A Two-Species Occupancy Model with a Continuous-Time Detection Process Reveals Spatial and Temporal Interactions
Detection/non-detection data are increasingly collected in continuous time, e.g., via camera traps or acoustic sensors. Application of occupancy modeling approaches to these datasets typically requires discretizing the dataset to detections over individual days or weeks, which precludes analysis of...
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| Published in | Journal of agricultural, biological, and environmental statistics Vol. 27; no. 2; pp. 321 - 338 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.06.2022
Springer Springer Nature B.V |
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| Abstract | Detection/non-detection data are increasingly collected in continuous time, e.g., via camera traps or acoustic sensors. Application of occupancy modeling approaches to these datasets typically requires discretizing the dataset to detections over individual days or weeks, which precludes analysis of temporal interactions between species or covariate relationships that change over fine temporal scales. To address this limitation, we developed a two-species occupancy model that assumes a temporal point process detection model. This model permits simultaneous analysis of species interactions in space (i.e., site occupancy) and time (i.e., activity patterns). The model is also capable of estimating the amount of time animals are available for detection, i.e., availability. We applied the model to detections of white-tailed deer (
Odocoileus virginianus
) and coyote (
Canis latrans
) collected via camera trapping. We found evidence of both temporal and spatial interactions between deer and coyote. Detection intensity of deer was greater and proportionally more diurnal where coyotes were present. At hunted sites, coyotes were more likely to occur at sites where deer were also present (and vice versa). These results highlight how two-species occupancy models with a continuous-time detection process can be used to infer temporal interactions between species. Our approach broadens the set of questions ecologists can ask regarding both spatial and temporal interactions between species, as well as fine-scale temporal covariates (e.g., weather). Our model should be increasingly applicable given the increasing availability of ecological data collected in continuous time. Supplementary materials accompanying this paper appear on-line |
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| AbstractList | Detection/non-detection data are increasingly collected in continuous time, e.g., via camera traps or acoustic sensors. Application of occupancy modeling approaches to these datasets typically requires discretizing the dataset to detections over individual days or weeks, which precludes analysis of temporal interactions between species or covariate relationships that change over fine temporal scales. To address this limitation, we developed a two-species occupancy model that assumes a temporal point process detection model. This model permits simultaneous analysis of species interactions in space (i.e., site occupancy) and time (i.e., activity patterns). The model is also capable of estimating the amount of time animals are available for detection, i.e., availability. We applied the model to detections of white-tailed deer (Odocoileus virginianus) and coyote (Canis latrans) collected via camera trapping. We found evidence of both temporal and spatial interactions between deer and coyote. Detection intensity of deer was greater and proportionally more diurnal where coyotes were present. At hunted sites, coyotes were more likely to occur at sites where deer were also present (and vice versa). These results highlight how two-species occupancy models with a continuous-time detection process can be used to infer temporal interactions between species. Our approach broadens the set of questions ecologists can ask regarding both spatial and temporal interactions between species, as well as fine-scale temporal covariates (e.g., weather). Our model should be increasingly applicable given the increasing availability of ecological data collected in continuous time. Supplementary materials accompanying this paper appear on-line Detection/non-detection data are increasingly collected in continuous time, e.g., via camera traps or acoustic sensors. Application of occupancy modeling approaches to these datasets typically requires discretizing the dataset to detections over individual days or weeks, which precludes analysis of temporal interactions between species or covariate relationships that change over fine temporal scales. To address this limitation, we developed a two-species occupancy model that assumes a temporal point process detection model. This model permits simultaneous analysis of species interactions in space (i.e., site occupancy) and time (i.e., activity patterns). The model is also capable of estimating the amount of time animals are available for detection, i.e., availability. We applied the model to detections of white-tailed deer ( Odocoileus virginianus ) and coyote ( Canis latrans ) collected via camera trapping. We found evidence of both temporal and spatial interactions between deer and coyote. Detection intensity of deer was greater and proportionally more diurnal where coyotes were present. At hunted sites, coyotes were more likely to occur at sites where deer were also present (and vice versa). These results highlight how two-species occupancy models with a continuous-time detection process can be used to infer temporal interactions between species. Our approach broadens the set of questions ecologists can ask regarding both spatial and temporal interactions between species, as well as fine-scale temporal covariates (e.g., weather). Our model should be increasingly applicable given the increasing availability of ecological data collected in continuous time. Supplementary materials accompanying this paper appear on-line |
| Audience | Academic |
| Author | Kays, Roland Parsons, Arielle W. Kellner, Kenneth F. Rota, Christopher T. Millspaugh, Joshua J. |
| Author_xml | – sequence: 1 givenname: Kenneth F. orcidid: 0000-0002-6755-0555 surname: Kellner fullname: Kellner, Kenneth F. email: kfkellner@esf.edu organization: Division of Forestry and Natural Resources, West Virginia University, Camp Fire Program in Wildlife Conservation, SUNY College of Environmental Science and Forestry – sequence: 2 givenname: Arielle W. surname: Parsons fullname: Parsons, Arielle W. organization: North Carolina Museum of Natural Sciences, Department of Forestry and Environmental Resources, North Carolina State University – sequence: 3 givenname: Roland surname: Kays fullname: Kays, Roland organization: North Carolina Museum of Natural Sciences, Department of Forestry and Environmental Resources, North Carolina State University – sequence: 4 givenname: Joshua J. surname: Millspaugh fullname: Millspaugh, Joshua J. organization: W.A. Franke College of Forestry and Conservation, Wildlife Biology Program, University of Montana – sequence: 5 givenname: Christopher T. surname: Rota fullname: Rota, Christopher T. organization: Division of Forestry and Natural Resources, West Virginia University |
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| Cites_doi | 10.1111/ecog.05411 10.1556/ComEc.9.2008.2.10 10.1890/02-5078 10.2193/2009-263 10.1890/12-0500.1 10.1007/978-4-431-99495-4 10.1080/01621459.2013.797356 10.1371/journal.pone.0176966 10.1002/ece3.1296 10.1007/s00442-001-0858-4 10.1198/jabes.2009.08038 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 10.1111/1365-2664.12432 10.2307/3802645 10.1007/s10980-015-0262-9 10.18637/jss.v076.i01 10.3150/12-BEJSP10 10.1890/09-0850.1 10.1016/j.prevetmed.2013.08.008 10.1007/s10164-019-00599-1 10.18637/jss.v040.i08 10.1111/2041-210X.12196 10.1111/1365-2664.12399 10.7554/eLife.38012 10.1198/108571101750524670 10.1046/j.1439-0310.2001.00647.x 10.1155/2010/459610 10.1111/2041-210X.12180 10.1111/j.2041-210X.2010.00050.x 10.1890/07-0298.1 10.1111/1365-2664.12076 10.1002/wmon.1015 10.1007/s13253-010-0053-3 10.1007/s10651-005-0006-0 10.1016/j.biocon.2012.12.016 10.1016/j.biocon.2016.09.001 10.1007/s13253-018-0327-8 10.1111/j.1442-9993.2008.01869.x 10.1016/j.biocon.2015.05.007 10.1111/biom.12763 10.2193/0091-7648(2006)34[211:UADRSA]2.0.CO;2 10.1111/j.1365-2664.2008.01509.x 10.1111/jzo.12416 10.1111/1365-2664.12700 10.1093/beheco/arv054 10.1890/09-0470.1 10.1007/BF00994199 10.1656/1092-6194(2004)011[0237:MAAPOE]2.0.CO;2 10.1890/12-2088.1 10.2193/0091-7648(2004)32[542:SRMCAH]2.0.CO;2 10.2307/3801296 10.1111/j.1469-7998.2012.00908.x 10.1111/2041-210X.12961 10.1111/2041-210X.12587 |
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| References | O’Connell AF, Nichols JD, Karanth KU (2011) Camera traps in animal ecology: methods and analyses. Springer, Tokyo BorchersDLDistillerGFosterRHarmsenBMilazzoLContinuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap surveyMethods Ecol Evol2014565666510.1111/2041-210X.12196 HigdonSDDigginsCACherryMJFordWMActivity patterns and temporal predator avoidance of white-tailed deer (Odocoileus virginianus) during the fawning seasonJ Ethol20193728329010.1007/s10164-019-00599-1 CressieNWikleCKStatistics for spatio-temporal data2011Hoboken, NJWiley1273.62017 WangYAllenMLWilmersCCMesopredator spatial and temporal responses to large predators and human development in the Santa Cruz mountains of CaliforniaBoil Conserv2015190233310.1016/j.biocon.2015.05.007 GarrardGEBekessySAMcCarthyMAWintleBAWhen have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveysAustral Ecol2008339869810.1111/j.1442-9993.2008.01869.x LingleSWilsonWFDetection and avoidance of predators in white-tailed deer (Odocoileus virginianus) and mule deer (Ohemionus)Ethology200110712514710.1046/j.1439-0310.2001.00647.x BeierPMcCulloughDRFactors influencing white-tailed deer activity patterns and habitat useWildl Monogr1990109351 WaddleJHDorazioRMWallsSCRiceKGBeauchampJSchumanMJMazzottiFJA new parameterization for estimating co-occurrence of interacting speciesEcol Appl2010201467147510.1890/09-0850.1 RotaCTFerreiraMARKaysRForresterTDKaliesELMcSheaWJParsonsAWMillspaughJJA multi-species occupancy model for two or more interacting speciesMethods Ecol Evol2016101164117310.1111/2041-210X.12587 WilliamsonSJHirthDHAn evaluation of edge use by white-tailed deerWildl Soc B198513252257 Lahoz-MonfortJJMorganBJTHarrisMPDauntFWanlessSFreemanSNBreeding together: modeling synchrony in productivity in a seabird communityEcology20139431010.1890/12-0500.1 KaysRParsonsAWBakerMCKaliesELForresterTCostelloRRotaCTMillspaughJJMcSheaWJData from: does hunting or hiking affect wildlife communities in protected areas?J Appl Ecol20175424225210.1111/1365-2664.12700 Guillera-ArroitaGMorganBJTRidoutMSLinkieMSpecies occupancy modeling for detection data collected along a transectJ Agr Biol Envir St20111630131728431271306.6228210.1007/s13253-010-0053-3 PollockKHWintersteinSRBunckCMCurtisPDSurvival analysis in telemetry studies: the staggered entry designJ Wildlife Manag19895371510.2307/3801296 KéryMRoyleJAApplied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance, and Species Richness in R and BUGS2016Elsevier, AmsterdamPrelude and Static Models1370.92004 DaiBDingSWahbaGMultivariate Bernoulli distributionBernoulli2013191465148331025591440.6222710.3150/12-BEJSP10 FroidevauxJSPZellwegerFBollmannKObristMKOptimizing passive acoustic sampling of bats in forestsEcol Evol2014244690470010.1002/ece3.1296 CherryMJConnerLMWarrenRJEffects of predation risk and group dynamics on white-tailed deer foraging behavior in a longleaf pine savannaBehav Ecol2015261091109910.1093/beheco/arv054 LesmeisterDBNielsonCKSchauberEMHellgrenECSpatial and temporal structure of a mesocarnivore guild in midwestern North AmericaWildl Monogr201519116110.1002/wmon.1015 KukielkaEBarasonaJACowieCEDreweJAGortazarCCotareloIVicenteJSpatial and temporal interactions between livestock and wildlife in south central Spain assessed by camera trapsPrevent Vet Med201311221322110.1016/j.prevetmed.2013.08.008 LingleSPellisSFight or flight? Antipredator behavior and the escalation of coyote encounters with deerOecologia200213115416410.1007/s00442-001-0858-4 RameshTKalleRSankarKQureshiQSpatio-temporal partitioning among large carnivores in relation to major prey species in western GhatsJ Zool201228726927510.1111/j.1469-7998.2012.00908.x RichmondOMWHinesJEBeissingerSRTwo-species occupancy models: a new parameterization applied to co-occurrence of secretive railsEcol Appl2010202036204610.1890/09-0470.1 CarpenterBGelmanAHoffmanMDLeeDGoodrichBBetancourtMBrubakerMGuoJLiPRiddellAStan: a probabilistic programming languageJ Stat Soft20177613210.18637/jss.v076.i01 WayJGOrtegaIMStraussEGMovement and activity patterns of eastern coyotes in a coastal, suburban environmentNortheast Nat20041123725410.1656/1092-6194(2004)011[0237:MAAPOE]2.0.CO;2 KaysRParsonsAWBakerMCKaliesELForresterTCostelloRRotaCTMillspaughJJMcSheaWJDoes hunting or hiking affect wildlife communities in protected areas?J Appl Ecol20165424225210.1111/1365-2664.12700 KaysRWGompperMERayJCLandscape ecology of eastern coyotes based on large-scale estimates of abundanceEcol Appl2008181014102710.1890/07-0298.1 MacKenzieDINicholsJDLachmanGBDroegeSAndrew RoyleJLangtimmCAEstimating site occupancy rates when detection probabilities are less than oneEcology20028322485510.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model. Methods Ecol Evol 5:397–406 AcevedoMAVillaneuva-RiveraLJFrom the field: Using automated digital recording systems as effective tools for the monitoring of birds and amphibiansWildl Soc B20063421121410.2193/0091-7648(2006)34[211:UADRSA]2.0.CO;2 RydénTConsistent and asymptotically normal parameter estimates for Markov modulated Poisson processesScand J Stat19952229530313632140838.62078 SchofieldMRBarkerRJGellingNContinuous-time capture-recapture in closed populationsBiometrics20187462663538253491414.6247310.1111/biom.12763 WimmerJTowseyMRoePWilliamsonISampling environmental acoustic recordings to determine bird species richnessEcol Appl2013231419142810.1890/12-2088.1 ChaoAAn overview of closed capture-recapture modelsJ Agr Biol Envir St2001615817510.1198/108571101750524670 RidoutMSLinkieMEstimating overlap of daily activity patterns from camera trap dataJ Agr Biol Envir St20091432233727508431306.6232710.1198/jabes.2009.08038 DorazioRMKaranthKUA hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recordersPLoS ONE201712e017696610.1371/journal.pone.0176966 SkaugHJMarkov-modulated Poisson processes for clustered line transect dataEnviron Ecol Stat200613199211222738610.1007/s10651-005-0006-0 SwihartRKPignatelloJJMattinaMJAversive responses of white-tailed deer, Odocoileus virginianus, to predator urinesJ Chem Ecol19911776777710.1007/BF00994199 MacKenzieDNicholsJRoyleJAPollockKBaileyLHinesJOccupancy estimation and modeling: inferring patterns and dynamic of species occurrence20172LondonAcademic Press1377.62016 Plummer M (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proc 3rd Int Workshop Distrib Stat Comput 124:1–10 BurtonACNeilsonEMoreiraDLadleASteenwegRFisherJTBayneEBoutinSWildlife camera trapping: a review and recommendations for linking surveys to ecological processesJ Appl Ecol2015567568510.1111/1365-2664.12432 KilgoJCRayHSRuthCMillerKVCan coyotes affect deer populations in southeastern North America?J Wildl Manag20107492993310.2193/2009-263 LangrockRBorchersDSkaugHJMarkov-modulated nonhomogeneous Poisson processes for modeling detections in surveys of marine mammal abundanceJ Am Stat Assoc201310884085131746670622497010.1080/01621459.2013.797356 BallardWBWhitlawHAYoungSJJenkinsRAForbesGJPredation and survival of white-tailed deer fawns in Northcentral New BrunswickJ Wildl Manag19996357457910.2307/3802645 Lahoz-MonfortJJMorganBJHarrisMPWanlessSFreemanSNA capture-recapture model for exploring multi-species synchrony in survivalMethods Ecol Evol201121162410.1111/j.2041-210X.2010.00050.x WebbSLGeeKLStricklandBKDemaraisSDeYoungRWMeasuring fine-scale white-tailed deer movements and environmental influences using GPS collarsInt J Ecol2010201011210.1155/2010/459610 TyreAJTenhumbergBFieldSANiejalkeDParrisKPossinghamHPImproving precision and reducing bias in biological surveys by estimating false negative error rates in presence-absence dataEcol Appl2003131790180110.1890/02-5078 R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org EddelbuettelDFrançoisRAllaireJUsheyKKouQRusselNChambersJBatesDRcpp: seamless R and C++\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$++$$\end{document} integrationJ Stat Soft20114018 Parsons AW, Forrester T, Baker-Whatton MC, McShea WJ, Rota CT, Schuttler SG, Millspaugh JJ, Kays R (2018) Mammal communities are larger and more diverse in moderately developed areas. eLife 7:e38012 SteinmetzRSeuaturienNChutipongWTigers, leopards, and dholes in a half-empty forest: assessing species interactions in a guild of threatened carnivoresBiol Conserv2013163687810.1016/j.biocon.2012.12.016 ChoquetRMarkov-modulated Poisson processes as a new framework for analysing capture-recapture dataMethods Ecol Evol2018992993510.1111/2041-210X.12961 CromsigtJPKuijperDPAdamMBeschtaRLChurskiMEycottAKerleyGIMysterudASchmidtKWestKHunting for fear: innovating management of human-wildlife conflictsJ Appl Ecol20135054454910.1111/1365-2664.12076 McSheaWJForresterTCostelloRHeZKaysRVolunteer-run cameras as distributed sensors for macrosystem mammal researchLandscape Ecol201531556610.1007/s10980-015-0262-9 Nichols JD, Bailey LL, O’Connell AF Jr, Talancy NW, Campbell Grant EH, Gilbert AT, Annand EM, Husband TP, Hines JE (2008) Multi-scale occupancy estimation and modelling using multiple detection methods. J Appl Ecol 45:1321–1329 ParsonsAWBlandCForresterTBaker-WhattonMCSchuttlerSGMcSheaWJCostelloRKaysRThe ecological impact of humans and dogs on wildlife in protected areas in eastern North AmericaBiol Conserv2016203758810.1016/j.biocon.2016.09.001 ToblerMWZúñiga HartleyACarrillo-PercasteguiSEPowellGVSpatiotemporal hierarchical modelling of species richness and occupancy using camera trap dataJ Ap DL Borchers (482_CR5) 2014; 5 KH Pollock (482_CR41) 1989; 53 SD Higdon (482_CR19) 2019; 37 M Kéry (482_CR24) 2016 T Ramesh (482_CR44) 2012; 287 JJ Lahoz-Monfort (482_CR28) 2013; 94 MW Tobler (482_CR55) 2015; 52 JP Cromsigt (482_CR12) 2013; 50 A Chao (482_CR8) 2001; 6 DB Lesmeister (482_CR30) 2015; 191 JK Vreeland (482_CR57) 2004; 32 SG Schuttler (482_CR51) 2017; 301 AJ Tyre (482_CR56) 2003; 13 JSP Froidevaux (482_CR16) 2014; 24 MR Schofield (482_CR50) 2018; 74 B Dai (482_CR13) 2013; 19 DI MacKenzie (482_CR34) 2002; 83 MJ Cherry (482_CR9) 2015; 26 T Rydén (482_CR48) 1995; 22 G Guillera-Arroita (482_CR18) 2011; 16 JJ Lahoz-Monfort (482_CR27) 2011; 2 EM Schliep (482_CR49) 2018; 23 HJ Skaug (482_CR52) 2006; 13 Y Wang (482_CR59) 2015; 190 WB Ballard (482_CR3) 1999; 63 482_CR39 482_CR37 MS Ridout (482_CR46) 2009; 14 P Beier (482_CR4) 1990; 109 482_CR36 RW Kays (482_CR20) 2008; 18 B Carpenter (482_CR7) 2017; 76 JG Way (482_CR60) 2004; 11 J Wimmer (482_CR63) 2013; 23 RK Swihart (482_CR54) 1991; 17 N Cressie (482_CR11) 2011 482_CR1 R Kays (482_CR22) 2017; 54 S Lingle (482_CR32) 2001; 107 WJ McShea (482_CR35) 2015; 31 GE Garrard (482_CR17) 2008; 33 AC Burton (482_CR6) 2015; 5 482_CR42 482_CR43 482_CR40 AW Parsons (482_CR38) 2016; 203 OMW Richmond (482_CR45) 2010; 20 SJ Williamson (482_CR62) 1985; 13 MA Acevedo (482_CR2) 2006; 34 R Choquet (482_CR10) 2018; 9 M Kéry (482_CR23) 2008; 9 D Eddelbuettel (482_CR15) 2011; 40 JC Kilgo (482_CR25) 2010; 74 R Kays (482_CR21) 2016; 54 JH Waddle (482_CR58) 2010; 20 RM Dorazio (482_CR14) 2017; 12 D MacKenzie (482_CR33) 2017 R Steinmetz (482_CR53) 2013; 163 R Langrock (482_CR29) 2013; 108 CT Rota (482_CR47) 2016; 10 SL Webb (482_CR61) 2010; 2010 E Kukielka (482_CR26) 2013; 112 S Lingle (482_CR31) 2002; 131 |
| References_xml | – reference: DaiBDingSWahbaGMultivariate Bernoulli distributionBernoulli2013191465148331025591440.6222710.3150/12-BEJSP10 – reference: VreelandJKDiefenbachDRWallingfordBDSurvival rates, mortality causes, and habitats of Pennsylvania white-tailed deer fawnsWildl Soc B20043254255310.2193/0091-7648(2004)32[542:SRMCAH]2.0.CO;2 – reference: Ait Kaci Azzou S, Singer L, Aebischer T, Caduff M, Wolf B, Wegmann D (2021) A sparse observation model to quantify species distributions and their overlap in space and time. Ecography. https://doi.org/10.1111/ecog.05411 – reference: BeierPMcCulloughDRFactors influencing white-tailed deer activity patterns and habitat useWildl Monogr1990109351 – reference: R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ – reference: RidoutMSLinkieMEstimating overlap of daily activity patterns from camera trap dataJ Agr Biol Envir St20091432233727508431306.6232710.1198/jabes.2009.08038 – reference: DorazioRMKaranthKUA hierarchical model for estimating the spatial distribution and abundance of animals detected by continuous-time recordersPLoS ONE201712e017696610.1371/journal.pone.0176966 – reference: RameshTKalleRSankarKQureshiQSpatio-temporal partitioning among large carnivores in relation to major prey species in western GhatsJ Zool201228726927510.1111/j.1469-7998.2012.00908.x – reference: MacKenzieDINicholsJDLachmanGBDroegeSAndrew RoyleJLangtimmCAEstimating site occupancy rates when detection probabilities are less than oneEcology20028322485510.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 – reference: LesmeisterDBNielsonCKSchauberEMHellgrenECSpatial and temporal structure of a mesocarnivore guild in midwestern North AmericaWildl Monogr201519116110.1002/wmon.1015 – reference: FroidevauxJSPZellwegerFBollmannKObristMKOptimizing passive acoustic sampling of bats in forestsEcol Evol2014244690470010.1002/ece3.1296 – reference: BallardWBWhitlawHAYoungSJJenkinsRAForbesGJPredation and survival of white-tailed deer fawns in Northcentral New BrunswickJ Wildl Manag19996357457910.2307/3802645 – reference: SchuttlerSGParsonsAWForresterTDBakerMCMcSheaWJCostelloRKaysRDeer on the lookout: how hunting, hiking and coyotes affect white-tailed deer vigilanceJ Zool201730132032710.1111/jzo.12416 – reference: Nichols JD, Bailey LL, O’Connell AF Jr, Talancy NW, Campbell Grant EH, Gilbert AT, Annand EM, Husband TP, Hines JE (2008) Multi-scale occupancy estimation and modelling using multiple detection methods. J Appl Ecol 45:1321–1329 – reference: AcevedoMAVillaneuva-RiveraLJFrom the field: Using automated digital recording systems as effective tools for the monitoring of birds and amphibiansWildl Soc B20063421121410.2193/0091-7648(2006)34[211:UADRSA]2.0.CO;2 – reference: KaysRWGompperMERayJCLandscape ecology of eastern coyotes based on large-scale estimates of abundanceEcol Appl2008181014102710.1890/07-0298.1 – reference: CromsigtJPKuijperDPAdamMBeschtaRLChurskiMEycottAKerleyGIMysterudASchmidtKWestKHunting for fear: innovating management of human-wildlife conflictsJ Appl Ecol20135054454910.1111/1365-2664.12076 – reference: ChoquetRMarkov-modulated Poisson processes as a new framework for analysing capture-recapture dataMethods Ecol Evol2018992993510.1111/2041-210X.12961 – reference: LangrockRBorchersDSkaugHJMarkov-modulated nonhomogeneous Poisson processes for modeling detections in surveys of marine mammal abundanceJ Am Stat Assoc201310884085131746670622497010.1080/01621459.2013.797356 – reference: KilgoJCRayHSRuthCMillerKVCan coyotes affect deer populations in southeastern North America?J Wildl Manag20107492993310.2193/2009-263 – reference: CressieNWikleCKStatistics for spatio-temporal data2011Hoboken, NJWiley1273.62017 – reference: ToblerMWZúñiga HartleyACarrillo-PercasteguiSEPowellGVSpatiotemporal hierarchical modelling of species richness and occupancy using camera trap dataJ Appl Ecol20155241342110.1111/1365-2664.12399 – reference: WangYAllenMLWilmersCCMesopredator spatial and temporal responses to large predators and human development in the Santa Cruz mountains of CaliforniaBoil Conserv2015190233310.1016/j.biocon.2015.05.007 – reference: KéryMRoyleJAApplied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance, and Species Richness in R and BUGS2016Elsevier, AmsterdamPrelude and Static Models1370.92004 – reference: RotaCTFerreiraMARKaysRForresterTDKaliesELMcSheaWJParsonsAWMillspaughJJA multi-species occupancy model for two or more interacting speciesMethods Ecol Evol2016101164117310.1111/2041-210X.12587 – reference: WaddleJHDorazioRMWallsSCRiceKGBeauchampJSchumanMJMazzottiFJA new parameterization for estimating co-occurrence of interacting speciesEcol Appl2010201467147510.1890/09-0850.1 – reference: SchliepEMGelfandAEClarkJSKaysRJoint temporal point pattern models for proximate species occurrence in a fixed area using camera trap dataJ Agr Biol Envir St20182333435738409751426.6235710.1007/s13253-018-0327-8 – reference: Lahoz-MonfortJJMorganBJTHarrisMPDauntFWanlessSFreemanSNBreeding together: modeling synchrony in productivity in a seabird communityEcology20139431010.1890/12-0500.1 – reference: KéryMSchmidtBImperfect detection and its consequences for monitoring for conservationCommun Ecol2008920721610.1556/ComEc.9.2008.2.10 – reference: ParsonsAWBlandCForresterTBaker-WhattonMCSchuttlerSGMcSheaWJCostelloRKaysRThe ecological impact of humans and dogs on wildlife in protected areas in eastern North AmericaBiol Conserv2016203758810.1016/j.biocon.2016.09.001 – reference: KukielkaEBarasonaJACowieCEDreweJAGortazarCCotareloIVicenteJSpatial and temporal interactions between livestock and wildlife in south central Spain assessed by camera trapsPrevent Vet Med201311221322110.1016/j.prevetmed.2013.08.008 – reference: McSheaWJForresterTCostelloRHeZKaysRVolunteer-run cameras as distributed sensors for macrosystem mammal researchLandscape Ecol201531556610.1007/s10980-015-0262-9 – reference: KaysRParsonsAWBakerMCKaliesELForresterTCostelloRRotaCTMillspaughJJMcSheaWJData from: does hunting or hiking affect wildlife communities in protected areas?J Appl Ecol20175424225210.1111/1365-2664.12700 – reference: TyreAJTenhumbergBFieldSANiejalkeDParrisKPossinghamHPImproving precision and reducing bias in biological surveys by estimating false negative error rates in presence-absence dataEcol Appl2003131790180110.1890/02-5078 – reference: RydénTConsistent and asymptotically normal parameter estimates for Markov modulated Poisson processesScand J Stat19952229530313632140838.62078 – reference: BurtonACNeilsonEMoreiraDLadleASteenwegRFisherJTBayneEBoutinSWildlife camera trapping: a review and recommendations for linking surveys to ecological processesJ Appl Ecol2015567568510.1111/1365-2664.12432 – reference: Lahoz-MonfortJJMorganBJHarrisMPWanlessSFreemanSNA capture-recapture model for exploring multi-species synchrony in survivalMethods Ecol Evol201121162410.1111/j.2041-210X.2010.00050.x – reference: Pollock LJ, Tingley R, Morris WK, Golding N, O’Hara RB, Parris KM, Vesk PA, McCarthy MA (2014) Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model. Methods Ecol Evol 5:397–406 – reference: RichmondOMWHinesJEBeissingerSRTwo-species occupancy models: a new parameterization applied to co-occurrence of secretive railsEcol Appl2010202036204610.1890/09-0470.1 – reference: WayJGOrtegaIMStraussEGMovement and activity patterns of eastern coyotes in a coastal, suburban environmentNortheast Nat20041123725410.1656/1092-6194(2004)011[0237:MAAPOE]2.0.CO;2 – reference: LingleSPellisSFight or flight? Antipredator behavior and the escalation of coyote encounters with deerOecologia200213115416410.1007/s00442-001-0858-4 – reference: Plummer M (2003) JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proc 3rd Int Workshop Distrib Stat Comput 124:1–10 – reference: SchofieldMRBarkerRJGellingNContinuous-time capture-recapture in closed populationsBiometrics20187462663538253491414.6247310.1111/biom.12763 – reference: SteinmetzRSeuaturienNChutipongWTigers, leopards, and dholes in a half-empty forest: assessing species interactions in a guild of threatened carnivoresBiol Conserv2013163687810.1016/j.biocon.2012.12.016 – reference: WimmerJTowseyMRoePWilliamsonISampling environmental acoustic recordings to determine bird species richnessEcol Appl2013231419142810.1890/12-2088.1 – reference: ChaoAAn overview of closed capture-recapture modelsJ Agr Biol Envir St2001615817510.1198/108571101750524670 – reference: WilliamsonSJHirthDHAn evaluation of edge use by white-tailed deerWildl Soc B198513252257 – reference: GarrardGEBekessySAMcCarthyMAWintleBAWhen have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveysAustral Ecol2008339869810.1111/j.1442-9993.2008.01869.x – reference: MacKenzieDNicholsJRoyleJAPollockKBaileyLHinesJOccupancy estimation and modeling: inferring patterns and dynamic of species occurrence20172LondonAcademic Press1377.62016 – reference: EddelbuettelDFrançoisRAllaireJUsheyKKouQRusselNChambersJBatesDRcpp: seamless R and C++\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$++$$\end{document} integrationJ Stat Soft20114018 – reference: KaysRParsonsAWBakerMCKaliesELForresterTCostelloRRotaCTMillspaughJJMcSheaWJDoes hunting or hiking affect wildlife communities in protected areas?J Appl Ecol20165424225210.1111/1365-2664.12700 – reference: Parsons AW, Forrester T, Baker-Whatton MC, McShea WJ, Rota CT, Schuttler SG, Millspaugh JJ, Kays R (2018) Mammal communities are larger and more diverse in moderately developed areas. eLife 7:e38012 – reference: BorchersDLDistillerGFosterRHarmsenBMilazzoLContinuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap surveyMethods Ecol Evol2014565666510.1111/2041-210X.12196 – reference: O’Connell AF, Nichols JD, Karanth KU (2011) Camera traps in animal ecology: methods and analyses. Springer, Tokyo – reference: HigdonSDDigginsCACherryMJFordWMActivity patterns and temporal predator avoidance of white-tailed deer (Odocoileus virginianus) during the fawning seasonJ Ethol20193728329010.1007/s10164-019-00599-1 – reference: WebbSLGeeKLStricklandBKDemaraisSDeYoungRWMeasuring fine-scale white-tailed deer movements and environmental influences using GPS collarsInt J Ecol2010201011210.1155/2010/459610 – reference: SwihartRKPignatelloJJMattinaMJAversive responses of white-tailed deer, Odocoileus virginianus, to predator urinesJ Chem Ecol19911776777710.1007/BF00994199 – reference: LingleSWilsonWFDetection and avoidance of predators in white-tailed deer (Odocoileus virginianus) and mule deer (Ohemionus)Ethology200110712514710.1046/j.1439-0310.2001.00647.x – reference: Guillera-ArroitaGMorganBJTRidoutMSLinkieMSpecies occupancy modeling for detection data collected along a transectJ Agr Biol Envir St20111630131728431271306.6228210.1007/s13253-010-0053-3 – reference: SkaugHJMarkov-modulated Poisson processes for clustered line transect dataEnviron Ecol Stat200613199211222738610.1007/s10651-005-0006-0 – reference: CarpenterBGelmanAHoffmanMDLeeDGoodrichBBetancourtMBrubakerMGuoJLiPRiddellAStan: a probabilistic programming languageJ Stat Soft20177613210.18637/jss.v076.i01 – reference: PollockKHWintersteinSRBunckCMCurtisPDSurvival analysis in telemetry studies: the staggered entry designJ Wildlife Manag19895371510.2307/3801296 – reference: CherryMJConnerLMWarrenRJEffects of predation risk and group dynamics on white-tailed deer foraging behavior in a longleaf pine savannaBehav Ecol2015261091109910.1093/beheco/arv054 – ident: 482_CR1 doi: 10.1111/ecog.05411 – volume-title: Occupancy estimation and modeling: inferring patterns and dynamic of species occurrence year: 2017 ident: 482_CR33 – volume: 9 start-page: 207 year: 2008 ident: 482_CR23 publication-title: Commun Ecol doi: 10.1556/ComEc.9.2008.2.10 – volume: 13 start-page: 1790 year: 2003 ident: 482_CR56 publication-title: Ecol Appl doi: 10.1890/02-5078 – volume: 74 start-page: 929 year: 2010 ident: 482_CR25 publication-title: J Wildl Manag doi: 10.2193/2009-263 – volume-title: Statistics for spatio-temporal data year: 2011 ident: 482_CR11 – volume: 94 start-page: 3 year: 2013 ident: 482_CR28 publication-title: Ecology doi: 10.1890/12-0500.1 – ident: 482_CR37 doi: 10.1007/978-4-431-99495-4 – volume: 108 start-page: 840 year: 2013 ident: 482_CR29 publication-title: J Am Stat Assoc doi: 10.1080/01621459.2013.797356 – volume: 12 start-page: e0176966 year: 2017 ident: 482_CR14 publication-title: PLoS ONE doi: 10.1371/journal.pone.0176966 – volume: 24 start-page: 4690 year: 2014 ident: 482_CR16 publication-title: Ecol Evol doi: 10.1002/ece3.1296 – volume: 131 start-page: 154 year: 2002 ident: 482_CR31 publication-title: Oecologia doi: 10.1007/s00442-001-0858-4 – volume: 14 start-page: 322 year: 2009 ident: 482_CR46 publication-title: J Agr Biol Envir St doi: 10.1198/jabes.2009.08038 – volume-title: Applied Hierarchical Modeling in Ecology: Analysis of Distribution, Abundance, and Species Richness in R and BUGS year: 2016 ident: 482_CR24 – volume: 83 start-page: 2248 year: 2002 ident: 482_CR34 publication-title: Ecology doi: 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2 – volume: 5 start-page: 675 year: 2015 ident: 482_CR6 publication-title: J Appl Ecol doi: 10.1111/1365-2664.12432 – volume: 63 start-page: 574 year: 1999 ident: 482_CR3 publication-title: J Wildl Manag doi: 10.2307/3802645 – volume: 31 start-page: 55 year: 2015 ident: 482_CR35 publication-title: Landscape Ecol doi: 10.1007/s10980-015-0262-9 – volume: 76 start-page: 1 year: 2017 ident: 482_CR7 publication-title: J Stat Soft doi: 10.18637/jss.v076.i01 – volume: 19 start-page: 1465 year: 2013 ident: 482_CR13 publication-title: Bernoulli doi: 10.3150/12-BEJSP10 – volume: 22 start-page: 295 year: 1995 ident: 482_CR48 publication-title: Scand J Stat – volume: 20 start-page: 1467 year: 2010 ident: 482_CR58 publication-title: Ecol Appl doi: 10.1890/09-0850.1 – volume: 112 start-page: 213 year: 2013 ident: 482_CR26 publication-title: Prevent Vet Med doi: 10.1016/j.prevetmed.2013.08.008 – volume: 37 start-page: 283 year: 2019 ident: 482_CR19 publication-title: J Ethol doi: 10.1007/s10164-019-00599-1 – volume: 40 start-page: 1 year: 2011 ident: 482_CR15 publication-title: J Stat Soft doi: 10.18637/jss.v040.i08 – volume: 13 start-page: 252 year: 1985 ident: 482_CR62 publication-title: Wildl Soc B – volume: 5 start-page: 656 year: 2014 ident: 482_CR5 publication-title: Methods Ecol Evol doi: 10.1111/2041-210X.12196 – volume: 52 start-page: 413 year: 2015 ident: 482_CR55 publication-title: J Appl Ecol doi: 10.1111/1365-2664.12399 – ident: 482_CR39 doi: 10.7554/eLife.38012 – volume: 6 start-page: 158 year: 2001 ident: 482_CR8 publication-title: J Agr Biol Envir St doi: 10.1198/108571101750524670 – volume: 107 start-page: 125 year: 2001 ident: 482_CR32 publication-title: Ethology doi: 10.1046/j.1439-0310.2001.00647.x – ident: 482_CR43 – volume: 2010 start-page: 1 year: 2010 ident: 482_CR61 publication-title: Int J Ecol doi: 10.1155/2010/459610 – ident: 482_CR40 – ident: 482_CR42 doi: 10.1111/2041-210X.12180 – volume: 2 start-page: 116 year: 2011 ident: 482_CR27 publication-title: Methods Ecol Evol doi: 10.1111/j.2041-210X.2010.00050.x – volume: 18 start-page: 1014 year: 2008 ident: 482_CR20 publication-title: Ecol Appl doi: 10.1890/07-0298.1 – volume: 50 start-page: 544 year: 2013 ident: 482_CR12 publication-title: J Appl Ecol doi: 10.1111/1365-2664.12076 – volume: 191 start-page: 1 year: 2015 ident: 482_CR30 publication-title: Wildl Monogr doi: 10.1002/wmon.1015 – volume: 16 start-page: 301 year: 2011 ident: 482_CR18 publication-title: J Agr Biol Envir St doi: 10.1007/s13253-010-0053-3 – volume: 13 start-page: 199 year: 2006 ident: 482_CR52 publication-title: Environ Ecol Stat doi: 10.1007/s10651-005-0006-0 – volume: 163 start-page: 68 year: 2013 ident: 482_CR53 publication-title: Biol Conserv doi: 10.1016/j.biocon.2012.12.016 – volume: 203 start-page: 75 year: 2016 ident: 482_CR38 publication-title: Biol Conserv doi: 10.1016/j.biocon.2016.09.001 – volume: 23 start-page: 334 year: 2018 ident: 482_CR49 publication-title: J Agr Biol Envir St doi: 10.1007/s13253-018-0327-8 – volume: 33 start-page: 986 year: 2008 ident: 482_CR17 publication-title: Austral Ecol doi: 10.1111/j.1442-9993.2008.01869.x – volume: 190 start-page: 23 year: 2015 ident: 482_CR59 publication-title: Boil Conserv doi: 10.1016/j.biocon.2015.05.007 – volume: 74 start-page: 626 year: 2018 ident: 482_CR50 publication-title: Biometrics doi: 10.1111/biom.12763 – volume: 34 start-page: 211 year: 2006 ident: 482_CR2 publication-title: Wildl Soc B doi: 10.2193/0091-7648(2006)34[211:UADRSA]2.0.CO;2 – ident: 482_CR36 doi: 10.1111/j.1365-2664.2008.01509.x – volume: 109 start-page: 3 year: 1990 ident: 482_CR4 publication-title: Wildl Monogr – volume: 301 start-page: 320 year: 2017 ident: 482_CR51 publication-title: J Zool doi: 10.1111/jzo.12416 – volume: 54 start-page: 242 year: 2017 ident: 482_CR22 publication-title: J Appl Ecol doi: 10.1111/1365-2664.12700 – volume: 26 start-page: 1091 year: 2015 ident: 482_CR9 publication-title: Behav Ecol doi: 10.1093/beheco/arv054 – volume: 54 start-page: 242 year: 2016 ident: 482_CR21 publication-title: J Appl Ecol doi: 10.1111/1365-2664.12700 – volume: 20 start-page: 2036 year: 2010 ident: 482_CR45 publication-title: Ecol Appl doi: 10.1890/09-0470.1 – volume: 17 start-page: 767 year: 1991 ident: 482_CR54 publication-title: J Chem Ecol doi: 10.1007/BF00994199 – volume: 11 start-page: 237 year: 2004 ident: 482_CR60 publication-title: Northeast Nat doi: 10.1656/1092-6194(2004)011[0237:MAAPOE]2.0.CO;2 – volume: 23 start-page: 1419 year: 2013 ident: 482_CR63 publication-title: Ecol Appl doi: 10.1890/12-2088.1 – volume: 32 start-page: 542 year: 2004 ident: 482_CR57 publication-title: Wildl Soc B doi: 10.2193/0091-7648(2004)32[542:SRMCAH]2.0.CO;2 – volume: 53 start-page: 7 year: 1989 ident: 482_CR41 publication-title: J Wildlife Manag doi: 10.2307/3801296 – volume: 287 start-page: 269 year: 2012 ident: 482_CR44 publication-title: J Zool doi: 10.1111/j.1469-7998.2012.00908.x – volume: 9 start-page: 929 year: 2018 ident: 482_CR10 publication-title: Methods Ecol Evol doi: 10.1111/2041-210X.12961 – volume: 10 start-page: 1164 year: 2016 ident: 482_CR47 publication-title: Methods Ecol Evol doi: 10.1111/2041-210X.12587 |
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| SubjectTerms | acoustics Activity patterns Agriculture Availability Biostatistics Cameras Canis latrans data collection Datasets Deer Health Sciences Mathematics and Statistics Medicine Monitoring/Environmental Analysis Odocoileus virginianus Sensors Species Statistics Statistics for Life Sciences weather White-tailed deer |
| Title | A Two-Species Occupancy Model with a Continuous-Time Detection Process Reveals Spatial and Temporal Interactions |
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