Keyword

Invasive species

16 record(s)
 
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  • This data contains the results of student and professional perceptions surveys conducted in the UK before and after e-Learning training, as part of a project to determine the effectiveness of e-Learning as a training tool. The dataset include two surveys; before and after e-Learning training. Students and professionals were given two separate surveys which were combined to create one dataset. The surveys were collected between September 2015 and July 2018. The objective of the survey was to collect data on participants’ awareness, risk perceptions and self-reported behaviours on biosecurity for invasive species. The topics on both the before and after survey included age, role at institution, field of work/study, field activity environments, cleaning methods for equipment, outerwear/footwear and transport and awareness and perceptions of risk around invasive species and biosecurity campaigns. The data does not include the aggregated cleaning scores that were used for the analysis nor has it excluded any participants that were not used in the final data analysis. The dataset has been anonymised by removing names of respondents, email addresses, departments or organisations worked for or studied in, and text responses which could have made the participant identifiable. The research was funded by NERC project no NE/N008391/1. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/ac271791-b722-489c-9b68-b37316ec826c

  • Marine fish, algae and invertebrate invasive alien species (IAS) data from Akrotiri and Dhekelia, Cyprus. Data were collected during an 19-month monitoring period starting in February 2017 and ending in September 2018. Sampling occurred seasonally, approximately once every 3 months, and used an underwater visual census (UVC) method. The UVC involved divers swimming at a steady pace along three 25m transects, with each transect separated by a 5m gap. The transects were randomly placed, covering a variety of habitats such as seagrass beds and rocky habitats where possible. Fish species were recorded and abundance estimated within 2.5m on each side of the transect. Benthic species (algae and invertebrates) were recorded from quadrats placed every 1m along each 25m transect. Sampling carried out by the University of Cyprus and volunteer divers from the Western Sovereign Base Area Sub Aqua Club as part of a Defra Darwin Initiative Plus project to ascertain baseline data on native and non-native marine species in the Sovereign Base Area of Akrotiri. Full details about this dataset can be found at https://doi.org/10.5285/519d42bf-51cc-42a4-8673-5f2044cfa19a

  • This dataset contains response data from Q sort exercises investigating attitudes towards non-native lizards in the UK conducted in 2017-18. Data have been collected using standard Q method techniques for combined qualitative and quantitative investigation into subjective viewpoints surrounding a research topic. The data provided are the final Q sort arrangements obtained from participants and provide the basis for further factor analysis. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/a9c314d8-8a87-4992-9677-d9705c380f10

  • Records of leaf damage caused by and parasitism of Cameraria ohridella in Britain in 2010 collected with a citizen science approach as part of the Conker Tree Science citizen science project, plus validation of the data. Over 3500 people in Great Britain provided data at a national scale on an invasive insect (horse-chestnut leaf-mining moth, Cameraria ohridella Deschka & Dimic; Lepidoptera: Gracillariidae) in order to address two hypotheses. Specifically: (1) whether the levels of damage caused to leaves of the horse-chestnut tree, Aesculus hippocastanum L., and (2) whether the level of parasitism of C. ohridella larvae were both greatest where C. ohridella had been present the longest Participants recorded leaf damage on an ordinal scale (0-4) during the summer (1st July to 15th October 2010). In order to assess the levels of parasitism of caterpillars of C. orhidella, we invited people to rear insects from horse chestnut leaves infested with C. ohridella. Participants sampled leaves during the first week of July 2010 (i.e. the first of the moth's gererations that year) and stored them in sealed plastic bags for two weeks. We then asked participants to report the number of leaf-mines, and to identify and count the insects in each category: adult C. ohridella moths, parasitoids, and other insects. Anyone could take part in rearing parasitoids, but we particularly focused on school children aged 8-11 by working with a team of eight trained volunteers across the country who directly contacted schools and led lessons in classes. The volunteers did not provide directive guidance during the time that the children were counting adult moths and parasitoids, so the data were not biased by our supervision. At the completion of the activity, we retained a randomly-selected subset of 669 samples that the children had counted. We also retained an additional 75 samples in which children had reported parasitoids. For all of these samples an expert blindly assessed the counts of leaf mines, adult C. ohridella moths and other insects. In order to assess how many years that C. orhidella had been present in a location, we used a long-term dataset collated by Forest Research (used with permission). These data showed under-sampling of the range of C. orhidella after 2006, so we also modeled the predicted arrival of C. orhidella based on a demographic model of spread parameterised in continental Europe by augementing the known distribution with a model of short-distance spread by the model. We ran the model twice, assuming two and three generations of C. ohridella, respectively. The project was supported by the Natural Environment Research Council and undertaken at the University of Bristol, UK. Full details about this dataset can be found at https://doi.org/10.5285/9f913f10-6e3d-449e-b8af-8fa2d06d7fd3

  • The datasets contains species presence and background points, and their associated environmental data for non-native common wall lizard (Podarcis muralis). These data are included for local and national scale modelling of likelihood of species presence, as used in the modelling software MaxEnt. The .asc files included are the raw spatial data of parameters (i.e., distance to nearest road) used in modelling at various local regions, from which SWD 'samples with data' were extracted. Outputs from the local MaxEnt models produced the .txt files included. These serve as landscape layer inputs (habitat suitability and movement cost layers) for modelling population growth and spatial spread in the Individual based modelling platform, RangeShifter. Subsequent outputs of projected population growth (number of individuals per landscape cell) and x/y coordinates for each cell, are presented in files with the prefix Pop.csv and avg.csv (averaged data over 50 replicate runs). Full details about this dataset can be found at https://doi.org/10.5285/8ae3f9ef-9a75-4237-afbd-e01abe02e75b

  • The dataset contains fruit counts (and counts of seeds within fruits), size measurements, and habitat characteristics for Pyracantha angustifolia, in Tafi Del Valle (Northwest Argentina). Data were obtained from field-based measurements of shrub characteristics in May 2019, when Pyracantha is fruiting. Also included are growth ring counts for shrubs of different sized used to predict the age of shrubs within the dataset. Full details about this dataset can be found at https://doi.org/10.5285/806eea81-1071-45af-a51e-d78f2a5fcd09

  • The UK Checklist of Freshwater Species is a collation of all the species (apart from algae) known to be found in association with fresh waters in the United Kingdom. The following eight major groups were identified as being associated with fresh waters in the UK: algae, amphibians, birds, fish, invertebrates, macrophytes, mammals and reptiles. Algae (except stoneworts) were not included in the UK Checklist of Freshwater Species as they are currently undergoing a major revision. Other microorganisms (bacteria, fungi and viruses) are also not included in this species list. The checklist was compiled to allow querying of freshwater species data in the Biological Records Centre (BRC) but to also to query freshwater species data from the BRC via the UK Lakes Portal (https://eip.ceh.ac.uk/apps/lakes/), as well as to update the freshwater species list supplied to the UK Species Inventory (UKSI) partners, such as Recorder 6, National Biodiversity Network (NBN) Atlas and iRecord. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/57653719-434b-4b11-9f0d-3bd76054d8bd

  • This dataset contains the number and fork length of Aphanius fasciatus and Gambusia holbrooki individuals caught in traps within pools and channels in Akrotiri SBA, Cyprus between 26th February 2018 and 19th March 2019. Fish were collected from submerged and semi-submerged traps within six pools and three channels within the lake approximately every two weeks before being measured, sexed as obvious males and then released. Traps were deployed for 24 hours before data were collected. Water temperature, dissolved oxygen and conductivity were also measured. Data were collected as part of a Defra Darwin Initiative Plus project to ascertain baseline data on native and non-native fish populations in the Sovereign Base Area of Akrotiri. Full details about this dataset can be found at https://doi.org/10.5285/ad7068c3-5225-437a-ae58-8422cb7e3454

  • These spatial layers quantify the predicted habitat suitability for Rhododendron ponticum across Scotland. These layers were developed with reference to this species role as reservoir host for Phytophthora plant pathogens, but should have value for management of Rhododendron ponticum as a problematic invasive species. The models were developed by combining biological records of R. ponticum with climate, soil, elevation and woodland cover data. The dataset contains averaged estimates for R. ponticum presence, associated standard deviation for each estimate and locations where environmental conditions in the study region strayed too far from the training set data. This research was funded by the Scottish Government under research contract CR/2008/55, 'Study of the epidemiology of Phytophthora ramorum and Phytophthora kernoviae in managed gardens and heathlands in Scotland' and involved collaborators from St Andrews University, Science and Advice for Scottish Agriculture (SASA), Scottish Natural Heritage (SNH), Forest Research, Forestry Commission and Centre for Ecology & Hydrology (CEH). Full details about this dataset can be found at https://doi.org/10.5285/b984a173-8d80-4781-8a53-bef7bcb0d198

  • This dataset comprises forest stand and species occurrence data for a selection of non-native species collected in the UK Sovereign Base Areas (SBA) of Cyprus in October 2015 and March 2017, with a particular focus on the area surrounding Lake Akrotiri in the Western SBA. The main focus for mapping was stands of Acacia saligna, Casuarina cunninghamiana, the eucalypts Eucalyptus camaldulensis and E. gomphocephala, and the forb Symphyotrichum squamatum. The typical accuracy of data capture was around 10-15 m precision, varying according to the presence of forest canopy. Full details about this dataset can be found at https://doi.org/10.5285/7c84e06d-bb1a-4aac-b1d7-33c11310d8a0