Silver fir growth (BAI) in the Pyrenees

The Excel file contains two sheets: 1) silver fir basal area increment from 1960 to 2000 in different populations across the Pyrenees; 2) silver fir growth (mean BAI), growth trend (BAI trend), Resistance index (Rt), Recovery index (Rc) and Resilience index (Rs) in different populations across the Pyrenees. In addition, we provide several explanatory variables describing topographic and stand characteristics that were used to explain the variation in the above mentioned variables

Changes in plant taxonomic and functional diversity patterns following treeline advances in the South Urals

<p><b><i>Background</i></b>: Treeline ecotones represent environmental boundaries that fluctuate in space and time and thus induce changes in plant taxonomic and functional diversity.</p> <p><b><i>Aims</i></b>: To study changes through time in taxonomic and functional plant diversity patterns along the treeline ecotone.</p> <p><b><i>Methods</i></b>: In 2002, vegetation was sampled along a gradient from upper montane forest to the treeline–alpine transition in the South Ural Mountains, Russia. In 2014, vegetation was resampled and plant functional traits were collected. We studied spatial and temporal changes in plant species composition, functional composition and functional diversity.</p> <p><b><i>Results</i></b>: Species composition and diversity changed along the elevational gradient. The functional composition in height, leaf area, specific leaf area and leaf nitrogen content decreased with elevation, whereas functional composition of leaf carbon content increased. We found a temporal shift towards shorter plants with smaller leaves in treeline sites. Functional richness varied in several traits along the elevational gradient, while functional dispersion showed a trend towards increased functional dispersion in height, specific leaf area and leaf nitrogen in the treeline–tundra transition.</p> <p><b><i>Conclusions</i></b>: Tree encroachment across the treeline ecotone has resulted in a shift in plant species relative abundances and functional diversity, possibly affecting plant community assembly patterns.</p

Data from: Size Matters a Lot: Drought-Affected Italian Oaks Are Smaller and Show Lower Growth Prior to Tree Death

Hydraulic theory suggests that tall trees are at greater risk of drought-triggered death caused by hydraulic failure than small trees. In addition the drop in growth, observed in several tree species prior to death, is often interpreted as an early-warning signal of impending death. We test these hypotheses by comparing size, growth, and woodanatomy patterns of living and now-dead trees in two Italian oak forests showing recent mortality episodes. The mortality probability of trees is modeled as a function of recent growth and tree size. Drift-diffusion-jump (DDJ) metrics are used to detect early-warning signals. We found that the tallest trees of the anisohydric Italian oak better survived drought contrary to what was predicted by the theory. Dead trees were characterized by a lower height and radial-growth trend than living trees in both study sites. The growth reduction of now-dead trees started about 10 years prior to their death and after two severe spring droughts during the early 2000s. This critical transition in growth was detected by DDJ metrics in the most affected site. Dead trees were also more sensitive to drought stress in this site indicating different susceptibility to water shortage between trees. Dead trees did not form earlywood vessels with smaller lumen diameter than surviving trees but tended to form wider latewood vessels with a higher percentage of vessel area. Since living and dead trees showed similar competition we did not expect that moderate thinning and a reduction in tree density would increase the short-term survival probability of trees.</p

The abundance of soil microbes (A) and nutrients (B) in May (open bars) and July (closed bars).

<p>The abundance of arbuscular mycorrhizal (AM) fungi, other fungi and bacteria was estimated as the content of respective ester-linked fatty acid (y-axis on graph A, see Methods for details). Soil N concentration was measured in % (left y-axis of graph B) whereas P and K content were measured as mg/kg (right y-axis of graph B). Significant differences (Linear Mixed-Effect Models; p<0.05) in the measured parameters over time are marked with *.</p

Data Sangüesa-Barreda et al. 2018_JEcol.xlsx

All data used in this publication are included in this file. Sheets are: 1) Mistletoe ocurrence data, 2) <i>Pinus sylvestris</i> ocurrence data, 3) <i>Pinus halepensis</i> ocurrence data, 4) Mistletoe cold tolerance, 5) d13C for mistletoe and tree hosts in 2012 and 2013, 6) mistletoe germination and establishment. The description for each variable is included in the data file.<br><br><p>For further details, we refer to our publication: Sangüesa-Barreda et al. (2018). Delineating limits: confronting predicted climatic suitability to field performance in mistletoe populations, accepted for publication in Journal of Ecology.</p

The mean, standard deviation (SD) and the range of the soil nutrient content, ester-linked fatty acid (ELFA) biomarkers of AM fungi, other fungi, bacteria and vegetation characteristics measured at 1 m² scale.

<p>Due to destructive sampling, vegetation characteristics were only measured in plots B and C (see explanation in Methods). Different letters (when present) mark a significant difference among means according to Tukey HSD test (p<0.05).</p

Summary statistics of the Linear Mixed-Effect Models analysis fitted to study the influence of sampling time (May or July) on soil nutrient content and ester-linked fatty acid (ELFA) biomarkers of arbuscular mycorrhizal (AM) fungi, other fungi and bacteria measured from plot A.

<p>The degrees of freedom of the numerator (Num DF) and denominator (Den DF), F statistic (F) and associated probability (P) for the sampling time (i.e. fixed factor) are presented.</p

Results of the generalized least square models performed to study the influence of environmental conditions on soil microbes.

<p>For each soil microbial group, the coefficient associated with the explanatory variable is presented. In addition, the AIC weight of the regression model (i.e. its importance as compared to other models containing a different subset of explanatory variables) and the R² are shown. * P<0.05; ** P<0.01.</p

Spatial pattern of soil phosphorus content and abundance of arbuscular mycorrhizal fungi (ester-linked fatty acid 16:1ω5c in soil) in plots A, B and C.

<p>Maps were created by kriging, using data from soil samples collected from 15×15 cm quadrats in each 105×105 cm plot.</p