A new insight for monitoring ungulates: density surface modelling of roe deer in a Mediterranean habitat
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Ungulates are especially difficult to monitor, and population estimates are challenging to obtain; nevertheless, such information is fundamental for effective management. This is particularly important for expanding species such as roe deer (Capreolus capreolus), whose populations dramatically increased in number and geographic distribution over the last decades. In an attempt to follow population trends and assess species ecology, important methodological advances were recently achieved by combining line or point sampling with geographic information systems (GIS). In this study, we combined density surface modelling (DSM) with line transect survey to predict roe deer density in northeastern Portugal. This was based on modelling pellet group counts as a function of environmental factors while taking into account the probability of detecting pellets and conversion factors to relate pellet density to animal density. We estimated a global density of 3.01 animals/100 ha (95 % CI 0.37–3.51) with a 32.82 % CV. Roe deer densities increased with increasing distance to roads as well as with higher percentage of cover areas and decreased with increasing distance to human populations. This recently developed spatial method can be advantageous to predict density over space through the identification of key factors influencing species abundance. Furthermore, surface maps for subset areas will enable to visually depict abundance distribution of wild populations. This will enable the assessment of areas where ungulate impacts should be minimized, allowing an adaptive management through time.
KeywordsCapreolus capreolus Iberian Peninsula Distance sampling Density surface models GAM
We are grateful to all the people who provided valuable assistance in the field. Likewise, several institutions provided invaluable support: Nature and Forestry Conservation Institute and especially Núcleo Florestal de Bragança. We would like to thank the University of Aveiro (Department of Biology) and FCT/MEC for the financial support to CESAM RU (UID/AMB/50017) through national funds and, where applicable, co-financed by the FEDER, within the PT2020 Partnership Agreement. TAM is partially funded by FCT, Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013.
This study was co-supported by the University of Aveiro (Department of Biology) and FCT/MEC financially supporting CESAM RU (UID/AMB/50017) through national funds and, where applicable, co-financed by the FEDER, within the PT2020 Partnership Agreement. TAM is partially funded by FCT, Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013. We thank the anonymous reviewers for constructive comments on the manuscript.
Compliance with ethical standards
This article does not contain any studies with human participants or animals performed by any of the authors.
Conflict of interest
The authors declare that they have no conflict of interest.
- Afonso R (2012) O Parque Natural de Montesinho e a promoção do desenvolvimento local. MsC dissertation in Urban and Regional Planning, Aveiro University, Aveiro, PortugalGoogle Scholar
- Apollonio M, Andersen R, Putman R (2010) European ungulates and their management in the 21st century. Cambridge University Press, CambridgeGoogle Scholar
- Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas LT (2001) Introduction to distance sampling. Oxford University Press, OxfordGoogle Scholar
- Buckland ST, Anderson DR, Burnham KP, Laake JL, Borchers DL, Thomas L (2004) Advanced distance sampling: estimating abundance of wildlife populations. Chapman & Hall, LondonGoogle Scholar
- Burnham KP, Buckland ST, Laake JL, Borchers DL, Marques TA, Bishop JRB, Thomas L (2004) Further topics in distance sampling advanced distance sampling. University Press, OxfordGoogle Scholar
- Burt ML, Paxton CGM (2006) Review of density surface modelling applied to JARPA survey data. Paper SC/D06/J4 presented to the JARPA Review Workshop, Tokyo, p 1–5Google Scholar
- Cañadas A, Hammond PS (2006) Model-based abundance estimates for bottlenose dolphins off southern Spain: implications for conservation and management. J Cetacean Res Manag 8(1):13Google Scholar
- R Development Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org. Accessed 25 Nov 2015
- Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Chapman and Hall, LondonGoogle Scholar
- Hedley SL, Buckland ST, Borchers DL (2004) Spatial distance sampling models. In: Advanced distance sampling: estimating abundance of biological populations. Oxford University Press, New York, pp 67-89Google Scholar
- Henrys BP (2005) Spatial distance sampling modeling of cetaceans observed from platforms of opportunity. MSc Dissertation, University of St. Andrews, ScotlandGoogle Scholar
- Miller DL (2014) Distance: a simple way to fit detection functions to distance sampling data and calculate abundance/density for biological populations. R package version 0.8.0. http://CRAN.R-project.org/package=Distance. Accessed 25 Nov 2015
- Miller DL, Rexstad EA, Burt ML, Bravingtion MV, Hedley S (2013b) dsm: Density surface modelling of distance sampling data. R package version 2.1.3. http://CRAN.R-project.org/package=dsm. Accessed 25 Nov 2015
- Mysterud A, Østbye E (1999) Cover as a habitat element for temperate ungulates: effects on habitat selection and demography. Wildl Soc Bull 27(2):385–394Google Scholar
- Newey S, Bell M, Enthoven S, Thirgood S (2003) Can distance sampling and dung plots be used to assess the density of mountain hares Lepus timidus? Wildlife Biol 3:185–192Google Scholar
- Petersen IK, Mackenzie M, Rexstad EA, Wisz MS, Fox AD (2011) Comparing pre- and post-construction distributions of long-tailed ducks Clangula hyemalis in and around the Nysted offshore wind farm, Denmark : a quasi-designed experiment accounting for imperfect detection, local surface features and autocorrelation. Technical Report 2011–1, CREEM Technical Report, no. 2011-1, University of St Andrews, St AndrewsGoogle Scholar
- Scott D, Bacon P, Irvine J (2002) Management of deer in woodlands—literature reviews of woodland design, and techniques for assessing populations and damage. Report to the Deer Commission for Scotland, Centre for Ecology and Hydrology, Aberdeenshire, ScotlandGoogle Scholar
- Torres RT, Santos JP, Linnell JDC, Virgós E, Fonseca C (2011) Factors affecting roe deer occurrence in a Mediterranean landscape, Northeastern Portugal. Mamm Biol 76:491–497Google Scholar
- Torres RT, Virgós E, Panzacchi M, Linnell JDC, Fonseca C (2012b) Life at the edge: roe deer occurrence at the opposite ends of their geographical distribution, Norway and Portugal. Mamm Biol 77:140–146Google Scholar
- Torres RT, Santos JP, Fonseca C (2013) Persistence of roe (Capreolus capreolus) and red (Cervus elaphus) deer pellet-groups in a Mediterrenean mosaic landscape. Mamm Biol 9:7–18Google Scholar
- Torres RT, Santos JP, Fonseca C (2014) Factors influencing red deer occurrence at the southern edge of their range: a Mediterranean ecosystem. Mamm Biol 79(1):52–57Google Scholar
- Vingada J, Fonseca C, Cancela J, Ferreira J, Eira C (2010) Ungulates and their management in Portugal. European ungulates and their management in the 21st century. Cambridge University Press, Cambridge, pp 392–418Google Scholar
- Wood SN (2006) Generalized additive models: an introduction with R. Chapman and Hall/CRC, FloridaGoogle Scholar