Abstract
For elusive species living in concealing habitats (e.g. deer in a forest habitat), indirect methods such as faecal pellet counts are considered more practical means of estimating population density and abundance. Accurate estimation of deer density using the faecal standing crop (FSC) method necessitates the reliable estimation of the mean time to decay of pellet groups present during the survey. Mean time to decay is generally habitat specific, and separate estimations should be made for each habitat type in the study area. In a confined mountainous area of Greece, the habitat-specific mean time to decay of roe deer pellet groups was estimated by locating and marking fresh pellet groups on several dates in the lead up to an FSC survey and returning to the marked signs at the time of the survey to record whether or not each pellet group had survived. Several logistic models were fitted to the data, and estimations were based on a multi-model inference (MMI) approach according to information theory. The highest mean time to decay was estimated in coniferous forests, while mid-ranged values were found in maquis shrubs, and the lowest mean time to decay was observed in open areas. MMI by model averaging, based on Akaike weights, is recommended for making robust parameter estimations and for dealing with uncertainty in model selection.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F (eds) Second international symposium on information theory. Akademiai Kiado, Budapest, pp 267–281
Akaike H (1983) Information measures and model selection. B Int Stat Inst 44:277–291
Barbero E, Palestrini C, Rolando A (1999) Dung beetle conservation: effects of habitat and resource selection (Coleoptera: Scarabaeoidea). J Insect Conserv 3:75–84. doi:10.1023/A:1009609826831
Buckland ST, Burnham KP, Augustin NH (1997) Model selection: an integral part of inference. Biometrics 53:603–618. doi:10.2307/2533961
Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, USA
Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York
Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, London
Hemami MR, Dolman PM (2005) The disappearance of muntjac (Muntiacus reevesi) and roe deer (Capreolus capreolus) pellet groups in a pine forest of lowland England. Eur J Wildl Res 51:19–24. doi:10.1007/s10344-004-0067-7
Hurvich CM, Tsai CL (1989) Regression and time series model selection in small samples. Biometrika 76:297–307. doi:10.1093/biomet/76.2.297
Katsanevakis S (2006) Modelling fish growth: model selection, multi-model inference and model selection uncertainty. Fish Res 81:229–235. doi:10.1016/j.fishres.2006.07.002
Katsanevakis S, Thessalou-Legaki M, Karlou-Riga C, Lefkaditou E, Dimitriou E, Verriopoulos G (2007) Information-theory approach to allometric growth of marine organisms. Mar Biol (Berl) 151:949–959. doi:10.1007/s00227-006-0529-4
Laing SE, Buckland ST, Burns RW, Lambie D, Amphlett A (2003) Dung and nest surveys: estimating decay rates. J Appl Ecol 40:1102–1111. doi:10.1111/j.1365-2664.2003.00861.x
Legakis A (1994) Community structure and species richness in the Mediterranean-type soil fauna. In: Arianoutsou M, Groves RH (eds) Plant-animal interactions in Mediterranean-type ecosystems. Kluwer Academic, Dordrecht, pp 37–46
Marques FFC, Buckland ST, Goffin D, Dixon CE, Borchers DL, Mayle BA et al (2001) Estimating deer abundance from line transect surveys of dung: sika deer in southern Scotland. J Appl Ecol 38:349–363. doi:10.1046/j.1365-2664.2001.00584.x
Massei G, Bacon P, Genov PV (1998) Fallow deer and wild boar pellet group disappearance in a Mediterranean area. J Wildl Manage 62:1086–1094. doi:10.2307/3802561
Mayle BA, Peace AJ, Gill RMA (1999) How many deer? A field guide to estimating deer population size. Forestry Commission, Edinburgh
McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman and Hall, London
R Development Core Team (2006) ‘R: A language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna. ISBN 3-900051-07-0, URL http://www.R-project.org)
Romero-Alcaraz E, Ávila JM (2000) Effect of elevation and type of habitat on the abundance and diversity of scarabaeoid dung beetle (Scarabaeoidea) assemblages in a Mediterranean area from southern Iberian peninsula. Zool Stud 39:351–359
Acknowledgements
We would like to thank the Forestry Agency of Kalavryta for their support and for the permission to conduct the present study in a restricted area. This study was part of the 03ED305 research project, implemented within the framework of the “Reinforcement Programme of Human Research Manpower” (PENED) and co-financed by E.U.—European Social Fund (75%) and the Greek Ministry of Development—GSRT (25%). The study complied with the current laws of Greece.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by E. Hadjisterkotis
Rights and permissions
About this article
Cite this article
Tsaparis, D., Katsanevakis, S., Ntolka, E. et al. Estimating dung decay rates of roe deer (Capreolus capreolus) in different habitat types of a Mediterranean ecosystem: an information theory approach. Eur J Wildl Res 55, 167–172 (2009). https://doi.org/10.1007/s10344-008-0233-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10344-008-0233-4