DOI: 10.5061/dryad.69p8cz9bh
title: Closely related species differ in their traits, but competition induces high intra-specific variability

description: Theories explaining community assembly assume that biotic and abiotic
 filters sort species into communities based on the values of their traits
 and are thus based on between-species trait variability (BTV).
 Nevertheless, these filters act on individuals rather than on species.
 Consequently, the selection is also influenced by intraspecific trait
 variability (ITV) and its drivers. These drivers may be abiotic (e.g.,
 water availability) or biotic (e.g., competition). Although closely
 related species should have similar traits, many of them coexist. We
 investigated the relative magnitudes of BTV and ITV in coexisting closely
 related species and how their individual traits differ under different
 drivers of ITV. We manipulated conditions in a greenhouse pot experiment
 with four common Carex species, where individuals of each species
 originated from four source localities. Individuals were grown in
 factorial combinations of two moisture levels, with and without a
 competitor (grass species Holcus lanatus, a frequent competitor). We
 analysed the variability of six morphological traits on individuals in the
 greenhouse and three morphological traits in the source localities.
 Species identity was the main determinant of differences in most traits.
 Competition exerted a greater effect than water availability. For leaf dry
 matter content (LDMC) and vegetative height, competition’s effect even
 exceeded the variability among species. On the contrary, for specific leaf
 area (SLA) and clonal spread, the interspecific differences exceeded ITV
 induced by experimental treatments. SLA measured in the greenhouse closely
 correlated with values measured in field populations, while LDMC did not.
 The variability caused by source locality of ramets in the greenhouse was
 small, although sometimes significant. Closely related species differ in
 their traits, but for some traits, ITV can exceed BTV. We can expect that
 ITV can modify the processes of community assembly, particularly among
 co-existing closely related species.
description: <em>Species and localities</em>
 For our greenhouse experiment, we chose four common
 <em>Carex</em> species (Table S1 in Appendix) able to co-occur
 although they differ especially in preference of soil moisture
 (Ellenberg-type indicator value for moisture is 4 for <em>C.
 caryophyllea</em>, 5 for <em>C. pilulifera</em>, 6 for
 <em>C. pallescens</em> and 8 for <em>C.
 panicea</em>; Chytrý et al., 2018). These species are all perennial
 herbs, hemicryptophytes, commonly distributed in the South Bohemia region,
 where they were recorded in all 12 × 11 km quadrats of grid mapping and in
 the majority of their 6 × 5.5 km sub-quadrats (Pladias – database of Czech
 flora and vegetation, www.pladias.cz, access
 June 13, 2024; Wild et al., 2019). These species are able to co-occur,
 although they also often occur separately, especially in the case of
 <em>C. caryophyllea</em> which prefers drier habitats.
 Although these <em>Carex</em> species frequently co-occur in
 relatively small plots (2x2 m, personal observation), they seldom achieve
 high cover, and the main competitor is commonly some dominant grass. The
 average cover of our <em>Carex</em> species in
 phytocenological relevés from Pladias – the database of Czech flora and
 vegetation (only relevés where the species is present considered) is
 relatively low (3.6% <em>C. caryophyllea</em>, 7.9%
 <em>C. panicea</em>, 2.4% <em>C. pallescens</em>,
 2.5% <em>C. pilulifera</em>) with a maximum recorded cover of
 38% (i.e., degree 3 of the Braun-Blanquet scale) for all species but
 <em>C. panicea,</em> which can become dominant (but only
 rarely and in special types of peatland meadows) (www.pladias.cz, access
 June 13, 2024; Chytrý et al., 2021). We used populations from four source
 localities (size 1-3 ha) of the South Bohemia region for each species. One
 locality contained all four species in mixtures, demonstrating they can
 coexist; together we used individuals from seven localities (Table S1 in
 Appendix). All localities were semi-natural meadows with regular mowing
 management, mostly once per year with the exception of one locality, which
 was not managed. From each population, we took 20 individuals in the form
 of small ramets (young vegetative rosettes, average fresh weight = 0.58 ±
 0.43 g (mean ± SD), average height = 5.05 ± 2.82 cm) at the beginning of
 April 2020 for the greenhouse experiment.  
 <em>Design of greenhouse experiment</em>
 Each individual was taken from the field to the greenhouse and
 left in water to develop roots for one week. After that, we planted each
 individual in its own pot (upper diameter 16 cm, lower diameter 10 cm,
 height 15 cm, volume 2 l) with peat substrate and sand mixture in the
 ratio of 1:2. From each population, 20 ramets were grown placing them
 randomly in five blocks in factorial combinations of two moisture levels
 (water treatment), and with and without competition of <em>Holcus
 lanatus</em> (competition treatment) (Figure 1a). Thus, we had four
 experimental treatment type combinations: CH – competition and high water
 treatment, CL – competition and low water treatment, NH – no-competition
 and high water treatment, NL – no-competition and low water treatment. In
 all our source localities (and similar localities in the area) sedges grew
 in the matrix of grasses, which dominate the meadows and thus are their
 main competitors. <em>Holcus lanatus</em> (Ellenberg-type
 indicator value for moisture is 6; Chytrý et al., 2018) is their typical
 representative. It is a clonal hemicryptophyte, typically with a height
 similar to <em>Carex</em> species from our experiment. It is
 one of the most frequently co-occurring species with <em>Carex
 </em>in our source localities; it is also suitable for experiments,
 as its germination rate is quite high and it establishes quickly (Tammaru
 et al., 2021). We used seeds from a commercial supplier (Planta
 Naturalis). For the competition treatment, ca. 45 seeds of
 <em>Holcus lanatus</em> were sown in each pot together with
 the <em>Carex</em> ramet at the time of ramet planting. After
 two weeks, <em>Holcus lanatus</em> was thinned to 15
 individuals per pot. One week after thinning, when <em>Holcus
 lanatus</em> was established, the water treatment was started. While
 the high water treatment consisted of keeping the saucer below the pot
 (volume of 380 ml) permanently filled with water, in the low water
 treatment meant saucers were only filled with water once the substrate was
 dried out. Because we could not measure the soil water content directly in
 every pot, we checked the effect of our water availability treatment on
 <em>Carex</em> biomass aboveground (18% decrease in low water
 treatment, F<sub>1,312</sub>=7.43, p=0.007), belowground (24%
 decrease in low water treatment, F<sub>1,309</sub>=19.76,
 p&lt;0.001) and in total (22% decrease in low water treatment,
 F<sub>1,307</sub>=14.95, p&lt;0.001)<em>
 </em>to prove that the low water treatment was sufficient to impose
 water stress. Because we used nutrient poor substrate (peat with sand),
 with fertility insufficient for <em>H. lanatus</em>, we used
 100 ml of fertilizer (15 ml of Vitality Komplex Agro on 1 l of water) on
 each pot two times. All plants were harvested from July 29 to August 1,
 2020. They were washed free of soil, separated into aboveground (leaves,
 stems, and flowers) and belowground (roots and rhizomes) parts, and
 processed for measuring functional traits as described below.
   <em>Measured functional traits and
 other variables</em> In the greenhouse
 experiment, we measured six functional traits characterizing different
 functions of <em>Carex</em> plants (Figure 1b). Vegetative
 Height was measured as the highest leaf apparently forming a rosette
 without stretching. To calculate SLA and LDMC, we took the fresh and dry
 weights of two leaves per individual and measured their area in ImageJ 1.x
 (Schneider et al., 2012). The number of ramets created by the mother plant
 in each pot (Clonal Spread further on) determined the ability of clonal
 multiplication. Belowground and aboveground biomass (with total biomass
 the sum of above- and belowground biomass) from each pot was oven-dried at
 60°C for 48 hours and subsequently weighed. From these values, we
 calculated Root/Shoot Ratio (i.e., dry biomass of only roots divided by
 dry aboveground biomass) and Aboveground/Belowground Biomass (i.e., dry
 aboveground biomass divided by dry belowground biomass of both roots and
 rhizomes) to differentiate belowground biomass as only roots, or including
 rhizomes, and to compare them with the aboveground biomass.
 To compare functional traits measured on plants grown under
 specific greenhouse conditions and experimentally influenced by water and
 competition treatments with traits measured in the source localities, we
 also measured three functional traits (Vegetative Height, SLA and LDMC) of
 10 individuals per population sampled from the field (i.e., field study)
 at the same time the greenhouse experiment was harvested, selected in the
 field according to Pérez-Harguindeguy et al. (2016).
 <em> </em> <em>Data
 analysis</em> <strong>Inter- and
 Intraspecific trait variability.</strong> We used Linear
 Mixed-Effects Models (LMM) with F tests of type III ANOVA using R version
 4.4.0, package “lmerTest” (Kuznetsova et al., 2020) to explain measured
 values of individual traits in the greenhouse experiment based on species
 identity (Species), source locality (Locality), experimental treatment
 (Competition and Water) and all their interactions. These factors were
 taken as fixed with experimental block (Block) as random effect because
 Block had significant effect on three out of six tested traits (Table S2
 in Appendix). For this analysis, after a check of the data normality, we
 used Log-transformed data for SLA, Vegetative Height, Root/Shoot Ratio and
 Aboveground/Belowground Biomass and Square Root-transformed data for
 Clonal Spread. The same test was used for aboveground, belowground and
 total biomass of <em>Carex</em> (all values Log-transformed)
 to check the effectivity of the water treatment. To examine BTV and ITV
 for plants from the field study, we used values measured in the field of
 Vegetative Height (Log-transformed), SLA, and LDMC in linear models (LM)
 with an F test of ANOVA, where we tested the effect of Species, Locality,
 and their interaction. For graphical representation (Figure 2), we
 calculated critical values for F (for individual factors both for
 greenhouse experiment and field study) reflecting appropriate degrees of
 freedom of each factor and α = 0.05.            
 Because we wanted to compare the explained variability of the greenhouse
 experiment with that of the field study represented by Sum of Squares, we
 reanalysed the LMM for the greenhouse experiment without the random factor
 Block and using a LM with F test of ANOVA, as in the case of the field
 study. The effect of Block, which was not included in this new analysis,
 was thus included in residuals of the model. Explained variability was
 composed by the Sum of Squares of Species, Locality, Experiment (i.e.,
 Competition, Water and their interaction; only for greenhouse experiment),
 Interactions (i.e., all interactions with Species and Locality; because
 the number of tested interactions was high, we summed up the Sum of
 Squares of all interactions with Species and Locality together for
 clarity) and Residuals (i.e., variability explained by other factors than
 were tested in the model, including the effect of Block for the greenhouse
 experiment). While the variability among Species expressed BTV, ITV
 included variability of Locality, Experiment, and Interactions. The
 variability of individuals from the same population (the same species from
 the same locality) under the same treatment combination is reflected by
 the Residuals (including the unexplained ITV, as well as sampling and
 measurement errors). Although one of our aims was to
 compare BTW and ITV, we should be aware that this comparison is context
 dependent and also depends on the design of the experimental setup. In
 particular, the explained variability depends on the degrees of freedom
 (generally, the explained variability increases with the degrees of
 freedom used). Nevertheless, the explained variability (expressed using
 Sum of Squares) is additive, which is a great advantage. Also, because the
 same design and data analysis model was used for all the traits, it is
 ideal for comparison of variability explained by individual factors in
 various traits. For direct comparison of effects of individual factors
 (i.e., species identity, individual experimental factors, source
 locality), the simple F-value might be more useful as a measure of the
 effect size. It is the ratio of variability explained in given
 experimental settings<em> </em>by an individual factor to its
 expected value under the null hypothesis that it does not have any effect.
 The disadvantage of F-value is that it is not additive. 
 <strong>Correlations of traits between greenhouse
 experiment and field study</strong><strong>. </strong>To
 assess how well the functional trait differences among and within species
 in nature were reflected in the values of their traits in the greenhouse
 experiment, we calculated the average values of functional traits from the
 field and greenhouse for each population and four experimental treatment
 type combinations (CH, CL, NH, NL). Subsequently, we compared the values
 from the field with values from the greenhouse using Major axis regression
 (MA) in R-package “lmodel2” (Legendre, 2018). We also calculated between
 group (i.e., between-species) and within group (i.e., within-species
 between localities) Pearson correlations between trait values from the
 field and greenhouse using function “statsBy” from the R-package “psych”
 (Revelle, 2024), where the grouping variable was
 <em>Carex</em> species. Nevertheless, especially in the case
 of between group correlations, the statistical test was not very relevant
 because it was calculated only from four values (i.e., four
 <em>Carex </em>species) which cannot give significant values
 for such a dataset. Thus, it was rather used to identify trends without
 testing for statistical significance.             For
 all figures, we used R-package ‘ggplot2’ (Wickham, 2016) and represented
 all data as non-transformed for easier visual interpretation.
description: # Closely related species differ in their traits, but competition induces
 high intra-specific variability
 [https://doi.org/10.5061/dryad.69p8cz9bh](https://doi.org/10.5061/dryad.69p8cz9bh) ## Description of the data and file structure We conducted a greenhouse experiment with four closely related *Carex* species, from four different populations each, and measured their functional traits under water availability and competition treatments. We were interested in the size of intraspecific differences caused both by experimental treatments (competition, water availability) and by the origin of individual transplants (source locality), as well as how ITV compares to BTV of these species. We also compared traits measured in a greenhouse with traits measured in the source localities to disentangle the effect of individual factors (genetic versus environmental) influencing ITV. We aimed to answer the following questions to guide our experiment: 1) How are the traits of individual species affected by competition and water availability, and how does this induced ITV compare with their interspecific differences? 2) Do closely related species respond similarly to competition and water availability? 3) How well are the functional trait differences in nature (among and within species) reflected in the trait values of the greenhouse experiment? We expected significant interspecific differences for individual functional traits, even for these closely related species (all species belonging to the subgenus *Carex* within the *Carex* genus) because, although these species can coexist, they also occur separately (they have different optima for soil moisture). We expected high effects of both competition and water availability on traits for different *Carex* species and possible, although weak, interactions between species and treatment and also some possible effect of the species’ source locality. All the above effects were expected to be rather trait specific, and we aimed to see which of them had the most stable BTV. However, we did not expect that differences in traits among populations of the same species from nature would be closely reflected by differences measured in the greenhouse experiment because genetic factors are the main drivers of ITV in standard greenhouse conditions, while ITV is mainly driven by environment in the field.  ### Files and variables #### File: Data.xlsx **Description:** There are two datasheets. Data_Greenhouse_experiment sheet represents data from greenhouse measurement. There is 5 Blocks, 7 localities, 4 *Carex* species (listed as only species names without genus). Competition and Water are experimental treatments. "Yes" means the presence of competitor, resp. high water level and "no" means the absence of competitor, resp. low water level. Other variables are measured traits in greenhouse. In the second sheet called Data_Field_study, there is 7 localities, 4 *Carex* species (abbreviated with first letters of species names - car (*caryophyllea*), pan (*panicea*), pal (*pallescens*), pil (*pilulifera*) and three traits measured in the field (SLA, LDMC and Vegetative Height). Missing values were indicating as "NA". ##### Variables * Block * Locality * Species * Competition * Water * RS (Root / Shoot ratio) * AB (Aboveground / Belowground Biomass) * LDMC [mg/g] * SLA [mm2/mg] * Vegetative_height [cm] * Clonal_Spread [nb.of ramets]