Lega_et_al_2014_Aquilegia_thalictrifolia_SSR_Genotypes

This file contains the genotypes of 9 SSR loci for 295 A. thalictrifolia individuals from 11 sampling localities. Missing data are indicated by '?'

Data and Readme

Data and Readm

Genotypes and phenotypes of adults and seeds from four Fagus sylvatica stands characterised by different management regimes

The archive contains microsatellite genotypes, geographical locations and phenotypic characteristics of adults and seeds of four beech stands characterised by different management regimes. Details are reported in the readme.txt file

Data from: Nitrogen deposition outweighs climatic variability in driving annual growth rate of canopy beech trees: evidence from long-term growth reconstruction across a geographic gradient

In this study, we investigated the role of climatic variability and atmospheric nitrogen deposition in driving long-term tree growth in canopy beech trees along a geographic gradient in the montane belt of the Italian peninsula, from the Alps to the southern Apennines. We sampled dominant trees at different developmental stages (from young to mature tree cohorts, with tree ages spanning from 35 to 160 years) and used stem analysis to infer historic reconstruction of tree volume and dominant height. Annual growth volume (<i>G</i>_V) and height (<i>G</i>_H) variability were related to annual variability in model simulated atmospheric nitrogen deposition and site-specific climatic variables, (<i>i.e.</i> mean annual temperature, total annual precipitation, mean growing period temperature, total growing period precipitation, and standard precipitation evapotranspiration index) and atmospheric CO₂ concentration, including tree cambial age among growth predictors. Generalized additive models (<i>GAM</i>), linear mixed-effects models (<i>LMM</i>), and Bayesian regression models (<i>BRM</i>) were independently employed to assess explanatory variables. The main results from our study were as follows: i) tree age was the main explanatory variable for long-term growth variability; ii) <i>GAM</i>, <i>LMM</i>, and <i>BRM </i>results consistently indicated climatic variables and CO₂ effects on <i>G</i>_V and <i>G</i>_H were weak, therefore evidence of recent climatic variability influence on beech annual growth rates was limited in the montane belt of the Italian peninsula; iii) instead, significant positive nitrogen deposition (<i>N</i>_dep) effects were repeatedly observed in <i>G</i>_V and <i>G</i>_H; the positive effects of <i>N</i>_dep on canopy height growth rates, which tended to level off at <i>N</i>_dep values greater than approximately 1.0 g m^-2 y^-1 were interpreted as positive impacts on forest stand above-ground net productivity at the selected study site

Parameters describing within-population genetic structure in the studied beech plots.

<p><i>F</i>₁, average kinship coefficient between individuals of the first distance class (0–20 m); <i>b</i>_F, regression slope of the kinship estimator <i>F</i>_ij computed among all pairs of individuals against geographical distances; <i>Sp</i>, intensity of SGS; <i>Nc</i>, mean number of clusters from GENELAND analyses<i>; θ</i>_ST, differentiation among clusters within each plot; <i>F</i>_IS, inbreeding coefficient estimated by INEst.</p>*<p><i>P</i><0.05,</p>**<p><i>P</i><0.01,</p>***<p><i>P</i><0.001.</p

Complexity of within-population genetic structure as measured by the standardized number of clusters in an area of 0.21 ha.

<p>0.21</p

Characteristics of investigated beech plots.

a<p>Plot codes were formed by the indication of country (G = Germany, NL = The Netherlands, A = Austria, F = France, I = Italy) and intensity of disturbance (l = low, h = high).</p>b<p>Austrian plots are subplots of Piotti et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073391#pone.0073391-Piotti1" target="_blank">[26]</a> plots.</p>c<p>Italian plots studied by Paffetti et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0073391#pone.0073391-Paffetti1" target="_blank">[25]</a> are subplots of the ones analyzed here.</p

Assessment of the power of the marker set to detect SGS by spatially explicit simulations.

<p>For illustration of the results, the distribution of the kinship coefficient <i>F</i>₁ between neighbours at generation 64 was used as the focal statistic (grey dots and boxplots) and compared to <i>i</i>) the no-structure 95% confidence intervals of <i>F</i>₁ from the Fh and Fl populations (dotted lines, see legend in the left panel) obtained by random shuffling of individual geographic locations, and <i>ii</i>) real <i>F</i>₁ values from Fh and Fl (black dots in the left panel) and their confidence intervals (grey areas). Results from simulations with 4 and 20 loci (right and left panels, respectively) are reported. Parameter settings for the 4 simulated scenarios were σ_g =12 m and D = 20 trees/ha (HIGH-SGS), σ_g =12 m and D = 35 trees/ha (Fl-like SGS), σ_g =29 m and D = 50 trees/ha (Fh-like SGS), σ_g =72 m and D = 145 trees/ha (LOW-SGS).</p

Correlograms from spatial autocorrelation analysis using the correlation coefficient <i>r</i> by Smouse & Peakall [29] and even distance classes.

<p>Shaded areas represent the 95% confidence interval obtained through random shuffling (1000 times) of individual geographic locations, black lines around mear <i>r</i> values represent 95% confidence intervals around mean r values generated by bootstrapping (1000 times) pair-wise comparisons within each distance class.</p