We established 40 common garden plots in each of two forest sites. One forest site was a forest fragment in Ciudad del Saber, Clayton (9°0′50″N, 79°35′W), on the Pacific side of the Isthmus. The area has lowland, semi-deciduous dry forest, with a clearly marked 3–4-month dry season and mean annual rainfall of 2010 mm. The other forest site was at Parque Nacional San Lorenzo (9°17′N, 79°58′W), on the Atlantic side of the Isthmus. The area is characteristic of lowland, evergreen wet forest, with a weak dry season and mean annual rainfall of 3020 mm. The plots were all placed in the shady forest understorey. In each plot, we planted one seedling of each of the study species. Seedlings were planted between July and December 2005 and harvested 1 year later, in December 2006 to January 2007, to measure traits.
The experimental design for the common garden plots in the field also included water supplementation and herbivore exclusion treatments. These experimental manipulations had little effect on the trait expression (see Table S2 in Supporting Information), and their effect on species performance has already been described in detail in Brenes-Arguedas, Coley & Kursar (2009). Hence, for this study, we ignored the experimental treatments for trait measurements and only mention them when necessary to describe the measures of performance.
Because the field seedlings were quite small, four traits that required larger individuals were measured on shade-house-grown plants. Shade-house plants were kept in 1-L pots, well watered and partly shaded in the facilities managed by Smithsonian Tropical Research Institute in Barro Colorado Island, located in the middle of the Isthmus.
Traits and performances
From a previous analysis, we know that plant performance differed significantly between the two experimental sites (Brenes-Arguedas, Coley & Kursar 2009). Preliminary analysis for this paper also showed that the expression of many of the plant traits differed between the dry and the wet sites (Table S2 and Fig. S1, Supporting information). To account for this, whenever possible, we evaluated the traits and performance separately for the two sites.
Growth performance (Gr) was measured as gross leaf production in the common gardens. We calculated the total number of new leaves produced by each seedling for the duration of the experiment and divided by the total number of months the plant was alive in the experiment. Leaf numbers were converted to leaf area by multiplying by the mean leaf area of each species such that leaf growth is in units of cm2 month−1. Gr differed significantly between sites but not due to watering or herbivore exclusion treatments (Brenes-Arguedas, Coley & Kursar 2009). Hence, we pooled all experimental treatments and calculated average Gr for each species in each site. Species ranks in Gr were largely maintained among sites (r = 0·85, P < 0·0001; Fig. S2, Supporting information), but the environmental conditions in the drier site yielded the highest Gr.
Leaf damage (Dmg) was measured at the end of the experiment, in November 2006. It is the average percentage of leaf area damaged for the three (or four, for species with opposite leaves) most apical, fully expanded leaves of the seedlings. It included 100% leaf loss when evidenced by scars in the stem. Dmg differed between sites and due to herbivore exclusion treatment but not due to watering (Brenes-Arguedas, Coley & Kursar 2009). Hence, for this analysis, we calculated mean Dmg per species at each site, only using the plots with no herbivore exclusion. Species ranks in Dmg also correlated between the two study sites (r = 0·55, P = 0·01; Fig. S2, Supporting information), with plots at the wet site showing the highest Dmg and discrimination among species (Brenes-Arguedas, Coley & Kursar 2009).
Drought performance (Dp) is the ratio of percentage seedling survival in the unwatered relative to the watered plots during the dry season. This is calculated only for the dry site, because there is rarely significant dry-season water stress at the wet site. The index of Dp ranges from zero, for species with the poorest drought performance, to one, for species with the highest drought performance (Engelbrecht & Kursar 2003). However, our experiment occurred in a year with a relatively short dry season in which many seedlings may not have experienced serious water stress and seedling mortality was low. Hence, we did not achieve maximum discrimination among species with respect to Dp. This was especially notable among the more drought-resistant species which, on occasion, had values higher than one, suggesting higher mortality in the watered plots.
Leaf life span (LL) was measured for seedlings without herbivore exclusion or water supplementation in both sites. A total of 190 leaves in both sites were marked upon expansion, and their survival was followed monthly until June 2006. Leaves still alive at the end of the measurements and those that died when the whole plant died were considered censored observations. We report leaf half-life = log(0·5)/log(1−ea), where a is the parameter obtained from fitting the regression log(percentage alive) = b + a (time in years). We pooled the data from the two sites because LL did not significantly differ between them (analysis not shown), and sample size was too small for any given site. However, most of the leaves that we followed were from the dry site. We were able to quantify LL for only 14 species, as not all species produced or lost enough leaves during the experiment.
At the end of the field experiment, we harvested six or more plants per species per site. To ensure that the allocation and tissue properties of the harvested plants reflected local conditions, we harvested only seedlings that had shown positive growth. We used a hand shovel to extract the seedlings from the ground with minimal effects on the roots and carefully separated the soil from the roots by submerging the seedlings in water.
Leaf toughness (Tgh) was measured as the average force needed to perforate a leaf. It was determined in the field immediately upon harvest using a Chatillon pressure gauge with a 3-mm-diameter rod. Due to the small size of the leaves of some species, we measured toughness only on 16 species.
Harvested seedlings were placed in humidified plastic bags upon collection and transported to the Barro Colorado Island Laboratory. For each harvested seedling, we separated leaves (including leafy cotyledons when present), stem and roots within 24 h of collection. We measured the area of one to three, fully expanded, undamaged leaves per seedling using a LI-3000 leaf area meter (LiCor Biosciences, Lincoln NE, USA). The entire root system of half of the harvested seedlings was spread out in a water-filled container and digitized with a flatbed scanner within 24 h of collection. The resulting images were analysed with Delta-T SCAN (Delta-T, Burwell, Cambridge, UK) to estimate total root length (mm). Subsequently, the dry weight to 0·1 mg was obtained for all plant parts (70 °C for at least 48 h). Here, we report Leaf Mass per Area (LMA), the leaf dry weight divided by the leaf fresh area; Specific Root Length (SRL), the root length divided by the root dry mass; Root Length per Plant Mass (RLPM), the root length divided by the whole-plant biomass; the Leaf, Stem and Root Mass Fractions (LMF, SMF and RMF, respectively), the dry weight of each tissue divided by the whole-plant biomass; and Leaf Area Ratio (LAR), the leaf area divided by the whole-plant biomass, here calculated as LMF/LMA.
Dry leaf material from three individuals per species from the unwatered plots of both sites was measured using continuous flow isotope ratio mass spectrometry (SIRFER Lab, University of Utah). We measured nitrogen and carbon content per mass (Nmass and Cmass: g (g DW of leaf)−1) and carbon isotope ratio (δ13C). Nitrogen content per area (Narea) was calculated as Nmass*LMA. Carbon isotope discrimination (Δ13Cleaf) was calculated as (δair−δ13C)/(1 + δ13C), where δair is the isotopic ratio of ambient air [here assumed to be −11·4 ‰, reported for the understorey of a tropical forest (Farquhar, Ehleringer & Hubick 1989)]. Δ13C leaf is always positive, and a high value of Δ13C leaf corresponds to a high internal concentration of CO2 in leaves (high ci). We analysed only unwatered seedlings because the stable isotope ratio of carbon is very sensitive to plant water status.
Stem density (SD) was measured for 2–10 individuals per species on potted plants grown in the shade house. A piece of stem 3–5 cm long was cut in half longitudinally, the bark and pith were removed, and we determined the volume using Archimedes' principle of water displacement. Stem sections were then dried at 70 °C for at least 48 h or to constant mass, to calculate dry weight per stem volume.
We measured light–response curves on one to two leaves from at least four plants per species grown in the shade house under homogenous light conditions. We used a LI-6400 gas exchange system (LiCor Biosciences, Lincoln NE, USA), recording at 14 light levels between 0 to 1500 mol m−2 s−1. We computed maximum assimilation (Amax) and dark respiration (Rdark) using Photosyn Assistant (Dundee Scientific, Scotland, UK). Rdark is reported as a positive value, such that larger numbers represent higher respiration rates.
Whole-stem hydraulic conductance (kws/la) was measured using a vacuum method (Kolb, Sperry & Lamont 1996). After gas exchange measurements, three to six plants per species were submerged in water, while stems were cut at the soil surface and leaves were removed. Total leaf area was determined. The cut stem was placed in 0·10 m KCl solution, and flow rates were measured sequentially at 0, −24, −47, −71, −59, −36, −12 and 0 kPa, using a Sartorius CP2250 balance with an accuracy of 0·05 mg (Precision Weighing Machines, Bradford, MA, USA). kws/la was calculated from the linear regression of flow as a function of pressure. This measurement was normalized by total leaf area, giving leaf area-specific, whole-stem hydraulic conductance.
For 13 species, we measured desiccation sensitivity (ΨLD50) as the leaf water potential (Ψ) at which 50% mortality occurs, as described in detail in Kursar et al. (2009). At least 20 plants of each species were denied water for several days up to 3 weeks. Each plant was measured once for Ψ and survival. Ψ was measured on two to five leaves at mid-morning with 5·6 mm2 leaf discs using leaf-cutter psychrometers (Merrill Engineering, Logan, UT, USA) interfaced with a CR7 datalogger (Campbell Instruments, Logan, UT, USA). We included three discs per leaf for well-watered plants and up to nine discs for plants with the lowest water status (Bennett & Cortes 1985). The psychrometers were calibrated with nine NaCl solutions spanning the range of −0·3 to −9 MPa. Water potentials between −9 and −12 MPa were extrapolated using the relationship between the water content per leaf disc and Ψ (Tyree et al. 2003). Survival was estimated by rewatering the plants and scoring for mortality beginning 2 weeks after rewatering. The ΨLD50 was estimated as dose–response or by interpolation. These data were incorporated into our analyses as negative values. Hence, a lower (more negative) ΨLD50 represents greater resistance to desiccation, whereas higher or less negative values of ΨLD50 correspond to plants that are more sensitive to desiccation.
All data were analysed using R software (R Development Core Team 2011). The list of traits and plant performance variables that we analyse in this paper is summarized in Table 1. Actual values for the traits per species per site are reported in the Supporting Information (Table S3 and Fig. S2, Supporting information). As most performance and trait measurements differed between sites, we ran the same analysis separately for the dry and the wet site. Both analyses had in common those measurements for which we had only one value for a species (those from the shade house, Dp and LL). Reassuringly, analyses from both sites yielded very similar results.
Table 1. List of performance variables and traits evaluated in this paper. Trait information includes the abbreviations used throughout the text (Abbr.), description, units, the number of species for which we had collected the trait (N) and the source of the seedlings used to collect the trait. As most traits showed some levels of plasticity when planted in the dry or wet site, we calculated separate values for each site when possible
|Performance||Gr||Growth: new leaf area produced per month||cm2 month−1||24||Dry + wet|
|Dmg||Leaf damage: percentage of leaf area lost at the end of the experiment in control (uncaged) subplots||%||23||Dry + wet|
|Dp||Drought performance: survival in control (unwatered) relative to watered treatments in the dry site||%||23||Drya|
|Allocational traits||Tgh||Leaf toughness with pressure gauge||kPa||17||Dry + wet|
|LMA||Leaf mass per area||g cm−2||23||Dry + wet|
|Cmass||Carbon content per leaf dry weight||%||23||Dry + wet|
|LL||Leaf life span||years||14||Mixedb|
|LAR||Leaf area ratio: leaf area per whole-plant biomass||cm2 g−1||23||Dry + wet|
|LMF||Leaf mass fraction: leaf mass per whole-plant biomass||g g−1||23||Dry + wet|
|SMF||Stem mass fraction: stem mass per whole-plant biomass||g g−1||23||Dry + wet|
|SD||Stem density||g cm−3||23||Shade house|
|RMF||Root mass fraction: root mass per whole-plant biomass||g g−1||23||Dry + wet|
|RLPM||Root length per whole-plant biomass||mm g−1||23||Dry + wet|
|SRL||Root length per root biomass ||mm g−1||23||Dry + wet|
|Physiological traits||Nmass||Nitrogen content per leaf dry weight ||% ||23||Dry + wet|
|Narea||Nitrogen content per leaf area||g of N cm−2 ||23||Dry + wet|
|Amax||Maximum carbon assimilation rate||μmol m−2 s−1||20||Shade house|
|Rdark||Dark respiration rate||μmol m−2 s−1||18||Shade house|
|ΨLD50||Leaf water potential at which 50% mortality occurs||MPa||13||Shade house|
| k ws/la ||Whole-stem hydraulic conductance per leaf area||g s−1 MPa−1 m−2||21||Shade house|
|Δ13Cleaf||Leaf carbon isotope enrichment above atmospheric δ13C||‰||23||Dry + wet|
Identification and description of ‘trait syndromes’ – To take into account that many traits probably covary due to structural or physiological reasons, we looked for combinations of covarying traits, or ‘trait syndromes’, using principal component analysis (PCA; ‘prcomp’ function). Because PCA is not very reliable when using a small sample size (number of species) relative to the number of variables (traits), we chose to report two separate PCAs based on subsets of the variables. We evaluated a number of possible combinations of traits, but settled for the separation between allocational and physiological traits, because they were relatively natural groupings of related traits, and we had no a priori expectation for a correlation between these two groups of traits. We classified as allocational those traits that described the structure or biomass allocation to plant parts (leaves, stems, roots; Table 1). We classified as physiological those traits that are more direct measurements of plant function and the chemical analysis of the leaves (Table 1). We excluded ΨLD50, Tgh and LL from the PCAs, due to low sample size (Table 1) and used nine allocational and six physiological traits for the analysis. To report the weight of each trait in the PC ordination, we calculated the factor structure correlations, which are the Pearson's r correlation coefficients between each trait and the ordination.