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:0ec7e549-57d4-4e2d-b2d3-2199e1578d84
dcterms:title "Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK (1970-2015)" ;
dcterms:identifier <https://catalogue.ceh.ac.uk/id/0ec7e549-57d4-4e2d-b2d3-2199e1578d84> ;
adms:identifier <https://doi.org/10.5285/0ec7e549-57d4-4e2d-b2d3-2199e1578d84> ;
dcterms:bibliographicCitation "Outhwaite, C.L., Powney, G.D., August, T.A., Chandler, R.E., Rorke, S., Pescott, O., Harvey, M., Roy, H.E., Fox, R., Walker, K., Roy, D.B., Alexander, K., Ball, S., Bantock, T., Barber, T., Beckmann, B.C., Cook, T., Flanagan, J., Fowles, A., Hammond, P., Harvey, P., Hepper, D., Hubble, D., Kramer, J., Lee, P., MacAdam, C., Morris, R., Norris, A., Palmer, S., Plant, C., Simkin, J., Stubbs, A., Sutton, P., Telfer, M., Wallace, I., Isaac, N.J.B. (2019). Annual estimates of occupancy for bryophytes, lichens and invertebrates in the UK (1970-2015) . NERC Environmental Information Data Centre. https://doi.org/10.5285/0ec7e549-57d4-4e2d-b2d3-2199e1578d84" ;
dcterms:description "This dataset provides annual estimates of species occupancy and species trend estimates in the form of growth rates for 5,293 UK invertebrate, bryophyte and lichen species for the period 1970 to 2015. Estimates are provided at the country level for England, Scotland, Wales and Northern Ireland as well as for the UK and Great Britain (GB) where possible. These data were generated using observations of species collated by UK recording schemes and societies as the input data for a Bayesian occupancy model. The outputs resulting from this modelling framework are presented in three forms: • 1000 samples from the modelled posterior distribution of the proportion of occupied sites for each species for each year and for each region analysed. • Summary tables from the model outputs detailing mean occupancy and associated statistics including credible intervals and rhat measure of convergence. • Derived species trend estimates in the form of annual percentage growth rates. Annual estimates derived from fine-grained data (1x1km squares) have not been determined for this set of species before, making this a unique dataset that broadens knowledge on UK biodiversity change. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability." ;
dcterms:provenance [
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rdfs:label "These data were generated using observations of species occurrence as the input data for a Bayesian occupancy model. The modelling framework implemented was based on that developed to improve precision of occupancy estimates when using biological records type data (Outhwaite et al., 2018; https://doi.org/10.1016/j.ecolind.2018.05.010). This allowed the estimation of species occupancy for the largest set of UK species to date. The input data used for this study consisted of species occurrence data collated from 29 UK recording schemes and societies. These data are presence only data that consist of a record of a species at a known time and location. Where possible, these input data were supplemented with additional occurrence data in the same form from the Biological Records Centre database and the wildlife recording app iRecord (https://www.brc.ac.uk/irecord/) These input datasets were then standardized according to the requirements of the model and to desired coverage. Observations of species had to meet the following criteria to be included in the final input dataset: - Date of the record known to the day - Location of the record known to 1x1km square precision - Record was from 1970 onwards - The location of the record was in the UK (strictly England, Northern Ireland, Scotland and Wales) Species names were checked with guidance from the scheme organisers, duplicate records were then removed from the dataset. The occupancy modelling framework used here was based on the random walk model tested by Outhwaite et al., 2018. Alterations made to the modelling framework include country level year effects in the state model and a categorical specification of list length in the detection model. For a complete description of the model and the changes implemented, see the associated data paper."
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