DOI: 10.5061/dryad.6m905qg71
title: Bromeliad populations perform distinct ecological strategies across a tropical elevation gradient

description: The effect of environmental gradients on the remarkable diversity of
 mountain-associated plants and on the species’ abilities to cope with
 climate change transcends species-specific strategies. For instance, our
 understanding of the impact of thermal gradients on ecological divergences
 in populations of widely distributed species is limited, although it could
 provide important insights regarding species’ response to climate change.
 Here, we investigated whether populations of an endemic species broadly
 distributed across an elevation gradient employ unique or multiple
 divergent ecological strategies according to specific environmental
 conditions. We hypothesized that populations employ distinct strategies,
 producing a tolerance-avoidance trade-off related to the thermal
 conditions they experience across elevations. We conducted our research
 with 125 individuals of Pitcairnia flammea (Bromeliaceae) sampled from
 various elevations spanning from sea level to ~2,200 meters and cultivated
 under the same conditions. To assess specific ecological strategies of P.
 flammea populations across elevations, we examined leaf temperature, heat
 and cold tolerances, as well as other structural/morphological, optical,
 physiological, and biochemical leaf traits. We majorly observed that
 water-saving traits diminish as elevation increases while membrane
 fluidity, majorly associated with unsaturated and very-long-chain lipids,
 enhances. Low-elevation individuals of P. flammea invest in water storage
 tissues, which likely prevent excessive water loss through the intense
 transpiration rates under warming periods. Conversely, high-elevation
 plants exhibit increased membrane fluidity, a possible response to the
 stiffening induced by low temperature. Our results revealed a
 tolerance-avoidance trade-off related to thermal strategies of populations
 distributed across an elevation gradient. Low-elevation plants avoid
 excessive leaf temperature by investing in water-saving traits to maintain
 transpiration rates. High-elevation individuals, in turn, tend to invest
 in membrane properties to tolerate thermal variations, particularly cold
 events. Our findings challenge the conventional notion that plants'
 vulnerability to warming depends on species-specific thermal tolerance by
 showing diverse thermal strategies on populations across an elevation
 gradient.
description: Plant sampling To characterize ecological
 strategies employed by distinct <em>P. flammea
 </em>populations, over two years, we collected ~20 individuals of
 <em>P. flammea</em> per population (according to population
 abundance) from eight localities along an elevation gradient from 0 to
 ~2,200 m a.s.l. (Fig. 2) for greenhouse cultivation, under the same
 environmental conditions. To acclimate the same genotype (genets) to warm
 and cold conditions for the thermal tolerance test (see the following
 subsection), we prioritized collecting individuals (i.e., genets) with at
 least two ramets. These genets were split and cultivated in a substrate
 mixture of equal parts of expanded clay and potting soil for at least one
 year in randomized blocks under sprinkler irrigation for five minutes,
 five times daily. The temperature inside the greenhouse during the
 cultivation averaged 19 °C, the air relative humidity averaged 57.7%, and
 the maximum photosynthetic photon flux density (PAR) was ~500 µmol
 m<sup>−2</sup> s<sup>−1</sup>. Below, we outline
 the methods for evaluating the thermal sensitivity, thermal tolerance, and
 each plant trait. For methodological clarity, we have categorized the
 plant traits into biochemical (lipidome), structural/morphological,
 optical, and physiological groups (Table 1). Leaf
 temperature and thermal tolerance To test whether
 leaves from distinct <em>P. flammea </em>populations growing
 under the same conditions sense the environmental temperature and how this
 difference affects the plants, we selected five individuals per population
 to be maintained for one hour (at noon) under 30 °C inside a controlled
 temperature chamber. After, we measured, in a random order, the
 temperature of the youngest fully expanded leaf of each individual, using
 an infrared camera (thermal imaging camera, model 871, Testo, Germany),
 recording 7.5–14 µm infrared spectrum with a thermal sensitivity
 &lt;0.08 °C (80 mK). We opted to exclusively measure the youngest
 leaves as they were developed under the greenhouse cultivation period. The
 leaf emissivity was set to 0.98 (Lopez et al., 2012). We estimated the
 leaf temperature from the thermal images considering the average
 temperature measured on the whole leaf using the IRSoft software v. 4.8
 (Testo, Germany). To infer the thermal tolerance range
 of each sampled <em>P. flammea</em> population while avoiding
 possible variability depending on the time of day or the season of the
 year (e.g., Chaves et al., 2018), we measured the heat and cold tolerance
 of eight to 13 individuals per population (according to the availability
 of ramets split at the beginning of cultivation) during the summer and
 winter, respectively. Before the heat and cold tolerance tests, the
 selected plants were grown for three days inside a controlled chamber with
 a 12-hour photoperiod and continuous temperatures of 30 °C and 15 °C,
 respectively (~30% RH). To prevent daily variation in thermal tolerance
 measures (e.g., Chaves et al., 2018), we measured the heat and cold
 tolerances shortly after noon and after sunrise, respectively, following
 Godoy et al. (2011). Leaf discs of 1.5 cm² extracted from the middle of
 the youngest fully expanded leaf of each individual were adapted to dark
 for 10 min at room temperature (~25 °C). Then, we proceeded with the first
 measurement of the potential quantum efficiency of PSII
 (F<sub>v</sub>/F<sub>m</sub>) using a pulse
 amplitude-modulated fluorometer (FMS1, Hansatech, UK). For heat tolerance,
 we exposed the samples to increasing temperatures from 27 to 60 °C, with a
 rate of 1 °C increase every 3 minutes, while performing
 F<sub>v</sub>/F<sub>m</sub> measurements every 3
 °C of temperature increase. For cold tolerance, we put the samples within
 individual plastic bags, which were exposed to decreasing temperatures
 from 20 to -22 °C at a similar rate while performing
 F<sub>v</sub>/F<sub>m</sub> measurements every 5
 °C of temperature decrease. Before the
 F<sub>v</sub>/F<sub>m</sub> measurements, samples
 were maintained at the target temperatures for about one minute, to
 equalize all leaf sample temperatures and to complete at least 10 min
 between each measurement. We used an ultra thermostatic water bath (2050,
 Thoth, Brazil) to increase and decrease temperature. To ensure accurate
 leaf sample temperatures, we attached K-type thermocouples connected to a
 digital thermometer to some samples (TH-096, Instrutherm, Brazil). To
 enable comparisons across populations, we first employed a sigmoid curve
 fitting approach to the acquired
 F<sub>v</sub>/F<sub>m</sub> measurements at each
 temperature using the 'drc' R-package (Ritz et al., 2015),
 following Knight and Ackerly (2003), Gimeno et al. (2009), and Godoy et
 al. (2011). This method allowed us to estimate critical temperatures
 (T<sub>c</sub>) at which there was a significant decrease in
 F<sub>v</sub>/F<sub>m</sub>, as well as
 temperatures corresponding to 15% (T<sub>15</sub>) and 50%
 (T<sub>50</sub>) reduction of the maximum
 F<sub>v</sub>/F<sub>m</sub> (refer to Fig.
 3A). Structural/morphological traits
 To characterize the leaf morphology, anatomical structure, and
 leaf surface, we measured 25 traits (see Table 1 for references) from 1.5
 cm² leaf discs extracted from the base, middle, and top regions of the
 youngest fully expanded leaf from eight to 21 individuals per population,
 all of which were healthy genets. For leaf area (LA) measurement, in
 particular, we estimated the total area of the sampled leaves, using the
 WinRHIZO image analysis system (Expression 12000XL, Regent Instruments,
 Canada). Please refer to the Supplementary Information for details
 regarding the sample preparation for the measurement of leaf structure and
 surface. Physiological and optical traits
 <strong> </strong>To characterize the leaf
 spectrometry, gas exchange, and PSII photochemistry, we measured nine
 traits (see Table 1) at noon, from the mid-region of the youngest fully
 expanded leaf of four to 14 healthy individuals per population. For leaf
 spectrometry, we estimated the reflectance, absorbance, and transmittance
 at noon using a handheld portable spectroradiometer (SpectraPen SP 256,
 PSI, Czech Republic). For leaf gas exchange, we measured the transpiration
 rate (E), stomatal conductance (gs), photosynthetic rate (Pn), and
 intercellular CO<sub>2</sub> concentration (Ci) using an
 infrared gas analyzer (LI-6400F, LI-COR, USA), under the air
 CO<sub>2</sub> concentration of ~400 μmol
 mol<sup>−1</sup> and PAR of 800 μmol m<sup>−2
 </sup>s<sup>−1</sup>. Lastly, we estimated the potential
 (F<sub>v</sub>/F<sub>m</sub>, after 15 min under
 dark conditions) and effective (ΦPS2, simultaneously to leaf gas exchange)
 quantum efficiency of PSII by using a modulated fluorometer (LCF6400–40,
 LI-COR, USA) attached to the LI-6400F system, following the pulse
 saturation method (λ = 630 nm, PAR ~6,000 µmol
 m<sup>-2</sup> s<sup>-1</sup>, 0.8
 s).<strong><br></strong> Biochemical
 traits (lipidome) To characterize the lipid
 composition of <em>P. flammea</em> leaves, we harvested the
 mid-region of the youngest fully expanded leaves from five to seven
 healthy individuals per population in summer morning. This timing was
 chosen to ensure high air humidity and minimize the likelihood of
 individuals experiencing drought or thermal stress. The samples were
 immediately frozen in liquid nitrogen and ground into a fine powder. We
 used the extraction method from Hummel et al. (2011) adjusted for
 <em>P. flammea</em> samples according to Matos et al. (2023).
 We performed ultra-high performance liquid chromatography coupled to mass
 spectrometry analysis operating a Thermo Scientific
 UltiMate<sup>TM</sup> 3000 RSLCnano system equipped with a
 Titan C18 column (100 mm x 2.1 mm x 1.9 µm particle size, Supelco
 Sigma-Aldrich, Bellefonte PA, USA), coupled to a Thermo Scientific
 Orbitrap Q-Exactive (Waltham MA, USA) mass spectrometer. We used MS
 full-scan followed by MS/MS analysis in the DDA mode for the five most
 intense peaks. We acquired full scan data between
 <em>m/z</em> 100 and 1500 in profile mode at 70000 resolution
 (at <em>m/z</em> 200) with automatic gain control set at 1 x
 10<sup>6</sup> and injection time set to 100 ms. For ESI (+),
 the spray voltage was 3.5 kV, and for ESI (-), it was 3.2 kV. The ion
 optics setting was S-Lens RF level 50, S-Lens 25 V; skimmer 15 V, and
 C-Trap RF 1010 V. For data preprocessing, we converted the raw MS data to
 .mzXML format using MSConvert 3.0 from ProteoWizard. MS-DIAL 4.9 software
 was used for data preprocessing, and alignment was done according to Matos
 et al. (2023).  The lipidomics dataset is deposited in the Metabolomics
 Workbench (Sud et al., 2016) under project ID PR001712 (http://dx.doi.org/10.21228/M8DM8V). We identified the lipids with an identification score cut-off of 80%. We filtered the peak spot viewer to display only the peaks that matched with the reference libraries and had MS/MS information. The internal lipid annotation in MS-DIAL is based on LipidBlast (Kind et al., 2013). To perform lipid enrichment analysis, we used the Lipid Mini-On software (Clair et al., 2019) that creates lipid ontology (LO) terms from the list of annotated lipids by using a text mining process that categorizes the lipids by their main classes and subclasses, and their total number of chain carbon and double bonds (unsaturations). The LO terms generated and their abundances are used as a query list to compare with the available lipid universe and generate a network of the over-representative LOs (see Fig. S5) based on Fisher’s exact test (<em>p</em> &lt; 0.05; Clair et al., 2019). Finally, to further compare across the elevation gradient, we analyzed the absolute and relative abundance of lipids from each over-represented class (i.e., hexosylceramide, phosphatidylglycerol, and very long chain fatty acids, a.k.a. VLCFA), as well as the abundance of lipids with distinct levels of unsaturation (including saturated, monounsaturated, diunsaturated, and polyunsaturated).
description: This README file was generated on 2024-01-31 by Cleber Chaves. GENERAL
 INFORMATION 1. Title of Dataset: Bromeliad populations perform distinct
 ecological strategies across a tropical elevation gradient
 1. Author Information A.
 Principal Investigator Contact Information Name: Cleber J. N. Chaves
 Institution: Universidade Estadual de Campinas Email:
 B. Associate or Co-investigator Contact
 Information Name: Clarisse Palma-Silva Institution: Universidade Estadual
 de Campinas Email: 2. Date of data collection
 (single date, range, approximate date): 2019-2023 3. Geographic location
 of data collection: Brazilian Atlantic Forest 4. Information about funding
 sources that supported the collection of the data: FAPESP 2020/14805-4
 FAPESP 2020/16696-8 FAPESP 2020/16696-8 FAPESP 2015/24351-2 FAPESP
 2023/01800-2 FAPESP 2021/10639-5 CAPES 88887.361034/2019-00 CNPq
 163094/2020-9 CNPq 308260/2021-0 CNPq 306662/2022-1 CNPq 304295/2022-1
 CNPq 302962/2022 SHARING/ACCESS INFORMATION 1. Licenses/restrictions
 placed on the data: CC0 1.0 Universal (CC0 1.0) Public Domain 2. Links to
 publications that cite or use the data: CHAVES, C. J. N.; CACOSSI, T. C.;
 MATOS, T. S.; BENTO, J. P. S. P.; SILVA, S. F.; SILVA-FERREIRA, M. V.;
 BARBIN, D. F.; MAYER, J. L. S.; SUSSULINI, A.; RIBEIRO, R. V.;
 PALMA-SILVA, C. Bromeliad populations perform distinct ecological
 strategies across a tropical elevation gradient. Functional Ecology, 2024
 1. Links to other publicly accessible locations of the data: None 2.
 Links/relationships to ancillary data sets: None 3. Was data derived from
 another source? No A. If yes, list source(s): NA 4. Recommended citation
 for this dataset: Chaves, Cleber et al. (Forthcoming 2024). Bromeliad
 populations perform distinct ecological strategies across a tropical
 elevation gradient [Dataset]. Dryad.
 DATA & FILE OVERVIEW
 1. File List: Root 'all_traits.csv'
 'script_leaf_physiology.R' Folder 'Thermal Tolerance'
 'Calc_t15t50.R' 'Cold_g1.csv' 'Cold_g2.csv'
 'Cold_g3.csv' 'Cold_g4.csv' 'Heat_g1.csv'
 'Heat_g2.csv' 'Heat_g3.csv' 'Heat_g4.csv'
 Folder 'Lipidome' 'script_lipid_pops.R'
 'Area_0_202262927.xlsx' 'Area_0_20226241518 POS BRO
 ALT.xlsx' 1. Relationship between files, if important: According to
 folders 2. Additional related data collected that was not included in the
 current data package: None 3. Are there multiple versions of the dataset?
 No A. If yes, name of file(s) that was updated: NA i. Why was the file
 updated? NA ii. When was the file updated? NA
 \#########################################################################
 ROOT DATA-SPECIFIC INFORMATION FOR: 'script_leaf_physiology.R':
 R script for all general data analysis DATA-SPECIFIC INFORMATION FOR:
 'all_traits.csv' 1. Number of variables: 90 2. Number of
 cases/rows: 126 3. Variable List: * ind: individual ID * pop: population
 of origin * latitude (°) * longitude (°) * altitude: elevation (m.a.s.l.)
 * temp_mean: average environmental tempearature (extracted from WorldClim)
 (°C) * temp_breadth: maximum minus minimum environmental temperature
 (extracted from WorldClim) (°C) * temp_min: minimum environmental
 tempearature (extracted from WorldClim) (°C) * temp_max: maximum
 environmental tempearature (extracted from WorldClim) (°C) * group_heat:
 methodological group identification to aclimatation for heat tolerance
 measurement * group_cold: methodological group identification to
 aclimatation for cold tolerance measurement * tc_cold: critical cold
 temperature estimated from cold tolerance measurement (°C) *
 max_temp_cold: minimum cold temperature with estimated with non-null Fv/Fm
 estimated from cold tolerance measurement (°C) * critical_zone_cold:
 max_temp_cold - tc_cold (°C) * t15_cold: temperature in which the sample
 reduces 15% of its original Fv/Fm under cold tolerance measurement (°C) *
 t50_cold: temperature in which the sample reduces 50% of its original
 Fv/Fm under cold tolerance measurement (°C) * tc_heat critical heat
 temperature estimated from heat tolerance measurement (°C) *
 max_temp_heat: maximum heat temperature with estimated with non-null Fv/Fm
 estimated from heat tolerance measurement (°C) * critical_zone_heat:
 max_temp_heat - tc_heat (°C) * t15_heat: temperature in which the sample
 reduces 15% of its original Fv/Fm under heat tolerance measurement (°C) *
 t50_heat: temperature in which the sample reduces 50% of its original
 Fv/Fm under heat tolerance measurement (°C) * tc_diff: tc_heat - tc_cold
 (°C) * max_temp_diff: max_temp_heat - max_temp_cold (°C) *
 critical_zone_diff: critical_zone_heat - critical_zone_cold (°C) *
 t15_diff: t15_heat - t15_cold (°C) * t50_diff: t50_heat - t50_cold (°C) *
 chr: relative water content (RWC) * is: succulence index * afe: specific
 leaf area (SLA) (cm²/g) * cmfs: dry leaf matter content (DMC) (g) * af:
 leaf area (LA) (cm²) * de: stomata density (units/mm²) * dt_aba: trichome
 density at adaxial leaf face (units/mm²) * dt_ada: trichome density at
 abaxial leaf face (units/mm²) * te_larg: stomata width (µm) * te_comp:
 stomata length (µm) * epidermis_ad: adaxial epidermis width (µm) *
 epidermis_ab: abaxial epidermis width (µm) * parenchyma_ch: chlorenchyma
 width (µm) * parenchyma_aq: water parenchyma width (µm) * mesophyll:
 mesophyll width (µm) * parenchyma_ch_prop: relative chlorenchyma width
 (considering mesophyll) * parenchyma_aq_prop: relative water parenchyma
 width (considering mesophyll) * vascular_bundle_length (µm) *
 vascular_bundle_width (µm) * vascular_bundle_area (µm) *
 vascular_bundle_perimeter (µm) * length_phloem (µm) * length_xylem (µm) *
 length_phloem_prop (µm) * length_xylem_prop (µm) * reflectance: leaf
 reflectance value (W m-2 nm-¹) * absorbance: leaf absorbance value (W m-2
 nm-¹) * transmittance: leaf transmittance value (W m-2 nm-¹) * Photo:
 Photosynthetic rate (µmol CO2 m-² s-¹) * Cond: Stomatal conductance (mol
 H2O m-² s-¹) * Trmmol: Transpiration rate (mmol H2O m-² s-¹) * Ci:
 Intercelular CO2 concentration (µmol CO2 mol air-¹) * Fo': Minimal F,
 dark adapted * Fm': Maximal F, dark adapted * Fs: Steady state F *
 Fv'/Fm': 1-Fo/Fm * PhiPS2: 1-Fs/Fm’ * ETR: Electron transport
 rate (µmol s-1) * Tair_normal: air temperature (°C) * Tleaf_normal: leaf
 temperature (°C) * T_airleaf_diff_normal: Tleaf_normal - Tair_normal (°C)
 * VpdL_normal: Vapor pressure deficit at leaf temp (kPa) * Tair_vaseline:
 air temperature on leaf samples with vaseline (°C) * Tleaf_vaseline:
 temperature of leaf sample with vaseline (°C) * T_airleaf_diff_vaseline:
 Tleaf_vaseline - Tleaf_vaseline (°C) * VpdL_vaseline: Vapor pressure
 deficit at leaf with vaseline temp (kPa) * Tdiff_norm_vaseline:
 Tleaf_vaseline - Tleaf_normal (°C) * Tleaf_air_vas_norm_ratio:
 Tair_vaseline - Tair_normal (°C) * abs_HexCer: Hexosylceramide content *
 rel_HexCer: relative Hexosylceramide content * abs_PG:
 Phosphatidylglycerol content * rel_PG: relative Phosphatidylglycerol
 content * abs_VLCFA: Very Long Chain Fatty Acids content * rel_VLCFA: Very
 Long Chain Fatty Acids relative content * abs_sat: saturated lipids
 content * rel_sat:: saturated lipids relative content * abs_monounsat:
 monounsaturated lipids content * rel_monounsat: monounsaturated lipids
 relative content * temp_IR_left: infrared temperature of the left side of
 the Leaf (with vaseline) (°C) * temp_IR_right: infrared temperature of the
 right side of the Leaf (°C) * temp_IR_black: infrared temperature of a
 black paper square (for comparison) (°C) * temp_IR_left_right:
 temp_IR_left - temp_IR_right (°C) * temp_IR_left_black: temp_IR_left -
 temp_IR_black (°C) * temp_IR_right_black: temp_IR_right - temp_IR_black
 (°C) 1. Missing data codes: NA 2. Specialized formats or other
 abbreviations used: None FOLDER 'Thermal Tolerance'
 DATA-SPECIFIC INFORMATION FOR: 'Calc_t15t50.R': R script for the
 estimation of thermal tolerance metrics DATA-SPECIFIC INFORMATION FOR:
 'Cold_g1.csv'/'Cold_g2.csv'/'Cold_g3.csv'/'Cold_g4.csv': raw data of Fv/Fm values of leaf samples under cooling temperatures (cold tolerance measurement). 1. Number of variables (individuals): 16/19/17/18 2. Number of cases/rows (temperatures): 13/10/10/10 Column names: individuals' IDs; Rows: temperature conditions Plants were randomly splitted in groups (g1-g4) to reduce time between measurements DATA-SPECIFIC INFORMATION FOR: 'Heat_g1.csv'/'Heat_g2.csv'/'Heat_g3.csv'/'Heat_g4.csv'/'Heat_g5.csv': raw data of Fv/Fm values of leaf samples under warming temperatures (heat tolerance measurement). 1. Number of variables (individuals): 18/18/17/13/9 2. Number of cases/rows (temperatures): 12/14/14/14/13 Column names: individuals' IDs; Rows: temperature conditions Plants were randomly splitted in groups (g1-g5) to reduce time between measurements FOLDER 'Lipidome' DATA-SPECIFIC INFORMATION FOR: 'script_lipid_pops.R': R script for the general lipidome analysis DATA-SPECIFIC INFORMATION FOR: 'Area_0_202262927.xlsx': raw non-normalized data of the intensity of each compound observed in each leaf samples in the negative mode 1. Number of variables: 65 2. Number of cases/rows: 99994 Column names: individuals' IDs; Rows: each observed compound in the negative mode(lipid) DATA-SPECIFIC INFORMATION FOR: 'Area_0_20226241518 POS BRO ALT.xlsx' raw non-normalized data of the intensity of each compound observed in each leaf samples in the positive mode 1. Number of variables: 55 2. Number of cases/rows: 34773 Column names: individuals' IDs; Rows: each observed compound in the positive mode(lipid)