Forest management-mortality interactions – quantification of management effects on tree mortality and implications for carbon cycling

Informations projet

ForMMI

N° de convention de subvention: 895158

DOI

10.3030/895158

Projet clôturé

Date de signature de la CE 21 Avril 2020

Date de début 1 Novembre 2020

Date de fin 31 Octobre 2022

Financé au titre de

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coût total

€ 224 933,76

Contribution de l’UE

€ 224 933,76

Coordonné par

THE UNIVERSITY OF BIRMINGHAM
United Kingdom

Periodic Reporting for period 1 - ForMMI (Forest management-mortality interactions – quantification of management effects on tree mortality and implications for carbon cycling)

Période du rapport: 2020-11-01 au 2022-10-31

Résumé du contexte et des objectifs généraux du projet

Forests form a major terrestrial carbon sink, but its size is subject to large uncertainties. Recent advances in the quantification of the sink have highlighted the need for accounting for the effects of forest management and tree mortality. However, management and mortality have so far been considered in isolation from each other and the lack of suitable management data has hindered the realistic consideration of management effects on forest dynamics in global vegetation models. As a large share of forests globally – and in Europe especially – are under human management, this lack of realistic representation of forest management and its effects on ecosystem dynamics are major uncertainties in assessments of the forest carbon sink. The overall objective of ForMMI was to improve the scientific understanding of current forest management regimes, assess how forest management affects tree mortality and quantify the implications this has for the carbon cycle.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

Forest harvest regimes were quantified in 11 European countries using re-measured plot data from forest inventories. Harvest regimes were described in terms of frequency and intensity of harvest events and the information from the forest inventory data was aggregated on a 1-degree resolution grid. In addition to this spatial characterisation of harvest regimes, we built predictive models using random forest algorithms, by linking the harvest variables to the pre-harvest status of the forest, but also the climatic, topographic and socio-economic factors, and the recent history of natural disturbances. These results showed variation in harvest strategies between countries and quantified the importance of different drivers in explaining harvest frequency and intensity.

Model simulations for 10 European countries were carried out in 0.5-degree resolution with the LPJ-GUESS model. For the simulation set-up, we used raster data for forest age in Europe and defined species compositions in each grid cell from the forest inventory data, leading to improved representation of current European tree species composition, that is strongly affected by human management. Forest structure in the simulation results were evaluated against the forest inventory data. This simulation set-up and evaluation builds a base for implementing the empirical harvest functions produced in ForMMI in the model.

Forest inventories in Europe planned and implemented by each country, leading to variation in the inventory sampling designs. This makes harmonisation of different data sets an important task for producing comparable information across different countries. Since this is a critical issue in ForMMI, we assessed how the different the sample plot designs between countries affect calculation of variables describing forest structure. These results showed that consistent estimated for tree size structure are achieved with most sample plot designs, when the size-dependent sampling probabilities of trees are accounted for. However, estimation of tree size structure from angle count plots has higher uncertainties. We also created an R package containing tools for carrying out similar assessments for user-defined variables, thus supporting data use of forest inventory data and harmonisation efforts in the future.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

The quantification of harvest regimes across 11 countries in Europe improved our scientific knowledge of how forests are managed in Europe, revealing patterns that were not visible from previously published research or available data sets, which have focused more on the overall wood flows from forests instead of characterising how harvest is carried out. As a major part of European forests are under management, and differences in harvest strategies have implications for both ecosystem dynamics and the services provided by the forests, this information is crucial in understanding the role of forests for harbouring biodiversity, storing carbon and providing ecosystem services.

Large-scale vegetation modelling efforts have recently put more emphasis on including forest management in the simulations. However, the realistic description of management has been hindered by the lack of data on how management is in fact carried out. Here, the quantification of European harvest regimes in ForMMI provide a powerful resource to inform studies of large-scale forest modelling, which otherwise are restricted to relatively simple rule-based approaches for assigning harvesting. When combined with simulations of forest structure from demographic models, they can provide an evidence-based counterfactual for simulations of the effect of future changes in forest harvest policy.

The work done in ForMMI for assessing the effects of different forest inventory sampling designs on estimating forest structure variables builds knowledge and tools for using forest inventory data across countries. This is a topic with increasing importance, as forest inventory data are increasingly needed and used to answer critical questions of the functioning of forests in large spatial scales, requiring a combination of data across countries. The harmonisation of the different data sources and the comparability of estimates across countries is crucial and shared tools for assessing the sampling design effects can make this effort lighter. This is especially important in Europe, where forest inventory sampling designs differ between each country.