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).

In 2021 and 2022 a total of 60 broadleaved woodland restoration sites from Central Scotland (2021) and English Midlands (2022) and a further six wildcard sites made up of ancient woodlands (2 in Scotland and 2 in England) and rewilding sites (2 in England) were surveyed to calculate metrics of ecological complexity from biodiversity and habitat structure data. In each woodland, we monitored ground flora (surveyed using quadrats), adult trees (within circular plots), tree seedlings and saplings (transects), volume of deadwood (measured within transects), canopy cover estimations, invertebrates on understorey vegetation (surveyed by tray beating) and (mostly) flying invertebrates (surveyed using Malaise traps). Information on site characteristics were collected, including age of the restoration site, former land-use and features of the surrounding landscape. Full details about this dataset can be found at https://doi.org/10.5285/8c997943-1f90-4897-87b3-491eaef534ec

This dataset contains genotype data of Single Nucleotide Polymorphisms (SNPs) and Simple Sequence Repeats (SSRs) from 18 populations of UK juniper, including the two subspecies hemisphaerica and nana. Plant material was collected in 2019. These data were gathered as part of a project on juniper conservation genetics that also includes an analysis of the quantitative genetics of UK junipers. The main goal of the project is to evaluate the standing genetic variation of populations using both quantitative and population genetic analyses. Full details about this dataset can be found at https://doi.org/10.5285/27e7df36-d689-4803-93b4-c15935693b83

[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 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

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 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 contains measurements from eleven traits on 285 plants that were grown in a randomised block designed common garden trial at the UKCEH Edinburgh. Seedlings were grown from seed during 2015-2018, and the traits were measured in December 2020. The trees are from sixteen different populations across the UK. Variables measured include: stem length, stem diameter, stem angle, main stem branches, needle length and width, extension, number of stems, number of internodes, length of internodes, and total spread. This data was gathered as part of a project on juniper conservation genetics that also includes an analysis of the population genetics of UK junipers. The main goal of the project is to evaluate the standing genetic variation of populations using both quantitative and population genetic analyses. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/330cf3ac-21c3-4fa8-ab76-528f8cb2fbb8

This dataset contains daily temperature and temperature variance measurements recorded at three nurseries in Scotland between 2007 and 2012. Each nursery contained Scots pine trees grown using seed collected from 21 native Caledonian pinewood provenances, with 8 seedlings from each of 10 families per provenance (total 1680 trees per nursery) included. This dataset was created as part of a multi-site long term garden experimental trial investigating the effect of nursery environment on Scots pine growth and development, and is part of the newLEAF and PROTREE projects. Full details about this dataset can be found at https://doi.org/10.5285/81841d93-41e2-47a7-b15a-92d1e1cf07f7