Guidance on estimation of abundance and density data of wild ruminant population: methods, challenges, possibilities. Issue 6 (19th June 2020)

• Record Type: Journal Article
• Authors: Grignolio, S.; Apollonio, M.; Brivio, F.; Vicente, J.; Acevedo, P.; P., Palencia; Petrovic, K.; Keuling, O.
• Is Part Of: EFSA supporting publications. Volume 17:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-19
• Subjects: Food -- Europe -- Safety measures -- Periodicals; Food -- Safety measures; Europe; Periodicals; Periodicals; 363.192094
• DOI: 10.2903/sp.efsa.2020.EN-1876
• Languages: English
• ISSNs: 2397-8325

Abstract

Abstract : The methods for estimating relative abundance and density in wild ruminant species are reviewed and insights on how to obtain reliable estimations by using those methods are provided. Eighteenmethods used in nineteenwild ruminant species widely distributed across Europe are reviewed. In accordance with the ENETWILD consortium objectives, we evaluate if different types of data can be used to generate harmonisedand comparable database at large scale and for calibration of hunting data into abundanceindices or population density. In addition, recommendations to select the methods to estimate the abundance or density and its implementations for ungulate populations are provided. How to increase the output quality provided by certain methods recognised as reliable (good accuracy and precision)and with the potential to be used for the validation and calibration ofother direct (i.e. based on observation of animals) or indirect (i.e. based on signs of animal activity) methods was recommended. Largely, the "counting" of large herbivores on a regional scale is often unfeasible, it can only be possible to accurately assess population status at local scale. We show that the habitat type plays a key role in the selection of the best method to determine density or relative abundance and that this is partially irrespective to species characteristics. A method that gives a density estimate rather than relative abundance, if possible, should be used. High‐quality hunting data statistics (collected at fine spatial resolution) have the highest availability and comparability potential across Europe, to give long‐term and large‐scale trends and should be used in predictive spatial modelling of wild ruminant relative abundance and density. Therefore, their standardized and harmonised collection is strongly recommended. On a local scale (e.g. management units), camera trapping is a method that can be conducted in different environmental conditions and at any time to collect robust data. In open areas, where camera trappingmay require an excessive effort, we suggest using methods involving the direct detection of animals (vantage points, linear transects, block counts, random points). This should be carried outby correctly defining the study areas (for instance by means of distance sampling) and by estimating the repeatability of the results.