. During summer, muskoxen spent about 69% of time foraging and 19% resting, without environmental constraints on foraging activity. During winter, time spent foraging decreased to 45%, whereas about 43% of time was spent resting, mediated by longer resting bouts than during summer. CONCLUSIONS Our results clearly indicate that female muskoxen follow an energy intake maximisation strategy during the arctic summer. During winter, our results were not easily reconcilable with just one dominant foraging strategy. The overall reduction in activity likely reflects higher time requirements for rumination in response to the reduction of forage quality (supporting an energy intake maximisation strategy). However, deep snow and low temperatures were apparent constraints to winter foraging, hence also suggesting attempts to conserve energy (net energy maximisation strategy). Our approach provides new insights into the year-round behavioural strategies of the largest Arctic herbivore and outlines a practical example of how to approximate qualitative predictions of upscaled optimal foraging theory using multi-year GPS tracking data.
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Affiliation(s)
- Larissa T. Beumer
- Department of Bioscience, Aarhus University, 4000 Roskilde, Denmark
- Arctic Research Centre, Aarhus University, 8000 Aarhus, Denmark
| | - Jennifer Pohle
- Department of Business Administration and Economics, Bielefeld University, 33615 Bielefeld, Germany
| | - Niels M. Schmidt
- Department of Bioscience, Aarhus University, 4000 Roskilde, Denmark
- Arctic Research Centre, Aarhus University, 8000 Aarhus, Denmark
| | | | - Jean-Pierre Desforges
- Department of Bioscience, Aarhus University, 4000 Roskilde, Denmark
- Arctic Research Centre, Aarhus University, 8000 Aarhus, Denmark
- Natural Resource Sciences, McGill University, Ste Anne de Bellevue, Quebec, H9X 3V9 Canada
| | - Lars H. Hansen
- Department of Bioscience, Aarhus University, 4000 Roskilde, Denmark
- Arctic Research Centre, Aarhus University, 8000 Aarhus, Denmark
| | - Roland Langrock
- Department of Business Administration and Economics, Bielefeld University, 33615 Bielefeld, Germany
| | - Stine Højlund Pedersen
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523 USA
- Department of Biological Sciences, University of Alaska Anchorage, Anchorage, AK 99508 USA
| | | | - Floris M. van Beest
- Department of Bioscience, Aarhus University, 4000 Roskilde, Denmark
- Arctic Research Centre, Aarhus University, 8000 Aarhus, Denmark
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57
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Arakawa T. Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods. Animals (Basel) 2020; 10:ani10050771. [PMID: 32365596 PMCID: PMC7278493 DOI: 10.3390/ani10050771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 04/16/2020] [Accepted: 04/27/2020] [Indexed: 11/16/2022] Open
Abstract
Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.
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Affiliation(s)
- Toshiya Arakawa
- Department of Mechanical Systems Engineering, Aichi University of Technology, Gamagori-shi, Aichi 443-0047, Japan
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58
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Vogel SM, Lambert B, Songhurst AC, McCulloch GP, Stronza AL, Coulson T. Exploring movement decisions: Can Bayesian movement-state models explain crop consumption behaviour in elephants (Loxodonta africana)? J Anim Ecol 2020; 89:1055-1068. [PMID: 31960413 DOI: 10.1111/1365-2656.13177] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/19/2019] [Indexed: 11/29/2022]
Abstract
Animal movements towards goals or targets are based upon either maximization of resource acquisition or risk avoidance, and the way animals move can reveal information about their motivation. We use hidden Markov models (HMMs) fitted in a Bayesian framework and hourly Global Positioning System fixes to distinguish animal movements into distinct states and analyse the influence of environmental variables on being in, and switching to, a particular state. Specifically, we apply our models to understand elephant movement decisions around agricultural fields, and crop consumption. As it is unclear what the role of habitat features are on this complex process, we analyse whether elephants target agricultural crops for consumption, or simply pass through them in search of water. Our HMMs separate elephant movements into two states: exploratory movements that are fast and directional, and encamped movements that are slow and meandering. For each elephant, we ran 16 models with each possible combination of selected habitat features (river, elephant corridor, agricultural field, trees), and repeated these analyses including interaction effects with both season and time of day. We used cross-validation to select the best model. In corridors, exploratory movements are dominant. Elephants mainly showed encamped movements at the river during the dry season, when temporary water sources have dried out and elephants relied on this permanent water source. In fields, males most often exhibited exploratory movements to and from the river, while females showed an increase in the frequency of encamped behaviour during the dry season and at night-the times when most crop consumption and movements through fields occur. Adaptation to risk could explain this behaviour, since foraging in fields is likely less risky under the cover of darkness and during the dry season when farmers are absent. This sex segregation in elephant movement decisions highlights the importance of predation risk in shaping movement patterns, which can result in sex segregation in responses to mitigation methods. The increase in encamped movements in the dry season suggests the importance of agricultural timing, and shows the potential for early ploughing and early-harvest crop types in order to reduce elephant crop consumption. Taking this into account could increase efficiency of elephant crop consumption mitigation.
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Affiliation(s)
- Susanne Marieke Vogel
- Department of Zoology Research and Administration Building, University of Oxford, Oxford, UK.,Ecoexist Trust, Maun, Botswana.,Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Bioscience, Aarhus University, Aarhus C, Denmark.,Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus C, Denmark
| | - Ben Lambert
- Department of Zoology Research and Administration Building, University of Oxford, Oxford, UK.,Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Anna Catherine Songhurst
- Department of Zoology Research and Administration Building, University of Oxford, Oxford, UK.,Ecoexist Trust, Maun, Botswana.,Agriculture and Life Sciences, Texas A&M, College Station, TX, USA
| | - Graham Paul McCulloch
- Department of Zoology Research and Administration Building, University of Oxford, Oxford, UK.,Ecoexist Trust, Maun, Botswana.,Agriculture and Life Sciences, Texas A&M, College Station, TX, USA
| | - Amanda Lee Stronza
- Ecoexist Trust, Maun, Botswana.,Agriculture and Life Sciences, Texas A&M, College Station, TX, USA
| | - Tim Coulson
- Department of Zoology Research and Administration Building, University of Oxford, Oxford, UK
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59
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Farhadinia MS, Michelot T, Johnson PJ, Hunter LTB, Macdonald DW. Understanding decision making in a food-caching predator using hidden Markov models. MOVEMENT ECOLOGY 2020; 8:9. [PMID: 32071720 PMCID: PMC7011357 DOI: 10.1186/s40462-020-0195-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 01/30/2020] [Indexed: 06/01/2023]
Abstract
BACKGROUND Tackling behavioural questions often requires identifying points in space and time where animals make decisions and linking these to environmental variables. State-space modeling is useful for analysing movement trajectories, particularly with hidden Markov models (HMM). Yet importantly, the ontogeny of underlying (unobservable) behavioural states revealed by the HMMs has rarely been verified in the field. METHODS Using hidden Markov models of individual movement from animal location, biotelemetry, and environmental data, we explored multistate behaviour and the effect of associated intrinsic and extrinsic drivers across life stages. We also decomposed the activity budgets of different movement states at two general and caching phases. The latter - defined as the period following a kill which likely involves the caching of uneaten prey - was subsequently confirmed by field inspections. We applied this method to GPS relocation data of a caching predator, Persian leopard Panthera pardus saxicolor in northeastern Iran. RESULTS Multistate modeling provided strong evidence for an effect of life stage on the behavioural states and their associated time budget. Although environmental covariates (ambient temperature and diel period) and ecological outcomes (predation) affected behavioural states in non-resident leopards, the response in resident leopards was not clear, except that temporal patterns were consistent with a crepuscular and nocturnal movement pattern. Resident leopards adopt an energetically more costly mobile behaviour for most of their time while non-residents shift their behavioural states from high energetic expenditure states to energetically less costly encamped behaviour for most of their time, which is likely to be a risk avoidance strategy against conspecifics or humans. CONCLUSIONS This study demonstrates that plasticity in predator behaviour depending on life stage may tackle a trade-off between successful predation and avoiding the risks associated with conspecifics, human presence and maintaining home range. Range residency in territorial predators is energetically demanding and can outweigh the predator's response to intrinsic and extrinsic variables such as thermoregulation or foraging needs. Our approach provides an insight into spatial behavior and decision making of leopards, and other large felids in rugged landscapes through the application of the HMMs in movement ecology.
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Affiliation(s)
- Mohammad S. Farhadinia
- Oxford Martin School and Department of Zoology, University of Oxford, 34 Broad St, Oxford, OX1 3BD UK
| | - Théo Michelot
- School of Mathematics and Statistics, University of St Andrews, The Observatory, Buchanan Gardens, St Andrews, KY169LZ UK
| | - Paul J. Johnson
- Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney House, Oxford, Oxfordshire OX13 5QL UK
| | - Luke T. B. Hunter
- Wildlife Conservation Society, Bronx, NY 10460 USA
- School of Life Sciences, Westville Campus, University of KwaZulu-Natal, Durban, South Africa
| | - David W. Macdonald
- Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney House, Oxford, Oxfordshire OX13 5QL UK
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60
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Joseph MB. Neural hierarchical models of ecological populations. Ecol Lett 2020; 23:734-747. [PMID: 31970895 DOI: 10.1111/ele.13462] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 10/17/2019] [Accepted: 12/23/2019] [Indexed: 01/20/2023]
Abstract
Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This article describes a class of hierarchical models parameterised by neural networks - neural hierarchical models. The derivation of such models analogises the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non-detection time series for hundreds of species, providing insights into colonisation and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modelling that combines the function representation power of neural networks with the inferential capacity of hierarchical models.
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Affiliation(s)
- Maxwell B Joseph
- Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, 80303, USA
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61
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Mastrantonio G, Grazian C, Mancinelli S, Bibbona E. New formulation of the logistic-Gaussian process to analyze trajectory tracking data. Ann Appl Stat 2019. [DOI: 10.1214/19-aoas1289] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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62
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Williams HJ, Taylor LA, Benhamou S, Bijleveld AI, Clay TA, de Grissac S, Demšar U, English HM, Franconi N, Gómez-Laich A, Griffiths RC, Kay WP, Morales JM, Potts JR, Rogerson KF, Rutz C, Spelt A, Trevail AM, Wilson RP, Börger L. Optimizing the use of biologgers for movement ecology research. J Anim Ecol 2019; 89:186-206. [PMID: 31424571 DOI: 10.1111/1365-2656.13094] [Citation(s) in RCA: 99] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Accepted: 08/08/2019] [Indexed: 10/26/2022]
Abstract
The paradigm-changing opportunities of biologging sensors for ecological research, especially movement ecology, are vast, but the crucial questions of how best to match the most appropriate sensors and sensor combinations to specific biological questions and how to analyse complex biologging data, are mostly ignored. Here, we fill this gap by reviewing how to optimize the use of biologging techniques to answer questions in movement ecology and synthesize this into an Integrated Biologging Framework (IBF). We highlight that multisensor approaches are a new frontier in biologging, while identifying current limitations and avenues for future development in sensor technology. We focus on the importance of efficient data exploration, and more advanced multidimensional visualization methods, combined with appropriate archiving and sharing approaches, to tackle the big data issues presented by biologging. We also discuss the challenges and opportunities in matching the peculiarities of specific sensor data to the statistical models used, highlighting at the same time the large advances which will be required in the latter to properly analyse biologging data. Taking advantage of the biologging revolution will require a large improvement in the theoretical and mathematical foundations of movement ecology, to include the rich set of high-frequency multivariate data, which greatly expand the fundamentally limited and coarse data that could be collected using location-only technology such as GPS. Equally important will be the establishment of multidisciplinary collaborations to catalyse the opportunities offered by current and future biologging technology. If this is achieved, clear potential exists for developing a vastly improved mechanistic understanding of animal movements and their roles in ecological processes and for building realistic predictive models.
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Affiliation(s)
- Hannah J Williams
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Lucy A Taylor
- Save the Elephants, Nairobi, Kenya.,Department of Zoology, University of Oxford, Oxford, UK
| | - Simon Benhamou
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS Montpellier, Montpellier, France
| | - Allert I Bijleveld
- NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Utrecht University, Den Burg, The Netherlands
| | - Thomas A Clay
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Sophie de Grissac
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Urška Demšar
- School of Geography & Sustainable Development, University of St Andrews, St Andrews, UK
| | - Holly M English
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Novella Franconi
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Agustina Gómez-Laich
- Instituto de Biología de Organismos Marinos (IBIOMAR), CONICET, Puerto Madryn, Chubut, Argentina
| | - Rachael C Griffiths
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - William P Kay
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Juan Manuel Morales
- Grupo de Ecología Cuantitativa, INIBIOMA-Universidad Nacional del Comahue, CONICET, Bariloche, Argentina
| | - Jonathan R Potts
- School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
| | | | - Christian Rutz
- Centre for Biological Diversity, School of Biology, University of St Andrews, St Andrews, UK
| | - Anouk Spelt
- Department of Aerospace Engineering, University of Bristol, University Walk, UK
| | - Alice M Trevail
- School of Environmental Sciences, University of Liverpool, Liverpool, UK
| | - Rory P Wilson
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
| | - Luca Börger
- Department of Biosciences, College of Science, Swansea University, Swansea, UK
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63
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Eisaguirre JM, Auger-Méthé M, Barger CP, Lewis SB, Booms TL, Breed GA. Dynamic-Parameter Movement Models Reveal Drivers of Migratory Pace in a Soaring Bird. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00317] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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64
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Péron G. The time frame of home-range studies: from function to utilization. Biol Rev Camb Philos Soc 2019; 94:1974-1982. [PMID: 31347250 DOI: 10.1111/brv.12545] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 06/18/2019] [Accepted: 06/21/2019] [Indexed: 12/23/2022]
Abstract
As technological and statistical innovations open new avenues in movement ecology, I review the fundamental implications of the time frame of home-range studies, with the aim of associating terminologies consistently with research objectives and methodologies. There is a fundamental distinction between (a) extrapolations of stationary distributions, associated with long time scales and aiming at asymptotic consistency, and (b) period-specific techniques, aiming at specificity but typically sensitive to the sampling design. I then review the difference between function and utilization in home-range studies. Most home-range studies are based on phenomenological descriptions of the time budgets of the study animals, not the function of the visited areas. I highlight emerging trends in automated pattern-recognition techniques for inference about function rather than utilization.
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Affiliation(s)
- Guillaume Péron
- University of Lyon, Laboratoire de Biométrie et Biologie Evolutive, CNRS, Université Lyon 1, UMR5558, F-69622, Villeurbanne, France
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Adam T, Griffiths CA, Leos‐Barajas V, Meese EN, Lowe CG, Blackwell PG, Righton D, Langrock R. Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13241] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Timo Adam
- Bielefeld University Bielefeld Germany
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66
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Campera M, Balestri M, Chimienti M, Nijman V, Nekaris KAI, Donati G. Temporal niche separation between the two ecologically similar nocturnal primates Avahi meridionalis and Lepilemur fleuretae. Behav Ecol Sociobiol 2019. [DOI: 10.1007/s00265-019-2664-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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67
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van Beest FM, Mews S, Elkenkamp S, Schuhmann P, Tsolak D, Wobbe T, Bartolino V, Bastardie F, Dietz R, von Dorrien C, Galatius A, Karlsson O, McConnell B, Nabe-Nielsen J, Olsen MT, Teilmann J, Langrock R. Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity - a multivariate hidden Markov model. Sci Rep 2019; 9:5642. [PMID: 30948786 PMCID: PMC6449369 DOI: 10.1038/s41598-019-42109-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 03/25/2019] [Indexed: 02/06/2023] Open
Abstract
Classifying movement behaviour of marine predators in relation to anthropogenic activity and environmental conditions is important to guide marine conservation. We studied the relationship between grey seal (Halichoerus grypus) behaviour and environmental variability in the southwestern Baltic Sea where seal-fishery conflicts are increasing. We used multiple environmental covariates and proximity to active fishing nets within a multivariate hidden Markov model (HMM) to quantify changes in movement behaviour of grey seals while at sea. Dive depth, dive duration, surface duration, horizontal displacement, and turning angle were used to identify travelling, resting and foraging states. The likelihood of seals foraging increased in deeper, colder, more saline waters, which are sites with increased primary productivity and possibly prey densities. Proximity to active fishing net also had a pronounced effect on state occupancy. The probability of seals foraging was highest <5 km from active fishing nets (51%) and decreased as distance to nets increased. However, seals used sites <5 km from active fishing nets only 3% of their time at sea highlighting an important temporal dimension in seal-fishery interactions. By coupling high-resolution oceanographic, fisheries, and grey seal movement data, our study provides a scientific basis for designing management strategies that satisfy ecological and socioeconomic demands on marine ecosystems.
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Affiliation(s)
- Floris M van Beest
- Marine Mammal Research, Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000, Roskilde, Denmark.
| | - Sina Mews
- Department of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Svenja Elkenkamp
- Department of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Patrick Schuhmann
- Department of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Dorian Tsolak
- Department of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Till Wobbe
- Department of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Valerio Bartolino
- Department of Aquatic Resources, Swedish University of Agricultural Sciences, Lysekil, SE-45321, Sweden
| | - Francois Bastardie
- National Institute for Aquatic Resources, Technical University of Denmark, Kemitorvet, Kgs. Lyngby, DK-2800, Denmark
| | - Rune Dietz
- Marine Mammal Research, Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000, Roskilde, Denmark
| | - Christian von Dorrien
- Thünen Institute of Baltic Sea Fisheries, Alter Hafen Süd 2, D-18069, Rostock, Germany
| | - Anders Galatius
- Marine Mammal Research, Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000, Roskilde, Denmark
| | - Olle Karlsson
- Department of Environmental Research and Monitoring, Swedish Museum of Natural History, Box 50007, SE-104 05, Stockholm, Sweden
| | - Bernie McConnell
- Sea Mammal Research Unit, University of St Andrews, St Andrews, KY16 8LB, United Kingdom
| | - Jacob Nabe-Nielsen
- Marine Mammal Research, Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000, Roskilde, Denmark
| | - Morten Tange Olsen
- Evolutionary Genomics Section, Natural History Museum of Denmark, Department of Biology, University of Copenhagen, Øster Voldgade 5-7, DK-1350, Copenhagen K, Denmark
| | - Jonas Teilmann
- Marine Mammal Research, Department of Bioscience, Aarhus University, Frederiksborgvej 399, DK-4000, Roskilde, Denmark
| | - Roland Langrock
- Department of Business Administration and Economics, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
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Affiliation(s)
- Théo Michelot
- School of Mathematics and StatisticsUniversity of Sheffield Sheffield UK
| | - Paul G. Blackwell
- School of Mathematics and StatisticsUniversity of Sheffield Sheffield UK
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Jiménez López ME, Palacios DM, Jaramillo Legorreta A, Urbán R. J, Mate BR. Fin whale movements in the Gulf of California, Mexico, from satellite telemetry. PLoS One 2019; 14:e0209324. [PMID: 30629597 PMCID: PMC6328206 DOI: 10.1371/journal.pone.0209324] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 12/04/2018] [Indexed: 11/18/2022] Open
Abstract
Fin whales (Balaenoptera physalus) have a global distribution, but the population inhabiting the Gulf of California (GoC) is thought to be geographically and genetically isolated. However, their distribution and movements are poorly known. The goal of this study was to describe fin whale movements for the first time from 11 Argos satellite tags deployed in the southwest GoC in March 2001. A Bayesian Switching State-Space Model was applied to obtain improved locations and to characterize movement behavior as either "area-restricted searching" (indicative of patch residence, ARS) or "transiting" (indicative of moving between patches). Model performance was assessed with convergence diagnostics and by examining the distribution of the deviance and the behavioral parameters from Markov Chain Monte Carlo models. ARS was the predominant mode behavior 83% of the time during both the cool (December-May) and warm seasons (June-November), with slower travel speeds (mean = 0.84 km/h) than during transiting mode (mean = 3.38 km/h). We suggest ARS mode indicates either foraging activities (year around) or reproductive activities during the winter (cool season). We tagged during the cool season, when the whales were located in the Loreto-La Paz Corridor in the southwestern GoC, close to the shoreline. As the season progressed, individuals moved northward to the Midriff Islands and the upper gulf for the warm season, much farther from shore. One tag lasted long enough to document a whale's return to Loreto the following cool season. One whale that was originally of undetermined sex, was tagged in the Bay of La Paz and was photographed 10 years later with a calf in the nearby San Jose Channel, suggesting seasonal site fidelity. The tagged whales moved along the western GoC to the upper gulf seasonally and did not transit to the eastern GoC south of the Midriff Islands. No tagged whales left the GoC, providing supporting evidence that these fin whales are a resident population.
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Affiliation(s)
- M. Esther Jiménez López
- Programa de Investigación de Mamíferos Marinos. Departamento Académico de Ciencias Marinas y Costeras, Universidad Autónoma de Baja California Sur, La Paz, Baja California Sur, México, Mezquitito, La Paz, México
| | - Daniel M. Palacios
- Marine Mammal Institute and Department of Fisheries and Wildlife, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America
| | | | - Jorge Urbán R.
- Programa de Investigación de Mamíferos Marinos. Departamento Académico de Ciencias Marinas y Costeras, Universidad Autónoma de Baja California Sur, La Paz, Baja California Sur, México, Mezquitito, La Paz, México
| | - Bruce R. Mate
- Marine Mammal Institute and Department of Fisheries and Wildlife, Oregon State University, Hatfield Marine Science Center, Newport, Oregon, United States of America
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Karelus DL, McCown JW, Scheick BK, van de Kerk M, Bolker BM, Oli MK. Incorporating movement patterns to discern habitat selection: black bears as a case study. WILDLIFE RESEARCH 2019. [DOI: 10.1071/wr17151] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Context Animals’ use of space and habitat selection emerges from their movement patterns, which are, in turn, determined by their behavioural or physiological states and extrinsic factors. Aim The aims of the present study were to investigate animal movement and incorporate the movement patterns into habitat selection analyses using Global Positioning System (GPS) location data from 16 black bears (Ursus americanus) in a fragmented area of Florida, USA. Methods Hidden Markov models (HMMs) were used to discern the movement patterns of the bears. These results were then used in step-selection functions (SSFs) to evaluate habitat selection patterns and the factors influencing these patterns. Key results HMMs revealed that black bear movement patterns are best described by three behavioural states: (1) resting (very short step-lengths and large turning angles); (2) encamped (moderate step-lengths and large turning angles); and (3) exploratory (long step-lengths and small turning angles). Bears selected for forested wetlands and marsh wetlands more than any other land cover type, and generally avoided urban areas in all seasons and when in encamped and exploratory behavioural states. Bears also chose to move to locations farther away from major roads. Conclusions Because habitat selection is influenced by how animals move within landscapes, it is essential to consider animals’ movement patterns when making inferences about habitat selection. The present study achieves this goal by using HMMs to first discern black bear movement patterns and associated parameters, and by using these results in SSFs to investigate habitat selection patterns. Thus, the methodological framework developed in this study effectively incorporates state-specific movement patterns while making inferences regarding habitat selection. The unified methodological approach employed here will contribute to an improved understanding of animal ecology as well as informed management decisions. Implications Conservation plans focused on preserving forested wetlands would benefit bears by not only providing habitat for resting and foraging, but also by providing connectivity through fragmented landscapes. Additionally, the framework could be applied to species that follow annual cycles and may provide a tool for investigating how animals are using dispersal corridors.
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Griffiths CA, Patterson TA, Blanchard JL, Righton DA, Wright SR, Pitchford JW, Blackwell PG. Scaling marine fish movement behavior from individuals to populations. Ecol Evol 2018; 8:7031-7043. [PMID: 30073065 PMCID: PMC6065275 DOI: 10.1002/ece3.4223] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 02/13/2018] [Accepted: 03/29/2018] [Indexed: 11/25/2022] Open
Abstract
Understanding how, where, and when animals move is a central problem in marine ecology and conservation. Key to improving our knowledge about what drives animal movement is the rising deployment of telemetry devices on a range of free-roaming species. An increasingly popular way of gaining meaningful inference from an animal's recorded movements is the application of hidden Markov models (HMMs), which allow for the identification of latent behavioral states in the movement paths of individuals. However, the use of HMMs to explore the population-level consequences of movement is often limited by model complexity and insufficient sample sizes. Here, we introduce an alternative approach to current practices and provide evidence of how the inclusion of prior information in model structure can simplify the application of HMMs to multiple animal movement paths with two clear benefits: (a) consistent state allocation and (b) increases in effective sample size. To demonstrate the utility of our approach, we apply HMMs and adapted HMMs to over 100 multivariate movement paths consisting of conditionally dependent daily horizontal and vertical movements in two species of demersal fish: Atlantic cod (Gadus morhua; n = 46) and European plaice (Pleuronectes platessa; n = 61). We identify latent states corresponding to two main underlying behaviors: resident and migrating. As our analysis considers a relatively large sample size and states are allocated consistently, we use collective model output to investigate state-dependent spatiotemporal trends at the individual and population levels. In particular, we show how both species shift their movement behaviors on a seasonal basis and demonstrate population space use patterns that are consistent with previous individual-level studies. Tagging studies are increasingly being used to inform stock assessment models, spatial management strategies, and monitoring of marine fish populations. Our approach provides a promising way of adding value to tagging studies because inferences about movement behavior can be gained from a larger proportion of datasets, making tagging studies more relevant to management and more cost-effective.
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Affiliation(s)
- Christopher A. Griffiths
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUK
- Institute for Marine and Antarctic StudiesUniversity of TasmaniaHobartTASAustralia
- Centre for EnvironmentFisheries and Aquaculture ScienceLowestoft LaboratoryLowestoftUK
| | | | - Julia L. Blanchard
- Institute for Marine and Antarctic StudiesUniversity of TasmaniaHobartTASAustralia
| | - David A. Righton
- Centre for EnvironmentFisheries and Aquaculture ScienceLowestoft LaboratoryLowestoftUK
| | - Serena R. Wright
- Centre for EnvironmentFisheries and Aquaculture ScienceLowestoft LaboratoryLowestoftUK
| | | | - Paul G. Blackwell
- School of Mathematics and StatisticsUniversity of SheffieldSheffieldUK
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Martorell-Barceló M, Campos-Candela A, Alós J. Fitness consequences of fish circadian behavioural variation in exploited marine environments. PeerJ 2018; 6:e4814. [PMID: 29796349 PMCID: PMC5961624 DOI: 10.7717/peerj.4814] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/27/2018] [Indexed: 12/23/2022] Open
Abstract
The selective properties of fishing that influence behavioural traits have recently gained interest. Recent acoustic tracking experiments have revealed between-individual differences in the circadian behavioural traits of marine free-living fish; these differences are consistent across time and ecological contexts and generate different chronotypes. Here, we hypothesised that the directional selection resulting from fishing influences the wild circadian behavioural variation and affects differently to individuals in the same population differing in certain traits such as awakening time or rest onset time. We developed a spatially explicit social-ecological individual-based model (IBM) to test this hypothesis. The parametrisation of our IBM was fully based on empirical data; which represent a fishery formed by patchily distributed diurnal resident fish that are exploited by a fleet of mobile boats (mostly bottom fisheries). We ran our IBM with and without the observed circadian behavioural variation and estimated selection gradients as a quantitative measure of trait change. Our simulations revealed significant and strong selection gradients against early-riser chronotypes when compared with other behavioural and life-history traits. Significant selection gradients were consistent across a wide range of fishing effort scenarios. Our theoretical findings enhance our understanding of the selective properties of fishing by bridging the gaps among three traditionally separated fields: fisheries science, behavioural ecology and chronobiology. We derive some general predictions from our theoretical findings and outline a list of empirical research needs that are required to further understand the causes and consequences of circadian behavioural variation in marine fish.
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Affiliation(s)
| | - Andrea Campos-Candela
- Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), Esporles, Spain.,Universidad de Alicante, Alicante, Spain
| | - Josep Alós
- Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-UIB), Esporles, Spain
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Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2017. [DOI: 10.1007/s13253-017-0299-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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74
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Variance estimation for integrated population models. ASTA-ADVANCES IN STATISTICAL ANALYSIS 2017. [DOI: 10.1007/s10182-017-0304-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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