DOI: 10.5061/dryad.rbnzs7hgq
title: Methods of species pool determination as predictors of survival in seeding and transplanting experiments

description: Community composition is limited by a species’ ability to reach,
 establish, and survive on a site. Establishment and survival are
 constrained by both abiotic conditions and biotic interactions that
 operate together on local scales. They decide which species from the pool
 will form the community. For this reason, it is very important to clearly
 define the species pool, against which the community composition is
 compared.  The effect of biotic and abiotic factors can be
 assessed experimentally, and the species pool by using estimation methods
 based on broad-scale observational data. We compared success of five
 species pool estimation methods in predicting establishment and survival
 in a seed/transplant addition experiment.  In four different
 locations, we added resident and non-resident species to plots with and
 without competition and tested the ability of the species to thrive in
 both competition-free gaps (constrained mainly by abiotic conditions) and
 in intact vegetation (complete community filter). In these treatments, we
 studied the seedling recruitment and survival, and the establishment and
 survival of pre-grown transplants. The ability of species pool assessment
 methods to predict species performance in individual treatments was
 compared. The comparison of results from individual treatments indicates
 the importance of individual components of the community filter. Species
 pool assessment methods, based on species co-occurrence patterns (Beals
 index, Favourability, and Unconstrained ordination), were the best
 predictors of species performance in the intact vegetation but were less
 successful in the competition-free environment. Methods based on
 co-occurrence patterns were the most effective for predicting seedling
 establishment, while seed germination alone and transplant survival were
 poorly predictable.  The biotic filter was the principal factor
 defining our community composition, especially for the process of seedling
 establishment. The roles of biotic and abiotic filters are very difficult
 to distinguish without an experimental approach and the ratio of their
 importance changes during plant ontogenesis.
description: <em>Study site</em>
 The seed/transplant addition experiment was
 conducted in four species-rich oligotrophic meadows in South Bohemia,
 Czech Republic. These four localities formed a moisture gradient. The
 wettest location, Ohrazení (48°57´N, 14°35´E, 510 m a.s.l.), is a wet
 meadow characterized as <em>Molinion</em>. Vrcov (48°55′N,
 14°39′E, 510 m a.s.l.) is a mesic meadow characterized as
 <em>Alopecurion</em> with some elements of
 <em>Molinion</em>. Zvíkov (48°59′N, 14°36′E, 500 m a.s.l.) is
 a mesic meadow characterized as <em>Arrhenatherion</em>
 (association <em>Poo-Trisetetum</em>). The driest location,
 Závraty (48°56´N, 14°23´E, 460 m a.s.l.), is a relatively dry grassland
 with the lowest productivity (Table S2 in Appendix S1) characterized as
 <em>Arrhenatherion</em> (association <em>Ranunculo
 bulbosi-Arrhenatheretum elatioris</em>). All these localities were
 extensively managed with a single mowing term at the end of June with the
 exception of Vrcov, where the meadow was mown twice a year (June and
 September). The localities were a maximum of 20 km apart and experience
 very similar climatic conditions (Table S3 in Appendix S1). We did not
 need any permission for our fieldwork.  <em>Species</em> We selected 30 meadow plant species (their
 Ellenberg indicator values for light varied from 5 to 8, for moisture from
 3 to 10 and for nutrients from 2 to 7), including both residents typical
 in target locations and non-residents typical in different habitats (Table
 S4 in Appendix S1). We chose those species which are not rare in the
 region, for which the seeds were available, and which provided high
 variability of their traits and ecological preferences. To increase the
 variability of ecological preferences, we also included two species of
 very wet habitats (<em>Potentilla supina</em>,
 <em>Eleocharis palustris</em>) and one mostly ruderal species
 (<em>Geum urbanum</em>) that are found only occasionally in
 very wet or disturbed meadows respectively. No forest species were
 included. On the other hand, we restricted our
 selection to herbaceous perennial species, to avoid the possible
 “successional” changes during the four years of the experiment. We also
 avoided <em>Carex</em> species that are known for their bad
 germination. We expected
 that values of indices expressing the species suitability (e.g., Beals
 index, Ellenberg indicator values, functional traits), which we used in
 further analyses, will be higher for resident than for non-resident
 species. Species residence was determined for each location individually:
 species were considered resident if present in at least one of the five
 phytosociological relevés (5x5m) recorded in each location in June 2016.
 These five relevés should be sufficient to thrive well in the locality –
 we expect that we might miss just very few transient species with rather
 erratic appearance in the locality. All non-resident species were present
 within a radius of 30 km from the locations (www.pladias.cz, access
 13<sup>th</sup> of November 2020), and are therefore part of
 the regional species pool. We chose only perennial species, with the
 exception of <em>Potentilla supina</em>, which could be both
 annual and perennial (Kleyer et al., 2008). <em>Seed introduction
 experiment</em> We used seeds from a commercial supplier (Planta
 Naturalis, Markvartice, Czech Republic) without any stratification. Seeds
 were sown into both the artificially created gaps (plots without
 competition from surrounding vegetation) and the intact vegetation
 (control plots with competition from surrounding vegetation) at the
 beginning of April 2016. In each location, two blocks, each containing 30
 gaps and 30 control plots (0.2m x 0.2m each), were established (details
 about design are in Appendix S1, Fig. S1a). In each block, each species
 was sown separately in one gap and one control plot (200 seeds per species
 were sown evenly throughout the entire area of each plot). The germination
 of seeds and survival of seedlings was monitored from April 2016 to
 September 2019, two times per each year (in spring and summer) with
 exception of the last year when one extra monitoring was done before the
 end of the experiment. The number of surviving individuals was used as the
 response in statistical analyses, and we call this value “performance”. At
 the beginning of the observational period, this characteristic was
 affected mainly by germination and establishment and later modified by
 survival. <em>Transplant introduction
 experiment</em> Seeds (from the same source as in the case of
 seed introduction experiment) were germinated in the middle of April 2016
 in a growth chamber (12 h light and 12 h darkness at 19°C) and
 subsequently, seedlings were replanted into a greenhouse, each individual
 in a separate pot (7x7x6.5cm). For the substrate, we used soil from the
 target locations (to ensure the natural composition of microbiota) mixed
 with sand in a ratio of 3:1 (to balance the level of nutrients increased
 in soil after its relocation from the field). After two months, we planted
 transplants, which had roughly
 the same size within the same species (Table S5 in Appendix
 S1), in the field.
 We used separate plots for transplant and seed introduction experiment. In
 each location (same as for the seed introduction experiment), the design
 of the transplant experiment was arranged in four random blocks with three
 treatments in each block (in addition to gap and control plots as in the
 seed addition experiment, clipped plots were added to exclude only
 aboveground competition; details about design are in Appendix S1, Fig.
 S1b). Two pre-grown transplants of each species were planted randomly in
 each plot in the middle of June 2016. The survival of transplants was
 monitored from June 2016 to April 2019, once in summer 2016, one spring
 and two summer monitoring in 2017, one spring and one summer monitoring in
 2018 and one spring monitoring in 2019 before the end of the
 experiment. <em>Data analysis</em>
 To assess how well a species “belongs in a
 community”, we used five different methods of species pool assessment
 (Table S4 in Appendix S1). These indicators were subsequently correlated
 with measures of species performance in the field experiment. The methods
 used were Ellenberg indicator values (EIV; Euclidean distance between EIV
 and CWM, approach 1b.), species functional traits (Gower's distance
 between functional traits and CWM, group 2), and three methods based on
 co-occurrences (approach 1c.): Beals index (BI), its corrected form
 (Favourability), and unconstrained ordination (UNO).
 We used EIV for moisture, light, and nutrients
 which were taken from a list of these values for the Czech Republic
 (Chytrý et al., 2018), and using the R function “dist”, we calculated the
 Euclidean distance for each species between three EIV and CWM of the
 target habitat (Appendix S2). Then, we used five species functional
 traits: canopy height determining competitive ability of species, specific
 leaf area (SLA) and leaf dry-matter content (LDMC) associated with leaf
 economic spectrum and thus trade-off between resource acquisition and
 conservation (all three traits taken from the LEDA database; Kleyer et
 al., 2008), lateral spread (with exclusion of freely dispersible organs)
 determining ability of clonal spread from the CLO-PLA 3.3 database
 (Klimešová et al., 2017), and seed mass which is related to reproductive
 ability. Average mass of one seed was derived from the real mass of 50
 seeds used in the experiment. We computed the Gower’s distance of each
 species between five functional traits and CWM of the target habitat in
 R-package “gower” (van der Loo, 2022, Appendix S3).  In Appendix S1 (Fig.
 S2), we also presented results for raw values (i.e., not distance from
 CWM) of each functional trait separately showing which functional traits
 are generally good predictors for species
 establishment. We used three methods of species pool
 determination based on species co-occurrence patterns calculated for each
 combination of sown species and experimental location: Beals index (BI),
 its corrected form (Favourability), and unconstrained ordination (UNO). We
 used the five phytosociological relevés recorded in each location as
 target vegetation, and the Czech National Phytosociological Database
 (Chytrý & Rafajová, 2013) as the reference database (stratified
 subsample, containing 31 512 relevés to reduce oversampling of some areas
 (Knollová et al., 2005)). We calculated abundance weighted form of BI,
 using the R-package “vegan” (Oksanen et al., 2019, Appendix S4).
 Favourability correction of Beals index (Real et al., 2006) was calculated
 using the R-package “fuzzySim” (Barbosa, 2015, Appendix S5). UNO was
 calculated using the R function “dark.pred.ca (Brown et al., 2019,
 Appendix S6). This method is based on the unconstrained ordination
 (Correspondence analysis) of the reference database. Then, the relevé of
 the target site is used as a supplementary sample, its coordinates are
 calculated, and the suitability of the location is determined based on the
 distance of the focal species’ position (i.e., its optimum) from the
 target site in the ordination space. We calculated BI, Favourability and
 UNO for each relevé separately and used the average value across the five
 relevés for each location for subsequent analyses. Because we consider the
 species pool to be fuzzy rather than a crisp set, we did not use any
 threshold to get species forming the pool, but we directly used the
 indicators calculated as a quantitative measure of species appropriateness
 for the community.  In each treatment, we calculated Pearson's
 correlation for each indicator with performance of seedlings germinated
 from sown seeds in the field (seedlings) and performance of pre-grown
 transplants (transplants). Note that high correlation also means good
 predictive power (Lepš & Šmilauer, 2020). In each treatment type,
 we used the average performance (from two and four replications for
 seedlings and transplants respectively) for every combination of
 observation time, location, and species. We also correlated the indicators with the
 ratios of seedling or transplant performance in different treatments, to
 reflect directly the effects of competition; “Clipped/Gap” represents the
 effect of belowground competition, “Control/Clipped” the effect of
 aboveground competition and “Control/Gap” the combined effect of both.
 Value 1 denotes no effect and 0 the strongest effect of competition.
 To compare the predictive power of different
 methods of species pool assessments, we used obtained correlation
 coefficients in general linear models (GLM) with F test of
 Analysis of variance (Anova), where we used each measuring
 time and each location as replications (Appendix S7).
 To analyse the changes in predictive power of different
 methods of species pool assessment over time, we calculated a repeated
 measurement analysis of the variance of median of correlation coefficients
 between seedling/transplant performance and different methods of species
 pool assessment using Linear Mixed Effects Models (LMEM) in the R-package
 “nlme” (Pinheiro et al., 2021, Appendix S8). Both in GLM and LMEM, we used
 correlation coefficient values for EIV (Euclidian distance) and functional
 traits (Gower distance) multiplied by -1, because these are distances from
 CWM and thus, they are assumed to be negative predictors. Thus, the
 positive values in graphs always mean prediction in correct direction. For
 the values of correlation coefficients, we showed in the figures the
 limits of significance (p&lt;0.05 in a two-tailed test) for a single
 correlation coefficient for the given number of species (30 in our case).
 It can be interpreted that if the average exceeds this limit, correlation
 coefficients for a given treatment are mostly significant. We also
 represented in Fig. 2 the interquartile ranges to show which percentage of
 correlation coefficients at different times falls into this range. To
 calculate the lower and upper quartiles, we used R-package “dplyr”
 (Wickham et al., 2022, Appendix S8). Finally, we used BI as the species pool
 determination method with, generally, the best predictive power for
 experimental seedling/transplant performance to compare its results in
 detail. We used the values of correlation coefficients of
 seedling/transplant performance with BI in GLM in the program R (Appendix
 S9). In the case of transplants, the effect of the geotextile lining
 (Textile) was included. For all figures, we used the R-package “ggplot2”
 (Wickham, 2016, Appendix S10).
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