Combining acoustic localisation and high-resolution land cover classification to study predator vocalisation behaviour
Elisabeth Bru
A B * , Bethany R. Smith C D , Hannah Butkiewicz E , Amy C. Fontaine F , Angela Dassow G , Jessica L. Owens H , Holly Root-Gutteridge I , Loretta Schindler J and Arik Kershenbaum K
+ Author Affiliations
- Author Affiliations
A Department of Zoology, University of Cambridge, Cambridge CB3 0JG, UK.
B Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot SL5 7PY, UK.
C School of Animal, Rural and Environmental Sciences, Nottingham Trent University, Brackenhurst Lane, Southwell NG25 0QF, UK.
D Mammal Society, Milton Abbas, Dorset DT11 0BL, UK.
E College of Natural Resources, University of Wisconsin-Stevens Point, 2100 Main Street, Stevens Point, WI 54481, USA.
F Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA.
G Biology Department, Carthage College, 2001 Alford Park Drive, Kenosha, WI 53140, USA.
H Unleashed Training, LLC, Daytona Beach, FL 32114, USA.
I School of Life Sciences, University of Lincoln, Beevor Street, Lincoln, LN6 7DL, UK.
J Department of Zoology, Faculty of Science, Charles University, Prague 128 44, Czech Republic.
K Girton College, and Department of Zoology, University of Cambridge, Cambridge CB3 0JG, UK.
Wildlife Research 50(12) 965-979 https://doi.org/10.1071/WR22007
Submitted: 18 January 2022 Accepted: 22 December 2022 Published: 13 February 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: The ecology of cryptic animals is difficult to study without invasive tagging approaches or labour-intensive field surveys. Acoustic localisation provides an effective way to locate vocalising animals using acoustic recorders. Combining this with land cover classification gives new insight into wild animal behaviour using non-invasive tools.
Aims: This study aims to demonstrate how acoustic localisation – combined with high-resolution land cover classification – permits the study of the ecology of vocalising animals in the wild. We illustrate this technique by investigating the effect of land cover and distances to anthropogenic features on coyote and wolf vocal behaviour.
Methods: We collected recordings over 13 days in Wisconsin, USA, and triangulated vocalising animals’ locations using acoustic localisation. We then mapped these locations onto land cover using a high-resolution land cover map we produced for the area.
Key results: Neither coyotes nor wolves vocalised more in one habitat type over another. Coyotes vocalised significantly closer to all human features than expected by chance, whereas wolves vocalised significantly further away. When vocalising closer to human features, coyotes selected forests but wolves showed no habitat preference.
Conclusions: This novel combination of two sophisticated, autonomous sensing-driven tools permits us to examine animal land use and behavioural ecology using passive sensors, with the aim of drawing ecologically important conclusions.
Implications: We envisage that this method can be used at larger scales to aid monitoring of vocally active animals across landscapes. Firstly, it permits us to characterise habitat use while vocalising, which is an essential behaviour for many species. Furthermore, if combined with additional knowledge of how a species’ habitat selection while vocalising relates to its general habitat use, this method could permit the derivation of future conclusions on prevailing landscape use. In summary, this study demonstrates the potential of integrating acoustic localisation with land cover classification in ecological research.
Keywords: anthropogenic disturbance, bioacoustics, Canis latrans, Canis lupus, habitat selection, howl, multilateration, passive acoustic monitoring, remote sensing.
References
Acevedo, MA, Corrada-Bravo, CJ, Corrada-Bravo, H, Villanueva-Rivera, LJ, and Aide, TM (2009). Automated classification of bird and amphibian calls using machine learning: a comparison of methods. Ecological Informatics 4, 206–214.| Automated classification of bird and amphibian calls using machine learning: a comparison of methods.Crossref | GoogleScholarGoogle Scholar |
Amorim, MCP, and Neves, ASM (2008). Male painted gobies (Pomatoschistus pictus) vocalise to defend territories. Behaviour 145, 1065–1083.
| Male painted gobies (Pomatoschistus pictus) vocalise to defend territories.Crossref | GoogleScholarGoogle Scholar |
Angeloni, L, Schlaepfer, MA, Lawler, JJ, and Crooks, KR (2008). A reassessment of the interface between conservation and behaviour. Animal Behaviour 75, 731–737.
| A reassessment of the interface between conservation and behaviour.Crossref | GoogleScholarGoogle Scholar |
Balčiauskas, L, Balčiauskienė, L, Litvaitis, JA, and Tijušas, E (2021). Adaptive monitoring: using citizen scientists to track wolf populations when winter-track counts become unreliable. Wildlife Research 48, 76–85.
| Adaptive monitoring: using citizen scientists to track wolf populations when winter-track counts become unreliable.Crossref | GoogleScholarGoogle Scholar |
Batista, GEAPA, and Monard, MC (2003). An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence 17, 519–533.
| An analysis of four missing data treatment methods for supervised learning.Crossref | GoogleScholarGoogle Scholar |
Berger-Tal, O, Blumstein, DT, Carroll, S, Fisher, RN, Mesnick, SL, Owen, MA, Saltz, D, St. Claire, CC, and Swaisgood, RR (2016). A systematic survey of the integration of animal behavior into conservation. Conservation Biology 30, 744–753.
| A systematic survey of the integration of animal behavior into conservation.Crossref | GoogleScholarGoogle Scholar |
Bivand R, Rundel C (2020) Rgeos: interface to Geometry Engine – Open Source (‘GEOS’). Available at https://cran.r-project.org/package=rgeos
Boitani L, Powell RA (2012) ‘Carnivore Ecology and Conservation: a Handbook of Techniques.’ (Oxford University Press: Oxford,UK)
Bolt, LM, Russell, DG, Coggeshall, EMC, Jacobson, ZS, Merrigan-Johnson, C, and Schreier, AL (2019). Howling by the river: howler monkey (Alouatta palliata) communication in an anthropogenically-altered riparian forest in Costa Rica. Behaviour 157, 77–100.
| Howling by the river: howler monkey (Alouatta palliata) communication in an anthropogenically-altered riparian forest in Costa Rica.Crossref | GoogleScholarGoogle Scholar |
Carrizosa, E, and Romero Morales, D (2013). Supervised classification and mathematical optimization. Computers & Operations Research 40, 150–165.
| Supervised classification and mathematical optimization.Crossref | GoogleScholarGoogle Scholar |
Chamundeeswari VV, Singh D, Singh K (2007) Unsupervised land cover classification of SAR images by contour tracing. In ‘2007 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)’. pp. 547–550. (IEEE) 10.1109/IGARSS.2007.4422852
Chen, Q, Kuang, G, Li, J, Sui, L, and Li, D (2013). Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity. IEEE Transactions on Geoscience and Remote Sensing 51, 1817–1825.
| Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity.Crossref | GoogleScholarGoogle Scholar |
Cherry, MJ, Howell, PE, Seagraves, CD, Warren, RJ, and Conner, LM (2016). Effects of land cover on coyote abundance. Wildlife Research 43, 662–670.
| Effects of land cover on coyote abundance.Crossref | GoogleScholarGoogle Scholar |
Christin, S, Hervet, E, and Lecomte, N (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution 10, 1632–1644.
| Applications for deep learning in ecology.Crossref | GoogleScholarGoogle Scholar |
Coelho, CM, De Melo, LFB, Sábato, MAL, Vaz Magni, EM, Hirsch, A, and Young, RJ (2008). Habitat use by wild maned wolves (Chrysocyon brachyurus) in a transition zone environment. Journal of Mammalogy 89, 97–104.
| Habitat use by wild maned wolves (Chrysocyon brachyurus) in a transition zone environment.Crossref | GoogleScholarGoogle Scholar |
Dramiński, M, Rada-Iglesias, A, Enroth, S, Wadelius, C, Koronacki, J, and Komorowski, J (2008). Monte Carlo feature selection for supervised classification. Bioinformatics 24, 110–117.
| Monte Carlo feature selection for supervised classification.Crossref | GoogleScholarGoogle Scholar |
Dufourq, E, Durbach, I, Hansford, JP, Hoepfner, A, Ma, H, Bryant, JV, Stender, CS, Li, W, Liu, Z, Chen, Q, Zhou, Z, and Turvey, ST (2021). Automated detection of Hainan gibbon calls for passive acoustic monitoring. Remote Sensing in Ecology and Conservation 7, 475–487.
| Automated detection of Hainan gibbon calls for passive acoustic monitoring.Crossref | GoogleScholarGoogle Scholar |
Enderle, DIM, and Weih, RC (2005). Integrating supervised and unsupervised classification methods to develop a more accurate land cover classification. Journal of the Arkansas Academy of Science 59, .
Frommolt, K-H, and Tauchert, K-H (2014). Applying bioacoustic methods for long-term monitoring of a nocturnal wetland bird. Ecological Informatics 21, 4–12.
| Applying bioacoustic methods for long-term monitoring of a nocturnal wetland bird.Crossref | GoogleScholarGoogle Scholar |
Galaverni, M, Palumbo, D, Fabbri, E, Caniglia, R, Greco, C, and Randi, E (2012). Monitoring wolves (Canis lupus) by non-invasive genetics and camera trapping: a small-scale pilot study. European Journal of Wildlife Research 58, 47–58.
| Monitoring wolves (Canis lupus) by non-invasive genetics and camera trapping: a small-scale pilot study.Crossref | GoogleScholarGoogle Scholar |
Gehring, TM, VerCauteren, KC, Provost, ML, and Cellar, AC (2010). Utility of livestock-protection dogs for deterring wildlife from cattle farms. Wildlife Research 37, 715–721.
| Utility of livestock-protection dogs for deterring wildlife from cattle farms.Crossref | GoogleScholarGoogle Scholar |
Graves, SJ, Asner, GP, Martin, RE, Anderson, CB, Colgan, MS, Kalantari, L, and Bohlman, SA (2016). Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data. Remote Sensing 8, .
| Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data.Crossref | GoogleScholarGoogle Scholar |
Gurarie, E, Suutarinen, J, Kojola, I, and Ovaskainen, O (2011). Summer movements, predation and habitat use of wolves in human modified boreal forests. Oecologia 165, 891–903.
| Summer movements, predation and habitat use of wolves in human modified boreal forests.Crossref | GoogleScholarGoogle Scholar |
Hagens, SV, Rendall, AR, and Whisson, DA (2018). Passive acoustic surveys for predicting species’ distributions: optimising detection probability. PLoS ONE 13, e0199396.
| Passive acoustic surveys for predicting species’ distributions: optimising detection probability.Crossref | GoogleScholarGoogle Scholar |
Harrington FH, Asa CS (2003) Wolf communication. In ‘Wolves: Behavior, Ecology, and Conservation’. (Eds LD Mech, L Boitani) pp. 66–103. (University of Chicago Press: Chicago, IL, USA)
Harrington, FH, and Mech, LD (1979). Wolf howling and its role in territory maintenance. Behaviour 68, 207–249.
| Wolf howling and its role in territory maintenance.Crossref | GoogleScholarGoogle Scholar |
Harrington, FH, and Mech, LD (1982). An analysis of howling response parameters useful for wolf pack censusing. The Journal of Wildlife Management 46, 686–693.
| An analysis of howling response parameters useful for wolf pack censusing.Crossref | GoogleScholarGoogle Scholar |
Hayes, MM, Miller, SN, and Murphy, MA (2014). High-resolution landcover classification using Random Forest. Remote Sensing Letters 5, 112–121.
| High-resolution landcover classification using Random Forest.Crossref | GoogleScholarGoogle Scholar |
Hays, GC, Bailey, H, Bograd, SJ, Bowen, WD, Campagna, C, Carmichael, RH, Casale, P, Chiaradia, A, Costa, DP, Cuevas, E, Nico de Bruyn, PJ, Dias, MP, Duarte, CM, Dunn, DC, Dutton, PH, Esteban, N, Friedlaender, A, Goetz, KT, Godley, BJ, et al. (2019). Translating marine animal tracking data into conservation policy and management. Trends in Ecology & Evolution 34, 459–473.
| Translating marine animal tracking data into conservation policy and management.Crossref | GoogleScholarGoogle Scholar |
He, H, and Garcia, EA (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21, 1263–1284.
| Learning from imbalanced data.Crossref | GoogleScholarGoogle Scholar |
Hijmans R (2022) Raster: geographic data analysis and modeling. R package version 3.6-11. Available at http://CRAN.R-project.org/package=raster
Hiscock, OH, Back, Y, Kleidorfer, M, and Urich, C (2021). A GIS-based land cover classification approach suitable for fine-scale urban water management. Water Resources Management 35, 1339–1352.
| A GIS-based land cover classification approach suitable for fine-scale urban water management.Crossref | GoogleScholarGoogle Scholar |
Hromada, SJ, Esque, TC, Vandergast, AG, Dutcher, KE, Mitchell, CI, Gray, ME, Chang, T, Dickson, BG, and Nussear, KE (2020). Using movement to inform conservation corridor design for Mojave desert tortoise. Movement Ecology 8, .
| Using movement to inform conservation corridor design for Mojave desert tortoise.Crossref | GoogleScholarGoogle Scholar |
Jia, K, Liang, S, Zhang, N, Wei, X, Gu, X, Zhao, X, Yao, Y, and Xie, X (2014). Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data. ISPRS Journal of Photogrammetry and Remote Sensing 93, 49–55.
| Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data.Crossref | GoogleScholarGoogle Scholar |
Joshi, AM, Narayan, EJ, and Gramapurohit, NP (2019). Vocalisation and its association with androgens and corticosterone in a night frog (Nyctibatrachus humayuni) with unique breeding behaviour. Ethology 125, 774–784.
| Vocalisation and its association with androgens and corticosterone in a night frog (Nyctibatrachus humayuni) with unique breeding behaviour.Crossref | GoogleScholarGoogle Scholar |
K. Lisa Yang Center for Conservation Bioacoustics at the Cornell Lab of Ornithology (2022) Raven Pro: interactive sound analysis software (version 1.6.3) [computer software]. The Cornell Lab of Ornithology, Ithaca, NY, USA. Available at https://ravensoundsoftware.com/
Kershenbaum, A, Owens, JL, and Waller, S (2019). Tracking cryptic animals using acoustic multilateration: a system for long-range wolf detection. The Journal of the Acoustical Society of America 145, 1619–1628.
| Tracking cryptic animals using acoustic multilateration: a system for long-range wolf detection.Crossref | GoogleScholarGoogle Scholar |
Kojola, I, Helle, P, Heikkinen, S, Lindén, H, Paasivaara, A, and Wikman, M (2014). Tracks in snow and population size estimation: the wolf Canis lupus in Finland. Wildlife Biology 20, 279–284.
| Tracks in snow and population size estimation: the wolf Canis lupus in Finland.Crossref | GoogleScholarGoogle Scholar |
Krebs, J, Ashcroft, R, and Webber, M (1978). Song repertoires and territory defence in the great tit. Nature 271, 539–542.
| Song repertoires and territory defence in the great tit.Crossref | GoogleScholarGoogle Scholar |
Larom, D, Garstang, M, Payne, K, Raspet, R, and Lindeque, M (1997). The influence of surface atmospheric conditions on the range and area reached by animal vocalizations. The Journal of Experimental Biology 200, 421–431.
| The influence of surface atmospheric conditions on the range and area reached by animal vocalizations.Crossref | GoogleScholarGoogle Scholar |
Li, X, Buzzard, P, Chen, Y, and Jiang, X (2013). Patterns of livestock predation by carnivores: human–wildlife conflict in Northwest Yunnan, China. Environmental Management 52, 1334–1340.
| Patterns of livestock predation by carnivores: human–wildlife conflict in Northwest Yunnan, China.Crossref | GoogleScholarGoogle Scholar |
Llaneza, L, García, EJ, Palacios, V, Sazatornil, V, and López-Bao, JV (2016). Resting in risky environments: the importance of cover for wolves to cope with exposure risk in human-dominated landscapes. Biodiversity and Conservation 25, 1515–1528.
| Resting in risky environments: the importance of cover for wolves to cope with exposure risk in human-dominated landscapes.Crossref | GoogleScholarGoogle Scholar |
Marques, TA, Thomas, L, Martin, SW, Mellinger, DK, Ward, JA, Moretti, DJ, Harris, D, and Tyack, PL (2013). Estimating animal population density using passive acoustics. Biological Reviews 88, 287–309.
| Estimating animal population density using passive acoustics.Crossref | GoogleScholarGoogle Scholar |
Marten, K, and Marler, P (1977). Sound transmission and its significance for animal vocalization. Behavioral Ecology and Sociobiology 2, 271–290.
| Sound transmission and its significance for animal vocalization.Crossref | GoogleScholarGoogle Scholar |
Meek, PD, Ballard, G-A, and Fleming, PJS (2015). The pitfalls of wildlife camera trapping as a survey tool in Australia. Australian Mammalogy 37, 13–22.
| The pitfalls of wildlife camera trapping as a survey tool in Australia.Crossref | GoogleScholarGoogle Scholar |
Milchram, M, Suarez-Rubio, M, Schröder, A, and Bruckner, A (2020). Estimating population density of insectivorous bats based on stationary acoustic detectors: a case study. Ecology and Evolution 10, 1135–1144.
| Estimating population density of insectivorous bats based on stationary acoustic detectors: a case study.Crossref | GoogleScholarGoogle Scholar |
Mitchell, N, Strohbach, MW, Pratt, R, Finn, WC, and Strauss, EG (2015). Space use by resident and transient coyotes in an urban–rural landscape mosaic. Wildlife Research 42, 461–469.
| Space use by resident and transient coyotes in an urban–rural landscape mosaic.Crossref | GoogleScholarGoogle Scholar |
Mu, X, Hu, M, Song, W, Ruan, G, Ge, Y, Wang, J, Huang, S, and Yan, G (2015). Evaluation of sampling methods for validation of remotely sensed fractional vegetation cover. Remote Sensing 7, 16164–16182.
| Evaluation of sampling methods for validation of remotely sensed fractional vegetation cover.Crossref | GoogleScholarGoogle Scholar |
Muchoney, D, Borak, J, Chi, H, Friedl, M, Gopal, S, Hodges, J, Morrow, N, and Strahler, A (2000). Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America. International Journal of Remote Sensing 21, 1115–1138.
| Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America.Crossref | GoogleScholarGoogle Scholar |
Okoniewski, JC, and Chambers, RE (1984). Coyote vocal response to an electronic siren and human howling. The Journal of Wildlife Management 48, 217–222.
| Coyote vocal response to an electronic siren and human howling.Crossref | GoogleScholarGoogle Scholar |
Olofsson, P, Foody, GM, Herold, M, Stehman, SV, Woodcock, CE, and Wulder, MA (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148, 42–57.
| Good practices for estimating area and assessing accuracy of land change.Crossref | GoogleScholarGoogle Scholar |
O’Gara, JR, Wieder, CA, Mallinger, EC, Simon, AN, Wydeven, AP, and Olson, ER (2020). Efficacy of acoustic triangulation for gray wolves. Wildlife Society Bulletin 44, 351–361.
| Efficacy of acoustic triangulation for gray wolves.Crossref | GoogleScholarGoogle Scholar |
Papin, M, Pichenot, J, Guérold, F, and Germain, E (2018). Acoustic localization at large scales: a promising method for grey wolf monitoring. Frontiers in Zoology 15, .
| Acoustic localization at large scales: a promising method for grey wolf monitoring.Crossref | GoogleScholarGoogle Scholar |
Pauleit, S, Ennos, R, and Golding, Y (2005). Modeling the environmental impacts of urban land use and land cover change – a study in Merseyside, UK. Landscape and Urban Planning 71, 295–310.
| Modeling the environmental impacts of urban land use and land cover change – a study in Merseyside, UK.Crossref | GoogleScholarGoogle Scholar |
R Core Team (2022) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at https://www.R-project.org/
Rhinehart, TA, Chronister, LM, Devlin, T, and Kitzes, J (2020). Acoustic localization of terrestrial wildlife: current practices and future opportunities. Ecology and Evolution 10, 6794–6818.
| Acoustic localization of terrestrial wildlife: current practices and future opportunities.Crossref | GoogleScholarGoogle Scholar |
Sadhukhan, S, Root-Gutteridge, H, and Habib, B (2021). Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method. Scientific Reports 11, .
| Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method.Crossref | GoogleScholarGoogle Scholar |
Schmitz, OJ (2008). Effects of predator hunting mode on grassland ecosystem function. Science 319, 952–954.
| Effects of predator hunting mode on grassland ecosystem function.Crossref | GoogleScholarGoogle Scholar |
Sen PC, Hajra M, Ghosh M (2020) Supervised classification algorithms in machine learning: a survey and review. In ‘Emerging Technology in Modelling and Graphics. Vol. 937. Advances in Intelligent Systems and Computing’. (Eds S Naman, S Bhattacharyya, T Saha) pp. 99–111. (Springer Nature: Cham, Switzerland) https://doi.org/10.1007/978-981-13-7403-6_18
Sibly, RM, Nott, HMR, and Fletcher, DJ (1990). Splitting behaviour into bouts. Animal Behaviour 39, 63–69.
| Splitting behaviour into bouts.Crossref | GoogleScholarGoogle Scholar |
Slabbekoorn, H, Yeh, P, and Hunt, K (2007). Sound transmission and song divergence: a comparison of urban and forest acoustics. The Condor 109, 67–78.
| Sound transmission and song divergence: a comparison of urban and forest acoustics.Crossref | GoogleScholarGoogle Scholar |
Smith, BR, Root-Gutteridge, H, Butkiewicz, H, Dassow, A, Fontaine, AC, Markham, A, Owens, J, Schindler, L, Wijers, M, and Kershenbaum, A (2022). Acoustic localisation of wildlife with low-cost equipment: lower sensitivity, but no loss of precision. Wildlife Research 49, 372–381.
| Acoustic localisation of wildlife with low-cost equipment: lower sensitivity, but no loss of precision.Crossref | GoogleScholarGoogle Scholar |
Sokolova, M, and Lapalme, G (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management 45, 427–437.
| A systematic analysis of performance measures for classification tasks.Crossref | GoogleScholarGoogle Scholar |
Stehman, SV (2009). Sampling designs for accuracy assessment of land cover. International Journal of Remote Sensing 30, 5243–5272.
| Sampling designs for accuracy assessment of land cover.Crossref | GoogleScholarGoogle Scholar |
Stehman SV, Foody GM (2009) Accuracy assessment. In ‘The SAGE Handbook of Remote Sensing’. SAGE Research Methods (Eds TA Warner, MD Nellis, GM Foody) pp. 297–309. (Sage Publications: Thousand Oaks, CA, USA)
Stevenson, ER, Lashley, MA, Chitwood, MC, Garabedian, JE, Swingen, MB, DePerno, CS, and Moorman, CE (2019). Resource selection by coyotes (Canis latrans) in a longleaf pine (Pinus palustris) ecosystem: effects of anthropogenic fires and landscape features. Canadian Journal of Zoology 97, 165–171.
| Resource selection by coyotes (Canis latrans) in a longleaf pine (Pinus palustris) ecosystem: effects of anthropogenic fires and landscape features.Crossref | GoogleScholarGoogle Scholar |
Story, M, and Congalton, RG (1986). Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing 52, 397–399.
Sutherland, WJ (1998). The importance of behavioural studies in conservation biology. Animal Behaviour 56, 801–809.
| The importance of behavioural studies in conservation biology.Crossref | GoogleScholarGoogle Scholar |
Séquin, ES, Jaeger, MM, Brussard, PF, and Barrett, RH (2003). Wariness of coyotes to camera traps relative to social status and territory boundaries. Canadian Journal of Zoology 81, 2015–2025.
| Wariness of coyotes to camera traps relative to social status and territory boundaries.Crossref | GoogleScholarGoogle Scholar |
Tanaka, T, Sugiura, H, and Masataka, N (2006). Sound transmission in the habitats of Japanese macaques and its possible effect on population differences in coo calls. Behaviour 143, 993–1012.
| Sound transmission in the habitats of Japanese macaques and its possible effect on population differences in coo calls.Crossref | GoogleScholarGoogle Scholar |
Taylor, RS, Oldland, JM, and Clarke, MF (2008). Edge geometry influences patch-level habitat use by an edge specialist in south-eastern Australia. Landscape Ecology 23, 377–389.
| Edge geometry influences patch-level habitat use by an edge specialist in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Therneau T, Atkinson B (2019) rpart: recursive partitioning and regression trees. Available at https://cran.r-project.org/package=rpart
Thinh, TV, Duong, PC, Nasahara, KN, and Tadono, T (2019). How does land use/land cover map’s accuracy depend on number of classification classes? SOLA 15, 28–31.
| How does land use/land cover map’s accuracy depend on number of classification classes?Crossref | GoogleScholarGoogle Scholar |
Thomsen, PF, and Willerslev, E (2015). Environmental DNA – an emerging tool in conservation for monitoring past and present biodiversity. Biological Conservation 183, 4–18.
| Environmental DNA – an emerging tool in conservation for monitoring past and present biodiversity.Crossref | GoogleScholarGoogle Scholar |
Tucker, CJ, Townshend, JRG, and Goff, TE (1985). African land-cover classification using satellite data. Science 227, 369–375.
| African land-cover classification using satellite data.Crossref | GoogleScholarGoogle Scholar |
Wickham, JD, Stehman, SV, Gass, L, Dewitz, J, Fry, JA, and Wade, TG (2013). Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sensing of Environment 130, 294–304.
| Accuracy assessment of NLCD 2006 land cover and impervious surface.Crossref | GoogleScholarGoogle Scholar |
Wickham, J, Stehman, SV, Gass, L, Dewitz, JA, Sorenson, DG, Granneman, BJ, Poss, RV, and Baer, LA (2017). Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD). Remote Sensing of Environment 191, 328–341.
| Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD).Crossref | GoogleScholarGoogle Scholar |
Wiedenhoeft JE, Walter S, Gross M, Kluge N, McNamara S, Stauffer G, Price-Tack J, Johnson R (2020) Wisconsin gray wolf monitoring report 15 April 2019 through 14 April 2020. (Ed. Bureau of Wildlife Management). Wisconsin Department of Natural Resources, Madison, WI, USA.
Wilson, DR, Battiston, M, Brzustowski, J, and Mennill, DJ (2014). Sound Finder: a new software approach for localizing animals recorded with a microphone array. Bioacoustics 23, 99–112.
| Sound Finder: a new software approach for localizing animals recorded with a microphone array.Crossref | GoogleScholarGoogle Scholar |
Zhou, W, Troy, A, and Grove, M (2008). Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data. Sensors 8, 1613–1636.
| Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Zimmer WMX (2011) ‘Passive Acoustic Monitoring of Cetaceans.’ (Cambridge University Press: Cambridge, UK)
