Abstract / Description of output
Recently climate change has contributed to the decline in forest health, and yearly European forest health 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. Forests in Germany have been badly affected and climate change now appears to be the major cause of defoliation (Eickenscheidt et al., 2019; Augustin et al., 2009). Thus, large scale forest conversions to more mixed forests with drought and heat resistant species are planned in some areas of Germany. This talk will cover the statistical aspects of a modelling project which has been informing decisions regarding this future forest conversion. Model selection is a challenge because of spatial confounding and the large number of correlated time varying environmental predictors. In addition there are computational challenges due to the large number of parameters and large sample sizes. A generalized additive mixed model is used for estimating spatio-temporal trends of defoliation, an indicator for tree health. Defoliation
is modelled as a function of site characteristics (topography, soil and climate) with
the aim of identifying the main factors associated with tree damage. The minimal model contains a space-time smoother and an AR1 process for temporal correlation. To eliminate predictors with negligible effects in the remaining set of predictors we use stability selection.
Variable selection using integrated backward selection is carried out repeatedly with resampled data yielding selection inclusion frequencies. The final set of predictors are the predictors with selection inclusion frequencies above a certain threshold.
is modelled as a function of site characteristics (topography, soil and climate) with
the aim of identifying the main factors associated with tree damage. The minimal model contains a space-time smoother and an AR1 process for temporal correlation. To eliminate predictors with negligible effects in the remaining set of predictors we use stability selection.
Variable selection using integrated backward selection is carried out repeatedly with resampled data yielding selection inclusion frequencies. The final set of predictors are the predictors with selection inclusion frequencies above a certain threshold.
Original language | English |
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Title of host publication | Book of the Short Papers SEAS IN 2023 |
Publisher | Pearson |
Pages | 157-162 |
Publication status | Published - 1 Nov 2023 |
Event | SEAS IN 2023 - Statistical Learning, Sustainability and Impact Evaluation - Ancona, Italy Duration: 21 Jun 2023 → 23 Jun 2023 |
Conference
Conference | SEAS IN 2023 - Statistical Learning, Sustainability and Impact Evaluation |
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Country/Territory | Italy |
City | Ancona |
Period | 21/06/23 → 23/06/23 |