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Prokopenko CM, Ellington EH, Robitaille A, Aubin JA, Balluffi-Fry J, Laforge M, Webber QMR, Zabihi-Seissan S, Vander Wal E. Friends because of foes: synchronous movement within predator-prey domains. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230374. [PMID: 39230459 PMCID: PMC11449165 DOI: 10.1098/rstb.2023.0374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 03/18/2024] [Accepted: 06/10/2024] [Indexed: 09/05/2024] Open
Abstract
For prey, movement synchrony represents a potent antipredator strategy. Prey, however, must balance the costs and benefits of using conspecifics to mediate risk. Thus, the emergent patterns of risk-driven sociality depend on variation in space and in the predators and prey themselves. We applied the concept of predator-prey habitat domain, the space in which animals acquire food resources, to test the conditions under which individuals synchronize their movements relative to predator and prey habitat domains. We tested the response of movement synchrony of prey to predator-prey domains in two populations of ungulates that vary in their gregariousness and predator community: (i) elk, which are preyed on by wolves; and (ii) caribou, which are preyed on by coyotes and black bears. Prey in both communities responded to cursorial predators by increasing synchrony during seasons of greater predation pressure. Elk moved more synchronously in the wolf habitat domain during winter and caribou moved more synchronously in the coyote habitat domains during spring. In the winter, caribou increased movement synchrony when coyote and caribou domains overlapped. By integrating habitat domains with movement ecology, we provide a compelling argument for social behaviours and collective movement as an antipredator response. This article is part of the theme issue 'The spatial-social interface: A theoretical and empirical integration'.
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Affiliation(s)
- Christina M Prokopenko
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
| | - E Hance Ellington
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
- Range Cattle Research and Education Center, University of Florida, 3401 Experiment Station Rd , Ona, FL, USA
| | - Alec Robitaille
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
| | - Jaclyn A Aubin
- Cognitive and Behavioural Ecology Interdisciplinary Program, Memorial University of Newfoundland, St. John's , NL, Canada
| | - Juliana Balluffi-Fry
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
| | - Michel Laforge
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
| | - Quinn M R Webber
- Cognitive and Behavioural Ecology Interdisciplinary Program, Memorial University of Newfoundland, St. John's , NL, Canada
| | - Sana Zabihi-Seissan
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
| | - Eric Vander Wal
- Department of Biology, Memorial University of Newfoundland, 232 Elizabeth Ave , St. John's, NL A1B 3X9, Canada
- Cognitive and Behavioural Ecology Interdisciplinary Program, Memorial University of Newfoundland, St. John's , NL, Canada
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2
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Kaur P, Ciuti S, Ossi F, Cagnacci F, Morellet N, Loison A, Atmeh K, McLoughlin P, Reinking AK, Beck JL, Ortega AC, Kauffman M, Boyce MS, Haigh A, David A, Griffin LL, Conteddu K, Faull J, Salter-Townshend M. A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data. MOVEMENT ECOLOGY 2024; 12:55. [PMID: 39107862 PMCID: PMC11304672 DOI: 10.1186/s40462-024-00494-6] [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: 01/15/2024] [Accepted: 07/15/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.
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Affiliation(s)
- Prabhleen Kaur
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland.
| | - Simone Ciuti
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland
| | - Federico Ossi
- Animal Ecology Unit, Research and Innovation Center (CRI), Fondazione Edmund Mach, San Michele all'Adige, Italy
- NBFC, National Biodiversity Future Center, 90133, Palermo, Italy
| | - Francesca Cagnacci
- Animal Ecology Unit, Research and Innovation Center (CRI), Fondazione Edmund Mach, San Michele all'Adige, Italy
- NBFC, National Biodiversity Future Center, 90133, Palermo, Italy
| | - Nicolas Morellet
- INRAE, CEFS, Université de Toulouse, Castanet-Tolosan, 31326, France
- LTSER ZA PYRénées GARonne, Auzeville-Tolosane, 31320, France
| | - Anne Loison
- Alpine Ecology Laboratory, Savoie Mont Blanc University, Chambéry, France
| | - Kamal Atmeh
- Biometrics and Evolutionary Biology Laboratory, Claude Bernard University Lyon 1, Lyon, France
| | - Philip McLoughlin
- Department of Biology, University of Saskatchewan, Saskatoon, Canada
| | - Adele K Reinking
- Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, USA
- Department of Ecosystem Science and Management, University of Wyoming, Laramie, USA
- Graduate Degree Program in Ecology, Colorado State University, Fort Collins, USA
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, USA
| | - Jeffrey L Beck
- Department of Ecosystem Science and Management, University of Wyoming, Laramie, USA
| | - Anna C Ortega
- Program in Ecology, University of Wyoming, Laramie, WY, 82071, USA
- Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, Laramie, USA
| | - Matthew Kauffman
- U.S. Geological Survey, Wyoming Cooperative Fish and Wildlife Research Unit, Laramie, USA
- Wyoming Cooperative Fish and Wildlife Research Unit, Department of Zoology and Physiology, University of Wyoming, Laramie, USA
| | - Mark S Boyce
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Amy Haigh
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland
| | - Anna David
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland
| | - Laura L Griffin
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland
| | - Kimberly Conteddu
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland
| | - Jane Faull
- Laboratory of Wildlife Ecology and Behaviour, School of Biology and Environmental Sciences, University College Dublin, Dublin, Ireland
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3
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Egan ME, Gorman NT, Crews S, Eichholz MW, Skinner D, Schlichting PE, Rayl ND, Bergman EJ, Ellington EH, Bastille-Rousseau G. Estimating encounter-habitat relationships with scale-integrated resource selection functions. J Anim Ecol 2024; 93:1036-1048. [PMID: 38940070 DOI: 10.1111/1365-2656.14133] [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: 11/22/2023] [Accepted: 05/27/2024] [Indexed: 06/29/2024]
Abstract
Encounters between animals occur when animals are close in space and time. Encounters are important in many ecological processes including sociality, predation and disease transmission. Despite this, there is little theory regarding the spatial distribution of encounters and no formal framework to relate environmental characteristics to encounters. The probability of encounter could be estimated with resource selection functions (RSFs) by comparing locations where encounters occurred to available locations where they may have occurred, but this estimate is complicated by the hierarchical nature of habitat selection. We developed a method to relate resources to the relative probability of encounter based on a scale-integrated habitat selection framework. This framework integrates habitat selection at multiple scales to obtain an appropriate estimate of availability for encounters. Using this approach, we related encounter probabilities to landscape resources. The RSFs describe habitat associations at four scales, home ranges within the study area, areas of overlap within home ranges, locations within areas of overlap, and encounters compared to other locations, which can be combined into a single scale-integrated RSF. We apply this method to intraspecific encounter data from two species: white-tailed deer (Odocoileus virginianus) and elk (Cervus elaphus) and interspecific encounter data from a two-species system of caribou (Rangifer tarandus) and coyote (Canis latrans). Our method produced scale-integrated RSFs that represented the relative probability of encounter. The predicted spatial distribution of encounters obtained based on this scale-integrated approach produced distributions that more accurately predicted novel encounters than a naïve approach or any individual scale alone. Our results highlight the importance of accounting for the conditional nature of habitat selection in estimating the habitat associations of animal encounters as opposed to 'naïve' comparisons of encounter locations with general availability. This method has direct relevance for testing hypotheses about the relationship between habitat and social or predator-prey behaviour and generating spatial predictions of encounters. Such spatial predictions may be vital for understanding the distribution of encounters driving disease transmission, predation rates and other population and community-level processes.
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Affiliation(s)
- Michael E Egan
- Cooperative Wildlife Research Laboratory, Southern Illinois University, Carbondale, Illinois, USA
| | - Nicole T Gorman
- Cooperative Wildlife Research Laboratory, Southern Illinois University, Carbondale, Illinois, USA
| | - Storm Crews
- Cooperative Wildlife Research Laboratory, Southern Illinois University, Carbondale, Illinois, USA
| | - Michael W Eichholz
- Cooperative Wildlife Research Laboratory, Southern Illinois University, Carbondale, Illinois, USA
| | - Dan Skinner
- Illinois Department of Natural Resources, Division of Wildlife Resources, Springfield, Illinois, USA
| | - Peter E Schlichting
- Illinois Department of Natural Resources, Division of Wildlife Resources, Springfield, Illinois, USA
| | | | - Eric J Bergman
- Colorado Parks and Wildlife, Fort Collins, Colorado, USA
| | - E Hance Ellington
- Department of Wildlife Ecology and Conservation, Range Cattle Research and Education Center, University of Florida, Ona, Florida, USA
| | - Guillaume Bastille-Rousseau
- Cooperative Wildlife Research Laboratory, Southern Illinois University, Carbondale, Illinois, USA
- School of Biological Sciences, Southern Illinois University, Carbondale, Illinois, USA
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4
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Fernandez CM, Krockenberger MB, Ho SYW, Crowther MS, Mella VSA, Jelocnik M, Wilmott L, Higgins DP. Novel typing scheme reveals emergence and genetic diversity of Chlamydia pecorum at the local management scale across two koala populations. Vet Microbiol 2024; 293:110085. [PMID: 38581768 DOI: 10.1016/j.vetmic.2024.110085] [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: 10/18/2023] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
To overcome shortcomings in discriminating Chlamydia pecorum strains infecting the koala (Phascolarctos cinereus) at the local level, we developed a novel genotyping scheme for this pathogen to inform koala management at a fine-scale subpopulation level. We applied this scheme to two geographically distinct koala populations in New South Wales, Australia: the Liverpool Plains and the Southern Highlands to South-west Sydney (SHSWS). Our method provides greater resolution than traditional multi-locus sequence typing, and can be used to monitor strain emergence, movement, and divergence across a range of fragmented habitats. Within the Liverpool Plains population, suspected recent introduction of a novel strain was confirmed by an absence of genetic diversity at the earliest sampling events and limited diversity at recent sampling events. Across the partially fragmented agricultural landscape of the Liverpool Plains, diversity within a widespread sequence type suggests that this degree of fragmentation may hinder but not prevent spread. In the SHSWS population, our results suggest movement of a strain from the south, where diverse strains exist, into a previously Chlamydia-free area in the north, indicating the risk of expansion towards an adjacent Chlamydia-negative koala population in South-west Sydney. In the south of the SHSWS where koala subpopulations appear segregated, we found evidence of divergent strain evolution. Our tool can be used to infer the risks of strain introduction across fragmented habitats in population management, particularly through practices such as wildlife corridor constructions and translocations.
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Affiliation(s)
- Cristina M Fernandez
- Faculty of Science, Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia
| | - Mark B Krockenberger
- Faculty of Science, Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia; Sydney Infectious Diseases, The University of Sydney, 176 Hawkesbury Road, Westmead, NSW 2145, Australia
| | - Simon Y W Ho
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Mathew S Crowther
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Valentina S A Mella
- Faculty of Science, Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia; School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Martina Jelocnik
- School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia; Centre for Bioinnovation, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
| | - Lachlan Wilmott
- NSW Department of Planning and Environment, Wollongong, NSW 2005, Australia
| | - Damien P Higgins
- Faculty of Science, Sydney School of Veterinary Science, The University of Sydney, Sydney, NSW 2006, Australia.
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5
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Albery GF, Bansal S, Silk MJ. Comparative approaches in social network ecology. Ecol Lett 2024; 27:e14345. [PMID: 38069575 DOI: 10.1111/ele.14345] [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: 05/03/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 01/31/2024]
Abstract
Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
- Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, District of Columbia, USA
| | - Matthew J Silk
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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6
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Yang A, Wilber MQ, Manlove KR, Miller RS, Boughton R, Beasley J, Northrup J, VerCauteren KC, Wittemyer G, Pepin K. Deriving spatially explicit direct and indirect interaction networks from animal movement data. Ecol Evol 2023; 13:e9774. [PMID: 36993145 PMCID: PMC10040956 DOI: 10.1002/ece3.9774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/11/2023] [Indexed: 03/29/2023] Open
Abstract
Quantifying spatiotemporally explicit interactions within animal populations facilitates the understanding of social structure and its relationship with ecological processes. Data from animal tracking technologies (Global Positioning Systems ["GPS"]) can circumvent longstanding challenges in the estimation of spatiotemporally explicit interactions, but the discrete nature and coarse temporal resolution of data mean that ephemeral interactions that occur between consecutive GPS locations go undetected. Here, we developed a method to quantify individual and spatial patterns of interaction using continuous-time movement models (CTMMs) fit to GPS tracking data. We first applied CTMMs to infer the full movement trajectories at an arbitrarily fine temporal scale before estimating interactions, thus allowing inference of interactions occurring between observed GPS locations. Our framework then infers indirect interactions-individuals occurring at the same location, but at different times-while allowing the identification of indirect interactions to vary with ecological context based on CTMM outputs. We assessed the performance of our new method using simulations and illustrated its implementation by deriving disease-relevant interaction networks for two behaviorally differentiated species, wild pigs (Sus scrofa) that can host African Swine Fever and mule deer (Odocoileus hemionus) that can host chronic wasting disease. Simulations showed that interactions derived from observed GPS data can be substantially underestimated when temporal resolution of movement data exceeds 30-min intervals. Empirical application suggested that underestimation occurred in both interaction rates and their spatial distributions. CTMM-Interaction method, which can introduce uncertainties, recovered majority of true interactions. Our method leverages advances in movement ecology to quantify fine-scale spatiotemporal interactions between individuals from lower temporal resolution GPS data. It can be leveraged to infer dynamic social networks, transmission potential in disease systems, consumer-resource interactions, information sharing, and beyond. The method also sets the stage for future predictive models linking observed spatiotemporal interaction patterns to environmental drivers.
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Affiliation(s)
- Anni Yang
- Department of Geography and Environmental SustainabilityUniversity of OklahomaOklahomaNormanUSA
- Department of Fish, Wildlife and Conservation BiologyColorado State UniversityColoradoFort CollinsUSA
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife ServicesNational Wildlife Research CenterColoradoFort CollinsUSA
| | - Mark Q. Wilber
- Forestry, Wildlife, and Fisheries, Institute of AgricultureUniversity of TennesseeTennesseeKnoxvilleUSA
| | - Kezia R. Manlove
- Department of Wildland Resources and Ecology CenterUtah State UniversityUtahLoganUSA
| | - Ryan S. Miller
- Center for Epidemiology and Animal HealthUnited States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary ServiceColoradoFort CollinsUSA
| | - Raoul Boughton
- Archbold Biological StationBuck Island RanchFloridaLake PlacidUSA
| | - James Beasley
- Savannah River Ecology LaboratoryWarnell School of Forestry and Natural ResourcesUniversity of GeorgiaSouth CarolinaAikenUSA
| | - Joseph Northrup
- Wildlife Research and Monitoring SectionOntario Ministry of Natural Resources and ForestryOntarioPeterboroughCanada
| | - Kurt C. VerCauteren
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife ServicesNational Wildlife Research CenterColoradoFort CollinsUSA
| | - George Wittemyer
- Department of Fish, Wildlife and Conservation BiologyColorado State UniversityColoradoFort CollinsUSA
| | - Kim Pepin
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife ServicesNational Wildlife Research CenterColoradoFort CollinsUSA
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7
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Webber QMR, Albery GF, Farine DR, Pinter-Wollman N, Sharma N, Spiegel O, Vander Wal E, Manlove K. Behavioural ecology at the spatial-social interface. Biol Rev Camb Philos Soc 2023; 98:868-886. [PMID: 36691262 DOI: 10.1111/brv.12934] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/25/2023]
Abstract
Spatial and social behaviour are fundamental aspects of an animal's biology, and their social and spatial environments are indelibly linked through mutual causes and shared consequences. We define the 'spatial-social interface' as intersection of social and spatial aspects of individuals' phenotypes and environments. Behavioural variation at the spatial-social interface has implications for ecological and evolutionary processes including pathogen transmission, population dynamics, and the evolution of social systems. We link spatial and social processes through a foundation of shared theory, vocabulary, and methods. We provide examples and future directions for the integration of spatial and social behaviour and environments. We introduce key concepts and approaches that either implicitly or explicitly integrate social and spatial processes, for example, graph theory, density-dependent habitat selection, and niche specialization. Finally, we discuss how movement ecology helps link the spatial-social interface. Our review integrates social and spatial behavioural ecology and identifies testable hypotheses at the spatial-social interface.
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Affiliation(s)
- Quinn M R Webber
- Department of Integrative Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada
| | - Gregory F Albery
- Department of Biology, Georgetown University, 37th and O Streets, Washington, DC, 20007, USA.,Wissenschaftskolleg zu Berlin, Wallotstraße 19, 14193, Berlin, Germany.,Leibniz Institute of Freshwater Ecology and Inland Fisheries, Müggelseedamm 310, 12587, Berlin, Germany
| | - Damien R Farine
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland.,Department of Collective Behavior, Max Planck Institute of Animal Behavior, Universitatsstraße 10, 78464, Constance, Germany.,Division of Ecology and Evolution, Research School of Biology, Australian National University, 46 Sullivans Creek Road, Canberra, ACT, 2600, Australia
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Nitika Sharma
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Orr Spiegel
- School of Zoology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Eric Vander Wal
- Department of Biology, Memorial University, St. John's, NL, A1C 5S7, Canada
| | - Kezia Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, 5200 Old Main Hill, Logan, UT, 84322, USA
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8
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Xu Z, MacIntosh AJ, Castellano-Navarro A, Macanás-Martínez E, Suzumura T, Duboscq J. Linking parasitism to network centrality and the impact of sampling bias in its interpretation. PeerJ 2022; 10:e14305. [PMID: 36420133 PMCID: PMC9677876 DOI: 10.7717/peerj.14305] [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: 09/28/2022] [Accepted: 10/05/2022] [Indexed: 11/19/2022] Open
Abstract
Group living is beneficial for individuals, but also comes with costs. One such cost is the increased possibility of pathogen transmission because increased numbers or frequencies of social contacts are often associated with increased parasite abundance or diversity. The social structure of a group or population is paramount to patterns of infection and transmission. Yet, for various reasons, studies investigating the links between sociality and parasitism in animals, especially in primates, have only accounted for parts of the group (e.g., only adults), which is likely to impact the interpretation of results. Here, we investigated the relationship between social network centrality and an estimate of gastrointestinal helminth infection intensity in a whole group of Japanese macaques (Macaca fuscata). We then tested the impact of omitting parts of the group on this relationship. We aimed to test: (1) whether social network centrality -in terms of the number of partners (degree), frequency of interactions (strength), and level of social integration (eigenvector) -was linked to parasite infection intensity (estimated by eggs per gram of faeces, EPG); and, (2) to what extent excluding portions of individuals within the group might influence the observed relationship. We conducted social network analysis on data collected from one group of Japanese macaques over three months on Koshima Island, Japan. We then ran a series of knock-out simulations. General linear mixed models showed that, at the whole-group level, network centrality was positively associated with geohelminth infection intensity. However, in partial networks with only adult females, only juveniles, or random subsets of the group, the strength of this relationship - albeit still generally positive - lost statistical significance. Furthermore, knock-out simulations where individuals were removed but network metrics were retained from the original whole-group network showed that these changes are partly a power issue and partly an effect of sampling the incomplete network. Our study indicates that sampling bias can thus hamper our ability to detect real network effects involving social interaction and parasitism. In addition to supporting earlier results linking geohelminth infection to Japanese macaque social networks, this work introduces important methodological considerations for research into the dynamics of social transmission, with implications for infectious disease epidemiology, population management, and health interventions.
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Affiliation(s)
- Zhihong Xu
- Wildlife Research Center, Kyoto University, Kyoto, Kyoto, Japan,Primate Research Institute, Kyoto University, Inuyama, Aichi, Japan
| | - Andrew J.J. MacIntosh
- Wildlife Research Center, Kyoto University, Kyoto, Kyoto, Japan,Primate Research Institute, Kyoto University, Inuyama, Aichi, Japan
| | - Alba Castellano-Navarro
- Ethology and Animal Welfare Section, Universidad CEU Cardenal Herrera, Valencia, Valencia, Spain,Institute of Biology, Universität Leipzig, Leipzig, Saxony, Germany
| | - Emilio Macanás-Martínez
- Ethology and Animal Welfare Section, Universidad CEU Cardenal Herrera, Valencia, Valencia, Spain,Institute of Biology, Universität Leipzig, Leipzig, Saxony, Germany
| | | | - Julie Duboscq
- UMR7206 Eco-Anthropologie, CNRS-MNHN-Université de Paris, Paris, Île-de-France, France,Department of Behavioural Ecology, Johann-Friedrich-Blumenbach Institute for Zoology and Anthropology, University of Göttingen, Göttingen, Lower Saxony, Germany
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9
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Gilbertson MLJ, Ketz AC, Hunsaker M, Jarosinski D, Ellarson W, Walsh DP, Storm DJ, Turner WC. Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus). MOVEMENT ECOLOGY 2022; 10:43. [PMID: 36289549 PMCID: PMC9608933 DOI: 10.1186/s40462-022-00342-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Dispersal is a fundamental process to animal population dynamics and gene flow. In white-tailed deer (WTD; Odocoileus virginianus), dispersal also presents an increasingly relevant risk for the spread of infectious diseases. Across their wide range, WTD dispersal is believed to be driven by a suite of landscape and host behavioral factors, but these can vary by region, season, and sex. Our objectives were to (1) identify dispersal events in Wisconsin WTD and determine drivers of dispersal rates and distances, and (2) determine how landscape features (e.g., rivers, roads) structure deer dispersal paths. METHODS We developed an algorithmic approach to detect dispersal events from GPS collar data for 590 juvenile, yearling, and adult WTD. We used statistical models to identify host and landscape drivers of dispersal rates and distances, including the role of agricultural land use, the traversability of the landscape, and potential interactions between deer. We then performed a step selection analysis to determine how landscape features such as agricultural land use, elevation, rivers, and roads affected deer dispersal paths. RESULTS Dispersal predominantly occurred in juvenile males, of which 64.2% dispersed, with dispersal events uncommon in other sex and age classes. Juvenile male dispersal probability was positively associated with the proportion of the natal range that was classified as agricultural land use, but only during the spring. Dispersal distances were typically short (median 5.77 km, range: 1.3-68.3 km), especially in the fall. Further, dispersal distances were positively associated with agricultural land use in potential dispersal paths but negatively associated with the number of proximate deer in the natal range. Lastly, we found that, during dispersal, juvenile males typically avoided agricultural land use but selected for areas near rivers and streams. CONCLUSION Land use-particularly agricultural-was a key driver of dispersal rates, distances, and paths in Wisconsin WTD. In addition, our results support the importance of deer social environments in shaping dispersal behavior. Our findings reinforce knowledge of dispersal ecology in WTD and how landscape factors-including major rivers, roads, and land-use patterns-structure host gene flow and potential pathogen transmission.
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Affiliation(s)
- Marie L J Gilbertson
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr, 53706, Madison, WI, USA.
| | - Alison C Ketz
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr, 53706, Madison, WI, USA
| | - Matthew Hunsaker
- Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr, 53706, Madison, WI, USA
| | - Dana Jarosinski
- Wisconsin Department of Natural Resources, 1500 N Johns St, 53533, Dodgeville, WI, USA
- Warnell School of Forestry and Natural Resources, University of Georgia, 180 E Green St, 30602, Athens, GA, USA
| | - Wesley Ellarson
- Wisconsin Department of Natural Resources, 1500 N Johns St, 53533, Dodgeville, WI, USA
| | - Daniel P Walsh
- U.S. Geological Survey, Montana Cooperative Wildlife Research Unit, University of Montana, 32 Campus Drive NS 205, 59812, Missoula, MT, USA
| | - Daniel J Storm
- Wisconsin Department of Natural Resources, 1300 West Clairemont Ave, 54701, Eau Claire, WI, USA
| | - Wendy C Turner
- U.S. Geological Survey, Wisconsin Cooperative Wildlife Research Unit, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr, 53706, Madison, WI, USA
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10
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He P, Klarevas‐Irby JA, Papageorgiou D, Christensen C, Strauss ED, Farine DR. A guide to sampling design for
GPS
‐based studies of animal societies. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Peng He
- Department of Collective Behaviour Max Planck Institute of Animal Behavior Constance Germany
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Constance Germany
- Department of Biology University of Konstanz Constance Germany
- Department of Evolutionary Biology and Environmental Science University of Zurich Zurich Switzerland
| | - James A. Klarevas‐Irby
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Constance Germany
- Department of Biology University of Konstanz Constance Germany
- Department of Evolutionary Biology and Environmental Science University of Zurich Zurich Switzerland
- Department of Migration Max Planck Institute of Animal Behavior Radolfzell Germany
- Mpala Research Centre Nanyuki Kenya
| | - Danai Papageorgiou
- Department of Collective Behaviour Max Planck Institute of Animal Behavior Constance Germany
- Department of Evolutionary Biology and Environmental Science University of Zurich Zurich Switzerland
| | - Charlotte Christensen
- Department of Collective Behaviour Max Planck Institute of Animal Behavior Constance Germany
- Department of Evolutionary Biology and Environmental Science University of Zurich Zurich Switzerland
- Mpala Research Centre Nanyuki Kenya
| | - Eli D. Strauss
- Department of Collective Behaviour Max Planck Institute of Animal Behavior Constance Germany
- Centre for the Advanced Study of Collective Behaviour University of Konstanz Constance Germany
- Department of Evolutionary Biology and Environmental Science University of Zurich Zurich Switzerland
| | - Damien R. Farine
- Department of Collective Behaviour Max Planck Institute of Animal Behavior Constance Germany
- Department of Evolutionary Biology and Environmental Science University of Zurich Zurich Switzerland
- Division of Ecology and Evolution, Research School of Biology Australian National University Canberra Australia
- Department of Ornithology National Museums of Kenya Nairobi Kenya
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11
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Wanelik KM, Farine DR. A new method for characterising shared space use networks using animal trapping data. Behav Ecol Sociobiol 2022; 76:127. [PMID: 36042847 PMCID: PMC9418289 DOI: 10.1007/s00265-022-03222-5] [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: 12/22/2021] [Revised: 07/25/2022] [Accepted: 07/28/2022] [Indexed: 12/03/2022]
Abstract
Abstract Studying the social behaviour of small or cryptic species often relies on constructing networks from sparse point-based observations of individuals (e.g. live trapping data). A common approach assumes that individuals that have been detected sequentially in the same trapping location will also be more likely to have come into indirect and/or direct contact. However, there is very little guidance on how much data are required for making robust networks from such data. In this study, we highlight that sequential trap sharing networks broadly capture shared space use (and, hence, the potential for contact) and that it may be more parsimonious to directly model shared space use. We first use empirical data to show that characteristics of how animals use space can help us to establish new ways to model the potential for individuals to come into contact. We then show that a method that explicitly models individuals’ home ranges and subsequent overlap in space among individuals (spatial overlap networks) requires fewer data for inferring observed networks that are more strongly correlated with the true shared space use network (relative to sequential trap sharing networks). Furthermore, we show that shared space use networks based on estimating spatial overlap are also more powerful for detecting biological effects. Finally, we discuss when it is appropriate to make inferences about social interactions from shared space use. Our study confirms the potential for using sparse trapping data from cryptic species to address a range of important questions in ecology and evolution. Significance statement Characterising animal social networks requires repeated (co-)observations of individuals. Collecting sufficient data to characterise the connections among individuals represents a major challenge when studying cryptic organisms—such as small rodents. This study draws from existing spatial mark-recapture data to inspire an approach that constructs networks by estimating space use overlap (representing the potential for contact). We then use simulations to demonstrate that the method provides consistently higher correlations between inferred (or observed) networks and the true underlying network compared to current approaches and requires fewer observations to reach higher correlations. We further demonstrate that these improvements translate to greater network accuracy and to more power for statistical hypothesis testing. Supplementary Information The online version contains supplementary material available at 10.1007/s00265-022-03222-5.
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12
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Albery GF, Clutton-Brock TH, Morris A, Morris S, Pemberton JM, Nussey DH, Firth JA. Ageing red deer alter their spatial behaviour and become less social. Nat Ecol Evol 2022; 6:1231-1238. [PMID: 35864228 PMCID: PMC10859100 DOI: 10.1038/s41559-022-01817-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 05/27/2022] [Indexed: 01/19/2023]
Abstract
Social relationships are important to many aspects of animals' lives, and an individual's connections may change over the course of their lifespan. Currently, it is unclear whether social connectedness declines within individuals as they age, and what the underlying mechanisms might be, so the role of age in structuring animal social systems remains unresolved, particularly in non-primates. Here we describe senescent declines in social connectedness using 46 years of data in a wild, individually monitored population of a long-lived mammal (European red deer, Cervus elaphus). Applying a series of spatial and social network analyses, we demonstrate that these declines occur because of within-individual changes in social behaviour, with correlated changes in spatial behaviour (smaller home ranges and movements to lower-density, lower-quality areas). These findings demonstrate that within-individual socio-spatial behavioural changes can lead older animals in fission-fusion societies to become less socially connected, shedding light on the ecological and evolutionary processes structuring wild animal populations.
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Affiliation(s)
- Gregory F Albery
- Department of Biology, Georgetown University, Washington DC, USA.
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.
- Wissenschaftskolleg zu Berlin, Berlin, Germany.
| | | | - Alison Morris
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Sean Morris
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | | | - Daniel H Nussey
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK
| | - Josh A Firth
- Department of Zoology, University of Oxford, Oxford, UK
- Merton College, University of Oxford, Oxford, UK
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13
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Clements SJ, Zhao Q, Silk MJ, Hodgson DJ, Weegman MD. Modelling associations between animal social structure and demography. Anim Behav 2022. [DOI: 10.1016/j.anbehav.2022.03.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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14
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Wilber MQ, Yang A, Boughton R, Manlove KR, Miller RS, Pepin KM, Wittemyer G. A model for leveraging animal movement to understand spatio-temporal disease dynamics. Ecol Lett 2022; 25:1290-1304. [PMID: 35257466 DOI: 10.1111/ele.13986] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/27/2021] [Accepted: 02/04/2022] [Indexed: 12/19/2022]
Abstract
The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.
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Affiliation(s)
- Mark Q Wilber
- Forestry, Wildlife, and Fisheries, Institute of Agriculture, University of Tennessee, Knoxville, Tennessee, USA
| | - Anni Yang
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA.,Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA.,Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, USA
| | - Raoul Boughton
- Archbold Biological Station, Buck Island Ranch, Lake Placid, Florida, USA
| | - Kezia R Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah, USA
| | - Ryan S Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, USA
| | - Kim M Pepin
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA
| | - George Wittemyer
- Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
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15
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Michalska-Smith M, Song Z, Spawn-Lee SA, Hansen ZA, Johnson M, May G, Borer ET, Seabloom EW, Kinkel LL. Network structure of resource use and niche overlap within the endophytic microbiome. THE ISME JOURNAL 2022; 16:435-446. [PMID: 34413476 PMCID: PMC8776778 DOI: 10.1038/s41396-021-01080-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 05/28/2021] [Accepted: 07/27/2021] [Indexed: 02/07/2023]
Abstract
Endophytes often have dramatic effects on their host plants. Characterizing the relationships among members of these communities has focused on identifying the effects of single microbes on their host, but has generally overlooked interactions among the myriad microbes in natural communities as well as potential higher-order interactions. Network analyses offer a powerful means for characterizing patterns of interaction among microbial members of the phytobiome that may be crucial to mediating its assembly and function. We sampled twelve endophytic communities, comparing patterns of niche overlap between coexisting bacteria and fungi to evaluate the effect of nutrient supplementation on local and global competitive network structure. We found that, despite differences in the degree distribution, there were few significant differences in the global network structure of niche-overlap networks following persistent nutrient amendment. Likewise, we found idiosyncratic and weak evidence for higher-order interactions regardless of nutrient treatment. This work provides a first-time characterization of niche-overlap network structure in endophytic communities and serves as a framework for higher-resolution analyses of microbial interaction networks as a consequence and a cause of ecological variation in microbiome function.
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Affiliation(s)
- Matthew Michalska-Smith
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, MN, USA.
- Department of Plant Pathology, University of Minnesota, St Paul, MN, USA.
| | - Zewei Song
- Department of Plant Pathology, University of Minnesota, St Paul, MN, USA
| | - Seth A Spawn-Lee
- Department of Geography, University of Wisconsin, Madison, WI, USA
- Center for Sustainability and the Global Environment (SAGE), University of Wisconsin, Madison, WI, USA
| | - Zoe A Hansen
- Department of Microbiology & Molecular Genetics, Michigan State University, East Lansing, MI, USA
| | - Mitch Johnson
- Department of Horticultural Science, University of Minnesota, St Paul, MN, USA
| | - Georgiana May
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, USA
| | - Elizabeth T Borer
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, USA
| | - Eric W Seabloom
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, USA
| | - Linda L Kinkel
- Department of Plant Pathology, University of Minnesota, St Paul, MN, USA
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16
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Livestock movement informs the risk of disease spread in traditional production systems in East Africa. Sci Rep 2021; 11:16375. [PMID: 34385539 PMCID: PMC8361167 DOI: 10.1038/s41598-021-95706-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/01/2021] [Indexed: 11/22/2022] Open
Abstract
In Africa, livestock are important to local and national economies, but their productivity is constrained by infectious diseases. Comprehensive information on livestock movements and contacts is required to devise appropriate disease control strategies; yet, understanding contact risk in systems where herds mix extensively, and where different pathogens can be transmitted at different spatial and temporal scales, remains a major challenge. We deployed Global Positioning System collars on cattle in 52 herds in a traditional agropastoral system in western Serengeti, Tanzania, to understand fine-scale movements and between-herd contacts, and to identify locations of greatest interaction between herds. We examined contact across spatiotemporal scales relevant to different disease transmission scenarios. Daily cattle movements increased with herd size and rainfall. Generally, contact between herds was greatest away from households, during periods with low rainfall and in locations close to dipping points. We demonstrate how movements and contacts affect the risk of disease spread. For example, transmission risk is relatively sensitive to the survival time of different pathogens in the environment, and less sensitive to transmission distance, at least over the range of the spatiotemporal definitions of contacts that we explored. We identify times and locations of greatest disease transmission potential and that could be targeted through tailored control strategies.
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17
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Sosa S, Jacoby DMP, Lihoreau M, Sueur C. Animal social networks: Towards an integrative framework embedding social interactions, space and time. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13539] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Sebastian Sosa
- IPHC UMR 7178 CNRS Université de Strasbourg Strasbourg France
| | | | - Mathieu Lihoreau
- Research Center on Animal Cognition (CRCA) Center for Integrative Biology (CBI) CNRS University Paul Sabatier – Toulouse III Toulouse France
| | - Cédric Sueur
- IPHC UMR 7178 CNRS Université de Strasbourg Strasbourg France
- Institut Universitaire de France Paris France
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18
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Brandell EE, Fountain-Jones NM, Gilbertson ML, Cross PC, Hudson PJ, Smith DW, Stahler DR, Packer C, Craft ME. Group density, disease, and season shape territory size and overlap of social carnivores. J Anim Ecol 2021; 90:87-101. [PMID: 32654133 PMCID: PMC9844152 DOI: 10.1111/1365-2656.13294] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 05/22/2020] [Indexed: 01/19/2023]
Abstract
The spatial organization of a population can influence the spread of information, behaviour and pathogens. Group territory size and territory overlap and components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates and environmental risk (e.g. indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography. Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We further aim to explore the mechanisms by which disease perturbations could cause changes in spatial organization. Here we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We use network analysis to describe spatial organization and connectivity of social groups. We then examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models. We demonstrate that lions and wolves were similar in that group-level factors, such as number of groups and shaped spatial organization more than population-level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may have altered lion spatial organization, highlighting the importance of including infectious disease epizootics in studies of behavioural and movement ecology. We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the complex relationships between demographics, social and spatial structure, abiotic and biotic conditions and pathogen infections.
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Affiliation(s)
- Ellen E. Brandell
- Center for Infectious Disease Dynamics & Department of Biology, Huck Institute for Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA 16802
| | | | - Marie L.J. Gilbertson
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, Minnesota 55108
| | - Paul C. Cross
- U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, Montana, USA 59715
| | - Peter J. Hudson
- Center for Infectious Disease Dynamics & Department of Biology, Huck Institute for Life Sciences, Pennsylvania State University, University Park, Pennsylvania, USA 16802
| | - Douglas W. Smith
- Yellowstone Center for Resources, Wolf Project, P.O. Box 168, Yellowstone National Park, WY 82190, USA
| | - Daniel R. Stahler
- Yellowstone Center for Resources, Wolf Project, P.O. Box 168, Yellowstone National Park, WY 82190, USA
| | - Craig Packer
- Department of Ecology, Evolution and Behavior, University of Minnesota, St Paul, Minnesota 55108
| | - Meggan E. Craft
- Department of Veterinary Population Medicine, University of Minnesota, St Paul, Minnesota 55108
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19
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Smith JE, Pinter-Wollman N. Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. J Anim Ecol 2020; 90:62-75. [PMID: 33020914 DOI: 10.1111/1365-2656.13362] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 09/15/2020] [Indexed: 12/11/2022]
Abstract
In the 4.5 decades since Altmann (1974) published her seminal paper on the methods for the observational study of behaviour, automated detection and analysis of social interaction networks have fundamentally transformed the ways that ecologists study social behaviour. Methodological developments for collecting data remotely on social behaviour involve indirect inference of associations, direct recordings of interactions and machine vision. These recent technological advances are improving the scale and resolution with which we can dissect interactions among animals. They are also revealing new intricacies of animal social interactions at spatial and temporal resolutions as well as in ecological contexts that have been hidden from humans, making the unwatchable seeable. We first outline how these technological applications are permitting researchers to collect exquisitely detailed information with little observer bias. We further recognize new emerging challenges from these new reality-mining approaches. While technological advances in automating data collection and its analysis are moving at an unprecedented rate, we urge ecologists to thoughtfully combine these new tools with classic behavioural and ecological monitoring methods to place our understanding of animal social networks within fundamental biological contexts.
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Affiliation(s)
| | - Noa Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, CA, USA
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20
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Albery GF, Kirkpatrick L, Firth JA, Bansal S. Unifying spatial and social network analysis in disease ecology. J Anim Ecol 2020; 90:45-61. [DOI: 10.1111/1365-2656.13356] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/24/2020] [Indexed: 01/18/2023]
Affiliation(s)
| | | | - Josh A. Firth
- Department of Zoology Edward Grey Institute University of Oxford Oxford UK
- Merton College Oxford University Oxford UK
| | - Shweta Bansal
- Department of Biology Georgetown University Washington DC USA
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