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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, 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
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
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
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
Bird species data from the UK Environmental Change Network (ECN) lowland terrestrial sites. These data were collected, using the British Trust for Ornithology (BTO)'s Common Bird Census methodology (CBC), at ECN's lowland terrestrial sites using a standard protocol. This protocol was abandoned in favour of the Breeding Bird Survey (Rennie et al (2017) UK Environmental Change Network (ECN) bird data: 1995-2015 https://doi.org/10.5285/5886c3ba-1fa5-49c0-8da8-40e69a10d2b5) in 1999; however, some sites continued to follow this protocol for a number of years after 1999 to allow comparison with the Breeding Bird Survey data. The CBC uses a mapping method in which a series of visits are made to all parts of a defined plot during the breeding season and contacts with birds by sight or sound are recorded on large-scale maps. Information from the series of visits is combined to estimate the number of territories found. Annual data are recorded but the date ranges available are variable for each site. ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage. Full details about this dataset can be found at https://doi.org/10.5285/8582a02c-b28c-45d2-afa1-c1e85fba023d
Larval mass and survival data for Meadow Brown butterflies (Maniola jurtina) originating from nine different source populations in the UK and reared under one of two host plant treatment groups (either control or drought stress) in an outdoor insectary at UKCEH under natural environmental conditions. Each individual larva was monitored at three growth check points throughout development: 49 days after hatching (pre-overwintering), 162 days after hatching (post overwintering during larval growth) and 309 days after hatching (late larval growth and pupation phase). Larval masses (mg) were recorded for all individuals that survived up to the second growth monitoring point and the number of larvae that survived until the third growth monitoring point were recorded. Full details about this dataset can be found at https://doi.org/10.5285/f26f391f-a17b-4a0d-85c7-ab8af85c3f1b
This dataset contains data pertaining to the phenotypes (height and budburst) and genotypes (via SNP array) for a subset of trees from a long term multi-site Scots pine experimental trial. Full details about this dataset can be found at https://doi.org/10.5285/52248442-a50f-4fc0-a73e-31c61cd27df9
This data set consists of data collected from one field season (2018) from the ORPHEE tree diversity experiment run by INRA Bordeaux in SW France. Data was collected on tree traits (tree height, shoot length), abundance of leaf miners, damage by leaf chewers, and leaf damage by oak powdery mildew, a foliar fungal pathogen. Data was collected from plots differing in tree species richness and drought treatment. Full details about this dataset can be found at https://doi.org/10.5285/35bb7278-a5c4-4819-9b1a-efa84a479d68