NORTHERN FOREST: A multi-driver framework for near-term iterative forecasting of ecosystem states

Informations

Funding country

Norway

Acronym

-

URL

-

Start date

1/1/2020

End date

12/31/2023

Budget

1,377,477 EUR

Fundings

Abstract

The state of an ecosystem is determined by an interplay between biological, physical and human factors. Due to anthropogenic climate change, this interplay is currently changing in many systems. However, because these changes are rapid and the interplay is complex, it is difficult to predict how the state of ecosystems will change in the long term. This implies that systems for near-term forecasting of changes in ecosystem state are all the more valuable. Such systems facilitate planning and adaptation and can thus be of great help to managers and other stakeholders. The goal of the project NORTHERN FOREST is to develop a system for near-term forecasting of insect outbreaks and forest damage in northern-boreal mountain birch forest. Outbreaks by geometrid moths have been an increasing problem in this ecosystem during the last 20 years because climate warming has allowed southern moth species to expand their ranges northwards and eastwards. The goal of NORTHERN FOERST is to develop statistical models that can forecast the risk of outbreaks and forest damage 1-3 years into the future, depending on factors like climate, forest productivity and grazing pressure by ungulates. The models will be developed based on existing data from long-term monitoring of outbreaks and forest damage, in combination with new climate- and productivity maps that will be developed during the project. The project will focus mainly on the mountain birch forest in former Finnmark County and has been developed in collaboration with the County governor of Troms and Finnmark, the Director of Forestry in Finnmark and the Finnmark Estate Agency. An important goal of the project is to facilitate dialogue between researchers and managers concerning the structure an interpretation of forecasting models, and how near-term forecasts can lead to concrete management actions. This goal is achieved through regular electronic meetings with the users, as well as annual workshops that gather researchers and users for presentation and interpretation of project results. As of November 2022, researchers in NORTHERN FOREST have developed downscaled climate data on temperature and precipitation with a spatial resolution of 100 m and 250 m, covering Troms og Finnmark as well as Nordland (only 250 m). Climate data with 10 m resolution are under development. All climate data from the project are openly available from the Norwegian Meteorological Institute. A forest productivity map for Finnmark has also been developed, and a beta version of the map is openly available from the Norwegian Institute for Bioeconomy research. For these spatial data products, we are also working on a solution for user-access based in the cloud-based processing platform Google Earth Engine. Downscaled climate data and productivity maps are stand-alone products from NORTHERN FOREST, but also form an important foundation for the statistical models for near-term forecasting of moth outbreaks and forest mortality that will be developed in the project. The forecasting models for outbreaks are based on analysis of long-term time series of moth larval abundance and are developed in a new statistical framework called «leave-future-out cross-validation». In this framework, the model is first fitted to the earliest years in the time series, and we assess how well the model is able to predict future datapoints while iteratively adding one year at a time to the model. Products from the downscaled climate data are used to define explanatory variables that represent weather and climate throughout the different life cycle stages of the moths, and machine learning techniques are used to identify the most relevant of these variables based on explained variance in the larval time series. As of November 2022, we have developed models that are ready to predict next year’s density of moth larvae. We are also well under way with developing similar models for forest mortality. To acquire better data for the development of these models, we have also conducted a field survey for mapping of forest health in Finnmark. This survey - which was stratified according to a satellite-derived index of outbreak-induced canopy loss - covered large parts of former Finnmark county and included measurements of roughly 13 000 birch stems. The survey has provided valuable data on the relationship between forest mortality and canopy loss, conditional on local site factors like climate and productivity.