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.
Valente , A M , Marques , T A , Fonseca , C & Torres , R T 2016 , ' A new insight for monitoring ungulates : density surface modelling of roe deer in a Mediterranean habitat ' European Journal of Wildlife Research , vol 62 , no. 5 , pp. 577–587 . DOI: 10.1007/s10344-016-1030-0
European Journal of Wildlife Research
© 2016, Springer. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at link.springer.com / https://doi.org/10.1007/s10344-016-1030-0
DescriptionWe 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.
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