Supporting Information Methods S1 and Table S1
Spatial and temporal variation in plant hydraulic traits and their relevance for climate
change impacts on vegetation
William R. L. Anderegg
Methods S1
Meta-analysis formulation and criteria
I sought to quantify the intra-specific variation of a key hydraulic trait thought to capture species
resistance to drought stress – the water potential at which 50% of hydraulic conductivity is lost
(P50). P50 is calculated by measuring a hydraulic a vulnerability curve, which plots the percent
loss hydraulic conductivity (PLC) as a function of water potential (Ψ) (e.g. Fig. 1). Many
techniques have been developed for vulnerability curves, although four methods accounted for
96% of published vulnerability curves (Cochard et al., 2013), and many functional forms have
been fit to these curves (Pammenter & Van der Willigen, 1998; Cochard et al., 2013). Metaanalysis have been conducted on inter-specific differences in P50 (Maherali et al., 2004), but no
study to my knowledge to date has quantified intra-specific differences across published studies.
I conducted an extensive literature search to identify studies that met the following
criteria: (1) included either multiple vulnerability curves (reported via figures, tables, or
mathematical functions) or P50 values reported for the same species, (2) analyzed these
vulnerability curves using the same measurement technique within a study, to avoid any
introduced bias in variation due to different measurement techniques or lab groups, (3) reported
values from multiple populations of the same species either over space or the same population
Anderegg – Supporting Information – 1
over time, (4) reported values from the same sub-species or cultivar (i.e. studies that measured
different sub-species or varieties/cultivars were not included), (5) quantified vulnerability curves
or P50 to water-stress-induced cavitation (i.e. not freeze-thaw cavitation), and (6) considered
plasticity in space or time primarily as a function of water availability (e.g. studies that
manipulated shade or nutrients not included). This literature search consisted of a hybrid of
researching the references in prominent intra-specific and inter-specific hydraulic trait studies
and then extensive searches of online databases of Google Scholar and Web of Science, using
keyword combinations of ‘hydraulic’, ‘P50’, ‘intra-specific’, ‘hydraulic trait’, ‘variation’, and
‘environment’. I did not to include species where P50 values were reported across multiple
studies, but only one P50 value was reported per study. There are several reasons for this. First,
using within-study reported variation in P50 values provides likely the most accurate snapshot of
variation because it standardizes by measurement technique, processing and measurement errors,
and sampling of tissues within a given tree (e.g. canopy location, time of day, season, etc.).
Second, it allows careful statistical quantification of effect size and sample size, which are
needed for meta-analysis and it is bad practice to integrate multiple studies to calculate a single
effect size (Koricheva et al., 2013) (i.e. studies can be combined where each study individually
can provide an estimate of effect size, which would not be possible when combining several
studies with one P50 value per study).
For each study, I collected information on P50, variability of P50 in a given vulnerability
curve (typically standard deviation or standard error), species, population/study treatment, P88,
minimum water potential, sample size, tissue measured (e.g. branch, root), plant age (seedling,
sapling, adult), measurement technique (e.g. air injection, centrifuge, Cavitron, dehydration,
etc.), and geographic location (region and lat/lon where available) of the study or plant
Anderegg – Supporting Information – 2
populations. P50 values were often drawn directly from those reported in tables, although P50
values were estimated from vulnerability curves using Adobe Acrobat software in ~30% of
cases.
Different methods in sample processing prior to constructing vulnerability curves used in
different labs, such as flushing versus not-flushing stems, can have a large impact on the shape of
the curves and P50 values reported (Sperry et al., 2012). While the methods used here of
calculating the CV within a given study (same methods across populations measured) should
largely mitigate this potential artifact, this is nonetheless an assumption that the same CV would
be derived from different protocols. Another hydraulic method caveat concerns the welldocumented artifacts in some vulnerability curves generated using the Cavitron centrifuge
technique (Cochard et al., 2005, 2010). If these potential errors are consistent across populations,
they should largely cancel out using the intra-study calculations of CV here. To assess the
robustness of the analyses performed here to this possible artifact, however, I recalculated all
statistical comparisons without studies that used the Cavitron method (N=9). All results were
robust with the removal of these studies.
Statistical methods
The literature search identified 33 studies, covering intra-specific variation of P50 over space or
time in 46 species, that met the four inclusion criteria above (Table S1). I then calculated the
coefficient of variation (CV, defined as the standard deviation/mean) of P50 for a given species
as the ‘effect size’ estimate for the meta-analysis. While meta-analyses are typically performed
using study means as effect sizes, any standardized metric can be selected as long as it meets the
following criteria: (1) the variable is comparable across studies (i.e. is standardized), (2) it
Anderegg – Supporting Information – 3
represents the magnitude and direction of the relationship of interest, and (3) is independent of
sample size (Wilson, 2001). The CV of P50 meets all of these criteria, as it standardizes the
variation by the mean of P50 for a given species, captures the intra-specific variation of P50, and
since both the standard deviation and mean are independent of sample size, CV is as well. To
ensure that P50 values were comparable both within and across studies, I analyzed only P50
values reported on branch tissues and the same ontogenetic stage within a study.
I then used fixed-effects linear models to test whether spatial or temporal CVs were
different and whether angiosperms exhibited higher spatial CVs than gymnosperms. I weighted
studies based on the inverse of standard error – the reported standard deviation in P50 within a
population, to account for differences in precision across studies, divided by the square root of
sample size in number of populations, to account for differences in sample size. This approach
has been used before in meta-analyses using CV as a metric of effect size (Chamberlain et al.
2014). For studies that did not provide standard deviations (N=12), I assumed a standard
deviations equal to the mean SD across all studies. To test the robustness, I performed this model
on spatial variability studies alone (N=26) and both spatial and temporal variability studies,
including a fixed effect for spatial vs. temporal. This combined model generated a preliminary
indication of whether spatial and temporal variability differed, although the limited sample size
of temporal studies (N=7) suggests that caution is recommended for interpreting generality.
To set intra-specific P50 variability in context with inter-specific and biome level
variability, I calculated the CV within a genus using the published database of Choat et al.
(2012) where more than two species per genus were recorded. I then classified each species to a
given plant functional type (PFT) of the Community Land Model (CLM 3.0) based on the
hierarchy presented in Lawrence & Chase (2007) and calculated the CV within PFTs and across
Anderegg – Supporting Information – 4
PFTs. Because information about sample sizes and variance in P50 is not included in the Choat
et al. database, weighting of effect sizes (here CV of P50) species or studies was not possible and
thus I performed an ANOVA on the unweighted CV values of (1) intra-specific P50 variation
over space, (2) inter-specific variation within a genus, and (3) inter-specific variation within a
PFT.
Finally, to examine how well PFT captured variability in hydraulic traits across species, I
used the classifications above and tested both P50 and hydraulic safety margin (P50 – minimum
midday water potential) values as a function of PFT using ANOVA. I repeated this analysis for
shrub-only and tree-only PFTs to isolate the effect of plant form on cross-PFT variation.
All analyses were performed in the R statistical software with meta-analysis mixedeffects models performed using the ‘metafor’ package (Viechtbauer, 2010).
Publication bias
Publication bias towards positive results (the ‘file drawer’ problem) should be considered as a
caveat in any meta-analysis. I suggest that publication bias is unlikely to drive the primary results
presented here for two interrelated reasons. First, the majority of studies examined multiple traits
beyond P50, which removes the pressure to publish significant results on any single trait and
makes publication bias less probable. Second, a large number of studies included in this metaanalysis presented negative or insignificant results (i.e. very little variation in P50), which
indicates that a strong publication bias towards only publishing positive results is less likely in
this literature. As is highlighted in many studies (e.g. Lemy et al., 2013), negative results of
minimal P50 variation over space are actually quite important because they suggest that this
Anderegg – Supporting Information – 5
prominent hydraulic trait is constrained by selection. The combination of these lines of evidence
suggests that publication bias is not likely to drive the trends observed here.
Table S1 Studies and species included in the meta-analysis of intra-specific hydraulic P50
variation over space and time. Group indicates whether angiosperm (A) or gymnosperm
(G). Space/time whether the study examined spatial or temporal variation in P50.
Experiment type indicates the study design. Npop indicates the number of populations
studied.
Group
Species
Space/time
Experiment type
A
Populus tremuloides
Spatial
Observational gradient across
elevation
2
A
Spatial
2
(Alder et al., 1996)
A
Cordia alliodora
Spatial
Drought experiment with
different growth conditions
Observational gradient across
moisture – riparian vs slope
Multiple populations
3
A
Populus tremula × Populus
alba
Acer grandidentatum
(Anderegg et al., 2013); High
elevation vulnerability curve from
unpublished data
(Awad et al., 2010)
3
(Choat et al., 2007)
A
Fagus Sylvatica
Spatial
Multiple populations
11
(Herbette et al., 2010)
A
Fagus Sylvatica
Spatial
Common garden with multiple
provenances
17
A
Baccharis sarothroides
Spatial
2
A
Quercus wislizenii
Spatial
Observational gradient across
moisture – riparian vs slope
Multiple populations
(Wortemann et al., 2011); CV
calculated from all provenance x
growth trail combinations
(Pockman & Sperry, 2000)
3
(Matzner et al., 2001)
A
Populus tremuloides
Spatial
Multiple populations
2
(Schreiber et al., 2011)
A
Spatial
Drought experiment with
different growth conditions
Humid versus arid site
8
(Fichot et al., 2010)
A
Populus deltoides x Populus
nigra
Eucalyptus camaldulensis
2
(Franks et al., 1995)
A
Cliffortia ruscifolia
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Leucadendron salignum
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Rhamnus ilicifolia
Spatial
Comparison of wet and dry
mountain ranges
3
(Jacobsen et al., in press)
A
Ceanothus megacarpus
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Ceanothus spinosus
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Quercus agrifolia
Spatial
Comparison of wet and dry
mountain ranges
3
(Jacobsen et al., in press)
A
Rhus ovata
Spatial
Comparison of wet and dry
mountain ranges
3
(Jacobsen et al., in press)
A
Quercus berberidifolia
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., In press)
A
Malosma laurina
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
Spatial
Spatial
Npop
Reference and notes
Anderegg – Supporting Information – 6
A
Ceanothus leucodermis
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Arctostaphylos grandulosa
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Adenostoma fasciculatum
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Ceanothus crassifolius
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Heteromeles arbutifolia
Spatial
Comparison of wet and dry
mountain ranges
2
(Jacobsen et al., in press)
A
Baccharis sarothroides
Spatial
2
(Pockman & Sperry, 2000)
A
Hymenoclea salsola
Spatial
Comparison of wet and dry
mountain ranges
Multiple populations
3
(Mencuccini & Comstock, 1997)
A
Ambrosia dumosa
Spatial
Multiple populations
3
(Mencuccini & Comstock, 1997)
A
Fagus sylvatica
Spatial
Multiple populations
4
A
Sorbus aucuparia
Spatial
Multiple populations
4
G
Pinus Pinaster
Spatial
Multiple populations
12
G
Pinus Sylvestrus
Spatial
Multiple populations
12
(Charra-Vaskou et al., 2012); Spatial
CV calculated as differences across
populations, averaged across
summer/winter samplings
(Charra-Vaskou et al., 2012); Spatial
CV calculated as differences across
populations, averaged across
summer/winter samplings
(Lamy et al., 2013); CV calculated
from all provenance x growth trail
combinations
(Martínez‐Vilalta et al., 2009)
G
Pinus pinaster
Spatial
Multiple populations
6
G
Pinus Pinaster
Spatial
Multiple populations
12
G
Pinus ponderosa
Spatial
Multiple populations
4
G
Cedrus libani
Spatial
Multiple populations
4
G
Pseudotsuga menziesii
Spatial
2
G
Pinus ponderosa
Spatial
2
(Stout & Sala, 2003)
G
Pinus canariensis
Spatial
Observational gradient across
moisture – riparian vs slope
Observational gradient across
moisture – riparian vs slope
Multiple populations
(Ladjal et al., 2005); for spatial
analyses used only 1998 values of
provenance x trial variations
(Stout & Sala, 2003)
16
(López et al., 2013)
G
Larix decidua
Spatial
Multiple populations
4
(Charra-Vaskou et al., 2012)
G
Picea abies
Spatial
Multiple populations
4
(Charra-Vaskou et al., 2012)
G
Pinus taeda
Spatial
2
(Hacke et al., 2000)
A
Acer negundo
Temporal
2
(Hacke et al., 2001)
A
Alnus incana
Temporal
2
(Hacke et al., 2001)
A
Betula occidentalis
Temporal
2
(Hacke et al., 2001)
A
Populus angustifolia
Temporal
2
(Hacke et al., 2001)
A
Populus tremuloides
Temporal
2
(Hacke et al., 2001)
A
Aesculus hippocastanum
petioles
Helianthus annuus
Temporal
Comparison of sand and loam
soil types
Stressed tissues with
centrifuge tension
Stressed tissues with
centrifuge tension
Stressed tissues with
centrifuge tension
Stressed tissues with
centrifuge tension
Stressed tissues with
centrifuge tension
Stressed tissues with
centrifuge tension
Stressed tissues with
centrifuge tension
2
(Hacke et al., 2001)
2
(Hacke et al., 2001)
A
Temporal
(Lamy et al., 2011)
(Corcuera et al., 2011); (Lamy et al.,
2013); CV calculated from all
provenance x growth trail
combinations
(Maherali & DeLucia, 2000)
Anderegg – Supporting Information – 7
A
Populus tremuloides
Temporal
Compared drought-induced
dying stems with healthy
Effects of drought after
multiple years of drought
experiment
Effects of drought after
drought experiment stopped
Effects of drought after
drought experiment stopped
Seasonal – summer versus
winter
2
(Anderegg et al., 2013);
A
Quercus ilex
Temporal
2
(Limousin et al., 2010)
A
Ligustrum vulgare
Temporal
2
Temporal
Seasonal – summer versus
winter
2
Adenostoma sparsifolium
Temporal
Seasonal comparison – wet
versus dry season
2
(Beikircher & Mayr, 2009); only
used irrigated and drought treatments
(Beikircher & Mayr, 2009); only
used irrigated and drought treatments
(Charra-Vaskou et al., 2012);
Temporal CV calculated as
differences between summer/winter,
averaged across spatial populations
(Charra-Vaskou et al., 2012);
Temporal CV calculated as
differences between summer/winter,
averaged across spatial populations
(Jacobsen et al., in press)
A
Viburnum lantana
Temporal
A
Fagus sylvatica
Temporal
A
Sorbus aucuparia
A
A
Adenostoma fasciculatum
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Arctostaphylos grandulosa
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Ceanothus crassifolius
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Ceanothus megacarpus
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Ceanothus spinosus
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Ceanothus oliganthus
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Ceanothus cuneatus
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Quercus agrifolia
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Quercus berberidifolia
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Rhus ovata
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
A
Malosma laurina
Temporal
Seasonal comparison – wet
versus dry season
2
(Jacobsen et al., in press)
G
Larix decidua
Temporal
Seasonal – summer versus
winter
2
G
Picea abies
Temporal
Seasonal – summer versus
winter
2
G
Cedrus atlantica
Temporal
3
G
Cedrus brevifolia
Temporal
3
(Ladjal et al., 2005)
G
Cedris libani var. libani
Temporal
3
(Ladjal et al., 2005)
G
Cedrus libani var. stenocoma
Temporal
Effects of drought after
drought experiment stopped
Effects of drought after
drought experiment stopped
Effects of drought after
drought experiment stopped
Effects of drought after
drought experiment stopped
(Charra-Vaskou et al., 2012);
Temporal CV calculated as
differences between summer/winter,
averaged across spatial populations
(Charra-Vaskou et al., 2012);
Temporal CV calculated as
differences between summer/winter,
averaged across spatial populations
(Ladjal et al., 2005)
3
(Ladjal et al., 2005)
2
2
Anderegg – Supporting Information – 8
References
Alder N, Sperry J, Pockman W. 1996. Root and stem xylem embolism, stomatal conductance,
and leaf turgor in Acer grandidentatum populations along a soil moisture gradient.
Oecologia 105: 293-301.
Anderegg WRL, Plavcová L, Anderegg LDL, Hacke UG, Berry JA, Field CB. 2013.
Drought's legacy: multiyear hydraulic deterioration underlies widespread aspen forest
die-off and portends increased future risk. Global Change Biology 19: 1188-1196.
Awad H, Barigah T, Badel E, Cochard H, Herbette S. 2010. Poplar vulnerability to xylem
cavitation acclimates to drier soil conditions. Physiologia Plantarum 139: 280-288.
Beikircher B, Mayr S. 2009. Intraspecific differences in drought tolerance and acclimation in
hydraulics of Ligustrum vulgare and Viburnum lantana. Tree Physiology 29: 765-775.
Charra-Vaskou K, Charrier G, Wortemann R, Beikircher B, Cochard H, Ameglio T, Mayr
S. 2012. Drought and frost resistance of trees: a comparison of four species at different
sites and altitudes. Annals of Forest Science 69: 325-333.
Choat B, Jansen S, Brodribb TJ, Cochard H, Delzon S, Bhaskar R, Bucci SJ, Feild TS,
Gleason SM, Hacke UG et al. 2012. Global convergence in the vulnerability of forests
to drought. Nature 491: 752-755.
Choat B, Sack L, Holbrook NM. 2007. Diversity of hydraulic traits in nine Cordia species
growing in tropical forests with contrasting precipitation. New Phytologist 175: 686-698.
Cochard H, Badel E, Herbette S, Delzon S, Choat B, Jansen S. 2013. Methods for measuring
plant vulnerability to cavitation: a critical review. Journal of Experimental Botany 64:
4779-4791.
Cochard H, Damour G, Bodet C, Tharwat I, Poirier M, Améglio T. 2005. Evaluation of a
new centrifuge technique for rapid generation of xylem vulnerability curves. Physiologia
Plantarum 124: 410-418.
Cochard H, Herbette S, Barigah T, Badel E, Ennajeh M, Vilagrosa A. 2010. Does sample
length influence the shape of xylem embolism vulnerability curves? A test with the
Cavitron spinning technique. Plant, Cell & Environment 33: 1543-1552.
Corcuera L, Cochard H, Gil-Pelegrin E, Notivol E. 2011. Phenotypic plasticity in mesic
populations of Pinus pinaster improves resistance to xylem embolism (P50) under severe
drought. Trees 25: 1033-1042.
Fichot R, Barigah TS, Chamaillard S, Le Thiec D, Laurans F, Cochard H, Brignolas F.
2010. Common trade‐offs between xylem resistance to cavitation and other physiological
traits do not hold among unrelated Populus deltoides× Populus nigra hybrids. Plant, Cell
& Environment 33: 1553-1568.
Franks P, Gibson A, Bachelard E. 1995. Xylem permeability and embolism susceptibility in
seedlings of Eucalyptus camaldulensis Dehnh. from two different climatic zones.
Functional Plant Biology 22: 15-21.
Hacke U, Sperry J, Ewers B, Ellsworth D, Schäfer K, Oren R. 2000. Influence of soil
porosity on water use in Pinus taeda. Oecologia 124: 495-505.
Anderegg – Supporting Information – 9
Hacke UG, Stiller V, Sperry JS, Pittermann J, McCulloh KA. 2001. Cavitation fatigue:
Embolism and refilling cycles can weaken the cavitation resistance of xylem. Plant
Physiology 125: 779-786.
Herbette S, Wortemann R, Awad H, Huc R, Cochard H, Barigah TS. 2010. Insights into
xylem vulnerability to cavitation in Fagus sylvatica L.: phenotypic and environmental
sources of variability. Tree Physiology 30: 1448-1455.
Jacobsen A, Pratt RB, Davis S, Tobin M. In press. Geographic and seasonal variation in
chaparral vulnerability to cavitation. Madrono.
Koricheva J, Gurevitch J, Mengersen K. 2013. Handbook of meta-analysis in ecology and
evolution: Princeton, NJ, USA: Princeton University Press.
Ladjal M, Huc R, Ducrey M. 2005. Drought effects on hydraulic conductivity and xylem
vulnerability to embolism in diverse species and provenances of Mediterranean cedars.
Tree Physiology 25: 1109-1117.
Lamy J-B, Bouffier L, Burlett R, Plomion C, Cochard H, Delzon S. 2011. Uniform selection
as a primary force reducing population genetic differentiation of cavitation resistance
across a species range. PLoS One 6: e23476.
Lamy JB, Delzon S, Bouche PS, Alia R, Vendramin GG, Cochard H, Plomion C. 2013.
Limited genetic variability and phenotypic plasticity detected for cavitation resistance in
a Mediterranean pine. New Phytologist 201: 874-886
Lawrence PJ, Chase TN. 2007. Representing a new MODIS consistent land surface in the
Community Land Model (CLM 3.0). Journal of Geophysical Research: Biogeosciences
(2005–2012) 112: 1-15.
Limousin J-M, Longepierre D, Huc R, Rambal S. 2010. Change in hydraulic traits of
Mediterranean Quercus ilex subjected to long-term throughfall exclusion. Tree
Physiology 30: 1026-1036.
López R, de Heredia UL, Collada C, Cano FJ, Emerson BC, Cochard H, Gil L. 2013.
Vulnerability to cavitation, hydraulic efficiency, growth and survival in an insular pine
(Pinus canariensis). Annals of Botany 111: 1167-1179.
Maherali H, DeLucia EH. 2000. Xylem conductivity and vulnerability to cavitation of
ponderosa pine growing in contrasting climates. Tree Physiology 20: 859-867.
Maherali H, Pockman WT, Jackson RB. 2004. Adaptive variation in the vulnerability of
woody plants to xylem cavitation. Ecology 85: 2184-2199.
Martínez‐Vilalta J, Cochard H, Mencuccini M, Sterck F, Herrero A, Korhonen J, Llorens
P, Nikinmaa E, Nolè A, Poyatos R. 2009. Hydraulic adjustment of Scots pine across
Europe. New Phytologist 184: 353-364.
Matzner SL, Rice KJ, Richards JH. 2001. Intra‐specific variation in xylem cavitation in
interior live oak (Quercus wislizenii A. DC.). Journal of Experimental Botany 52: 783789.
Mencuccini M, Comstock J. 1997. Vulnerability to cavitation in populations of two desert
species, Hymenoclea salsola and Ambrosia dumosa, from different climatic regions.
Journal of Experimental Botany 48: 1323-1334.
Pammenter N, Van der Willigen C. 1998. A mathematical and statistical analysis of the curves
illustrating vulnerability of xylem to cavitation. Tree Physiology 18: 589-593.
Pockman WT, Sperry JS. 2000. Vulnerability to xylem cavitation and the distribution of
Sonoran Desert vegetation. American Journal of Botany 87: 1287-1299.
Anderegg – Supporting Information – 10
Schreiber SG, Hacke UG, Hamann A, Thomas BR. 2011. Genetic variation of hydraulic and
wood anatomical traits in hybrid poplar and trembling aspen. New Phytologist 190: 150160.
Sperry JS, Christman MA, Torres-Ruiz JM, Taneda H, Smith DD. 2012. Vulnerability
curves by centrifugation: is there an open vessel artefact, and are ‘r’shaped curves
necessarily invalid? Plant, Cell & Environment 35: 601-610.
Stout DL, Sala A. 2003. Xylem vulnerability to cavitation in Pseudotsuga menziesii and Pinus
ponderosa from contrasting habitats. Tree Physiology 23: 43-50.
Viechtbauer W. 2010. Conducting meta-analyses in R with the metafor package. Journal of
Statistical Software 36: 1-48.
Wilson DB. 2001. Practical meta-analysis: Thousand Oaks, CA, USA: Sage Publications.
Wortemann R, Herbette S, Barigah TS, Fumanal B, Alia R, Ducousso A, Gomory D,
Roeckel-Drevet P, Cochard H. 2011. Genotypic variability and phenotypic plasticity of
cavitation resistance in Fagus sylvatica L. across Europe. Tree Physiology 31: 11751182.
Anderegg – Supporting Information – 11