Introduction

Understanding the species composition of local communities and its dynamics over larger regional scales is a central goal in community ecology and an essential prerequisite to predict how environmental change (e.g., habitat fragmentation) may affect the assembly of local and regional communities (Cardoso et al., 2017; Schlägel et al., 2020; Thompson et al., 2020). Several attempts have been made to categorize dynamics shaping community assemblies from a local to a regional (i.e., metacommunity) scale. Major drivers of community assembly are niche selection, dispersal, stochastic demography, and colonization-extinction processes. Based on these drivers, Leibold et al. (2004) denominated four distinct archetypes of metacommunity dynamics: Neutral model, patch dynamics, species sorting, and mass effects. Subsequent studies on different study systems (e.g., rock pools, lakes, rivers, ponds, terrestrial systems) have attempted to apply these different archetypes to natural metacommunities (e.g., Bloch et al., 2006; Declerck et al., 2011; Dias et al., 2015; Kulkarni et al., 2019; Meynard et al., 2013), thereby contributing to our understanding of the underlying dynamics of metacommunities in terrestrial and aquatic systems. However, dynamics of a metacommunity system (and, in effect, assigned archetype) varied depending on the studied functional/ecological group (Pandit et al., 2009; Pineda et al., 2022; Tonkin et al., 2015), time scales (Castillo-Escrivà et al., 2020; Li et al., 2022), geographic scale (Dümmer et al., 2016; Santos et al., 2016), and focal taxon (Tonkin et al., 2015). Likely, any metacommunity is - to a varying degree - influenced by environmental filtering/niche selection, dispersal limitation, species interactions and stochastic drift. Therefore, the research focus switched from assigning discrete archetypes to identifying core processes underlying metacommunity dynamics, i.e., environmental filtering/niche selection, dispersal limitation, stochasticity/drift, and species interactions (Driscoll & Lindenmayer, 2009; Leibold & Chase, 2018; Padial et al., 2014; Thompson et al., 2020; Vellend, 2010). Under this more recent view, the relative impact of these various processes can be quantified using different statistical frameworks, i.e., Hierarchical Modelling of Species Communities (HMSC) (Ovaskainen et al. 2017), Joint Species Distribution Models (JDSM) (Leibold et al., 2022), and variance partitioning, thus disentangling major drivers from processes of less importance for a given metacommunity system (Cottenie, 2005; Legendre, 2008). While aquatic metacommunity studies have been targeted by a number of ecological studies (Cottenie et al., 2003; Gálvez et al., 2020; Gascón et al., 2016; Hill et al., 2017; Martin et al., 2021; Ramos et al., 2022; Zhao et al., 2016), the temporal aspect is often not included or only limited to a momentary view. It has been recently emphasized that such metacommunity studies should also consider the temporal component (i.e., a higher temporal sampling resolution both within and between multiple years) (Chaparro et al., 2018; Cottenie & De Meester, 2005; Da Silva et al., 2019; Gray et al., 2012; Horváth et al., 2016, 2019; Kulkarni et al., 2019; Padial et al., 2009; Smit et al., 2017; Wang et al., 2020).
DNA metabarcoding has the potential to greatly facilitate accumulation of the high-resolution data necessary to infer metacommunity processes, as it allows for simultaneous high throughput species determination across diverse taxa (Baird & Hajibabaei, 2012; Taberlet et al., 2012). Traditionally, species determination has been predominantly relied on morphological analysis to identify species (Pilgrim et al., 2011; Souza et al., 2019). This approach was often limiting biomonitoring programs, as morphological species determination can be tedious. Furthermore, in some groups (e.g., zooplankton) it is challenging even for experts, because of cryptic species and phenotypic plasticity, relative to ecosystem conditions (Karlsson & Winder, 2018; Xiong et al., 2019). As an alternative, species identification by DNA sequence analysis of specific genetic regions (Hebert et al., 2003) has been increasingly applied throughout the last decades, an approach commonly known as DNA barcoding. While traditional DNA barcoding uses short generic sequences to identify individual taxa, DNA metabarcoding supports the simultaneous identification of entire assemblages using high-throughput sequencing (Taberlet et al., 2012). This approach provides consistency, accuracy, and high taxonomic resolution in species identification (Baird & Hajibabaei, 2012).
Among aquatic systems, ponds (defined as lacustrine areas < 1 ha) are the most widespread water bodies on Earth (Meerhoff & Jeppensen, 2009). Their ecological value has been long time neglected (Céréghino et al., 2007), but has recently attracted increasing attention (Céréghino et al., 2014, Hill et al., 2021; Jeffries, 2012). Despite their small size (Williams et al., 2004), ponds often support a disproportionately high species richness compared to other freshwater habitats (e.g., rivers, lakes; Davies et al., 2008). They therefore contribute significantly to regional diversity (Hill et al., 2016, 2021) and provide important ecosystem services, e.g., support for pollinators (Stewart et al., 2017), watering life stock (Vico et al., 2020), and climate change mitigation (Coutts et al., 2013). Furthermore, ponds are highly replicated along environmental and spatial gradients, promoting a high compositional turnover (De Meester et al., 2005; Vad et al., 2017). Kettle holes are ponds that comprise remnant water bodies formed by retreating glaciers after the last glacial maximum. They frequently experience pronounced wet-dry cycles or show a tendency to high water overflows (Kalettka & Rudat, 2006). Nowadays, they are often surrounded by agricultural land and can be viewed as “inverse islands” supporting high biodiversity and often encompassing endangered species in increasingly fragmented landscapes (Davies et al., 2008; Gerke et al., 2010; Ungaro et al., 2014).
Zooplankton kettle hole metacommunities represent a suitable model system to test predictions of the metacommunity theory, as they include a variety of phylogenetically unrelated groups, i.e., rotifers and crustaceans (Cladocerans, ostracods and copepods), with different life history and dispersal capabilities (Cáceres & Soluk, 2002; Cottenie & De Meester, 2005; Dias et al., 2015; Frisch et al., 2012). Because kettle holes have been a persistent part of the landscape for so long, each kettle hole could theoretically harbour its peculiar biodiversity due to monopolization and the adaptation to a specific environmental condition (De Meester et al., 2002, De Meester et al., 2016). Many zooplankton species produce an extensive egg bank (Brendonck & De Meester, 2003; De Stasio, 1989; Hairston, 1996) that serves within a pond as an insurance against unfavourable conditions and a source for the pelagic population (Bell & Weithoff, 2003; Hairston et al., 2000). Hence, a kettle holes inventory comprises all species present in both the resting egg bank and the active community. Furthermore, resting eggs represent an effective passive dispersal pathway even for long distance dispersal (Fontaneto, 2019; Incagnone et al., 2015; Lopes et al., 2016; Pinceel et al., 2015). Dispersed resting stages can distribute many species in a certain region independent from the actual prevalent environmental conditions. Thus, the metacommunity assembly is not only related to interactions among extant communities (i.e., dispersal in space among open water habitat patches), but could also be driven by dispersal in time from the egg bank into the local waterbody. Hence, a comprehensive assessment of a local zooplankton community should consider all living specimens and viable resting stages, both those present in the water body and those in active egg banks, an aspect rarely considered in previous zooplankton metacommunity analyses (Cottenie & De Meester, 2005; Declerck et al., 2011), but whose importance has recently been emphasized (Wisnoski et al. 2019).
We performed a fully PCR- and Next Generation Sequencing (NGS)-based integrative analysis of both active and dormant (resting stages) zooplankton communities to infer the relative importance of different processes for zooplankton metacommunity assembly in a kettle hole system, an approach which has – to the best of our knowledge – only rarely been applied before. Previous studies in similar systems (e.g., ponds, rockpools) and a meta-study of Cottenie (2005) have already suggested that environmental filtering/species sorting is a major driver of these systems (Kulkarni et al., 2019; Vanschoenwinkel et al., 2010). Species sorting implies sufficient dispersal for species to reach suitable habitats. Here, we specifically aim at disentangling the relative role of dispersal through space (among kettle holes) and dispersal through time (i.e., from the dormant community in the egg bank to the active community in the open water of a kettle hole), relative to environmental conditions. To overcome limitations of previous studies in terms of sampling frequency, seasonal/annual resolution, and habitat type (sediment and open water), we sampled 24 kettle holes in the northeastern German Uckermark region over a two-year period. We applied a multi-marker DNA metabarcoding approach using a combination of two markers (fragments of the nuclear 18S and the mitochondrial cytochrome oxidase subunit I gene; COI) on bulk DNA to determine the overall composition of the entire zooplankton community. Specifically, we used this approach (I) to assess spatial (among kettle holes) and temporal (among dormant and active community within a kettle hole) connectivity; (II) to identify environmental parameters influencing the local community structure; and (III) to infer dominating metacommunity processes in the study system. As such, we also aim at providing a proof-of-principle for such a DNA-based metacommunity analysis, by discussing prospects, but also limitations and potential pitfalls of this approach.
 
Methods

Sampling

Over a period of two years (2019 and 2020), samples from open water were collected from 24 kettle holes in northeastern Germany (Quillow catchment, Uckermark, figure 1; details see table S1). In the first year, a total of eight sampling campaigns with a sampling interval of one month were carried out from March to October. In the second year, the sampling period was the same, but the sampling interval was once every two months (4 sampling campaigns). To collect water samples, we filtered 10 L of water from each kettle hole (1 L each taken from 10 different parts of the water body, representing all accessible parts of the pond) through a 30 µm plankton net (Hydrobios, acc. Apstein). We transferred the remaining 15 mL of water to a 50 mL centrifuge tube, to which 1.5 mL of sodium acetate and 33 mL of EtOH were added (Ladell et al., 2016). The samples were stored in -20 °C until DNA extraction took place. This resulted in a total number of 121 samples for the two years (note that water samples could only be taken if a pond had not dried out at the time of sampling; cf. table S1). Environmental parameters, i.e., pH and water temperature (Portamess® 911, Knick), were determined by measuring 50 mL of water three times, retaining the mean value for further analysis. Surrounding field crops as well as recent or ongoing agricultural practices were recorded during each sampling event (see details at Dryad: doi:10.5061/dryad.kprr4xh74 ) . Wind data was recorded using an anemometer (Vantage Pro2™, Davis) during the whole campaign period. Sediment samples were collected from all accessible kettle holes (n=17) at the end of the first sampling year (2019) to capture the total post-season resting stages deposited in the sediment. Therefore, four sediment samples were randomly taken within a 1 m2 rectangle using a Gardena® bulb planter, the first 5 cm of the active egg bank (Brendonck & De Meester, 2003) were taken, pooled, and stored in 4 °C until processing.

Hatching experiment

To discriminate between viable resting stages (to be included into our assessment) and dead animals/eggs (to be excluded from further analysis), we performed a hatching experiment. Sediment samples were manually homogenized and transferred to a Ø 19 cm glass petri dish. The samples were dried at 30 °C in a drying oven (BINDER FD 115-E2) for approx. four days. Once the samples were completely dry, they were again manually homogenized, weighed, and distributed into four replicates, each with 14 g sediment in a 165 mL vessel. 50 mL of tap water was added to the sediment of each replicate and samples were incubated at 15 °C (16/8 cycle) for four weeks in a climate chamber (RU|MED Rubarth Apparate GmbH). Twice per week, 15 mL of the supernatant (hence comprising hatched live specimens) were collected and stored in 1.5 mL sodium acetate and 33 mL 95% EtOH. These samples were further processed as described in the DNA extraction section and pooled for metabarcoding.

DNA extraction

To extract total DNA, samples were centrifuged at 5000 g, 0 °C for 35 minutes. Pellet DNA extraction was performed with a commercial DNeasy™ Tissue Kit (QIAGEN) following the standard protocol for human and animal tissues (QIAGEN, user manual p.30-33). To ensure a sufficient final yield of DNA, we doubled the amounts of chemicals during the step 1-3 (tissue lysis buffer, lysis buffer and proteinase K; lysis overnight) and performed the elution step twice with 30 µL AE-buffer. After a control of the concentration and purity via a spectral photometer (NanoDrop ND-1000, Thermo Scientific®), the DNA was stored at -20 °C.

Species identification via DNA metabarcoding

We used a combined fragment approach (18S and COI) to increase the accuracy of the species identification (Zhang et al. 2018). To identify species present in each sample, we targeted the mitochondrial cytochrome oxidase subunit I and the nuclear 18S gene using primers of Leray et al. (2013) (Leray fragment, length 313 bp; mlCOIintF 5’ - GGW ACW GGW TGA ACW GTW TAY CCY CC - 3’, jgHco 5’ - TAA ACT TCA GGG TGACCA AAR AAY CA—3’) and Zhang et al. (2018) (Zhang fragment, length 310 – 620 bp; length varies in different species; UNI18s 5’-AGG GCA AKY CTG GTG CCA GC-3’, UNI18SR 5’-GRC GGT ATC TRA TCG YCT T-3’), respectively. Amplification of the fragments, library preparation, next generation sequencing and blasting against reference databases were performed by a commercial company (AIM GmbH, Leipzig, Germany) following Morinière et al. (2016, 2019): From each sample, 5 µL of extracted genomic DNA was used together with the MyTaq PCR Kit (Bioline, Luckenwalde, Germany) and barcode primers adapted to High Throughput Sequencing (HTS). Amplification for COI and 18S was conducted under the PCR thermocycler conditions described by Leray et al. (2013) and Zhang et al. (2018), respectively, using 25 µL reaction volume. Amplification success and fragment length was assessed by gel electrophoresis. Amplified DNA was purified using a 1.0 % sodium acetate and 70.0 % EtOH purification method (Green et al., 2016) and resuspended in 50 µL molecular water for each sample before proceeding. Illumina Nextera XT (Illumina Inc., San Diego, USA) indices were ligated to the PCR product in a second PCR reaction using the same annealing temperature as in the first amplification PCR reaction (for thermocycling profiles see Leray et al., 2013; Zhang et al., 2018), but with only 7-9 cycles. Ligation success was confirmed by gel electrophoresis. DNA concentrations were measured using a Qubit fluorometer (Life Technologies, Carlsbad, USA) and samples were combined into 40 µL pools containing equimolar concentrations of 100 ng each. To remove unwanted smaller fragments and residual primer dimers, the amplicon pools were combined with NGS magnetic beads (MagSi-NGSPrep Plus, Magtivio) and purified using a 0.8 to 1.0 ratio of beads to amplicon PCR product. A final elution volume of 20 µL was used. HTS was performed on an Illumina MiSeq using v3 chemistry (2*300bp, 600 cycles, maximum of 25mio paired end reads). The COI run of all 138 samples resulted in a total number of 26715829 reads, while the 18S run resulted in 16576630 reads. Sequences were quality filtered (minimum length COI: 100bp, 18S: 300bp) using vsearch 2.9.1 and trimmed using the software cutadapt 1.18 (Martin, 2011) in Python 2.7.15. After dereplication, singleton sequences were removed and non-singleton sequences were clustered with a 98 % identity threshold using vsearch (v.2.9.1) (Rognes et al., 2016). After chimera detection the total number of operational taxonomic units (OTUs) were blasted against NCBI, BOLD (COI only), and Silva (18S only). 9950 OTUs matched in the reference databases for COI and 3336 for 18S. Subsequently, species lists were separately analysed. OTUs which had a ≥ 97 % hit identity were extracted and only hits to zooplankton species were retained, by combining different OTUs assigning to the same species. Two sediment samples had to be excluded from further analysis (Meta_37_p, Meta_1598_p) because no zooplankton species were detected. From the remaining 136 samples, relative read numbers were calculated by dividing the read number per species in the sample by the total read number of that sample (Deagle et al., 2019; Zamora-Terol et al., 2020) which is a reliable proxy for biomass (Schenk et al., 2019). For each species, fragment-specific relative read number values were averaged, in an attempt to balance potential primer/fragment-specific species detection biases. The resulting combined species list across amplified fragments with mean relative read numbers as a proxy for relative abundance (Krueger et al., 2021) was used for all subsequent analyses.

 

Statistical analysis

All statistical analyses were performed using R version 4.0.5 (R Core Team, 2020). Permanency of water containment per kettle hole was defined as number of sampling events (2019 only) with water containment in the kettle hole (values 1-8). For some summary statistics, kettle holes were further classified into two hydroperiod-types based on the timespan of water containment, i.e., ephemeral (permanency 1-4) and permanent (permanency 5-8).
Any kettle hole was assessed over two subsequent years (2019 and 2020), by comparing data from water samples collected in the same month across both years (n=60 from 15 kettle holes) using diversity indices (Species richness S, Shannon Index H', and Simpson Index D1) calculated with the diversity and specnumber function implemented in vegan 2.5-7 (Oksanen et al., 2020). These three different indices were considered, as each of them has a different emphasis on species richness, abundance, and evenness (Martin et al., 2016). A non-metric multidimensional scaling (NMDS) approach based on Bray-Curtis dissimilarities and k = 2 in combination with a Permanova (based on 999 permutations), using the metaMDS function in vegan, was applied to assess the difference in community species compositions within kettle holes across years. As these analyses did not reveal significant differences among 2019 and 2020, further analyses focused on the more densely sampled year 2019.
Environmental, temporal, and spatial parameters/processes in species numbers and composition of the open water zooplankton communities within a year was assessed across the eight sampling campaigns in 2019 (n= 91 metabarcoded samples from 24 kettle holes). To avoid overfitting, explanatory variables were checked for collinearity using Pearson correlation coefficients (threshold > 0.7; Dormann et al., 2013). Non collinear explanatory variables (pairwise correlation < 0.7; field crops, location, number of neighbouring kettle holes, permanency, pH, water temperature, time (month), pond size, season) were included in the model selection, while average wind direction and speed were excluded, as they were highly correlated to time (month).
Species richness (S) and Shannon Index (H') were correlated with environmental parameters (pH, water temperature, surrounding field crops, kettle hole size, kettle hole location (i.e., assigned to the three categories of north, south, or mid based on location; for details see Dryad: doi:10.5061/dryad.kprr4xh74), numbers of neighbouring kettle holes in a 500 m radius, permanency, time (month), and season with a generalized linear mixed effects model (Gelman & Hill, 2006; Zuur et al., 2009). For Species richness (S) the lme4 package v.1.1-27.1 (Bates et al., 2015) and a quasipoisson distribution were used, while for Shannon Index (H’) the glmmADMB package v.0.8.3.3. (Bolker et al., 2012) with a gaussian distribution was applied. Kettle hole ID, referring to repeated observations from the same kettle hole, was used as a random effect. The diversity (H') as response variable and predictor variables (pH, water temperature, kettle hole size, permanency, time) were Tukey-transformed to obtain a distribution approximate to normal. To identify the model that explains most of the variance, model selection was conducted based on the Akaike information criterion (AIC), using the dedgre function in the R package MuMIn (Barton, 2016). We confirmed normality and homoscedasticity of the model residuals via QQ-plots (data not shown).
To test if communities of different hydroperiod types differed from one another, a NMDS ordination plot based on Bray-Curtis dissimilarities (k=2) and a subsequent Permanova with 999 permutations were calculated. To identify environmental parameters influencing community composition, a distance-based redundancy analysis (dbRDA) was performed using the dbrda function implemented in the vegan package. This analysis was performed on Bray-Curtis dissimilarities to infer community abundance relationships and on Jaccard dissimilarities as a measure of species presence/absence being related to environmental and temporal parameters. To avoid overfitting, explanatory variables were first checked for collinearity using Pearson correlation coefficient (see above). Non collinear explanatory variables (field crops, permanency, pH, water temperature, neighbouring kettle holes, kettle holes size, month, season) were fed into a forward stepwise variable selection with 9999 permutations based on the ordistep function in vegan. To include distance as an explanatory variable, so called principal coordinates of neighbouring matrices (PCNMs; Borcard & Legendre, 2002; Borcard et al., 2004; Dray et al., 2006) were generated using the pcnm function implemented in vegan based on the coordinates of the kettle holes and subsequently passed on into a forward step selection. Significant explanatory variables (environment, temporal and PCNMs) were included in variance partitioning (Legendre, 2008), using the varpart function in vegan. This was done to partition explained variance and to assign it to environmental, temporal, and spatial processes, respectively. Variance fractions were tested for significance using an ANOVA-like permutation test (anova.cca function) with 999 permutations. To evaluate the influence of the individual kettle hole (which may represent a unique combination of environmental parameters) within a season on community composition, a Permanova (999 permutations) was conducted based on the Bray-Curtis dissimilarities. To further correlate community dissimilarities with geographic (km) resp. environmental distances (i.e., Gower’s distance; Gower, 1971), Mantel and partial Mantel tests were performed (using the functions mantel and mantel.partial in vegan) among all pairs of kettle holes. Gower’s distance (calculated using gower.dist in statMatch v. 1.4.0; D’Orazio, 2012) was implemented, as it combines the unique kettle-hole specific environmental differences among any pair of kettle holes into a single distance measure.
To assess the potential for recruitment from the resting stages located in the sediment of any kettle hole (dispersal through time), we compared sediment and water samples from the same year (2019), using the same diversity indices and the NMDS (and k = 2) and Permanova (based on 999 permutations) approach described above.
Results

Species detection and identification

During the blast search, 2422847 COI reads (from 9550 OTUs) and 2768328 18S reads (from 3336 OTUs) had scored a positive blast hit. After filtering out all blast hits that had a hit rate of ≥ 97 %, 2422708 (599 OTUs) and 960645 (1463 OTUs) reads remained for COI and 18S, respectively. Filtering for zooplankton species resulted in a final read count of 1761637 (153 OTUs) for COI and 348851 (58 OTUs) for 18S. After fusing OTUs which detected the same species, a total of 65 zooplankton species were recovered in the 136 metabarcoded samples (full data available at Dryad: doi:10.5061/dryad.kprr4xh74) . 11 species were detected by both fragments, while each fragment detected 27 unique species. All 65 species were found in the water samples (n=121). When comparing open water and sediment (2019 only), we found 58 species in the water samples (n=75), while only 9 of them were detected in the 15 sediment samples (figure 2). Among the zooplankton-specific hits of all open water samples, the species-specific percentage of hits (as a proxy of relative abundances; Krueger et al., 2021) revealed copepods and cladocerans (Cyclops insignis: 25.8 %, Cyclops strenuus: 16.7 %, Thermocyclops crassus 16.1 %, Daphnia magna: 4.8 %) and some rotifers (Polyarthra dolichoptera: 7.1 % and Synchaeta pectinata: 5.2 %) to be the most frequent hits. The sediment was dominated by Brachionus calyciflorus (34.7 %) and Daphnia magna (20.0 %).

Consistency in community composition, species richness and diversity between subsequent years

Comparing kettle hole specific barcoding results for the same month of two subsequent years (2019 and 2020), there was no difference in species composition between the two years, neither for ephemeral nor for permanent kettle holes (figure S1, table 1). Furthermore, species diversity (S, H', D1) across subsequent years did not differ significantly (figure S2).
Community composition in the open water in relation to environmental parameters and season
The calculation of the Permanova of the 2019 water samples shows a significant difference in community composition between ephemeral and permanent kettle holes (F-value: 7.50, R2: 0.078, adjusted p-value 0.001) (figure 3). The dbRDA forward step selection identified “surrounding field crop”, “permanency”, “kettle hole size”, and “season” as significant explanatory variables regarding Bray-Curtis dissimilarities among kettle holes and “surrounding field crop” and “permanency” regarding Jaccard dissimilarities among kettle holes (table 2, figure S3). Variation partitioning revealed that 20.7 % of the observed variance in Bray-Curtis dissimilarities was explained by all explanatory variables (thereof environment: 10.5 %, season: 0.9 %, and distance: 2.8 %). For Jaccard dissimilarities, 21.0 % of the observed variance was explained by explanatory variables (thereof environment: 12.9 %, season: 2.6 %, and distance: 5.5 %; figure 4). Regarding Bray-Curtis dissimilarities, 7.0 % of the variance were explained by the combination of environment and distance, while all other interaction variances were neglectable. A Permanova, including Pond ID to infer the influence of individual kettle hole peculiarities, explained a high proportion of variability in species composition (38.5 %) by the individual kettle hole, while the season explained a further 9.2 % of the variability (table 3). The performed Mantel tests showed a significant correlation (R: 0.131, p-value: 0.024) between the pairwise geographic distance among kettle holes and the observed Bray-Curtis dissimilarities of the respective communities (figure 5), and a highly significant correlation between Gower’s distance (as a measure for environmental difference) and observed Bray-Curtis dissimilarities (R: 0.218, p-value: 0.001; figure 6). These results were also confirmed by partial Mantel tests based on pooled (geographic distance: R: 0.119, p-value: 0.034; Gower’s distance: R: 0.212, p-value: 0.002) and individual data (geographic distance: R: 0.148, p-value: 0.0001; Gower’s distance: R: 0.225, p-value: 0.0001) (table S2).

Species richness and diversity

Kettle hole-specific temporal dynamics of species richness (S) over all eight sampling campaigns of 2019 did not follow a clear common trend, neither for permanent nor for ephemeral kettle holes (figure 7). GLMMs revealed that species richness (S) was related pond permanency and pH (tables S3, S4), with a generally higher species richness at intermediate pH (figure 8) and in permanent rather than in ephemeral kettle holes (figure S4). This trend was also observed for the Shannon (H') and Simpson Index (D1), but did not yield statistical significance in a Wilcoxon test (figure S4). The GLMM based on Shannon Index (H') reveals this diversity measure to be dependent on the kettle hole location (table S5, table S6).

Sediment egg bank vs. open water

When comparing species richness and diversity indices, a large difference between dormant and active communities became evident, with a significantly lower number of species in dormant communities. Only species from Cladocera, Copepoda, and Rotifera were detected in the sediment samples, with the highest number of species belonging to rotifers (cf. figure 2, figure S5). Species richness was higher in ephemeral than in permanent dormant communities (figure 9). The Shannon Index (H') was significantly different among all cohorts. The Simpson Index (D1) generally resembled the same pattern of difference, but the pairwise comparisons involving ephemeral active communities did not yield statistical significance (figure 9). There was a consistent difference in species composition between sediment and water samples, both for ephemeral and permanent kettle holes (figure 10, table 4). The among kettle hole variation in species composition was higher in the sediment than in the open water (figure 10), but the former may be influenced by the low absolute number of species recovered from the sediment (cf. figure 2; see data on Dryad for details). These differences in species composition among sediment samples did not correlate to the geographic distance among the sampled kettle holes (R:0.125, p-value: 0.109) (figure S6). We found differences in species composition between the ephemeral and permanent kettle holes in the open water, but not in the sediment samples (figure 10, table 4).
 

Discussion

Multi-marker DNA barcoding for zooplankton community inference

We present an analytical pipeline for time-efficient DNA-metabarcoding species identification and diversity inference in zooplankton communities, based on total DNA extracted from water samples. These bulk tissue samples were either taken directly from the open water of ponds or resulted from hatching experiments on resting stages from sediment. We used a two-fragment DNA metabarcoding approach (COI and 18S) which significantly improved species recognition by offsetting some of the errors that may have occurred due to incompleteness of reference databases, but potentially also due to primer bias, i.e., biased amplification successes among different taxonomic groups (Cicala et al., 2021; Clarke et al., 2017; Leite et al., 2021; Stefanni et al., 2018; Zhang et al., 2018). Indeed, without the use of two markers, a fair proportion of species (41.5 %) in our kettle holes would have remained undetected, potentially reducing the power of our biodiversity assessment. It should be noted that utilization of public uncurated sequence databases may have an influence on species detection, in two ways: (1) taxonomic misassignments of sequences will go undetected and lead to erroneous species assignment; (2) public databases may be incomplete regarding species potentially encountered in a DNA metabarcoding study. While the former could only be mitigated by carefully curated data bases not available to date and beyond the scope of individual barcoding studies, taxonomic misassignment because of data base incompleteness is minimized by applying stringent established thresholds of sequence identity (here, ≥ 97 %). This approach is conservative in the sense of avoiding false positives, at the expense of potentially underestimating species diversity (as OTUs not assigned with certainty to a species were excluded). Indeed, field studies in the area (albeit not in the same years and kettle holes) have revealed a total of 89 zooplankton species (Colangeli, 2018) by microscopic identification. Our study revealing 65 species in total may hence have underestimated the true diversity.
 

Inferred patterns and processes of zooplankton metacommunity assembly

We found zooplankton communities from kettle holes to be mostly influenced by environmental factors (hydroperiod, pH, kettle hole size and surrounding field crops), with smaller but significant seasonal and spatial (pond location) impacts, in line with other studies on aquatic metacommunities (e.g., Gálvez et al., 2020; Ramos et al., 2022; Vanschoenwinkel et al., 2007). Species composition and numbers within a kettle hole hydroperiod type (permanent vs. ephemeral) remained relatively stable in subsequent years, suggesting little between-year variability in composition. Our monthly within-year sampling over eight months revealed seasonal variation in the zooplankton community, which is well known from temperate, dimictic lakes (Sommer et al. 1986) and has been recently also described in small ponds (e.g., Florencio et al., 2016 for microcrustaceans, Onandia et al., 2021 for rotifers). Species richness was mainly determined by permanency of water containment and pH (as in Tavernini, 2008), while the species diversity, measured by the Shannon Index was affected by kettle hole location, pointing towards some connectivity among adjacent kettle holes. According to our analyses, the community composition is strongly affected by the environmental factors of the local habitat, such as kettle hole size, surrounding field crops, and permanency/hydroperiod (permanent vs. ephemeral; Kulkarni et al., 2019). Differences in water persistence, as among the ephemeral and permanent ponds, can influence species exchange (i.e., beta diversity; Dulić et al., 2014; Florencio et al., 2016; Ripley & Simovich, 2009). Ephemeral kettle holes are more dynamic due to dryfall and subsequent rewetting, which might lead to a faster succession of species, sometimes correlated with pond size (Basińska et al., 2014, a finding also supported by our analysis), and allows for a restart of otherwise less competitive species from the egg bank (Incagnone et al., 2015; Serrano & Fahd, 2005; Tilman, 1994). Additionally, dryfall of kettle holes can reduce the occurrence of predator species like amphibians and macroinvertebrates which potentially affect the zooplankton community (Taylor & Mahoney, 1990). In contrast, the permanent kettle holes are more stable in environmental parameters and less variable in species composition (Vagaggini et al., 2011) because they are filled with water (most of the time) and therefore tend to harbour species adapted to a stable environment (Brendonck et al., 2017; De Block et al., 2008; Wellborn et al., 1996). Surrounding field crops comprised a further parameter influencing zooplankton communities, which could be linked to differences in agricultural practises (i.e., fertilizer/pesticide applications) and timings of their application depending on the respective crop. Compounds from agricultural practises entering water bodies by surface runoff can affect entire zooplankton communities (Andrade et al., 2022) or specific taxonomic groups (e.g., cladocerans, Relyea, 2005). Furthermore, the height of the different field crops could facilitate or restrict passive dispersal pathways of different zooplankton species due to ground-surface dispersal which is known to be a dispersal strategy for some species (e.g., daphnid ephippia; Brendonck & De Meester, 2003; Pinceel et al., 2016; Schurr et al., 2005). The temporal density of field crops can even affect active dispersers by sheltering effects (Wratten et al., 2003). In summary, the impact of field crops is indirect and complex in nature. While it is significant in our study, we are so far unable to infer the distinct underlying mechanism.
Season was a dominating factor in community assembly in kettle holes, to which seasonally fluctuating water availability may contribute. Indeed, seasonality in zooplankton communities has been linked to the hydroperiod of ponds in previous studies (e.g., Florencio et al., 2016; Zokan & Drake, 2015). Differences in hydroperiod further substantially contributed to the variation in species composition among kettle holes. That may reflect that ephemeral ponds with more dynamic environments may favour species with specific adaptations such as higher resting stage production, faster development, and resistance to dryfall and associated conditions (Both et al., 2011; Wellborn et al., 1996). It has been observed in Daphnia carinata that juveniles from temporary ponds have a faster growth rate than those from stable permanent ponds (Drapes et al., 2021). The number of species observed in the kettle holes was also dependent on hydroperiod, with higher numbers in permanent than in ephemeral kettle holes, a finding supported by a long term mesocosm study (Zokan & Drake, 2015), but contrary to findings of Kuczyńska-Kippen & Pronin (2018) which report temporary ponds of shorter hydroperiods to generally exhibit a higher zooplankton diversity.
We further found a significant proportion of the variation in species composition to be attributed to the individual kettle hole, as found in a comparable study of Montaña et al. (2021). This pattern may be associated with monopolization/priority effects of certain species in individual kettle holes that affect subsequent colonization success and interactions between species (Jenkins & Buikema, 1998). Such priority effects can persist for thousands of years (Ventura et al. 2014). Alternatively, the location of any specific kettle hole could be peculiar in further environmental parameters not considered here, such as geological characteristics or hidden connectivity among kettle holes (Vyse et al., 2020). A possibly unique set of environmental parameters in each kettle hole could also explain the differences in species diversity (measured by Shannon Index) and the different seasonal dynamics of community composition and species richness in our individual kettle holes and may have contributed to the unexplained part of the observed variance in species composition. This environmental impact is further supported by the correlation of Gower’s distance (as proxy for heterogeneity of kettle holes) and community composition differences, such that the more similar local environments are, the more similar is their species composition (see also Kulkarni et al., 2019).
A correlation between geographic distance with zooplankton community dissimilarity on a geographic scale of ~14.0 km was found in our study. Passively dispersed organisms, as zooplankton, are dependent on dispersal vectors (e.g., Fontaneto, 2019). If they are wind-dependent, their dispersal effectiveness strongly depends on the geographic scale (Horváth et al., 2016; Vanschoenwinkel et al., 2009). They can also depend on animals, so called mobile links (Jeltsch et al., 2013; Lundberg & Moberg, 2003), to get dispersed on a landscape scale (Brochet et al., 2009; Frisch et al., 2007; Moreno et al., 2016; Vanschoenwinkel et al., 2008, 2009). These mobile links often do not necessarily create a linear dispersal framework, as they may not explore habitats in a purely distance-related manner and may have other selective criteria, such as landscape configuration (habitat quality, connectivity, competition etc.). In addition, these factors may change temporally, especially in agricultural landscapes (due to farming/cultivation activities). These complex and confounding factors may result in fluctuating and non-linear dispersal dynamics for passive disperses (like zooplankton), which confound any distance-based patterns, should they exist on the scale of the analysed metacommunity (Burel & Baudry, 2005; Kleyheeg et al., 2017; Kloskowski et al., 2010). Furthermore, individual zooplankton species might have different dispersal rates/abilities (Cáceres & Soluk et al., 2002; Frisch et al., 2007; Vanschoenwinkel et al., 2009) and pathways (Lopes et al., 2016), a pattern we did not consider in our community approach.
While our study indicates some impact of the spatial configuration (on our scale of 14.0 km), this explained less variance than environment, pointing towards environmental filtering as the dominant process, a finding in line with most other zooplankton metacommunity studies (e.g., Gálvez et al., 2020; Ramos et al., 2022; Vanschoenwinkel et al., 2007). In this context, recruitment from the local dormant community (dispersal in time) may be of key importance. In our study, we indeed made an effort to not only assess the dormant community, but to prove its viability by hatching experiments.
We did not find differences in species composition in the sediment egg bank among the kettle holes within the study area (Uckermark region), irrespective of local environmental parameters (such as hydroperiod or pH) and geographic distance. While this finding fits well with the notion of the egg bank as a reservoir of species waiting for suitable conditions to hatch, this interpretation is based on low absolute numbers of species revealed in our sediment analyses. We cannot rule out the possibility that our hatching conditions acted as an unconscious filter, as we may have overlooked some species that did not hatch under our experimental conditions due to not using an isolation method (e.g., sugar floatation; Vandekerkhove et al., 2004), different bet-hedging strategies or hatching cues among zooplankton species (Brendonck & De Meester, 2003; De Mester & Jager, 1993; García‐Roger et al., 2014). While this was an unavoidable trade-off because we intended to examine “true” dispersal (time/space), which only occurs when viable resting stages are present/transported, future studies might aim to further improve sampling or hatching conditions. Despite of the limitations in absolute species numbers, we were able to infer a higher species richness in the resting egg bank of ephemeral, relative to permanent kettle holes, in line with findings of Olmo et al. (2020).
The similarity of the species found as viable resting stages in the sediment of different kettle holes, irrespective of distance, points towards sufficient dispersal within our metacommunity to homogenize dormant communities among kettle holes (Cottenie et al., 2003), at least for the subset of abundant species regularly found in the egg bank.  For these species, there is no indication for apparent dispersal limitations among our kettle holes. Yet, we found a high species heterogeneity in the active communities among them, with the highest explained variance by environmental factors (10.5 % / 12.9 %), followed by spatial components (2.8 % / 5.5 %). The former indicates environmental filtering/species sorting. The latter could point towards some dispersal limitations among species not encountered in our egg bank hatching experiments, leading to an increased similarity in species composition among geographically proximate kettle holes. A meta-analysis of Cottenie (2005), covering 158 datasets, reported the combination of species sorting and spatial effects (dispersal limitations/mass effects) as the second most common inferred combination of metacommunity dynamics, a pattern also found in recent studies (Castillo-Escrivà et al., 2010; Farooq et al., 2022; Vanschoenwinkel et al., 2007). Though, mass effects alone are considered unlikely to play a dominant role in natural systems (Howeth & Leibold, 2010; Leibold & Chase, 2018; Michels et al, 2010). While we significantly confirm the impact of environmental and – to a lesser degree – spatial configuration as important drivers of metacommunity composition, the majority of observed variance remains unexplained. In how far this is due to neutral processes/demographic stochasticity (Leibold & Chase, 2018; Rosindell et al., 2011) or additional parameters not considered here, could not be inferred in the course of this study. Further, the prevalence of environmental filtering/niche selection over dispersal limitations for metacommunity assembly found in our study may be related to the geographic scale of our study. This might change across spatial scales, with environmental filtering as the main process on the smaller spatial scale we had focussed on, while dispersal limitations potentially becoming a structuring force over a larger spatial scale (Declerck et al., 2011; Heino et al., 2015).
The data on which our study is based meet the criteria proposed for the analysis of metacommunity dynamics (Logue et al., 2011) by providing proxies for species abundances, spatial data, and environmental data. In addition, we were able to compare community dynamics across seasons as well as to gain insight into the dormant community and thus putative dispersal through time in our kettle hole system. Focusing on two subsequent years allowed us to confirm interannual consistency in metacommunity composition, yet we cannot draw conclusions about evolutionary (Pillar & Duarte, 2010) or historical community assembly processes (Fukami et al., 2010).

Conclusions

We were able to demonstrate that a multi-marker metabarcoding approach can significantly improve species detection compared to using only one marker by balancing the effect of primer bias/differential amplification success and incompleteness of reference databases for zooplankton species. DNA metabarcoding enabled us to assess zooplankton communities in two years at 12 different dates, yielding altogether 136 bulk DNA samples, in a time-efficient manner. Using traditional specimen-specific morphological determination instead of bulk DNA, we would not have been able to conduct such a comprehensive study with the same time-dense sampling setup, potentially reducing the power of our analysis. We would advocate inclusion of the dormant community in a zooplankton metacommunity assessment. Our own data in that regard revealed a surprisingly low number of species. While future studies may attempt to improve sampling or hatching conditions, our study provides a proof-of-principle that such samples could be fed into the same DNA metabarcoding pipelines as samples from active communities, facilitating downstream comparison.
Our study of local zooplankton communities in quasi-insular ponds embedded in an unfavourable agricultural landscape matrix indeed confirmed that the studied kettle hole system comprises a metacommunity, as we find strong connectivity between the observed communities (active and dormant communities). We were able to identify environmental drivers such as surrounding field crops, kettle hole size, and permanency in water containment/hydroperiod explaining a major part of the observed variance in zooplankton communities. Field crops might be a surrogate for the use of agrochemicals, land use and physical boundaries. According to our study, environmental filtering plays a dominant role in zooplankton community assembly, while spatial effects were less prominent, but significant. With our approach, we could not further dissect these spatial effects as to respective contributions of dispersal limitations or mass effects (Leboucher et al., 2020).
Our focus of the study was on identifying the underlying processes and influences (spatial/temporal), based on established metacommunity theory (Leibold & Chase, 2018). We however did not address coexistence theory. Future studies should put more emphasis on the internal structure of metacommunities, applying the recently proposed new conceptual framework of Leibold et al. (2022). This would advance our comprehension of how specific species and sites contribute to the global system of metacommunities, thus expanding our knowledge of the complex and interactive relationships between processes and patterns in metacommunities.
Acknowledgements
We thank the farmers and landowners for their cooperation in permitting the sampling of the kettle holes. The authors would also like to acknowledge Dr. Binia De Cahsan and Dr. Michael Westbury for their assistance in analysing the metabarcoding data. Maxi Tomowski, Jonas Stiegler, and Victor Parry are thanked for their help with statistics and writing the code in R, and Maxi Tomowski for her assistance in the field. We thank Jessica Wolter and Tina Birnbach for their help in conducting the hatching experiment and Dr. Marijke Autenrieth for assistance with the data visualization. This work was supported by Deutsche Forschungsgemeinschaft (DFG) in the framework of the BioMove Research Training Group (DFG-GRK 2118). Three anonymous reviewers helped us - with their expertise and thoughtful comments - to substantially improve this manuscript.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Data accessibility statement
Sample metadata, taxonomic assessment and relative read counts per sample and species are available from the Dryad repository (doi:10.5061/dryad.kprr4xh74). DNA amplicon sequences are available from the GenBank of NCBI at https://www.ncbi.nlm.nih.gov/ under the BioProject number: PRJNA851569 and BioSample numbers: SAMN29249391 - SAMN29249528. COI amplicon raw reads are available in the Sequence Read Archive (SRA) under the accession numbers: SRR19895316-SRR19895453, and SRR19896128-SRR19896265 for 18S, respectively.
Author Contributions
K.K., R.T. and G.W. designed the research. K.K. coordinated the fieldwork, collected samples, performed laboratory processing of samples, and analysed the data with input from R.T. and G.W. K.K. produced the graphics and wrote the manuscript with the support of R.T. and G.W. All authors approved the final version of the manuscript.
 
 
Table 1: Permanova performed with 999 permutations based on Bray-Curtis dissimilarities on water samples from permanent and ephemeral kettle holes of two different years.
Pairwise PERMANOVA F1 R2 Adjusted p
2019 ephemeral vs. 2020 ephemeral 0.877 0.052 1.000
2019 permanent vs. 2020 permanent 1.403 0.034 0.936
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2: Results of stepwise forward selection of the dbRDA to avoid overfitting of explanatory variables. Best explanatory variables were identified using 9999 permutations. Asteriks indicate significant results.
ANOVA forward step selection dbRDA (Bray-Curtis dissimilarities) Df AIC F p-value
Permanency 1 143.70 7.447 0.005*
Field crops 8 143.23 1.951 0.005*
Kettle hole size 1 141.37 3.127 0.005*
Season 2 141.46 1.506 0.040*
PCNM (1, 2, 6) - - - -
ANOVA forward step selection dbRDA (Jaccard dissimilarities) Df AIC F p-value
Permanency 1 128.66 3.114 0.005*
Season 2 128.62 1.932 0.005*
Field crops 8 128.48 1.810 0.005*
PCNM (1, 3, 4) - - - -
 
 
 
 
 
 
 
PERMANOVA Df SumsOfSqs MeanSqs F1 R2 p
Season 2 2.9110 1.45549 5.9889 0.09210 0.001*
Pond ID 23 12.1722 0.52923 2.1776 0.38510 0.001*
Season*Pond ID 17 4.8589 0.28582 1.1761 0.15373 0.074
Residuals 48 11.6655 0.24303   0.36907  
Total 90 31.6076     1.00000  
 
Table 3: Permanova performed with 999 permutations on community composition based on Bray-Curtis dissimilarities of water samples from permanent and ephemeral kettle holes. The asterisk indicate significant results.
 
 
 
Table 4: Permanova performed with 999 permutations based on Bray-Curtis dissimilarities on water and sediment samples from permanent and ephemeral kettle holes. Asterisk indicate significant results.
Pairwise PERMANOVA F1 R2 Adjusted p
Sediment ephemeral vs. water ephemeral 9.243464 0.4351204 0.006*
Sediment ephemeral vs. sediment permanent 1.934268 0.1295188 0.198
Water ephemeral vs. water permanent 3.501158 0.2121765 0.048*
Water permanent vs. sediment permanent 3.904207 0.2180609 0.006*
 
 
 
 
 
Figure 1: Sampling sites at 24 kettle holes located in the northeastern German Uckermark.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2: Detected species in sediment and water samples of different kettle hole types based on 15 sediment resp. 75 water samples (data for 2019 only).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3: NMDS ordination plot (stress score: 0.20) of water samples from all 91 samples from the 2019 campaign. Permanency shown in the legend. Ephemeral kettle holes are shown in green, permanent kettle holes are shown in blue.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4: Variance partitioning based on distance-based redundancy analysis (dbRDA) on A) Abundances (Bray-Curtis) and B) Incidences (Jaccard) dissimilarities among kettle holes. Environment contains for A) permanency, surrounding field crop and kettle hole size and B) permanency and crop. Distance is based on significant PCNMs (for A) PCNM1, PCNM2, PCNM6, and B) PCNM1, PCNM3, PCNM4). Asteriks indicate significant fractions: * ≤ 0.05, ** ≤ 0.01, *** ≤ 0.001.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 5: Correlation between pairwise geographic distances and Bray Curtis dissimilarities among the studied kettle holes (n = 24).
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6: Mantel test based on Gower’s distances (Surrounding field crop, permanency, hydroperiod, water temperature, number of neighbouring kettle holes, kettle hole size and average wind direction/speed) and community Bray-Curtis dissimilarities among the studied kettle holes (n = 24).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 7: Trends in species richness of individual kettle holes. A) permanent kettle holes, B) ephemeral kettle holes. Kettle holes that were dry in a given month are indicated by a bar below the x-axis.
 
 
 
 
 
 
 
 
 
 
 
 
Figure 8: Species richness (S) as a function of different measured pH-values (smooth best fit).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 9: A) Species richness, B) Shannon Index and C) Simpson Index of the different kettle hole types and sample origins. Dots indicate sample variation. Significance levels of pairwise comparisons via Wilcoxon test are indicated by brackets and Asterisks, non-significant comparisons are indicated by “NS”.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 10: NMDS ordination plot (stress score: 0.13) of sediment and water samples, separated in ephemeral and permanent kettle holes. Connection between the points indicate the same kettle hole.