DOI: 10.5281/zenodo.14825295
title: Norway spruce root rot risk in Finland

description: This record contains the data layers related to the publication: 

Susanne Suvanto, Juha Heikkinen, Eero Holmström, Juha Honkaniemi, Tuula Piri, Jarkko Hantula, Tapio Räsänen, Kirsi Riekki, Juha-Antti Sorsa, Harri Hytönen, Henna Höglund, Tuomas Rajala, Aleksi Lehtonen, Mikko Peltoniemi, Rot or not? Uncovering the spatial patterns and drivers of Norway spruce root rot with harvester data, Forestry: An International Journal of Forest Research, 2025; cpaf061, https://doi.org/10.1093/forestry/cpaf061

The files contain the final root rot risk map, presenting the probability for > 10% of Norway spruce stems to be affected by root rot in clear cutting, and the raster data layers needed for the calculation of the map. These include raster files representing each covariate in the fixed effects model and the raster layers for the prediction and variation of the spatial random effects model component. Plese find more details on the models and the data layers from the manuscript linked above.

Important: Before using the data in this archive, please read carefully the documentation here and in the Suvanto et al. (2025) paper listed above!
description: The files included in this archive are described below. Please note that in some cases the raster values are multiplied turned into integers in order to reduce the file size, please find the details in the column titled "Processing". 




File name
Description
Processing


FinalRiskMap_pred_lahoYli10Pros_10-09-24_INT10_MASKED.tif


The final root risk map, presenting the probability for > 10% of sawlog-sized Norway spruce stems to be affected by root rot in a clear cut. 

Calculation presented in Eq. 3 in the manuscript,  P(RotPercent > 10%)



The values in the file are multiplied with 10 and transformed to integers. The percentage values presented in the manuscript are achieved by dividing with 10.





randomPred_rast_smooth_10-09-24_sigma40_s120_16m_MASKED_INT1000.tif

(within random_effects_layers.zip)



Prediction with the spatial random effects model, using the locations of the harvester data and their rot status.

In the article this is variable Pred_kriging, and the raster layers are processed as described in the "Processing to control privacy and uncertainty" section of the manuscript.

The values in the file are multiplied with 1000 and transformed to integers. Divide by 1000 to get the values reported in the article.




randomVar_focalMeanGapFill_10-09-24_16m_MASKED_INT1000.tif

(within random_effects_layers.zip)



Variation of the random effects model, using the locations of the harvester data and their rot status.

In the article this is varibale Var_kriging, and the raster layers are processed as described in the "Processing to control privacy and uncertainty" section of the manuscript.

The values in the file are multiplied with 1000 and transformed to integers. Divide by 1000 to get the values reported in the article.




DistCoast.tif

DistRail.tif

DistWater.tif

FertileSite.tif

NonForestPercent.tif

PopDens.tif

These files contain the raster files for each independent variable in the model, except for DBH, which was set to a constant (mean of the model training data). The raster names correspond to the variable abbreviations in Eq. 1 in the paper.
For the distance-variables where log-transformation was carried out in the model (DistCoast and DistWater) the minimum value was set to 1 before applying the transformation.


covariate_scaling_attributes.csv
Scaling attributes for the covariates (center and scale, see details in Suvanto et al. 2025). As the variables were scaled before model fitting, they need to be scaled with the same attributes before using the model.
 


forest_mask_MVMI_int.tif
The forest mask used to mask out non-forest pixels in the final map.