UK Environmental Change Network (ECN) rabbit and deer data: 1993-2015

Rabbit and deer data from the UK Environmental Change Network (ECN) terrestrial sites. These data are collected by transect at ECN's terrestrial sites using a standard protocol. The protocol uses an index method based on dropping counts (of deer, rabbits - and where appropriate sheep and Grouse) to estimate relative abundance. They represent twice-yearly continuous records from 1993 to 2015. ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage.

Data are collected by transect at all of ECN's terrestrial sites using a standard protocol. The protocol uses an index method based on dropping counts to estimate relative abundance. Prior to deposit of the data in the EIDC, data are managed by the ECN Data Centre at CEH Lancaster according to defined protocols.

Verification steps include numeric range checks (i.e. checking if a value falls within a specified range), categorical checks (e.g. checking that a species code appears on the standard code list), formatting (i.e. that the dataset conforms to the specified data format) and logical integrity checks (i.e. checking the data make sense, e.g. that the dates in one dataset match those in a related dataset).

Appropriate range settings for ECN variables have been selected following discussion with specialists in each field. Where data fall outside these ranges, a cautious approach has been adopted towards discarding data on the principle that apparent errors may be valid outliers. Such values are discarded only if there is a clear explanation (e.g. an instrumentation error) and corrections are made where possible. If the reason is unclear, the values are stored, but are qualified using pre-defined quality codes or free-text descriptions. Data providers also use these codes or free text to describe factors affecting sampling outside their control, instrument damage or site management effects.