Assessing precision and requirements of three methods to estimate roe deer density

English version

Autoři: Andrea Marcon ^aff001; Daniele Battocchio ^aff001; Marco Apollonio ^aff001; Stefano Grignolio ^aff001
Působiště autorů: Department of Veterinary Medicine, University of Sassari, Sassari, Italy ^aff001
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222349

Souhrn

Roe deer (Capreolus capreolus) is the most abundant cervid in Europe and, as such, has a considerable impact over several human activities. Accurate roe deer population size estimates are useful to ensure their proper management. We tested 3 methods for estimating roe deer abundance (drive counts, pellet-group counts, and camera trapping) during two consecutive years (2012 and 2013) in the Apennines (Italy) in order to assess their precision and applicability. During the study period, population density estimates were: drive counts 21.89±12.74 roe deer/km² and pellet-group counts 18.74±2.31 roe deer/km² in 2012; drive counts 19.32±11.12 roe deer/km² and camera trapping 29.05±7.48 roe deer/km² in 2013. Precision of the density estimates differed widely among the 3 methods, with coefficients of variation ranging from 12% (pellet-group counts) to 58% (drive counts). Drive counts represented the most demanding method on account of the higher number of operators involved. Pellet-group counts yielded the most precise results and required a smaller number of operators, though the sampling effort was considerable. When compared to the other two methods, camera trapping resulted in an intermediate level of precision and required the lowest sampling effort. We also discussed field protocols of each method, considering that volunteers, rather than technicians, will more likely be appointed for these tasks in the near future. For this reason, we strongly suggest that for each method managers of population density monitoring projects take into account ease of use as well as the quality of the results obtained and the resources required.

Klíčová slova:

Europe – Population size – Forests – Technicians – Deer – Population density – Radii – Defecation

Zdroje

1. Gaillard J-M, Loison A, Toïgo C. Variation in life history traits and realistic population models for wildlife management. Animal Behavior and Wildlife Conservation. Washington, D.C.: Island Press; 2003. pp. 115–132.

2. Morellet N, Gaillard J-M, Hewison AJM, Ballon P, Boscardin Y, Duncan P, et al. Indicators of ecological change: new tools for managing populations of large herbivores. Journal of Applied Ecology. 2007;44: 634–643. doi: 10.1111/j.1365-2664.2007.01307.x

3. Meriggi A, Sotti F, Lamberti P, Gilio N. A review of the methods for monitoring roe deer European populations with particular reference to Italy. Hystrix, the Italian Journal of Mammalogy. 2008; doi: 10.4404/hystrix-19.2–4421

4. Williams BK, Nichols JD, Conroy MJ. Analysis and Management of Animal Populations. Academic Press; 2002.

5. Morellet N, Klein F, Solberg E, Andersen R. The census and management of populations of ungulates in Europe. Ungulate management in Europe: problems and practices. 2010; 106–143.

6. Putman R, Langbein J, Green P, Watson P. Identifying threshold densities for wild deer in the UK above which negative impacts may occur. Mammal Review. 2011;41: 175–196. doi: 10.1111/j.1365-2907.2010.00173.x

7. Apollonio M, Andersen R, Putman R. European Ungulates and Their Management in the 21st Century. Cambridge University Press; 2010.

8. Mayle BA, Peace AJ, Gill RMA. How many deer? A fieldguide to estimating deer populations. Edinburgh. UK: Forestry Commission; 1999.

9. Marques FFC, Buckland ST, Goffin D, Dixon CE, Borchers DL, Mayle BA, et al. Estimating deer abundance from line transect surveys of dung: sika deer in southern Scotland. Journal of Applied Ecology. 2001;38: 349–363. doi: 10.1046/j.1365-2664.2001.00584.x

10. Andersen J. Analysis of a Danish Roe-deer Population (Capreolus Capreolus (L.)): Based Upon the Extermination of the Total Stock. Vildtbiologisk Station Kalø. 1953;

11. Bongi P, Mattioli L, Luccarini S, Apollonio M. Il censimento del capriolo in Toscana: verifica delle metodologie utilizzate e manuale di applicazione. Compagnia delle Foreste; 2009.

12. Putman RJ. Facts from faeces. Mammal Review. 1984;14: 79–97. doi: 10.1111/j.1365-2907.1984.tb00341.x

13. Anderson DR. Response to Engeman: Index Values Rarely Constitute Reliable Information. Wildlife Society Bulletin (1973–2006). 2003;31: 288–291.

14. White GC, Burnham KP. Program MARK: survival estimation from populations of marked animals. Bird Study. 1999;46: S120–S139. doi: 10.1080/00063659909477239

15. C.E.M.A.G.R.E.F. Methodes de recensement des populations de chevreuils. Note technique. 1984;51: 1–65.

16. Strandgaard H. The roe deer (Capreolus capreolus) population at Kalo and the factors regulating its size. Danish Rev of Game Biol. 1972;7: 1–205.

17. Pielowski Z. Some aspects of population structure and longevity of field roe deer. http://katalog.pan.pl/webpac-bin/223bzbsPL/wgbroker.exe?new+-access+top+search+open+NR+ee95400552. 1984; doi: 10.4098/AT.arch.84-2

18. Ratcliffe PR. Distribution and current status of Sika Deer, Cervus nippon, in Great Britain. Mammal Review. 1987;17: 39–58. doi: 10.1111/j.1365-2907.1987.tb00047.x

19. Koda R, Agetsuma N, Agetsuma-Yanagihara Y, Tsujino R, Fujita N. A proposal of the method of deer density estimate without fecal decomposition rate: a case study of fecal accumulation rate technique in Japan. Ecological Research. 2011;26: 227–231. doi: 10.1007/s11284-010-0757-4

20. Bennett LJ, English PF, McCain R. A Study of Deer Populations by Use of Pellet-Group Counts. The Journal of Wildlife Management. 1940;4: 398. doi: 10.2307/3796010

21. Acevedo P, Ruiz-Fons F, Vicente J, Reyes-García AR, Alzaga V, Gortázar C. Estimating red deer abundance in a wide range of management situations in Mediterranean habitats. Journal of Zoology. 2008;276: 37–47. doi: 10.1111/j.1469-7998.2008.00464.x

22. Periago ME, Leynaud G. Density estimates of Mazama gouazoubira (Cervidae) using the pellet count technique in the arid Chaco (Argentina). Ecología Austral. 2009;19: 73–77.

23. Plhal R, Kamler J, Homolka M, Drimaj J. An assessment of the applicability of dung count to estimate the wild boar population density in a forest environment. Journal of Forest Science. 2014;60: 174–180. doi: 10.17221/99/2013-JFS

24. McCallum J. Changing use of camera traps in mammalian field research: habitats, taxa and study types: Camera trap use and development in field ecology. Mammal Review. 2013;43: 196–206. doi: 10.1111/j.1365-2907.2012.00216.x

25. Carbone C, Christie S, Conforti K, Coulson T, Franklin N, Ginsberg JR, et al. The use of photographic rates to estimate densities of tigers and other cryptic mammals. Animal Conservation. 2001;4: 75–79. doi: 10.1017/S1367943001001081

26. Morimando F, Focardi S, Andreev R, Capriotti S, Ahmed A, Lombardi S, et al. A Method for Evaluating Density of Roe Deer, Capreolus capreolus (Linnaeus, 1758), in a Forested Area in Bulgaria Based on Camera Trapping and Independent Photo Screening.: 8.

27. Jennelle CS, Runge MC, MacKenzie DI. The use of photographic rates to estimate densities of tigers and other cryptic mammals: a comment on misleading conclusions. Animal Conservation. 2002;5: 119–120. doi: 10.1017/S1367943002002160

28. Stephens PA, Pettorelli N, Barlow J, Whittingham MJ, Cadotte MW. Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology. 2015;52: 1–6. doi: 10.1111/1365-2664.12383

29. Rowcliffe JM, Field J, Turvey ST, Carbone C. Estimating animal density using camera traps without the need for individual recognition. Journal of Applied Ecology. 2008;45: 1228–1236. doi: 10.1111/j.1365-2664.2008.01473.x

30. Davis ML, Stephens PA, Willis SG, Bassi E, Marcon A, Donaggio E, et al. Prey Selection by an Apex Predator: The Importance of Sampling Uncertainty. Walker S, editor. PLoS ONE. 2012;7: e47894. doi: 10.1371/journal.pone.0047894 23110122

31. Bassi E, Donaggio E, Marcon A, Scandura M, Apollonio M. Trophic niche overlap and wild ungulate consumption by red fox and wolf in a mountain area in Italy. Mammalian Biology—Zeitschrift für Säugetierkunde. 2012;77: 369–376. doi: 10.1016/j.mambio.2011.12.002

32. Grignolio S, Merli E, Bongi P, Ciuti S, Apollonio M. Effects of hunting with hounds on a non-target species living on the edge of a protected area. Biological Conservation. 2011;144: 641–649. doi: 10.1016/j.biocon.2010.10.022

33. Merli E, Grignolio S, Marcon A, Apollonio M. Wild boar under fire: the effect of spatial behaviour, habitat use and social class on hunting mortality. Journal of Zoology. 2017;303: 155–164. doi: 10.1111/jzo.12471

34. Mattioli L, Capitani C, Avanzinelli E, Bertelli I, Gazzola A, Apollonio M. Predation by wolves (Canis lupus) on roe deer (Capreolus capreolus) in north-eastern Apennine, Italy. Journal of Zoology. 2004;264: 249–258. doi: 10.1017/S095283690400576X

35. Neff DJ. The Pellet-Group Count Technique for Big Game Trend, Census, and Distribution: A Review. The Journal of Wildlife Management. 1968;32: 597. doi: 10.2307/3798941

36. Buckland ST. Review of deer count methodology. Unpublished report to the Scottish Office, Agriculture and Fisheries Department, Edinburgh, UK. 1992;

37. Webbon CC, Baker PJ, Harris S. Faecal density counts for monitoring changes in red fox numbers in rural Britain: Faecal density counts for monitoring foxes. Journal of Applied Ecology. 2004;41: 768–779. doi: 10.1111/j.0021-8901.2004.00930.x

38. Mitchell B, Rowe JJ, Ratcliffe P, Hinge M. Defecation frequency in Roe deer (Capreolus capreolus) in relation to the accumulation rates of faecal deposits. Journal of Zoology. 1985;207: 1–7. doi: 10.1111/j.1469-7998.1985.tb04910.x

39. Fattorini L, Ferretti F, Pisani C, Sforzi A. Two-stage estimation of ungulate abundance in Mediterranean areas using pellet group count. Environmental and Ecological Statistics. 2011;18: 291–314. doi: 10.1007/s10651-010-0133-0

40. Sutherland WJ. 11 The twenty commonest surveying sins. Ecological Census Techniques: A Handbook. 1996. p. 317.

41. Brus DJ, Spätjens LEEM, JJ de Gruijter. A sampling scheme for estimating the mean extractable phosphorus concentration of fields for environmental regulation. Geoderma. 1999;89: 129–148. doi: 10.1016/S0016-7061(98)00123-2

42. Barabesi L, Franceschi S, Marcheselli M. Properties of design-based estimation under stratified spatial sampling with application to canopy coverage estimation. The Annals of Applied Statistics. 2012;6: 210–228. doi: 10.1214/11-AOAS509

43. Rowcliffe JM, Kays R, Carbone C, Jansen PA. Clarifying assumptions behind the estimation of animal density from camera trap rates: Density Estimates From Camera Trap Rates. The Journal of Wildlife Management. 2013;77: 876–876. doi: 10.1002/jwmg.533

44. Zero VH, Sundaresan SR, O’Brien TG, Kinnaird MF. Monitoring an Endangered savannah ungulate, Grevy’s zebra Equus grevyi: choosing a method for estimating population densities. Oryx. 2013;47: 410–419. doi: 10.1017/S0030605312000324

45. Cusack JJ, Swanson A, Coulson T, Packer C, Carbone C, Dickman AJ, et al. Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania. J Wildl Manage. 2015;79: 1014–1021. doi: 10.1002/jwmg.902 26640297

46. Carbone C, Cowlishaw G, Isaac NJB, Rowcliffe JM. How far do animals go? Determinants of day range in mammals. Am Nat. 2005;165: 290–297. doi: 10.1086/426790 15729658

47. Andersen R, Duncan P, Linnell JDC. The European roe deer: the biology of success. 1998.

48. Fattorini L. Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities. Biometrika. 2006;93: 269–278. doi: 10.1093/biomet/93.2.269

49. Lioy S, Braghiroli S, Dematteis A, Meneguz PG, Tizzani P. Faecal pellet count method: some evaluations of dropping detectability for Capreolus capreolus Linnaeus, 1758 (Mammalia: Cervidae), Cervus elaphus Linnaeus, 1758 (Mammalia: Cervidae) and Lepus europaeus Pallas, 1778 (Mammalia: Leporidae). Italian Journal of Zoology. 2014; 1–7. doi: 10.1080/11250003.2014.963178

50. Caravaggi A, Zaccaroni M, Riga F, Schai-Braun SC, Dick JTA, Montgomery WI, et al. An invasive-native mammalian species replacement process captured by camera trap survey random encounter models. Williams R, Chauvenet A, editors. Remote Sensing in Ecology and Conservation. 2016;2: 45–58. doi: 10.1002/rse2.11

51. Pfeffer SE, Spitzer R, Allen AM, Hofmeester TR, Ericsson G, Widemo F, et al. Pictures or pellets? Comparing camera trapping and dung counts as methods for estimating population densities of ungulates. Rowcliffe M, O’Brien T, editors. Remote Sensing in Ecology and Conservation. 2017; doi: 10.1002/rse2.67

52. Bubnicki JW, Churski M, Kuijper DPJ. TRAPPER: an open source web-based application to manage camera trapping projects. Poisot T, editor. Methods in Ecology and Evolution. 2016;7: 1209–1216. doi: 10.1111/2041-210X.12571

53. Niedballa J, Sollmann R, Courtiol A, Wilting A. camtrapR: an R package for efficient camera trap data management. Jansen P, editor. Methods in Ecology and Evolution. 2016;7: 1457–1462. doi: 10.1111/2041-210X.12600

54. Massei G, Kindberg J, Licoppe A, Gačić D, Šprem N, Kamler J, et al. Wild boar populations up, numbers of hunters down? A review of trends and implications for Europe: wild boar and hunter trends in Europe. Pest Management Science. 2015;71: 492–500. doi: 10.1002/ps.3965 25512181

Assessing precision and requirements of three methods to estimate roe deer density

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