Modelling tree survival - a tool for mitigation of climate change effects on forests

Modelling tree survival - a tool for mitigation of climate change effects on forests

  • Thu 31 Jan 19

    14:00 - 16:00

  • Colchester Campus

    STEM 3.1

  • Event speaker

    Dr Nicole Augustin (University of Bath)

  • Event type

    Lectures, talks and seminars

  • Event organiser

    Mathematical Sciences, Department of

  • Contact details

    Dr Andrew Harrison

Joint work with Alice Davis, Axel Albrecht, Heike Puhlmann, Stefan Meining, Simon Wood and Karim Anaya-Izquierdo
Forests are economically, recreationally and ecologically important, providing timber and wildlife habitat and acting as a carbon sink, among many ecosystem services. Forest health is monitored in Europe by The International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects (ICP Forests) in cooperation with the European Union. More recently climate change has contributed to the decline in forest health and monitoring data are increasingly being used to investigate the effects of climate change on forests in order to decide on forest management strategies for mitigation.
Here we model extensive yearly data on tree mortality and crown defoliation, an indicator of tree health, from a monitoring survey carried out in Souther Germany, which includes a part of the ICP transnational grid. On a changing grid, defoliation, mortality and other tree and site specific variables are recorded.  In some cases the grid locations are no longer observed which leads to censored data, also recruitment of trees happens throughout when new grid points are added. 

We model tree survival as a function of the predictor variables on climate, soil characteristics and water budget. We are interested in the process leading to tree mortality rather than prediction and this requires the inclusion of all potential drivers of tree mortality in the model.  We use a smooth additive Cox model which allows for random effects taking care of dependence between neighbouring trees and non-linear smooth functions of time varying predictors and functional predictors. At each of 2385 locations 24 trees were observed between 1983 and 2016, with not all locations being observed yearly. Altogether a total of 57720 trees are observed making the analysis computationally challenging. 

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