ADAPTForRes is a muti-institutional forest research project. It has three measures (pillars) which aim to increase forest resilience: 1-Forest Genetic Options, 2- Forest Management Practices and 3-Forest protection measures. Each pillar has a number of tasks, with associated objectives, and tasks are conducted by national experts in their field of research that contribute to pillar goals. The main aim of pillar 1 is to assess whether the current range of forest reproductive material (FRM) is suitably adapted for climate change and whether additional FRM has potential to assist adaption, while maintaining sustainable production or conservation goals. Pillar 2 aims to conduct research into forest management options for minimising the impacts of climate change through enhanced mitigation (assessment of afforestation options). It will also evaluate the potential of using diverse and adaptive sustainable forest management practices, which may offer the potential to increase the resilience of Irish forests. Such practices may have potential to minimise risk of carbon loss from existing forest stocks owing to the expected increasing frequency of natural disturbance. Pillar 3 will conduct research in forest protection, including a global horizon scanning and pest risk assessment of key forest species, and examine the potential of an innovative risk-based surveillance network (using sentinel sites) to detect and track the progression of pathogens in Irish forests.
This post will be supervised by Prof Ken Byrne and will consider the trade-offs and synergies relating to stand and landscape management of forests for carbon sequestration and how “climate-smart” options might be developed for forestry. The post will deal with the following tasks relating to Pillar 2:
• Investigate the potential use of models (e‧g. CBM, MOTTI growth simulator, FORMIT-M) to simulate stand level carbon storage in Irish forests and in harvested wood products.
• Investigate the potential use of Remsoft-Woodstock to simulate landscape level simulation of carbon storage in Irish forests.
• Conduct a literature review of the synergies/trade-offs between stand and landscape level carbon sequestration in Irish forests.
• Application of models for simulation of forest development and stand-level management (e‧g. standard stand management with clearcutting, business as usual, rewetting, CCF, stand conservation, mixed species silviculture) on C sequestration and wood product lifetimes.
• Investigate effect of changes in stand-level management on landscape-level C-sequestration and C-storage in altered wood products lifetimes.
• Investigate the resilience of forest carbon stocks to impacts of disturbance regimes (e‧g. wind, fire, disease).
• Present project results at national and international conferences.
• Submit papers for publication in high impact scientific journals.
• Contribute to project management in collaborate with the wider ADAPTFORRES project team.
DESCRIPTION:
Essential Criteria:
• A Doctoral Degree (Level 10) in Forest Science, Environmental Science, Environmental Engineering or a cognate discipline.
• Demonstrated experience of (1) working with models to simulate carbon cycling in terrestrial ecosystems and/or carbon storage in harvested wood products or (2) working with models to simulate biogeochemical cycles in terrestrial ecosystems.
• Demonstrated experience in data collation and statistical analysis of experimental data.
• A record of scientific outputs as evidenced by scientific publications and/or conference publications.
• Excellent organisational skills and the ability to work to deadlines.
• Excellent oral and written communication and interpersonal skills.
Desirable Criteria:
• Knowledge of the terrestrial carbon cycle, and forest ecosystems in particular.
• Experience with modelling of soil and biomass carbon cycling in terrestrial ecosystems.
• Experience with data mining, forest inventory data and Earth Observation data.
• Relevant post-doctoral research experience.
• Experience of project management.
• Ability to work as part of a team as well as independently.
• Demonstrable experience of data mining and database management.
• Ability to network with industry stakeholders.
