1
|
Yu JH, Napoli JL, Lovett-Barron M. Understanding collective behavior through neurobiology. Curr Opin Neurobiol 2024; 86:102866. [PMID: 38852986 PMCID: PMC11439442 DOI: 10.1016/j.conb.2024.102866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 02/16/2024] [Accepted: 03/07/2024] [Indexed: 06/11/2024]
Abstract
A variety of organisms exhibit collective movement, including schooling fish and flocking birds, where coordinated behavior emerges from the interactions between group members. Despite the prevalence of collective movement in nature, little is known about the neural mechanisms producing each individual's behavior within the group. Here we discuss how a neurobiological approach can enrich our understanding of collective behavior by determining the mechanisms by which individuals interact. We provide examples of sensory systems for social communication during collective movement, highlight recent discoveries about neural systems for detecting the position and actions of social partners, and discuss opportunities for future research. Understanding the neurobiology of collective behavior can provide insight into how nervous systems function in a dynamic social world.
Collapse
Affiliation(s)
- Jo-Hsien Yu
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA. https://twitter.com/anitajhyu
| | - Julia L Napoli
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA. https://twitter.com/juliadoingneuro
| | - Matthew Lovett-Barron
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, 92093, USA.
| |
Collapse
|
2
|
Sanders NJ, Salguero-Gómez R, Evans DM, Gaillard JM, Lancaster LT. Journal of Animal Ecology in 2023: Looking back and looking forward. J Anim Ecol 2024; 93:370-372. [PMID: 38566325 DOI: 10.1111/1365-2656.14061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 04/04/2024]
Affiliation(s)
- Nathan J Sanders
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Darren M Evans
- School of Natural and Environmental Sciences, Newcastle University, Newcastle-upon-Tyne, UK
| | - Jean-Michel Gaillard
- Laboratoire de Biométrie et Biologie Evolutive, Université de Lyon, Université Lyon 1, CNRS, Villeurbanne, France
| | | |
Collapse
|
3
|
Farine DR. Modelling animal social networks: New solutions and future directions. J Anim Ecol 2024; 93:250-253. [PMID: 38234253 DOI: 10.1111/1365-2656.14049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 01/19/2024]
Abstract
Research Highlight: Ross, C. T., McElreath, R., & Redhead, D. (2023). Modelling animal network data in R using STRAND. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.14021. One of the most important insights in ecology over the past decade has been that the social connections among animals affect a wide range of ecological and evolutionary processes. However, despite over 20 years of study effort on this topic, generating knowledge from data on social associations and interactions remains fraught with problems. Redhead et al. present an R package-STRAND-that extends the current animal social network analysis toolbox in two ways. First, they provide a simple R interfaces to implement generative network models, which are an alternative to regression approaches that draw inference by simulating the data-generating process. Second, they implement these models in a Bayesian framework, allowing uncertainty in the observation process to be carried through to hypothesis testing. STRAND therefore fills an important gap for hypothesis testing using network data. However, major challenges remain, and while STRAND represents an important advance, generating robust results continues to require careful study design, considerations in terms of statistical methods and a plurality of approaches.
Collapse
Affiliation(s)
- Damien R Farine
- Division of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
- Department of Evolutionary Biology and Environmental Science, University of Zurich, Zurich, Switzerland
- Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany
| |
Collapse
|
4
|
Ding SS, Fox JL, Gordus A, Joshi A, Liao JC, Scholz M. Fantastic beasts and how to study them: rethinking experimental animal behavior. J Exp Biol 2024; 227:jeb247003. [PMID: 38372042 PMCID: PMC10911175 DOI: 10.1242/jeb.247003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.
Collapse
Affiliation(s)
- Siyu Serena Ding
- Max Planck Institute of Animal Behavior, 78464 Konstanz, Germany
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany
| | - Jessica L. Fox
- Department of Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Andrew Gordus
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Abhilasha Joshi
- Departments of Physiology and Psychiatry, University of California, San Francisco, CA 94158, USA
| | - James C. Liao
- Department of Biology, The Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL 32080, USA
| | - Monika Scholz
- Max Planck Research Group Neural Information Flow, Max Planck Institute for Neurobiology of Behavior – caesar, 53175 Bonn, Germany
| |
Collapse
|
5
|
English HM, Börger L, Kane A, Ciuti S. Advances in biologging can identify nuanced energetic costs and gains in predators. MOVEMENT ECOLOGY 2024; 12:7. [PMID: 38254232 PMCID: PMC10802026 DOI: 10.1186/s40462-024-00448-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024]
Abstract
Foraging is a key driver of animal movement patterns, with specific challenges for predators which must search for mobile prey. These patterns are increasingly impacted by global changes, principally in land use and climate. Understanding the degree of flexibility in predator foraging and social strategies is pertinent to wildlife conservation under global change, including potential top-down effects on wider ecosystems. Here we propose key future research directions to better understand foraging strategies and social flexibility in predators. In particular, rapid continued advances in biologging technology are helping to record and understand dynamic behavioural and movement responses of animals to environmental changes, and their energetic consequences. Data collection can be optimised by calibrating behavioural interpretation methods in captive settings and strategic tagging decisions within and between social groups. Importantly, many species' social systems are increasingly being found to be more flexible than originally described in the literature, which may be more readily detectable through biologging approaches than behavioural observation. Integrating the effects of the physical landscape and biotic interactions will be key to explaining and predicting animal movements and energetic balance in a changing world.
Collapse
Affiliation(s)
- Holly M English
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland.
| | - Luca Börger
- Department of Biosciences, Swansea University, Swansea, UK
| | - Adam Kane
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland
| | - Simone Ciuti
- School of Biology and Environmental Science, University College Dublin, Belfield, Dublin, Ireland
| |
Collapse
|
6
|
Plum F, Bulla R, Beck HK, Imirzian N, Labonte D. replicAnt: a pipeline for generating annotated images of animals in complex environments using Unreal Engine. Nat Commun 2023; 14:7195. [PMID: 37938222 PMCID: PMC10632501 DOI: 10.1038/s41467-023-42898-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 10/24/2023] [Indexed: 11/09/2023] Open
Abstract
Deep learning-based computer vision methods are transforming animal behavioural research. Transfer learning has enabled work in non-model species, but still requires hand-annotation of example footage, and is only performant in well-defined conditions. To help overcome these limitations, we developed replicAnt, a configurable pipeline implemented in Unreal Engine 5 and Python, designed to generate large and variable training datasets on consumer-grade hardware. replicAnt places 3D animal models into complex, procedurally generated environments, from which automatically annotated images can be exported. We demonstrate that synthetic data generated with replicAnt can significantly reduce the hand-annotation required to achieve benchmark performance in common applications such as animal detection, tracking, pose-estimation, and semantic segmentation. We also show that it increases the subject-specificity and domain-invariance of the trained networks, thereby conferring robustness. In some applications, replicAnt may even remove the need for hand-annotation altogether. It thus represents a significant step towards porting deep learning-based computer vision tools to the field.
Collapse
Affiliation(s)
- Fabian Plum
- Department of Bioengineering, Imperial College London, London, UK.
| | | | - Hendrik K Beck
- Department of Bioengineering, Imperial College London, London, UK
| | - Natalie Imirzian
- Department of Bioengineering, Imperial College London, London, UK
| | - David Labonte
- Department of Bioengineering, Imperial College London, London, UK.
| |
Collapse
|
7
|
Brickson L, Zhang L, Vollrath F, Douglas-Hamilton I, Titus AJ. Elephants and algorithms: a review of the current and future role of AI in elephant monitoring. J R Soc Interface 2023; 20:20230367. [PMID: 37963556 PMCID: PMC10645515 DOI: 10.1098/rsif.2023.0367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/23/2023] [Indexed: 11/16/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.
Collapse
Affiliation(s)
| | | | - Fritz Vollrath
- Save the Elephants, Nairobi, Kenya
- Department of Biology, University of Oxford, Oxford, UK
| | | | - Alexander J. Titus
- Colossal Biosciences, Dallas, TX, USA
- Information Sciences Institute, University of Southern California, Los Angeles, USA
| |
Collapse
|
8
|
Ko H, Lauder G, Nagpal R. The role of hydrodynamics in collective motions of fish schools and bioinspired underwater robots. J R Soc Interface 2023; 20:20230357. [PMID: 37876271 PMCID: PMC10598440 DOI: 10.1098/rsif.2023.0357] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/02/2023] [Indexed: 10/26/2023] Open
Abstract
Collective behaviour defines the lives of many animal species on the Earth. Underwater swarms span several orders of magnitude in size, from coral larvae and krill to tunas and dolphins. Agent-based algorithms have modelled collective movements of animal groups by use of social forces, which approximate the behaviour of individual animals. But details of how swarming individuals interact with the fluid environment are often under-examined. How do fluid forces shape aquatic swarms? How do fish use their flow-sensing capabilities to coordinate with their schooling mates? We propose viewing underwater collective behaviour from the framework of fluid stigmergy, which considers both physical interactions and information transfer in fluid environments. Understanding the role of hydrodynamics in aquatic collectives requires multi-disciplinary efforts across fluid mechanics, biology and biomimetic robotics. To facilitate future collaborations, we synthesize key studies in these fields.
Collapse
Affiliation(s)
- Hungtang Ko
- Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA
| | - George Lauder
- Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
| | - Radhika Nagpal
- Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA
- Computer Science, Princeton University, Princeton, NJ, USA
| |
Collapse
|
9
|
Hansen MJ, Domenici P, Bartashevich P, Burns A, Krause J. Mechanisms of group-hunting in vertebrates. Biol Rev Camb Philos Soc 2023; 98:1687-1711. [PMID: 37199232 DOI: 10.1111/brv.12973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/19/2023]
Abstract
Group-hunting is ubiquitous across animal taxa and has received considerable attention in the context of its functions. By contrast much less is known about the mechanisms by which grouping predators hunt their prey. This is primarily due to a lack of experimental manipulation alongside logistical difficulties quantifying the behaviour of multiple predators at high spatiotemporal resolution as they search, select, and capture wild prey. However, the use of new remote-sensing technologies and a broadening of the focal taxa beyond apex predators provides researchers with a great opportunity to discern accurately how multiple predators hunt together and not just whether doing so provides hunters with a per capita benefit. We incorporate many ideas from collective behaviour and locomotion throughout this review to make testable predictions for future researchers and pay particular attention to the role that computer simulation can play in a feedback loop with empirical data collection. Our review of the literature showed that the breadth of predator:prey size ratios among the taxa that can be considered to hunt as a group is very large (<100 to >102 ). We therefore synthesised the literature with respect to these predator:prey ratios and found that they promoted different hunting mechanisms. Additionally, these different hunting mechanisms are also related to particular stages of the hunt (search, selection, capture) and thus we structure our review in accordance with these two factors (stage of the hunt and predator:prey size ratio). We identify several novel group-hunting mechanisms which are largely untested, particularly under field conditions, and we also highlight a range of potential study organisms that are amenable to experimental testing of these mechanisms in connection with tracking technology. We believe that a combination of new hypotheses, study systems and methodological approaches should help push the field of group-hunting in new directions.
Collapse
Affiliation(s)
- Matthew J Hansen
- Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, 12587, Germany
| | - Paolo Domenici
- IBF-CNR, Consiglio Nazionale delle Ricerche, Area di Ricerca San Cataldo, Via G. Moruzzi No. 1, Pisa, 56124, Italy
- IAS-CNR, Località Sa Mardini, Torregrande, Oristano, 09170, Italy
| | - Palina Bartashevich
- Faculty of Life Science, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin, 10115, Germany
- Cluster of Excellence "Science of Intelligence," Technical University of Berlin, Marchstr. 23, Berlin, 10587, Germany
| | - Alicia Burns
- Faculty of Life Science, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin, 10115, Germany
- Cluster of Excellence "Science of Intelligence," Technical University of Berlin, Marchstr. 23, Berlin, 10587, Germany
| | - Jens Krause
- Fish Biology, Fisheries and Aquaculture, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, Berlin, 12587, Germany
- Faculty of Life Science, Humboldt-Universität zu Berlin, Invalidenstrasse 42, Berlin, 10115, Germany
- Cluster of Excellence "Science of Intelligence," Technical University of Berlin, Marchstr. 23, Berlin, 10587, Germany
| |
Collapse
|
10
|
Ozogány K, Kerekes V, Fülöp A, Barta Z, Nagy M. Fine-scale collective movements reveal present, past and future dynamics of a multilevel society in Przewalski's horses. Nat Commun 2023; 14:5096. [PMID: 37669934 PMCID: PMC10480438 DOI: 10.1038/s41467-023-40523-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/31/2023] [Indexed: 09/07/2023] Open
Abstract
Studying animal societies needs detailed observation of many individuals, but technological advances offer new opportunities in this field. Here, we present a state-of-the-art drone observation of a multilevel herd of Przewalski's horses, consisting of harems (one-male, multifemale groups). We track, in high spatio-temporal resolution, the movements of 238 individually identified horses on drone videos, and combine movement analyses with demographic data from two decades of population monitoring. Analysis of collective movements reveals how the structure of the herd's social network is related to kinship and familiarity of individuals. The network centrality of harems is related to their age and how long the harem stallions have kept harems previously. Harems of genetically related stallions are closer to each other in the network, and female exchange is more frequent between closer harems. High movement similarity of females from different harems predicts becoming harem mates in the future. Our results show that only a few minutes of fine-scale movement tracking combined with high throughput data driven analysis can reveal the structure of a society, reconstruct past group dynamics and predict future ones.
Collapse
Affiliation(s)
- Katalin Ozogány
- ELKH-DE Behavioural Ecology Research Group, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary.
- Department of Evolutionary Zoology and Human Biology, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary.
| | - Viola Kerekes
- Hortobágy National Park Directorate, Sumen u. 2, Debrecen, 4024, Hungary
| | - Attila Fülöp
- ELKH-DE Behavioural Ecology Research Group, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary
- Department of Evolutionary Zoology and Human Biology, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary
- Evolutionary Ecology Group, Hungarian Department of Biology and Ecology, Babeș-Bolyai University, Str. Clinicilor 5-7, 400006, Cluj-Napoca, Romania
- Centre for Systems Biology, Biodiversity and Bioresources (3B), Babeș-Bolyai University, Str. Clinicilor 5-7, 400006, Cluj-Napoca, Romania
- STAR-UBB Institute of Advanced Studies in Science and Technology, Babeş-Bolyai University, Str. Mihail Kogălniceanu 1, 400084, Cluj-Napoca, Romania
| | - Zoltán Barta
- ELKH-DE Behavioural Ecology Research Group, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary
- Department of Evolutionary Zoology and Human Biology, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary
| | - Máté Nagy
- MTA-ELTE "Lendület" Collective Behaviour Research Group, Hungarian Academy of Sciences, Pázmány P. Stny. 1A, Budapest, 1117, Hungary.
- Department of Biological Physics, Eötvös Loránd University, Pázmány P. Stny. 1A, Budapest, 1117, Hungary.
- MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Pázmány P. Stny. 1A, Budapest, 1117, Hungary.
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitätsstraße 10, 78457, Konstanz, Germany.
| |
Collapse
|
11
|
Rathore A, Sharma A, Shah S, Sharma N, Torney C, Guttal V. Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings. PeerJ 2023; 11:e15573. [PMID: 37397020 PMCID: PMC10309051 DOI: 10.7717/peerj.15573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 05/25/2023] [Indexed: 07/04/2023] Open
Abstract
Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.
Collapse
Affiliation(s)
- Akanksha Rathore
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
| | - Ananth Sharma
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
| | - Shaan Shah
- Department of Electrical Engineering, Indian Institute of Technology, Bombay, Mumbai, India
| | - Nitika Sharma
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, United States of America
| | - Colin Torney
- School of Mathematics and Statistics, University of Glasgow, Glasgow, United Kingdom
| | - Vishwesha Guttal
- Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India
| |
Collapse
|
12
|
Baldwin RW, Beaver JT, Messinger M, Muday J, Windsor M, Larsen GD, Silman MR, Anderson TM. Camera Trap Methods and Drone Thermal Surveillance Provide Reliable, Comparable Density Estimates of Large, Free-Ranging Ungulates. Animals (Basel) 2023; 13:1884. [PMID: 37889800 PMCID: PMC10252056 DOI: 10.3390/ani13111884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/28/2023] [Accepted: 06/02/2023] [Indexed: 10/29/2023] Open
Abstract
Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark-resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season.
Collapse
Affiliation(s)
- Robert W. Baldwin
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
| | - Jared T. Beaver
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
- Wake Forest University Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA
- Department of Animal and Range Sciences, Montana State University, Bozeman, MT 59717, USA
| | - Max Messinger
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
- Wake Forest University Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA
| | - Jeffrey Muday
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
| | - Matt Windsor
- Pilot Mountain State Park, North Carolina State Parks, 1792 Pilot Knob Park Rd, Pinnacle, NC 27043, USA;
| | - Gregory D. Larsen
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
- Wake Forest University Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA
| | - Miles R. Silman
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
- Wake Forest University Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA
| | - T. Michael Anderson
- Department of Biology, Wake Forest University, Winston-Salem, NC 27109, USA; (R.W.B.); (M.M.); (J.M.); (G.D.L.); (M.R.S.); (T.M.A.)
- Wake Forest University Center for Energy, Environment, and Sustainability, Wake Forest University, Winston-Salem, NC 27109, USA
| |
Collapse
|
13
|
Arnon E, Uzan A, Handel M, Cain S, Toledo S, Spiegel O. ViewShedR: a new open-source tool for cumulative, subtractive and elevated line-of-sight analysis. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221333. [PMID: 37388309 PMCID: PMC10300669 DOI: 10.1098/rsos.221333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 06/08/2023] [Indexed: 07/01/2023]
Abstract
Many environmental and ecological studies require line of sight (LOS) and/or viewshed analyses. While tools for performing these analyses from digital elevation models (DEMs) are widespread, they are either too restrictive, inaccessible or pricey and difficult to use. This methodological gap is potentially imperative for scholars using solutions like telemetry tracking systems or spatial ecology landscape mapping. Here we present ViewShedR-a free, open-source and intuitive graphical user-interface application for performing LOS calculations, including cumulative, subtractive (areas covered by towers A + B or by A but not by B, respectively), and elevated-target analyses. ViewShedR is implemented in the widely used R environment, thus facilitating usage and further modification by end-users. We provide two working examples for ViewShedR in the context of permanent animal-tracking systems requiring simultaneous tag-detection by multiple towers (receivers): first, the ATLAS system for terrestrial animals in the Harod Valley, Israel; and second, an acoustic telemetry array for marine animals in the Dry Tortugas, Florida. ViewShedR allowed effective tower deployment and finding partially detected tagged animals in the ATLAS system. Similarly, it allowed us to identify reception shadows cast by islands in the marine array. We hope ViewShedR will facilitate deployment of tower arrays for tracking, communication networks and other ecological applications.
Collapse
Affiliation(s)
- Eitam Arnon
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Assaf Uzan
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Michal Handel
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Faculty of Architecture and Town Planning, Technion – Israel Institute of Technology, Haifa 3200003, Israel
| | - Shlomo Cain
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Sivan Toledo
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Orr Spiegel
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| |
Collapse
|