1. Species range limits are thought to result from a decline in demographic performance at range edges. However, recent studies reporting contradictory patterns in species demographic performance at their edges cast doubt on our ability to predict climate change demographic impacts. To understand these inconsistent demographic responses at the edges, we need to shift the focus from geographic to climatic edges and analyse how species responses vary with climatic constraints at the edge and species’ ecological strategy.

2. Here we parameterised integral projection models with climate and competition effects for 27 tree species using forest inventory data from over 90,000 plots across Europe. Our models estimate size-dependent climatic responses and evaluate their effects on two life trajectory metrics: lifespan and passage time - the time to grow to a large size. Then we predicted growth, survival, lifespan, and passage time at the hot and dry or cold and wet edges and compared them to their values at the species climatic centre to derive indices of demographic response at the edge. Using these indices, we investigated whether differences in species demographic response between hot and cold edges could be explained by their position along the climate gradient and functional traits related to their climate stress tolerance.

3. We found that at cold and wet edges of European tree species, growth and passage time were constrained, whereas at their hot and dry edges, survival and lifespan were constrained. Demographic constraints at the edge were stronger for species occurring in extreme conditions, i.e. in hot edges of hot-distributed species and cold edges of cold-distributed species. Species leaf nitrogen content was strongly linked to their demographic responses at the edge. In contrast, we found only weak links with wood density, leaf size, and xylem vulnerability to embolism.

4. Synthesis. Our study presents a more complicated picture than previously thought with demographic responses that differ between hot and cold edges. Predictions of climate change impacts should be refined to include edge and species characteristics.

As part of the FunDivEUROPE project (http://www.fundiveurope.eu/) forest inventory data was compiled from the National Forest Inventories (NFIs) of selected European countries (Belgium, Finland, France, Germany, Spain and Sweden) (see Baeten et al. 2013). NFIs provide a systematic large-scale representation of a country’s forest structure and are statistically representative of the variability in forest types over large environmental and management gradients.

This dataset contains the harmonised plot and tree data from permanent sample plot data from Finland and the National Forest Inventory of Sweden used in Kunstler et al (2020), as compiled in the FunDivEUROPE project. The geographic plot coordinates have been blurred by up to 0.02 degrees from those the coordinates supplied by the NFIs and only trees with a diameter at breast height of 10 cm or more were used in Kunstler et al (2020) and thus included in this dataset.

Data from the National Forest Inventories of France, Germany (BWI 1 and 2) and Spain (IFN2 and 3) were also used in Kunstler et al (2020), and can be downloaded from the following websites:

• France: https://inventaire-forestier.ign.fr/spip.php?article532
• Germany: https://bwi.info/Download/de/BWI-Basisdaten/ACCESS2003/
• Spain: https://www.miteco.gob.es/en/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/ifn3_bbdd_descargas.htm.aspx and https://www.miteco.gob.es/en/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/ifn2_descargas.aspx

In addition, the dataset includes the Water availability index (wai) and Sum of growing degree days above 5.5 °C (sgdd) from in Kunstler et al (2020), which were extracted based on the exact coordinates of each plot. wai was computed using precipitation (P, extracted from E-OBS, Moreno & Hasenauer, 2016)  and potential evapotranspiration (PET) from the Climatic Research Unit (Harris, Jones, Osborn, & Lister, 2014) data-set, as (P − PET)/PET. Sum of growing degree days above 5.5 °C was computed with daily temperature was extracted from E-OBS, a high resolution (1 km2) downscaled climate data-set (Moreno & Hasenauer, 2016) for the years between the two surveys plus two years before the first survey.

Overview of NFI survey and sampling designs

Permanent sample plot data from Finland

The FunDivEUROPE project was given permanent sample plot data from Finland sampled in the period 1985-1986 to 1995 as a sub-sample from the eighth National Forest Inventory (NFI8, see https://www.luke.fi/en/natural-resources/forest/forest-resources-and-forest-planning/forest-resources/ and http://radar.luke.fi/catalog/search/resource/details.page?uuid=%7B939297A5-3096-40BE-9856-3A4BD72408C0%7D). The sample plots are in a systematic grid across the country of plot clusters in forested areas (Reinikainen et al 2000, Mäkipää & Heikkinen, 2003). In Southern Finland the grid is 16 by 16 square km, with four plots in each cluster at 400 m intervals, while in Northern Finland the grid is a 24 by 32 km rectangle with three plots per cluster, at 600 m. intervals. These permanent sample plot data were sampled using a variable radius technique with two concentric circular subplots of radius 5.64 m for trees under 10.5 cm of d.b.h. (i.e. 100 m2) and 9.77 m for trees of d.b.h. 10.5 cm or higher (i.e. 300 m2).

The field manuals for the two surveys are available online for 1985 (https://jukuri.luke.fi/handle/10024/522617) and 1995 (https://jukuri.luke.fi/handle/10024/522624).

Swedish National Forest Inventory

The FunDivEUROPE project was given data from two censuses of the Swedish National Forest Inventory (see http://www.slu.se/nfi). Plots in the first census were surveyed between 2003 and 2005 and plots in the second census were surveyed between 2008 and 2010. The permanent inventory uses a randomly planned regular sampling grid and includes about 4,500 permanent tracts, each surveyed every five years.

The tracts are rectangular and have different dimensions depending on the location within the country.  Each tract has between 4 and 8 circular sample plots. Trees greater than 10 cm d.b.h. are sampled in a 10 m radius.

References

Baeten, L., Verheyen, K., Wirth, C., Bruelheide, H., Bussotti, F., Finér, L., . . . Scherer-Lorenzen, M. (2013). A novel comparative research platform designed to determine the functional signif- icance of tree species diversity in European forests. Perspectives in Plant Ecology, Evolution and Systematics, 15(5), 281–291.

Harris, I., Jones, P., Osborn, T., & Lister, D. (2014). Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset: updated high-resolution grids of monthly climatic observations. International Journal of Climatology, 34(3), 623–642.

Kunstler (2020). Demographic performance of European tree species at their hot and cold climatic edges. Journal of Ecology.

Mäkipää, R., & Heikkinen, J. (2003). Large-scale changes in abundance of terricolous bryophytes and macrolichens in Finland. Journal of Vegetation Science, 14(4), 497–508.

Moreno, A., & Hasenauer, H. (2016). Spatial downscaling of European climate data: Spatial Downscaling of European Climate Data. International Journal of Climatology, 36(3), 1444–1458.

Reinikainen, A., Mäkipää, R, Vanha-Majamaa, I. & Hotanen, J-P. (2000). Kasvit muuttuvassa metsäluonnossa. Kustannusosakeyhtiö Tammi, 2000.

The data provided have blurred geographic coordinates (random number up to 0.02 degree).

When opening the datafiles in Excel be aware that the Swedish plot codes are numeric and Excel can round the numbers.

When determining natural mortality only trees with the tree status 4 (dead and stem present) should be considered as dying due to natural mortality.

Metadata

Three datafiles are provided here: 

trees_finland_and_sweden.csv

Tree information from the Finnish and Swedish National Forest Inventories.

plots_finland_and_sweden.csv

Plot information from the Finnish and Swedish National Forest Inventories.

climate_wai_sgdd.csv

Climate variables for all the plots in Kunstler et al (2020).