DOI: 10.5061/dryad.djh9w0w52
title: Adaptation potential of Neotropical montane oaks to drought events: wood anatomy sensitivity in Quercus delgadoana and Quercus meavei

description: Climate and local water availability are major evolutionary drivers of
 adaptive variation and plasticity in the hydraulic architecture of
 Tropical Montane Cloud Forest (TMCF) tree species. Between-year xylem
 vessel variability is key to understanding the adaptation potential of
 wood anatomy of trees to drought. How wood anatomical features have been
 influenced by the typical TMCF climate and how tree species persist in
 these environments remain open questions, particularly in the context of
 predicted extreme climate events in the future. Here, we evaluated the
 effects of changes in temperature, precipitation, and evapotranspiration
 during drought events on ring-width and anatomical vessel traits
 (hydraulic diameter, vessel density, vessel grouping index, and
 vulnerability index) for two relict endemic and threatened oak species
 (Quercus delgadoana and Q. meavei) from a Mexican TMCF. The study species
 differed in their functional and ecological vessel anatomical traits, and
 their wood anatomical differences are related to specific environmental
 requirements. However, the Ring Width Indices (RWI) calculated for these
 species indicate that both have high resistance and recovery, and thus
 high resilience to drought events. Ring-width and vessel functional traits
 show differences in the between-year variability of xylem traits
 associated with the hydraulic efficiency of these oak species, which is
 crucial to understanding how they avoid drought-induced embolism and
 cavitation in vessel conduits. These results provide evidence for the
 existence of specific hydraulic systems that determine functional wood
 anatomy in response to climatic variation and drought in the study
 species. Further research assessing the wood anatomical adaptation to
 different climatic variables and identifying the xylem functional traits
 that underlie these adaptations, along with the mechanism allowing tree
 species persistence in these environments, is essential to gain insight
 into the responses of TMCF to future drought events.
description: <em><strong>Sampling techniques and chronology development
 </strong></em> We collected wood cores from 20 trees from each
 oak species with a diameter at breast height (DBH; 1.3
 m) ≥ 40 cm. We
 collected two cores per
 tree at breast height with an increment borer of 5 mm inner diameter
 (Häglof®, Langsele, Sweden) (Stokes & Smiley, 1996). We filled the
 holes with wood plugs treated with a mixture of 80% ethanol and 20%
 purified water (Thiercelin et al., 1972). The wood cores were air-dried at
 room temperature, glued to wooden supports, and sanded with a succession
 of coarse grit sandpapers (400, 600, 1000, 1200, and 2000) until the xylem
 cell structure was visible up to 100x magnified (Speer, 2010; Stokes
 & Smiley, 1996). We removed wood dust inside the lumina with a
 hairdryer (Rodríguez-Ramírez et al., 2018). Permission to
 collect the wood samples was granted directly by the Commissioner of the
 Medio Monte Natural Protected Area. Wood cores are stored at the
 Biogeography and Systematics Laboratory, Faculty of Science, Universidad
 Nacional Autónoma de México (UNAM), Mexico City,
 Mexico. Ring widths were measured to the nearest 0.001
 mm using a stereoscopic microscope (Olympus® SZ61, Olympus Corporation,
 Center Valley, PA, USA) with TSAP-Win v.4.67c software and a Velmex Tree
 Ring Measuring System (Velmex, Inc., Bloomfield, NY, USA). We
 statistically verified the dating accuracy through the COFECHA software
 (Holmes, 1983). To minimise the non-climatic variance associated with
 local climatic variations, and thus maximise the evidence for drought, we
 removed non-climatic trends from each tree-ring series using a cubic
 spline with a 50% response over 10-year periods (Gareca et al., 2010;
 Rodríguez-Ramírez et al., 2020). We standardised raw ring-width series by
 applying autoregressive modelling to remove serial correlation using the
 ARSTAN software to calculate the mean value of detrended Ring-Width
 Indices (RWI; a standardised index for annual radial growth; Cook
 & Holmes, 1996). We evaluated the quality of the chronologies
 using EPS (Expressed Population Signal) as a measure of the total signal
 present in the chronology, considering values &gt; 0.85 (Briffa, 1999)
 and the mean correlation coefficient between tree-ring series
 (R-bar; Wigley et al.,
 1984). In addition, we evaluated the signal-to-noise ratio (SNR), which
 expresses the strength of the recognized common signal between trees in a
 chronology. <strong>Climate-growth
 correlations</strong> We retrieved climatic data from the CLICOM
 database of the National Meteorological Service of Mexico
 (http://clicom-mex.cicese.mx/). We extracted the mean monthly maximum temperature
 (Tmax), total monthly precipitation (Prec), and total monthly
 evapotranspiration (EvT) data for a nearby weather station (Agua Blanca,
 20.3° N, 98.4° W; 2157 m asl), which kept records from 1974 to 2016. We
 used Spearman's correlation coefficients (rS; because the climatic
 variables tend to change simultaneously; Pouteau et al., 2018) as a
 similarity measure to assess growth sensitivity to climate in the study
 species. We used the standard chronology and monthly climate data for
 Tmax, Prec, and EvT for a period spanning from the previous growth year
 (Jun (-1)) to the current growth year (Sep). We conducted this analysis
 with SigmaStat v.4 (Jandel Scientific, 2016).
 <strong>Identification
 of historical drought events</strong>
 To select tree rings formed
 during drought events for subsequent digitization (Rodríguez-Ramírez
 & Luna-Vega, 2020), we used specific historical drought years
 (1976, 1983, 1991, 1999, and 2012; directly from the Agua Blanca weather
 station). Droughts indicated by these data were confirmed using
 information from the Mexican Drought Atlas (Stahle et al., 2016). The
 chronologies were compared with monthly values of the Palmer Drought
 Severity Index (PDSI) to estimate drought intensity. The relationship of
 narrow rings with historical droughts allowed the identification of
 specific drought events (through the calculation of z-scores; Eq. 1) in
 the RWI for both species (1976, 1983, 1991, 1999 and
 2012).             We calculated the z-score for each
 oak chronology as: <em>z<sub>t</sub>= (X<sub>t
 </sub>- Xbar)/S              </em>(1)
 where z is the standardised
 value of the drought year, xt is the reconstructed drought ranking, Xbar
 is the mean of the
 reconstructed chronology, and s is the standard deviation of the
 reconstructed chronology. By plotting the z-score and the ≤ 5th percentile, we identified
 the historical drought years estimated to have been significantly mild or
 harsh (Jones & Hulme, 1996).
 <strong>2.5 Effect of
 drought events on tree growth</strong>
 We performed a Superposed
 Epoch Analysis (SEA; Mooney et al., 1993) to examine the effects of the
 five historical drought years on the RWI related to the growth of the two
 oaks. SEA links the RWI time series to a series of drought events. For
 each drought event, a 5-year window was considered, including 2 years
 before and 2 years after the year of the event. The 5-year windows for all
 events were superimposed and averaged to obtain the mean pattern of RWI
 associated with drought events. The mean RWI pattern for the selected
 years was statistically tested for significance (95% confidence interval)
 by running 1000 Monte Carlo simulations (Mooney et al., 1993) using random
 years from the RWI dataset. We performed these analyses in R using the
 dplr package (Bunn, 2008). Next, to determine the effect of historical
 drought on RWI for each species, we assessed three sensitivity indices,
 namely resistance (Rt; Eq. 2), recovery (Rc; Eq. 3), and resilience (Rs;
 Eq. 4), based on Lloret et al. (2011), as follows:
 Resistance
 (<em>Rt</em>) = Ring width<sub>t </sub>/ Ring
 width<sub>t-2     </sub> (2)
 Recovery
 (<em>Rc</em>)= Ring width<sub>t+2</sub> / Ring
 width<sub>t       </sub> (3)
 Resilience
 (<em>Rs</em>) = Ring width<sub>t+2 </sub>/ Ring
 width<sub>t-2  </sub>(4)
 where Ring width is the growth
 width of the annual ring in year t, Ring width-2 is the mean ring width
 for the 2 years preceding
 year t, and Ring widtht+2 is the mean ring width for the 2 years following
 year t (Anderegg & Meinzer, 2015). We calculated the indices at
 the individual tree level for each year during the 1930-2012 period.
 Finally, we used ANOVA and post-hoc Tukey's test to compare the means
 of these indicators between year type (drought vs. non-drought) and
 species. These analyses were performed using BoxPlotR: a web tool for
 creating box plots
 (http://shiny.chemgrid.org/boxplotr/).
 <strong>2.7 Tree-ring
 digitalization and measurement of xylem anatomical
 traits</strong> To evaluate the drought effect on vessel traits,
 we randomly selected 10 cores for each oak species to obtain digital wood
 core images for vessel traits measurements. We first prepared the wood
 cores using the finest grit (2500, Wetordry™). Digital wood core images
 were taken using a stereoscopic microscope (Leica Z16 APOA) with a depth
 of field of 13 to 51 μm. Digital wood core images were produced using a
 digital camera (Leica DFC 490) and saved in TIFF format with a 1.3
 μm per pixel resolution (Rodríguez-Ramírez et
 al., 2018). Within each digital wood core image, we identified the area
 occupied by each growth ring between two wood rays (~16.5 µm wide × 54.4
 µm long). We located the annual growth ring boundaries using Adobe
 Illustrator CC v.23.0.5 (www.adobe.com; Rodríguez-Ramírez &
 Luna-Vega, 2020). For each oak species, we calculated the
 following vessel traits: hydraulic diameter (DH), vessel density (VD),
 vessel grouping index (VG), and vulnerability index (VI). We measured
 vessel traits in digital wood core images for the tree-rings previously
 identified for three different moments: before, during, and after each
 drought event (Rodríguez-Ramírez et al. 2020). Vessel traits (DH, VD, VG,
 and VI) were calculated for each digital tree ring area using ImageJ-Fiji4
 (Schneider et al., 2012). Hydraulic diameter (DH; in µm) is associated
 with fundamental environmental conditions and maximises climatic effects
 (García-González et al., 2016), where D is the vessel diameter (raised to the 4th and
 the 5th power), and N is the number of conduits within tree-ring
 containing n vessels (Souto-Herrero et al., 2017).
 To estimate vessel density
 (VD; number per mm2), we counted the number of
 vessels in each selected image of each wood core and expressed this figure
 as 'number per mm2' in the area between two wood rays per
 growth ring
 (Rodríguez-Ramírez & Luna-Vega, 2020).
 To assess the vessel grouping
 index (VG; the total number of vessels divided by the total number of
 vessels groups), we first added the number of individual vessels to the number of vessel
 groups (Scholz et al., 2013a).                                                
           Carlquist's vulnerability index (VI; with
 values ranging from 1.0 to 3.0) sensu Hoeber et al. (2014) is commonly
 used to reveal species
 adaptations to either xeric (values close to 1.0) or mesic conditions
 (values close to 3.0). Narrow and abundant vessels show low vulnerability,
 in consistency with the trade-off between vessel size and embolism
 resistance. VI was determined by dividing the mean vessel diameter by
 vessel density (Carlquist, 2020).                                               
             The differences in the values of the vessel
 traits between the drought years (DY) and the non-drought years (NDY) were
 evaluated for each study species through two-way repeated measures-ANOVA
 and pairwise comparisons of all pairs of species-year type combinations,
 with the rstatics (Kassambara, 2023), and ggplot2 (Wickham et al., 2021)
 R-packages.
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