Cryptic genetic variation and adaptation to waterlogging in Caledonian Scots pine, Pinus sylvestris L.

Exposure to a contrasting novel environment such as waterlogging under common garden conditions can trigger release of otherwise unobservable (cryptic) genetic variation. Under a flooding treatment, there was a greater increase in variability in Scots pine populations originating from drier sites in Scotland which likely reflects a relative absence of past selection. Under climate change this cryptic genetic variation may provide considerable potential to adapt.

Dothistroma literature (M-Z)

A list of all literature available (updated to December 2015) about Dothistroma in alphabetical order (M-Z)

Dothistroma literature (D-F)

A list of all literature available (updated to December 2015) about Dothistroma in alphabetical order (D-F)

Dothistroma literature (A-C)

A list of all literature available (updated to December 2015) about Dothistroma in alphabetical order (A to C)

A review of the effects of forest management intensity on ecosystem services for northern European temperate forests with a focus on the UK

Abstract Ecosystem services (ES) are the benefits that people receive from ecosystems. Understanding the impact of forest management on their supply can inform policy and practice for meeting societal demand. The objectives of this paper are to identify and review the effect of management intensity on priority ES supply and identify synergies and trade-offs among ES […]

Quantifying the hydrological effect of woodland creation in the Camowen and Drumragh catchments, Omagh, Northern Ireland

A rainfall-runoff model based on the Soil Conservation Service (SCS) Runoff Curve Number method has been applied to the catchments draining to Omagh, Northern Ireland, to assess the potential effect of woodland creation on flood flows. Realistic woodland creation targets that would extend woodland cover to 13.8% of the catchment was predicted to reduce more […]

Urban Forests in a European Perspective: what can the National Forest Inventory tell us?

This pilot project demonstrates that the NFI data, in combination with the DEGURBA approach, provides existing data on European urban forests. Now that we know what we have, there is potential to quantify and qualify the state and trends of European urban forests. Moving forward we can draw on existing policies and concepts such as […]

Practical solutions for bottlenecks in ecosystem services mapping

Palomo I, Willemen L, Drakou E, Burkhard B, Crossman N, Bellamy C, Burkhard K, Campagne CS, Dangol A, Franke J, Kulczyk S, Le Clec’h S, Malak DA, Muñoz L, Narusevicius V, Ottoy S, Roelens J, Sing L, Thomas A, Van Meerbeek K, Verweij P (2018). One Ecosystem 3: e20713. Abstract Ecosystem services (ES) mapping is becoming mainstream […]

EcoServ-GIS: a toolkit for mapping ecosystem services

Mapping ecosystem services at a county and region scale The EcoServ-GIS toolkit generates maps illustrating the need for each service as well as the capacity for service provision, using scientifically-based, standardised methods and widely available datasets. It provides users with the facility to overlay these maps to show how well demand and capacity coincide in space, […]

Microbiome and infectivity studies reveal complex polyspecies tree disease in Acute Oak Decline

Teamwork between Forest Research, Bangor University and others has for the first time, tracked down the cause of the stem bleeding symptoms of acute oak decline (AOD). Using the latest scientific methods they have revealed a multi-bacterial cause of the stem bleeding and pioneered novel methods for analysing the causes of complex plant diseases. AOD is […]

Pitch Canker Field guide

Field guide This document details signs and symptoms of the named tree pest(s) or disease(s) and provides advice on what to do if you suspect the pest or disease is present. Related pages Research and advice on tree pests and diseases Tree Health Diagnostic and Advisory Service Tree pest and disease posters

Harvesting Machine Census 2016 (FR_TDJR173)

Harvesting Machine Census 2016 (FR_TDJR173)