Optimizing the regeneration of spruce-dominated stands suffering from Heterobasidion root rot in Finland

forests
Bayesian modelling
Authors
Affiliations

Eero Holmström

Natural Resources Institute Finland

Juha Honkaniemi

Natural Resources Institute Finland

Anssi Ahtikoski

Natural Resources Institute Finland

Tuomas Rajala

Natural Resources Institute Finland

Jarkko Hantula

Natural Resources Institute Finland

Tuula Piri

Natural Resources Institute Finland

Juha Heikkinen

Natural Resources Institute Finland

Susanne Suvanto

Natural Resources Institute Finland

Tapio Räsänen

Metsäteho Oy

Juha-Antti Sorsa

Metsäteho Oy

Kirsi Riekki

Metsäteho Oy

Henna Höglund

Finnish Forest Centre

Aleksi Lehtonen

Natural Resources Institute Finland

Mikko Peltoniemi

Natural Resources Institute Finland

Published

February 13, 2025

Doi

Abstract

Heterobasidion root rot is a destructive fungal disease causing extensive damage in conifer forests throughout the Northern hemisphere. The effective spreading of the causal agent Heterobasidion sp. from one tree generation to the next makes the disease a persistent problem for forestry. Here, we present a precision-forestry method for optimizing the regeneration of spruce-dominated stands suffering from Heterobasidion root rot. Our method prevents the inter-generational spread of the disease while aiming for high financial or climate change mitigation value. The method uses harvester data with non-parametric clustering or Bayesian modeling to delineate the stand into healthy and infected “microstands.” Through simulations of forest growth and Heterobasidion dynamics, the optimal species to plant in each microstand to maximize either bare land value (BLV, interest rate 2%) or net CO2 removals by living tree biomass is determined, subject to the condition that regeneration leads to disease eradication. In Finnish conditions, the method recommends pine on mesic heath sites (MT) and combinations of pine and spruce on herb-rich sites (OMT) to maximize BLV. To maximize CO2 removals, the method suggests a variety of tree species compositions including birch. In comparison to regenerating using only spruce, the predicted mean financial gain from the method is 1320 ± 40 EUR/ha on MT and 400 to 800 EUR/ha on OMT. Direct gains in CO2 removals are difficult to achieve due to prevailing management practices for infected stands. The method offers financial and carbon-wise support for decision-making while diversifying forests and cleansing sites of root rot disease.

Figure 8: Since the DBSCAN delineation approaches are based purely on the spatial configuration of known rotten stumps of sawlog-sized spruce trees, they do not take into account the unknown status of smaller-than sawlog-sized spruce trees. The Bayes approach, on the other hand, can take these unknown values into account through imputing values for the unknown rot states of stumps. This can lead to predictions for new infected areas that the DBSCAN approach does not capture, as shown for two example stands here.