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Mammal Research

, Volume 63, Issue 4, pp 477–484 | Cite as

Use of track counts and camera traps to estimate the abundance of roe deer in North-Eastern Italy: are they effective methods?

  • Toni Romani
  • Carmelinda GiannoneEmail author
  • Emiliano Mori
  • Stefano Filacorda
Original Paper

Abstract

Population density of European roe deer Capreolus capreolus was estimated in six forest areas of North-Eastern Italy through the use of different methods. The most effective method to estimate a population density is always case-dependent and, thus, varies across study areas. Particularly, drive count and vantage point count estimates (i.e. counts by hunters) have been reported to be the most effective to assess deer densities in woodlands, but they require a high volunteer human presence, which limit their feasibility. Results of count by hunters were thus compared with estimates obtained through camera trapping and track counts. Surveys were all carried out between 2014 and 2015. The three-used method provided us with comparable density results, suggesting that they all may be applied in the study area. Track-count survey was shown to be—with equal effectiveness—the cheapest method to infer roe deer density in forest areas (i.e. near 28% cheaper than camera trapping). As to our study sites, we therefore suggest that the proposal of track-count method might provide wildlife managers with a cost-effective alternative to other count methods to estimate roe deer population density. However, it is noteworthy that track-count method may also lead to lower density estimates than the drive counts; an apparent difference in the accuracy between methods needs to be considered when choosing for a certain count method.

Keywords

Capreolus capreolus Drive counts Vantage point counts Camera trapping Track counts Economic costs 

Notes

Acknowledgements

We would like to thank the Friuli Venezia-Giulia Region for sharing their data, Cristina Comuzzo and hunters from the SIPS (Società Italiana Pro-Segugio- www.enci.it) project for their support with camera trapping and Yannick Fanin for his support in Doberdò del Lago Reserve. We thank CyberTracker Conservation for the software. Part of this research was carried out within the project named “L’interazione tra gli ungulati, attività cinofilia, caccia con l’uso dei cani da seguita e grandi e meso carnivori” funded by the Società Italiana Pro-Segugio- www.enci.it.

Author contributions

TR, CG and SF conceived the idea and collected all the data; EM helped in the paper organization and wrote part of it based on the data collected by other authors. Two anonymous reviewers kindly improved the first version of our manuscript with their comments.

References

  1. Acevedo P, Ferreres J, Jaroso R, Duràn M, Escudero MA, Marco J, Gortàzar C (2010) Estimating roe deer abundance from pellet group counts in Spain: an assessment of methods suitable for Mediterranean woodlands. Ecol Indic 10:1226–1230CrossRefGoogle Scholar
  2. Ancillotto L, Notomista T, Mori E, Bertolino S, Russo D (2018) Assessment of detection methods and vegetation associations for introduced Finlayson’s squirrels (Callosciurus finlaysonii) in Italy. Environ Manage 61:875–883CrossRefPubMedGoogle Scholar
  3. Andersen R, Duncan P, Linnell JDC (1998) The European roe deer: the biology of success. Scandinavian University Editions, OsloGoogle Scholar
  4. Anile S, Ragni B, Randi E, Mattucci F, Rovero F (2014) Wildcat population density on the Etna volcano, Italy: a comparison of density estimation methods. J Zool (Lond) 293:252–261CrossRefGoogle Scholar
  5. Aulak W, Babińska-Werka J (1990) Estimation of roe deer density based on the abundance and rate of disappearance of their faeces from the forest. Acta Theriol 35:111–120CrossRefGoogle Scholar
  6. Breed GA, Costa DP, Jonsen ID, Robinson PW, Mills-Flemming J (2012) State-space methods for more completely capturing behavioral dynamics from animal tracks. Ecol Model 235:49–58CrossRefGoogle Scholar
  7. Cagnacci F, Focardi S, Heurich M, Stache A, Hewison AJM, Morellet N, Kjellander N, Linnell JDC, Mysterud A, Neteler M, Delucchi L, Ossi F, Urbano F (2011) Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 120:1790–1802CrossRefGoogle Scholar
  8. Cagnacci F, Cardini A, Ciucci P, Ferrari N, Mortelliti A, Preatoni DG, Russo D, Scandura M, Wauters LA, Amori G (2012) Less is more: a researcher’s survival guide in time of economic crisis. Hystrix 23:1–7Google Scholar
  9. Carbone C, Cowlishaw G, Isaac NJB, Rowcliffe JM (2005) How far do animals go? Determinants of day range in mammals. Am Nat 165:290–297CrossRefPubMedGoogle Scholar
  10. Cetin M, Sevik H (2016) Evaluating the recreation potential of Ilgaz Mountain National Park in Turkey. Environ Monit Assess 188:52CrossRefPubMedGoogle Scholar
  11. Chavel EE, Mazerolle MJ, Imbeau L, Drapeau P (2017) Comparative evaluation of three sampling methods to estimate detection probability of American red squirrels (Tamiasciurus hudsonicus). Mamm Biol 83:1–9CrossRefGoogle Scholar
  12. Corlatti L, Gugiatti A, Pedrotti L (2016) Spring spotlight counts provide reliable indices to track changes in population size of mountain-dwelling red deer Cervus elaphus. Wildl Biol 22:268–276CrossRefGoogle Scholar
  13. Côté SD, Rooney TP, Tremblay J-P, Dussault C, Waller DM (2004) Ecological impacts of deer overabundance. Annu Rev Ecol Evol Syst 35(1):113–147CrossRefGoogle Scholar
  14. Coulon A, Morellet N, Goulard M, Cargnelutti B, Angibault JM, Hewison AJM (2008) Inferring the effects of landscape structure on roe deer (Capreolus capreolus) movements using a step selection function. Landsc Ecol 23:603–614CrossRefGoogle Scholar
  15. Daniels MJ (2006) Estimating roe deer Cervus elaphus populations: an analysis of variation and cost-effectiveness of counting methods. Mammal Rev 36:235–247CrossRefGoogle Scholar
  16. D'Eon RG (2001) Using snow-track surveys to determine deer winter distribution and habitat. Wildl Soc Bull 29:879–887Google Scholar
  17. Elzinga CL, Salzer DW, Willoughby JW, Gibbs DP (2001) Monitoring plant and animal populations. Blackwell Scientific Publications Editions, AbingdonGoogle Scholar
  18. Ferretti F, Bertoldi G, Sforzi A, Fattorini L (2011) Roe and fallow deer: are they compatible neighbours? Eur J Wildl Res 57:775–783CrossRefGoogle Scholar
  19. Focardi S, Montanaro P, Isotti R, Ronchi F, Scacco M, Calmanti R (2005) Distance sampling effectively monitored a declining population of Italian roe deer Capreolus capreolus italicus. Oryx 39:421–428CrossRefGoogle Scholar
  20. Formozov AN (1932) Formula for quantitative censusing of mammals by tracks. Russ J Zool 11:66–69 [in Russian]Google Scholar
  21. Foster RJ, Hamsen BJ (2012) A critique of density estimation from camera-trap data. J Wildl Manag 76:224–236CrossRefGoogle Scholar
  22. Garel M, Bonenfant C, Hamann J-L, Klein F, Gaillard J-M (2010) Are abundance indices derived from spotlight counts reliable to monitor red deer Cervus elaphus populations? Wildl Biol 16:77–84CrossRefGoogle Scholar
  23. Horcajada-Sánchez F, Navarro-Castilla Á, Boadella M, Barja I (2018) Influence of livestock, habitat type, and density of roe deer (Capreolus capreolus) on parasitic larvae abundance and infection seroprevalence in wild populations of roe deer from central Iberian peninsula. Mamm Res 63:213–222CrossRefGoogle Scholar
  24. Iborra O, Lumaret JP (1997) Validity limits of the pellet group counts in wild rabbit (Oryctolagus cuniculus). Mammalia 61:205–218CrossRefGoogle Scholar
  25. Keeping D (2014) Rapid assessment of wildlife abundance: estimating animal density with track counts using body mass–dayrange scaling rules. Anim Conserv 17:486–497CrossRefGoogle Scholar
  26. Keeping D, Pelletier R (2014) Animal density and track counts: understanding the nature of observations based on animal movements. PLoS One 9:e96598CrossRefPubMedPubMedCentralGoogle Scholar
  27. Kremen C, Merenlender AM, Murphy DD (1994) Ecological monitoring: a vital need for integrated conservation and development programs in the tropics. Conserv Biol 8:1–10CrossRefGoogle Scholar
  28. Laurenzi A, Bodino N, Mori E (2016) Much ado about nothing: assessing the impact of a problematic rodent on agriculture and native trees. Mamm Res 61:65–72CrossRefGoogle Scholar
  29. Lovari S, Serrao G, Mori E (2017) Woodland features determining home range size of roe deer. Behav Process 140:115–120CrossRefGoogle Scholar
  30. Mandujano S (2005) Track count calibration to estimate density of white-tailed deer (Odocoileus virginianus) in Mexican dry tropical forest. Southwest Nat 50:223–229CrossRefGoogle Scholar
  31. Mandujano S, Gallina S (1995) Comparison of deer censusing methods in tropical dry forest. Wildl Soc Bull 23:180–186Google Scholar
  32. Massei G, Bacon P, Genov PV (1998) Fallow deer and wild boar pellet group disappearance in a Mediterranean area. J Wildl Manag 62:1086–1094CrossRefGoogle Scholar
  33. Morellet N, Van Moorter B, Cargnelutti B, Angibault JM, Lourtet B, Merlet J, Ladet S, Hewison AJM (2011) Landscape composition influences roe deer habitat selection at both home range and landscape scales. Landsc Ecol 6:999–1010CrossRefGoogle Scholar
  34. Mori E, Di Bari P, Coraglia M (2017) Interference between roe deer and northern chamois in the Italian Alps: are Facebook groups effective data sources? Ethol Ecol Evol 30:277–284.  https://doi.org/10.1080/03949370.2017.1354922 CrossRefGoogle Scholar
  35. Mysterud A, Meisingset EL, Veiberg V, Langvatn R, Solberg EJ, Egil L, Stenseth NC (2007) Monitoring population size of red deer Cervus elaphus: an evaluation of two types of census data from Norway. Wildl Biol 13:285–298CrossRefGoogle Scholar
  36. O'Connell AF, Nichols JD, Karanth KU (2010) Camera traps in animal ecology: methods and analyses. Springer Science & Business Media, New YorkGoogle Scholar
  37. Parsons AW, Forrester T, McShea WJ, Baker-Whatton MC, Millspaugh JJ, Kays R (2017) Do occupancy or detection rates from camera traps reflect deer density? J Mammal 98:1547–1557CrossRefGoogle Scholar
  38. Pęksa Ł, Ciach M (2015) Negative effects of mass tourism on high mountain fauna: the case of the Tatra chamois Rupicapra rupicapra tatrica. Oryx 49:500–505CrossRefGoogle Scholar
  39. Pépin D, Adrados C, Mann C, Janeau G (2004) Assessing real daily distance traveled by ungulates using differential GPS locations. J Mammal 85:774–780CrossRefGoogle Scholar
  40. Putman RJ (1986) Foraging by roe deer in agricultural areas and impact on arable crops. J Appl Ecol 23:91–99CrossRefGoogle Scholar
  41. Putman R, Apollonio M, Andersen R (2011) Ungulate management in Europe: problems and practices. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  42. R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org. Accessed 22 May 2018
  43. Reynolds JH, Thompson WL, Russell B (2011) Planning for success: identifying effective and efficient survey designs for monitoring. Eur J Wildl Res 144:1278–1284Google Scholar
  44. Roberts CW, Pierce BL, Braden AW, Lopez RR, Silvy NJ, Frank PA, Ransom D (2006) Comparison of camera and road survey estimates for white-tailed deer. J Wildl Manag 70:263–267CrossRefGoogle Scholar
  45. Romani T (2016) Stima delle densità di ungulati attraverso l’uso di diverse tecniche in Friuli Venezia-Giulia. Tesi di Laurea Specialistica in Nutrizione e Risorse Animali, Univesità di Udine, Anno Accademico 2015–2016Google Scholar
  46. Rowcliffe JM, Field J, Turvey ST, Carbone C (2008) Estimating animal density using camera traps without the need for individual recognition. J Appl Ecol 45:1228–1236CrossRefGoogle Scholar
  47. Silveira L, Jacomo AT, Diniz-Filho JAF (2003) Camera trap, line transect census and track surveys: a comparative evaluation. Biol Conserv 114:351–355CrossRefGoogle Scholar
  48. Soper HE, Young AW, Cave BM, Lee A, Pearson K (1917) On the distribution of the correlation coefficient in small samples. Appendix II to the papers of “Student” and R. A. Fisher. A cooperative study. Biometrika 11:328–413Google Scholar
  49. Stephens O, Zaumyslova Y, Miquelle DG, Myslenkov AI, Hayward GD (2006) Estimating population density from indirect sign: track counts and the Formozov–Malyshev–Pereleshin formula. Anim Conserv 9:339–348CrossRefGoogle Scholar
  50. Thulin CG, Malmsten J, Ericsson G (2015) Opportunities and challenges with growing wildlife populations and zoonotic diseases in Sweden. Eur J Wildl Res 61:649–656CrossRefGoogle Scholar
  51. Tobler MW, Powell GV (2013) Estimating jaguar densities with camera traps: problems with current designs and recommendations for future studies. Biol Conserv 159:109–118CrossRefGoogle Scholar
  52. Villafañe-Trujillo ÁJ, López-González CA, Kolowski JM (2018) Throat patch variation in tayra (Eira barbara) and the potential for individual identification in the field. Diversity 10:7CrossRefGoogle Scholar
  53. Ward AI, White PC, Critchley CH (2004) Roe deer Capreolus capreolus behaviour affects density estimates from distance sampling surveys. Mammal Rev 34:315–319CrossRefGoogle Scholar
  54. Yoccoz NG, Nichols JD, Boulinier T (2001) Monitoring of biological diversity in space and time. Trends Ecol Evol 16:446–453CrossRefGoogle Scholar
  55. Zaccaroni M, Dell’Agnello F, Ponti G, Riga F, Vescovini C, Fattorini L (2017) Vantage point counts and monitoring roe deer. J Wildl Manag 82:354–361.  https://doi.org/10.1002/jwmg.21385 CrossRefGoogle Scholar

Copyright information

© Mammal Research Institute, Polish Academy of Sciences, Białowieża, Poland 2018

Authors and Affiliations

  1. 1.Kokulandela.org - CyberTracker Italia AssociationUdineItaly
  2. 2.Research Unit of Behavioural Ecology, Ethology and Wildlife Management – Dipartimento di Scienze della VitaUniversità di SienaSienaItaly
  3. 3.Department of Agricultural, Food, Environmental and Animal Sciences –DI4AUniversity of UdineUdineItaly

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