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Forest Research

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  • This dataset consists of tick sampling and microclimate data from Exmoor, Richmond and New Forest study sites; as well as ARCGIS risk maps that model tick abundance driven by climate surfaces and host abundance. Tick sampling data (91 files, each representing a day of sampling) indicate tick abundance (distinguishing larvae, nymphs, adult males and adult females), vegetation height, soil moisture, temperature and relative humidity. Static risk map files indicate modeled tick abundance: 251 landcover files for the three sites, as well as 36 ArcView map files. The study is part of the NERC Rural Economy and Land Use (RELU) programme. Many people take pleasure from activities in forests and wild lands in the UK and others are being encouraged to participate. Unfortunately, there are risks and one of the most insidious is the possibility (albeit tiny) of acquiring a disease from wild animals; for example, ticks can be vectors of the bacterial infection leading to Lyme Disease. Both diagnosis and treatment can be problematic so prevention of acquiring such disease is highly desirable. Surprisingly little is known about how best to warn countryside users about the potential for disease without scaring them away or spoiling their enjoyment. Answering such questions was the goal of this project, and required the integration of a diverse set of scientific skills, and an understanding of the views of those who manage countryside, those who have contracted zoonotic diseases and those who access the land. This project combined knowledge from three strands of work, namely risk assessment, risk perception and communication, and scenario analysis. The study sites were selected to provide a range of environmental conditions and countryside use. Peri-urban parkland, accessible lowland forest and heath and remote upland forest were chosen as represented by Richmond Park on the fringe of Greater London, the New Forest in Southern England, and Exmoor in South West England. The following additional data from this same research project are available at the UK Data Archive under study number 6892 (see online resources): Lyme disease risk perception data resulting from tick imagery vignette experiments, Lyme disease patient interviews and surveys, residents and countryside staff focus groups, forest manager interviews, and multiple scoring procedures of animal social representation; as well as Lyme and tick risk communication data resulting from interviews with organisations and content analysis of risk warning information leaflets, Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

  • This dataset consists of a survey of the vegetational impacts of deer in 20 forests as part of the NERC Rural Economy and Land Use (RELU) programme. It is widely accepted, at least in principle, that most kinds of natural resources are best handled collaboratively. Collaborative management avoids conflict and enhances the efficiency with which the resource is managed. However, simply knowing that collaboration is a good idea does not guarantee that collaboration can be achieved. In this project, the researchers have addressed issues of conflict and collaboration in ecological resource management using the example of wild deer in Britain. Deer are an excellent example since they highlight problems around ownership and because they offer both societal benefits and drawbacks. Wild deer are not owned, though the land they occupy is. As deer move around, they usually cross ownership boundaries and thus provoke potential conflicts between neighbouring owners who have differing management goals. Deer themselves are valued and a key component of the natural environment, but their feeding commonly limits or prevents woodland regeneration and can thus be harmful to ecological quality. Deer provide jobs but they also provoke traffic accidents. This study used a variety of methods from across the natural and social sciences, including choice experiments, semi-structured interviews with individuals and focus groups. It also incorporated the use of participatory GIS to map deer distributions and habitat preferences in conjunction with stakeholders. The study confirmed conventional wisdom about the importance of collaboration. However, it also showed that there were many barriers to achieving effective collaboration in practice, such as contrasting objectives, complex governance arrangements, power imbalances and personal relationships. Mechanisms for enhancing collaboration, such as incentives and incorporating deer within broader landscape management objectives, were examined. Though these proposals were worked out for the case of deer, they are likely to be applicable much more widely and should be considered in other cases of disputed or rapidly changing ecological resource management. This dataset consists of a survey of the vegetational impacts of deer in 20 forests. The interview and focus group transcripts, and the choice experiment datasets from this study are available at the UK Data Archive under study number 6545 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

  • Phenotypes (growth and phenology) for Scots pine trees in a long-term common garden trial planted in three sites in Scotland, surveyed annually from 2013 to 2020. Full details about this dataset can be found at https://doi.org/10.5285/1c9367fb-ea87-47a1-8257-d9fed54215e7

  • [THIS DATASET HAS BEEN WITHDRAWN]. Phenotypes (growth and phenology) for Scots pine trees in a long-term common garden trial planted in three sites in Scotland, surveyed annually from 2013 to 2020. Full details about this dataset can be found at https://doi.org/10.5285/f463bc5c-bb79-4967-a8dc-f662f57f7020

  • [THIS DATASET HAS BEEN WITHDRAWN]. Phenotypes for Scots pine mother trees and their cones/seed from 21 populations across Scotland in 2007. The seed was used to establish a long-term multisite common garden trial at three nurseries/field sites. Full details about this dataset can be found at https://doi.org/10.5285/ac687a66-135e-4c65-8bf6-c5a3be9fd9aa

  • Phenotypes (growth, phenology and form) for Scots pine trees in a long-term common garden trial grown in three nurseries in Scotland and surveyed from 2007 to 2011. Full details about this dataset can be found at https://doi.org/10.5285/29ced467-8e03-4132-83b9-dc2aa50537cd

  • [This dataset is embargoed until October 31, 2024]. This dataset contains a model, input data and outputs of the Emerald Ash Borer (EAB; Agrilus planipennis) lifecycle and spread across Great Britain. Nine different scenarios are considered related to how certain we are that EAB will arrive through known pathways related to wood imports (70%, 50%, 30%) and the probability that EAB would escape at port rather than at the onwards depots (25%, 50%, 75%). The model outputs can be used to predict the best places to locate surveillance technologies (i.e., girdled trees or traps) and included in this dataset are optimised trap locations for 27 scenarios (three trapping types for each of the nine different scenarios).

  • This data set comprises two years of data (2016 and 2017) from one trial (Hucking, Kent, UK) and one year (2017) from a second trial (Hartshorne, Derbyshire, UK). Data was collected on tree traits (tree height, shoot length, tree provenance), abundance of foliar insect herbivores (gallers, leaf manipulators and leaf miners) and leaf damage by oak powdery mildew, a foliar fungal pathogen. Data was collected from plots differing in tree diversity (provenance and species diversity). Full details about this dataset can be found at https://doi.org/10.5285/cbccb101-c877-4e43-ac70-e8a852b51f07

  • A species by quadrat matrix showing the percentage cover of understory herbaceous plants in ancient and recent woodlands varying in age and isolation. Percentage cover was calculated for each species individually. The data was collected on the Isle of Wight, woodlands spanned the entire island and were not situated in one area. All data was collected in the summer of 2021 over a period of 3 weeks covering the last 2 weeks of may May and the first week of June. This time was chosen as this is when a large subset of woodland plants are in flower. Woodlands were sampled in blocks of three, each block contains an ancient woodland, a recent woodland adjacent to the ancient woodland and another recent woodland of similar age and size but isolated from the ancient woodland. Each woodland had six quadrats taken, systematically placed at four corners and two in the centre. Within each quadrat the percentage cover of all understory herbs was recorded. This data was collected to measure the colonisation credit of recently planted woodlands, and to observe how much this might vary under differing degrees of isolation. This data could also be used to compare all sorts of biodiversity metrics between connected and isolated recent woodlands. It could also be used to compare beta diversity metrics between woodlands of varying degrees of isolation Full details about this dataset can be found at https://doi.org/10.5285/7c2b2878-1d15-4ddd-9d7e-cf50bd65f652

  • This dataset presents a compendium of field-based earthworm data sources and associated meta-data from across the United Kingdom and Ireland (‘Worm source’). These were compiled up to 2021 and include 257 data sources, the earliest dating back to 1891. Source meta-data covers the type of quantitative earthworm data (i.e. incidence, abundance, biomass, taxa), methodological details (e.g. sampling method/s, location/s, whether sampled plots were natural or experimental, sampling year/s), and environmental information (e.g. habitat/land-use, inclusion of climate data and basic soil properties). Data sources were collected through literature searches on Web of Science and Google Scholar, as well as directly from original authors/data holders where possible. The data sources were compiled with the aim of gathering quantitative data on earthworm species and populations to develop earthworm abundance and niche models, and toward a modelling framework for earthworm impacts on soil processes. This work is part of the Soil Organic Carbon Dynamics (SOC-D) project funded by the NERC UK-SCAPE programme (Grant reference NE/R016429/1). Full details about this dataset can be found at https://doi.org/10.5285/1a1000a8-4e7e-4851-8784-94c7ba3e164f