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  • These data show the presence/absence and identification of Cryptosporidium species from the results of a molecular survey of various upland river biota aquatic invertebrates, biofilms, mammal droppings and fish guts, gills and faeces. Samples were collected from various upland influenced sites from around Wales between 2012 and 2015 and were collected. Additionally, otter samples from UK-wide project were also tested. Sample collection was primarily undertaken by DURESS researchers at Cardiff University. Sample testing and analysis was performed at the Cryptosporidium Reference Unit, Public Health Wales Microbiology, Swansea. DNA was extracted using a commercially available kit (Gentra PureGene), Qiagen stool and tissue DNA kits for the fish and mammal samples. These data were collected to provide new information required for the production of a catchment pathogen model to inform ecosystems (dis)services analysis of land use change scenarios for the Diversity in Upland Rivers for Ecosystem Service Sustainability (DURESS) project, part of the NERC Biodiversity and Ecosystem Service Sustainability (BESS) BESS Programme. Full details about this dataset can be found at

  • 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. Full details about this dataset can be found at