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A new insight for monitoring ungulates: density surface modelling of roe deer in a Mediterranean habitat

Research output: Contribution to journalArticle


Ana M. Valente, Tiago A. Marques, Carlos Fonseca, Rita Tinoco Torres

School/Research organisations


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.



Original languageEnglish
Pages (from-to)577–587
Number of pages11
JournalEuropean Journal of Wildlife Research
Issue number5
Early online date28 Jul 2016
Publication statusPublished - Oct 2016

    Research areas

  • Capreolus capreolus, Density surface models, Distance sampling, GAM, Iberian Peninsula

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