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Data from: Disturbance history is a key driver of tree lifespan in temperate primary forests

Citation

Pavlin, Jakob et al. (2021), Data from: Disturbance history is a key driver of tree lifespan in temperate primary forests, Dryad, Dataset, https://doi.org/10.5061/dryad.1ns1rn8ts

Abstract

AIMS

We examined differences in lifespan among the dominant tree species (spruce (Picea abies (L.) H. Karst.), fir (Abies alba Mill.), beech (Fagus sylvatica L.), and maple (Acer pseudoplatanus L.)) across primary mountain forests of Europe. We ask how disturbance history, lifetime growth patterns, and environmental factors influence lifespan.

LOCATIONS

Balkan mountains, Carpathian mountains, Dinaric mountains.

METHODS

Annual ring widths from 20,600 cores from primary forests were used to estimate tree life spans, growth trends, and disturbance history metrics. Mixed models were used to examine species-specific differences in lifespan (i.e. defined as species-specific 90th percentiles of age distributions), and how metrics of radial growth, disturbance parameters, and selected environmental factors influence lifespan.

RESULTS

While only a few beech trees surpassed 500 years, individuals of all four species were older than 400 years. There were significant differences in lifespan among the four species (beech > fir > spruce > maple), indicating life history differentiation in lifespan. Trees were less likely to reach old age in areas affected by more severe disturbance events, whereas individuals that experienced periods of slow growth and multiple episodes of suppression and release were more likely to reach old age. Aside from a weak but significant negative effect of vegetation season temperature on fir and maple lifespan, no other environmental factors included in the analysis influenced lifespan.

CONCLUSIONS

Our results indicate species-specific biological differences in lifespan, which may play a role in facilitating tree species coexistence in mixed temperate forests. Finally, natural disturbances regimes were a key driver of lifespan, which could have implications for forest dynamics if regimes shift under global change.

Methods

This study was conducted in primary temperate mountain forests of the Carpathian Mountains and the Balkan peninsula, spanning from beech-dominated and mixed forests (hereafter referred to as beech forests) at lower elevations to spruce-dominated forests at higher elevations. The dataset used for this study is a part of the REMOTE network (www.remoteforests.org), which is focused on surveying remaining tracts of primary forest landscapes in Europe and long-term study of their dynamics. The plot network has a hierarchical sampling scheme, with plots located within stands, and multiple stands organized in larger landscapes. For this study, we split the dataset into 11 landscapes, based on geographic location and forest type. They include 7 beech-dominated landscapes (i.e. Albania, Bulgaria, Croatia, Central Slovakia beech, Eastern Slovakia beech, Northern Romania beech, and Southern Romania beech) and 4 spruce-dominated landscapes (i.e. Central Slovakia spruce, Ukraine spruce, Northern Romania spruce, and Southern Romania spruce). The landscapes comprise a total of 35 spruce-dominated stands and 33 beech-dominated stands. 971 plots were analysed in total.

The lifespan of 20600 tree cores was determined following standard dendrochronological procedures. All tree cores that had more than 20 missing rings to the pith, as well as the cores with estimated age of less than 50 years were excluded from further studies. Disturbance chronologies were derived from temporal patterns in inter-annual tree growth. We used the original approach of Lorimer and Frelich (1989), in which each core is screened for (1) abrupt, sustained increases in radial growth (i.e releases) and (2) rapid early growth rates (i.e. gap-recruited trees), both of which provide indirect evidence of mortality of a former canopy tree. The events were aggregated at the plot level by smoothing and detecting peaks in their distributions. Ultimately we used the severity and timing of the most severe event on the plot as a proxy of past disturbance. Latitude, slope, aspect (transformed to northness), and the average temperature of the vegetation season (by downscaling the Worldclim gridded data (Fick and Hijmans, 2017) for the period 1970–2000) were used as proxies for environmental conditions. For each core also the number of releases, minimum 10-year growth, maximum 10-year growth and early growth (average growth rate in the first 50 years) were used as proxies of individual growth. Densities of the oldest trees were calculated as densities of trees ≥ species-specific 90th percentile of age on the stand level, while the share of the oldest canopy trees in the canopies was also calculated on the stand level as the share of trees ≥ species-specific 90th percentile of age that were characterized as canopy trees among all the canopy trees detected on the plots.

Usage Notes

The dataset contains the age data resulting from dendrochronological analyses. It is comprised of three tables related to tree lifespan in the study area: (Data90all), (Densities), and (Share_old_canopy_trees).

Individual core (tree) data is included in the first table (Data90all). Each row shows the data for the specific tree (core). Each tree is characterized by a unique “treeid”. “plot_id” is the identifier of the plot, “stand” is the identifier of the stand, “landscape” is the identifier of the landscape, “foresttype” is the identifier of the respective forest type, “species” marks the tree species, and “age” its age. “over90q” marks whether the tree was ≥ species-specific 90th percentile of age in that case the value is 1, while in the case the tree was < species-specific 90th percentile of age 0 was assigned. “n_release” marks the number of release events identified in the respective tree chronology. “lat” marks the latitude [˚] of the respective plot, “lng” marks the longitude [˚] of the respective plot, and “altitude_m” marks the altitude [m] of the respective plot. “incr_50” represents the average growth of the tree in the first 50 years [mm/year], while “growth_max” represents maximum 10-year running mean of annual growth rates [mm/year], and “growth_min” represents minimum 10-year running mean of annual growth rates [mm/year]. “dbh_mm” marks the tree’s DBH [mm]. “slope” represents the slope [˚] of the respective plot, while “aspect” represents the aspect [˚] of the respective plot. “northness” is cosine transformed aspect. “temp_mean_vegetseason” represents the average temperature of the vegetation season [˚C] on the respective plot. “disturbance_severity” marks the severity of the most severe disturbance event [% of canopy removed] on the respective plot, while “disturbance_year” marks the year of this event. “Pairplot” marks the respective pair of plots.

The densities of the trees ≥ species-specific 90th percentile of age on the stand level are included in the second table (Densities). Each row represents the density of the ≥ species-specific 90th percentile of age of a particular species in a particular stand. “landscape” marks the respective landscape, “stand” marks the respective stand, while “species” marks the particular species which the density on the stand level stands for. “standsize” represents the size of the sampled area [m2] in the particular stand. “n_stand” represents the number of trees of a particular species ≥ species-specific 90th percentile of age [n/ha] in the particular stand. “density” stands for the density of the trees of a particular species ≥ species-specific 90th percentile of age [n/ha] in the particular stand, while “stand _density” represents the cumulative density of all the trees ≥ species-specific 90th percentile of age [n/ha] in the particular stand.

The final table (Share_old_canopy_trees) represents the shares of the trees ≥ species-specific 90th percentile of age in the canopy layer. “landscape” marks the respective landscape, “stand” marks the respective stand, while “species” marks the particular species which the density on the stand level stands for. “n_total” stands for all the canopy trees in the particular stand, while “n” marks the number of canopy trees ≥ species-specific 90th percentile of age in the respective stand. “share” stands for the share of the trees of a particular species ≥ species-specific 90th percentile of age in the canopy layer [%] in the particular stand, while “stand _share” represents the cumulative share of all the trees ≥ species-specific 90th percentile of age in the canopy [%] in the particular stand.

Funding

Czech Ministry of Education, Youth and Sports, Award: LTT20016

Fakulta Lesnická a Drevarská, Česká Zemědělská Univerzita v Praze, Award: A_19_21

European Regional Development Fund, Award: ITMS 313011T721

Česká Zemědělská Univerzita v Praze

European Regional Development Fund, Award: No. CZ.02.1.01/0.0/0.0/16 _019/0000803

Czech Ministry of Education, Youth and Sports, Award: LTT20016