10.5061/DRYAD.1NS1RN8TS
Pavlin, Jakob
0000-0001-8514-3446
Czech University of Life Sciences Prague
Nagel, Thomas A.
University of Ljubljana
Svitok, Marek
Technical University of Zvolen
Pettit, Joseph L.
Czech University of Life Sciences Prague
Begović, Krešimir
Czech University of Life Sciences Prague
Mikac, Stjepan
University of Zagreb
Dikku, Abdulla
PSEDA-ILIRIA
Toromani, Elvin
Agricultural University of Tirana
Panayotov, Momchil
University of Forestry
Zlatanov, Tzvetan
Bulgarian Academy of Sciences
Haruta, Ovidiu
University of Oradea
Dorog, Sorin
University of Oradea
Chaskovskyy, Oleh
Ukrainian National Forestry University
Mikoláš, Martin
Czech University of Life Sciences Prague
Janda, Pavel
Czech University of Life Sciences Prague
Frankovič, Michal
Czech University of Life Sciences Prague
Rodrigo, Ruffy
Czech University of Life Sciences Prague
Vostarek, Ondřej
Czech University of Life Sciences Prague
Synek, Michal
Czech University of Life Sciences Prague
Dušátko, Martin
Czech University of Life Sciences Prague
Kníř, Tomáš
Czech University of Life Sciences Prague
Kozák, Daniel
Czech University of Life Sciences Prague
Kameniar, Ondrej
Czech University of Life Sciences Prague
Bače, Radek
Czech University of Life Sciences Prague
Čada, Vojtěch
Czech University of Life Sciences Prague
Trotsiuk, Volodymyr
Swiss Federal Institute for Forest, Snow and Landscape Research
Schurman, Jonathan S.
Czech University of Life Sciences Prague
Saulnier, Mélanie
Czech University of Life Sciences Prague
Buechling, Arne
Czech University of Life Sciences Prague
Svoboda, Miroslav
Czech University of Life Sciences Prague
Data from: Disturbance history is a key driver of tree lifespan in
temperate primary forests
Dryad
dataset
2021
disturbance
European beech
growth patterns
longevity
Norway spruce
silver fir
site conditions
sycamore maple
Czech Ministry of Education, Youth and Sports*
LTT20016
Czech University of Life Sciences Prague
https://ror.org/0415vcw02
A_19_21
European Commission
https://ror.org/00k4n6c32
ITMS 313011T721
Czech University of Life Sciences Prague
https://ror.org/0415vcw02
European Commission
https://ror.org/00k4n6c32
No. CZ.02.1.01/0.0/0.0/16 _019/0000803
Czech Ministry of Education, Youth and Sports
LTT20016
2021-07-25T00:00:00Z
2021-07-25T00:00:00Z
en
3956896 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.
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.