How much information do we gain from multiple-year sampling in natural pond research?

Journal Pre-proof How much information do we gain from multiple-year sampling in natural pond research? ¨ Patzig Marlene, Thomas Kalettka, Gabriela Onandia, Dagmar Balla, Gunnar Lischeid

PII:

S0075-9511(19)30174-4

DOI:

https://doi.org/10.1016/j.limno.2019.125728

Reference:

LIMNO 125728

To appear in:

Limnologica

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16 August 2019

Revised Date:

15 October 2019

Accepted Date:

16 October 2019

¨ Please cite this article as: Patzig M, Thomas K, Gabriela O, Dagmar B, Gunnar L, How much information do we gain from multiple-year sampling in natural pond research?, Limnologica (2019), doi: https://doi.org/10.1016/j.limno.2019.125728

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How much information do we gain from multiple-year sampling in natural pond research?

Pätzig, Marlene1,4, Thomas Kalettka1, Gabriela Onandia1,2, Dagmar Balla1, Gunnar Lischeid1,2,3

Working group Small Water Bodies in Agricultural Landscapes, Research Platform “Data”, Leibniz

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Centre for Agricultural Landscape Research ZALF e.V., Eberswalder Str. 84, 15374 Müncheberg, Germany 2

Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Altensteinstr. 34, 14195

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Berlin, Germany 3

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University of Potsdam, Karl-Liebknecht-Str. 24-25, 14476 Potsdam, Germany

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Corresponding author: [email protected] , T: +49 33432 82 470, F: +49 33432 82 301

Abstract

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Natural ponds are perceived as spatially and temporally highly variable ecosystems. This perception is in contrast to the often-applied sampling design with high spatial but low temporal

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replication. Based on a data set covering a period of six years and 20 permanently to periodically inundated ponds, we investigated whether this widely applied sampling design is sufficient to identify

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differences between single ponds or single years with regard to water quality and macrophyte community composition as measures of ecosystem integrity.

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In our study, the factor “pond”, which describes differences between individual ponds, explained 56 % and 63 %, respectively, of the variance in water quality and macrophyte composition. In contrast, the factor “year” that refers to changes between individual years, contributed less to understand the observed variability in water quality and macrophyte composition (10% and 7% respectively, of the variance explained). The low explanation of variance for “year” and the low yearto-year correlation for the single water quality parameter or macrophyte coverage values, respectively, indicated high but non-consistent temporal variability affecting individual pond patterns. 1

In general, the results largely supported the ability of the widely applied sampling strategy with about one sampling date per year to capture differences in water quality and macrophyte community composition between ponds. Hence, future research can be rest upon sampling designs that give more weight to the number of ponds than the number of years in dependence on the research question and the available resources. Nonetheless, pond research would miss a substantial amount of information (7 to 10 % of the variance explained), when the sampling would generally be restricted to one year. Moreover, we expect that the importance of multiple-year sampling will likely increase in periods and regions of higher hydrological variability compared to the average hydrological conditions

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encountered in the studied period.

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Keywords: water quality, macrophytes, space, time, kettle holes, conservation

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1. Introduction

Natural ponds, further referred to as ponds, are shallow ecosystems that range from a few

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square metres up to several hectares (<10 ha), and are perceived as highly spatially and temporally variable ecosystems (EPCN 2008). Their spatial and temporal variability results mainly from

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pronounced water level fluctuations, including drying and rewetting events, and their heterogeneous geomorphological features (e.g., Bauder, 2005; Kalettka & Rudat, 2006). Consequently, ponds can

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represent hotspots of biodiversity and biogeochemistry in the terrestrial landscape matrix, as is the case of the glacially created ponds in the agricultural Pleistocene landscapes of Europe and North

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America (Euliss et al., 2004; Pätzig et al., 2012; Reverey et al., 2018; Onandia, et al. 2018). Owing to the large number of ponds on the global scale, they are considered to significantly contribute to global nutrient cycling and freshwater biodiversity (Biddanda, 2017; Cheng and Basu, 2017; Downing, 2010; Downing et al., 2008; Kalinkat et al., 2017). Valorisation of the ecosystem services they provide, assessments of actual threats, or planning of measures to ensure their long-term integrity requires a sound understanding of the spatial and temporal variability of their water quality and biota.

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Ponds have been subject to numerous studies with regard to biodiversity and biogeochemistry, especially in relation to their pronounced hydrological dynamics, often against the background of climate change effects (e.g., Altenfelder et al., 2016; Reverey et al., 2018; Rocarpin et al., 2016). Several studies provided evidence for their high inter- and intra-annual variability in terms of the hydrological regime, water quality, macrophyte and macroinvertebrate community composition (Boix et al., 2016; Jeffries, 2008; Lischeid et al., 2018; Rhazi et al., 2009; Schneider and Frost, 1996; Serrano and Zunzunegui, 2008). Macrophyte community composition (further referred to as macrophyte composition) was found to be mainly determined by the timing, length and frequency of

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the hydroperiod (Kneitel, 2014; Schriever and Williams, 2013). Water quality is also indirectly

influenced by the hydrological dynamics of ponds through the evaporative enrichment of solute concentrations and by differing sources of water entering into ponds, such as surface runoff or

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discharging groundwater (Goldyn et al., 2015; Nitzsche et al., 2017; Sahuquillo et al., 2012). Water

quality and macrophyte composition are both established indicators for the ecological state of aquatic

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ecosystems (European Commission, 2000). While water quality parameters are often measured to monitor relatively rapid changes in trophic state and concentrations of water pollutants, macrophytes

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are viewed as suitable long-term indicators for ecosystem health due to their general slow response to environmental changes (Hilt et al., 2018; Schaumburg et al., 2004). Yet, there are no universal

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guidelines to assess properly the quality condition of small and shallow aquatic ecosystems. Methods for selected groups of organisms only exist for certain areas (e.g. Oertli et al., 2005; Solimini et al.,

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2008), but there is a need to develop standardized holistic approaches for an international context. Recognizing ponds as highly dynamic ecosystems contradicts the common sampling design

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that features high spatial resolution but often considers just one-time sampling for macrophytes and one to a few samples per year for water quality (e.g., Angelibert et al., 2010; Oertli et al., 2005; Sager and Lachavanne, 2009). Pond studies that follow such an approach often neglect the high intra- and inter-annual variability of ponds for sampling capacity reasons, which potentially limits the interpretation of their results as pointed out by Serrano et al. (2017). Pond research that focuses solely on the explanation of the variability of local conditions (spatial patterns) is thus often plagued by the low explanatory power of the analysed variables. For example, this low explanatory power has been 3

found between land use, pond water quality and biota in ponds of the Pleistocene landscapes of temperate European lowlands (Joniak et al., 2017; Lischeid et al., 2018; Nitzsche et al., 2016; Serrano et al., 2017). The pond studies of Joniak et al. (2017) and Nitzsche et al. (2016) as well as the glacially created ponds of the Quillow region included in the study of Serrano et al. (2017) did not consider multiple sampling times for ponds or for the surrounding land cover, implicitly restricting the findings to steady-state conditions. Given the well-known strong intra- and inter-annual variability in single ponds (e.g., Bennion and Smith, 2000; Jeffries, 2008; Lischeid et al., 2018; Schneider and Frost, 1996), the question arises whether the widely applied annual or seasonal sampling of ponds (i.e., one

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or a few times in a year) is actually sufficient to identify differences between single ponds with regard to water quality and macrophytes as measures for ecosystem integrity. This results in the compelling necessity to systematically quantify the spatial and temporal variability of pond water quality and

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macrophyte composition (Biggs et al., 2017).

Against this background, we systematically analysed spatial and temporal variations in water

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quality and macrophyte composition of 20 ponds located in the young moraine landscape of northeastern Germany. Water quality and macrophyte composition were assessed once a year during the

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climax of the macrophyte growing season at least four times between 1994 and 1999. Thus, the data are limited with respect to the intra-annual resolution. Such a database does not allow assessing intra-

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annual variability in pond water quality and macrophyte composition, but it permits analyses of the variability across more than three years (i.e. typical time span of many research projects). With four to

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six years of information about water quality and macrophytes, we focused on the question of whether multiple-year sampling (i.e. longer than three years) is able to explain the observed patterns in pond

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water quality and macrophytes. Our first objective was to test whether the variability in water quality and macrophytes composition among ponds sampled in the same year (spatial variability) is larger than the pond inter-annual variability. Second, we verified how much the sampling design, and in particular the number of ponds and years sampled, affects the explained amount of variance in water quality and macrophytes composition. Last, we examined the inter-annual variability of single water quality parameters and coverage of dominant macrophyte species separately to analyse their interannual stability. The outcome was used to verify the general outcomes of the two multivariate data 4

sets, i.e. water quality and macrophyte composition. The results of this study are intended to guide future research directions and to improve current sampling and monitoring designs, both crucial prerequisites for the understanding of pond ecosystem functions and the subsequent development of conservation and management strategies.

2. Data and methods 2.1. Selection of study sites This study explored one data set of water quality and one of macrophyte composition

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collected in 20 ponds (<2 ha) between 1994 and 1999. Sampling was performed once a year at the

climax of the macrophyte growing season, between the end of July and the end of August. Information about macrophytes at the climax of the growing season is considered an integrated measure of the

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biogeochemical state of the pond (European Commission, 2000). Macrophyte composition and water quality were simultaneously sampled in at least four years at each study pond. The ponds were located

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in three regions in the intensively cultivated agricultural young moraine landscape of the state of Brandenburg, Germany (Fig. 1 & Appendix). The young moraine landscape, with different interglacial

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stages, is characterized by a hilly to hummocky topography with a patchwork of internal or closed catchments that contain glacially created small, shallow ponds, also known as “kettle holes” in Europe

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or “potholes” in North America (Tiner, 2003; Kalettka and Rudat, 2006). Bölkendorf is the northernmost region of the study area and is characterized by a moderately

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rolling to hilly topography. Müncheberg and Lietzen are situated 50 to 60 km further south and have a mixed (moderately rolling to hilly) and a very hilly topography, respectively (Pätzig et al., 2012). All

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ponds considered were surrounded by arable fields or long-term fallows. The mean annual climatic water balance in the study period from 1994 to 1999 was negative (-33 mm), with 636 ± 119 mm of precipitation (corrected, after Richter, 1995) and a potential evapotranspiration of 672 ± 45 mm. In comparison, the climatic water balance over the long term (1991-2017) was -77 mm with a mean precipitation of 640 mm and 718 mm average potential evapotranspiration (ZALF weather station at Müncheberg, Germany, ZALF refers to Leibniz Centre for Agricultural Landscape Research).

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Although the climatic water balance during the study period was less negative than the long-term mean, the evapotranspiration during summer months regularly led to desiccation of the ponds. According to the hydrogeomorphological classification of Kalettka & Rudat (2006), the 20 ponds selected covered the range of areal sizes from small (≤0.1 ha) to very large (>1 ha) with a mean area of 0.47 ha and the range of depths from wadeable (≤1.6 m) to deep (>4 m) with a mean depth of 2.8 m (Tab. 1). With respect to the water regime, ponds were classified as either ponds that exhibit occasional overflow of the top shore (overflow type, N=8) or ponds that have no overflow but store their water in the hollow (storage type, N=12). Independent of their overflow tendency, all 20 ponds

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were either permanently water inundated (permanent, N=6), dry every few years (semi-permanent,

N=10) or with a short drying period starting in autumn (periodic, N=4). The selection did not include ponds with a usually long dry period starting in early summer (episodic), which is a common feature

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in the region (Pätzig et al., 2012). The distribution of the different hydrogeomorphological types

reflects the geomorphological characteristics of each study region, with more “Big Storage” ponds in

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Lietzen, rather “Big Overflow” ponds in Bölkendorf, and predominantly “Small” ponds in

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Müncheberg (sensu Kalettka & Rudat, 2006)(Tab. 1).

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2.2. Water quality parameters We considered 9 physicochemical water quality parameters with 92 to 93 observations each (Tab. 2 & Appendix). Electric conductivity (EC), oxygen concentration (O2), pH, and water temperature (Temp) were measured once in situ using electronic devices (WTW, Weilheim, Germany). For the remaining five hydrochemical compounds one water sample was collected from each pond and stored at 4°C. Water samples were analysed within 48 hours after sampling using standard methods, including ion chromatography for chloride and sulphate and photometric

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measurement of (ammonium) nitrogen and soluble reactive phosphorous, respectively. Total

phosphorus was analysed as soluble reactive phosphorus after potassium peroxodisulphate digestion, according to German standards DIN (see also Lischeid and Kalettka, 2012). The range of summer

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values obtained for each water quality parameter was in the range of previously published data for

ponds in north-eastern Germany (Goldyn et al., 2015; Lischeid et al., 2018; Pätzig et al., 2012). The

2.3. Macrophyte data

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water quality data used in this study are open accessible under DOI 10.4228/ZALF.DK.136.

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Macrophyte species and coverage (i.e., aggregated coverage of vascular plants, Characeae and filamentous algae) growing in the inundated area of the kettle hole were mapped, either by accessing

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the ponds with chest waders or with a rake from a boat. A total of 25 macrophyte species or taxa were recorded comprising three plant life forms: hydrophytes (17), amphiphytes (5) and helophytes (3).

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Chara globularis Thuill 1799, filamentous green algae and Ranunculus subgenus Batrachium were classified as hydrophytes. The average macrophyte species number was three (min-max: 1-9) per pond and year. A detailed description of macrophyte species identification and classification of plant life forms is given in Pätzig et al. (2012). Macrophyte coverage was determined in 1 to 5% percentage steps and converted to the 14-part Londo scale (Londo, 1976) (Tab. 3). The coverage data are open accessible under DOI 10.4228/ZALF.DK.136.

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2.4. Preprocessing Prior to the statistical analyses, water quality data were checked for values below the detection limit and for correlation between variables. Substituting values below the detection limit with half of the detection limit might substantially distort the analysis if more than a few non-detects were present (Helsel, 2005; 2006). Since the share of values below the detection limit was below 10% for all 9 water quality variables (Tab. 2), all values below the detection limit (left-censored data) were substituted with one-half of the detection limit. For the four parameters ammonium, chloride, sulphate, and soluble reactive phosphorous, one out of 93 values was missing. We replaced each of them by the

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global mean of the respective parameter (Tab. 2). Inter-correlation was examined with a Pearson correlation matrix. None of the 9 variables exceeded the usual cut-off level of r = 0.95 for inter-

correlated variables (Anderson et al., 2008). Instead, the correlation between the parameters was

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mostly low to moderate with the highest correlation calculated between total phosphorous and soluble reactive phosphorous (R² = 0.74). Finally, all 9 water quality parameters were normalized (z-

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2.5. Statistical analysis

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transformed) before the statistical analysis.

All statistical analyses were carried out using the software R 3.5.1 (R Core Team, 2018). The

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variability of water quality and macrophyte composition between ponds and years was estimated with global two-way crossed permutational multivariate analyses of variance (PERMANOVAs) using the

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functions ‘adonis’ and ‘adonis2’ of the R package ‘vegan’ with 999 permutations for all analyses (Oksanen et al., 2018). First, we performed a PERMANOVA for the complete data set (N=93). Owing

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to the unbalanced design (Appendix), we applied the function ‘adonis2’ (Oksanen et al., 2018). The resemblance matrix was based on Euclidian distances for water quality variables and based on BrayCurtis dissimilarity for macrophytes. The significance of each factor was assessed, accounting for marginal effects to ensure that the results were not affected by the order of the factors (Oksanen et al., 2018). For all PERMANOVA and correlation analyses, the significance level was set to 0.05. The R squared values of the PERMANOVA result tables were used to compare the predictive power of the variability between ponds with the variability between years for the overall variance. 8

The results of the global PERMANOVA may be due to differences in the groups´ means (location) but also to differences in variance (multivariate dispersions) among groups or because of both aspects. Hence, additional information about multivariate dispersion provides a prerequisite for a clear interpretation of the results obtained by the PERMANOVA routine (Anderson et al., 2008). Thus, we tested for differences in multivariate dispersion of water quality and macrophyte composition between groups of the factors “pond” and “year” based on Euclidian distance or BrayCurtis dissimilarity, respectively, using the function ‘betadisper’ of the ‘vegan’ package incorporating ‘vegdist’. Differences in multivariate dispersion were calculated by averaging the distance of group

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members to the group centroid in multidimensional space. Group members are, for example, annual replicates of a single pond or all sampled ponds within a single year. The coordinates of each group

member (object) of the underlying principal coordinate analysis (PCoA) were graphically depicted in

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2-dimensional PCoA ordinations. PCoA is conceptually similar to a principal component analysis (PCA) but uses a (dis)similarity matrix that preserves distances. Therefore, PCoA can be clearly

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interpreted with closely located group members being similar and group members farther apart being more dissimilar. Hence, our PCoA shows the differences in location and dispersion between single

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ponds or single years, respectively (Oksanen et al., 2018).

Furthermore, we performed an uncertainty analysis to estimate the influence of different

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sampling designs with varying numbers of observations for the factors pond and year on the assigned variance. The complete data set (N=93) was unbalanced, with 20 ponds but only six years.

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Furthermore, the number of ponds available for each year differed between 13 and 18, while the number of years varied between four and six for each pond (Appendix). Therefore, we created three

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sub-data sets that represented three different variants of balances between the pond and year factors. The first variant reflected a case where more years than ponds were sampled (6x3); in the second variant, ponds and years were equally represented with five groups each (5x5); and in the third variant, the case of three years and six ponds was considered (3x6). Since we selected only sub-data sets without missing values, all three variants for the uncertainty analysis were balanced with regard to the number of groups per factor, meaning in each of the above selections all ponds shared the same years and all years the same ponds. We performed the PERMANOVA routine using the function ‘adonis’ 9

with 999 permutations for each available unique combination. For example, in the case of the 5x5 variant, one combination would be the years 1995 to 1999 and the ponds B10, B13, B53, L12 and M03, while another combination would be the same years and the ponds B10, B13, B53, L12 and M18 (Appendix). The first variant (6x3) was present only once in the complete data set, the 5x5 variant occurred in 22 unique combinations and the 6x3 variant in 570 unique combinations. The resulting Rsquared values of the 5x5 and 6x3 variant were compared with the single value for the complete data set. Finally, to examine the differences in the inter-annual stability of pond patterns among all 9

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water quality parameters, we calculated the Spearman correlation coefficient for all possible year-toyear combinations for each water quality parameter. Water quality parameters were sorted according to the median Spearman correlation coefficient. High correlation coefficients indicate inter-annual

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stability. Weak correlation coefficients indicate high inter-annual variability of the water quality

parameters, meaning that in some years, the respective water quality parameters exhibited similar

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concentration levels between ponds, and in some years, the water quality parameter concentration levels between ponds were not similar. To examine the inter-annual variability of different macrophyte

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species, the coverage was averaged across ponds for the six dominant species and one category pooling all other species. In accordance with the water quality parameters, we then calculated the

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3. Results

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Spearman correlation coefficient for all possible year-to-year combinations.

3.1. Results of multivariate analyses

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For the complete multidimensional data sets, we obtained significant differences between the 20 ponds (Df = 19, p < 0.05) and the six sampled years (Df = 5, p < 0.05) for the nine water quality parameters and for the macrophyte composition according to the PERMANOVA routine. The variance explained by differences between ponds clearly exceeded the variance explained by differences between years (Fig. 2). Differences between ponds described 63% of the variation in macrophyte composition, while only 7% of the variation could be attributed to inter-annual differences. Similarly, 56% of the variability in water quality was explained by differences between ponds while 10% was 10

explained by inter-annual changes (Fig. 2). The unexplained variance was relatively high and similar, with approximately 30% for both macrophyte composition and water quality data (Fig. 2). The test of multivariate homogeneity of dispersion revealed non-significant differences in water quality among ponds (pseudo-F = 1.7) and among years (pseudo-F = 1.2), indicating that significant differences in the PERMANOVA resulted from differences in group means (pseudo-Fpond = 5.9, pseudo-Fyear = 4.9). Accordingly, the dispersion of water quality was rather similar among ponds and years, while differences in group means occurred at least for some ponds and years (Fig. 3A & B). Generally, the dispersion of water quality for each of the years was quite large, indicating high

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variability among ponds within one particular year (Fig. 3B). That does not mean that one single pond was similar in water quality across the years; rather, it indicates non-consistent patterns among years, resulting in low explanatory power (low variance) for the year factor.

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In contrast to water quality, the dispersion of macrophyte composition was significantly different among the analysed ponds (pseudo-F = 2.3). Hence, pond macrophytes differed in dispersion and

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group means, but according to the higher pseudo-F of 7.7 of the PERMANOVA, the differences in location were stronger than those in dispersion (Fig. 3C). For the factor year, the dispersion of

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macrophyte composition was not significant (pseudo-F = 0.7), which would indicate significant differences in location. Nonetheless, the PCoA ordination showed neither differences in dispersion nor

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in location for macrophyte composition between years (Fig. 3D). In accordance with water quality, dispersion within one year was quite large (Fig. 3D) indicating that macrophyte composition between

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ponds was different within one year but not necessarily similar across years for one pond. Since the explanatory power of the year factor was slightly lower for macrophytes (explained variance 7%) than

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for water quality (explained variance 10%), the macrophyte composition of ponds seemed to be slightly less structured among years.

3.2. Results of uncertainty analysis For the complete but unbalanced data set, which included 30% fewer years than ponds (6 years x 20 ponds), the predictive power of the factor “pond” was considerably higher than that of the factor “year”. This finding was generally confirmed by the three balanced sub-data set variants for both 11

water quality and macrophyte composition data, except for one case (Fig. 4). The balanced variant with only half the number of years compared to the number of ponds (3 years x 6 ponds) showed, on average, nearly the same results as for the complete data set (Fig. 4). For this 3x6 variant, the factor “pond” was significantly different with regard to differences between individual ponds for all 570 unique combinations. In contrast, 16% of the unique combinations were not significant for the factor “year” with regard to differences between years compared to the complete data set. Likewise, the overall balanced variant with five ponds and five years (5x5) showed distinct differences in the variance explained by ponds and years, even though the differences were smaller for the complete data

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set and the 3x6 variant (Fig. 4). In this 5x5 variant, the factor “pond” again was significantly different for all 22 unique combinations, while “year” only differed in 50% of them. Generally, the ratio of

number of years to number of ponds influenced the explained variance by ponds or years, respectively.

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We found a clear sequence of increasing explained variance in the variability between ponds with a

decreasing ratio of number of years to number of ponds in the variants “6x3”, “5x5”, “3x6” and 6x20

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(“cp”) (Fig. 4).

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3.3. Inter-annual variations in water quality parameters and dominant macrophyte species The inter-annual stability of the local patterns in the 9 water quality parameters differed

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substantially within the monitored period (rs: 0.20 – 0.82) (Fig. 5). In general, most water quality parameters exhibited rather high variance in their correlation coefficients, indicating relatively similar

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spatial patterns of solute concentration among the ponds in some years but not in others (Fig. 5). At the extremes, chloride concentration and electric conductivity exhibited the highest correlation among

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different years, indicating higher temporal stability, while ammonium showed a very low correlation, indicating higher inter-annual variability. The inter-annual stability of plant coverage patterns averaged over the single ponds among the six

dominant and other macrophyte species also considerably differed within the monitored period (rs: 0.05 – 0.59) (Fig. 5). The median correlation coefficient varied independently from average species coverage (Fig. 5). For example, Ceratophyllum submersum L. and Lemna minor L. were the most dominant species in the flooded pond area, but the correlation coefficients were only 0.4 and 0.2, 12

respectively (Fig. 5). Compared to water quality parameters, the variance was large for all macrophyte species, and the correlation coefficients were generally lower, indicating higher inter-annual variability.

4. Discussion Despite a rapid increase in the amount of scientific literature on ponds, there are fundamental gaps in basic knowledge about the spatial and temporal variation patterns of biotic and abiotic compartments in natural ponds (Biggs et al., 2017). Pond research typically aims at increasing spatial

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replication because of the observed high spatial variability between ponds and due to logistical

constraints. There are a range of studies that included 50 and many more ponds sampled within one year, neglecting year-to-year changes that may have caused the observed non-significant or weak

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relationships between presumed implicit static variables (Joniak et al., 2017; Nitzsche et al., 2017). In contrast, research focusing on the inter-annual variability of ponds is rare, usually includes fewer

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ponds (≤30, e.g., Jeffries, 2008; Rhazi et al., 2009), and focuses on relatively short time spans (1 to 3 years, e.g., Angelibert et al., 2010; Oertli et al., 2005; Sager and Lachavanne, 2009). However, some

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recommend larger data sets to mitigate the effect of hydro-meteorological anomalies (Serrano et al., 2017). In this study, we aimed to contribute to the topic with a systematic analysis of the explanatory

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power of variability in water quality and macrophyte composition between ponds compared to variability between years in a pond-rich landscape of north-eastern Germany, where a series of pond

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studies have been performed (e.g., Lischeid et al., 2018; Onandia et al., 2018; Pätzig et al., 2012). Our study was based on a data set with 20 permanent to periodic ponds annually sampled in a period of

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relatively balanced hydrological conditions (six years from 1994 to 1999), reflecting the typical tradeoff between spatial and temporal replication in pond research. Therefore, we were not able to consider the role of other spatial and temporal scales, such as the high within-pond variability (e.g., Catalan et al., 2014; Joniak et al., 2007; Reverey et al., 2018) or the strong inherent seasonal dynamics (Kayler et al., 2018; Kazanjian et al., 2018; Lischeid et al., 2018) of biotic and abiotic pond elements in the analysis.

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4.1. Variability among individual ponds and non-consistent patterns across years The explanatory power of the factor “pond” was relatively high compared to the factor “year”. A short time period of four to six years was enough to explain more than 55% of the variability among ponds in terms of both water quality and macrophyte composition. Hence, despite the well-known pronounced temporal variability of the ponds, they appeared to be relatively well separable with this sampling frequency during a period of average hydrological conditions. Thus, our results largely support the typical sampling resolution with high spatial but low temporal replication for questions related to the scaling of findings in single ponds to the landscape scale, but inter-annual variability

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should not be neglected as discussed below.

The low explanatory power of inter-annual variability compared to variability among ponds and at the same time high inter-annual dispersion seemed to be a result of the non-consistent patterns of

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water quality parameters and macrophyte composition across the years. Furthermore, we assume that the low variance explained by the factor “year” for macrophyte composition can be partly explained

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by the focus of the study on the inundated area of the pond, which is less exposed to desiccation and rewetting effects than the pond shore, and therefore subject to less variations in macrophytes

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composition (Rhazi et al., 2009). In addition, because the hydrological conditions were relatively balanced during the study period and we did not include episodic ponds, our study considered only

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moderate water level fluctuations (Tab. 2). The analysed data sets lacked pronounced differences in the hydrological conditions across the years, which might have caused the lack of structured

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differences in water quality and macrophytes across the years. It is well documented that the behaviour of these ecosystems can change dramatically when the climatic conditions of consecutive years shift to

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extremes. For example, studies on glacially created ponds found large differences in water quality between subsequent years as a result of extremely different precipitation patterns (Goldyn et al., 2015). Similarly, sudden changes in water level within one year were shown to significantly shift the macrophyte community composition (Kuczyńska-Kippen and Nagengast, 2006). Longer time series are needed to account for the effects of more variable hydrological conditions or shifts between different states of water quality or dominance relationships of macrophytes.

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Generally, a single measurement per year cannot reflect the inherent seasonal dynamics of ponds. Given the described high variability within a year (e.g., Bennion and Smith, 2000; Lischeid et al., 2018; Schneider and Frost, 1996), we assume that it is highly unlikely that the ponds in this study were all at exactly the same stage at the same time of the year with regard to the natural temporal dynamics of water quality and seasonal succession in macrophyte composition. This could explain part of the unexplained variance of our data sets (approximately 30% at the pond level, see Fig. 4). The unexplained variance might be reduced by sampling frequencies that are adapted to the temporal dynamics and functioning of (single) ponds in addition to longer time series. A valuable contribution

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of future studies would be the analysis of a high temporal-resolution multi-annual water quality and

macrophyte data set of ponds to characterize the intra-annual variability in comparison with the interannual variability of single ponds. High-resolution monitoring can be achieved by using new

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technologies, such as unmanned aerial systems in combination with in situ nutrient monitoring

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techniques, to apply adaptive sampling (Anderson and Gaston, 2013; Blaen et al., 2016).

4.2. Sampling design affects the explained variance

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We demonstrated that the proportion of ponds to years in the sampling design affected the results of the statistical analyses (PERMANOVA) with a clear sequence of decreasing explained variance in

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the variability between ponds with an increasing share of years. However, the results of the balanced design (5x5) suggested that the stronger explanatory power of the factor “pond” in comparison to

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“year” was not solely an effect of the unbalanced design. Given that our study included only permanently to periodically water-inundated, but not episodically water-inundated ponds, we expect

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that the total variance would increase while the variance explained by the factor year might decrease if a higher number of ponds with higher hydrological variability, such as episodic ponds, were included in the analysis. On the other hand, if the analysed data covered periods with stronger differences in hydrological conditions than this study (Tab. 1), we would expect more systematic differences between the years and consequently a higher explanatory power of the factor “year”.

4.3. General outcomes of the multivariate data set vs. single parameter/species 15

The single water quality parameters and macrophyte species (single entities) exhibited distinct differences in their inter-annual stability. For water quality, the observed differences in temporal stability were in accordance with earlier studies in the region (Kleeberg et al., 2016; Lischeid and Kalettka, 2012; Lischeid et al., 2018; Reverey et al., 2016). While the larger group of parameters, especially redox-sensitive substances such as ammonium or phosphorous, reflect the internal changes between oxic and anoxic conditions as a result of dynamic biogeochemical processes, the smaller group consisting of chloride and electric conductivity mirrors the geogenic background through hydrological coupling to subsurface water (cf., Lischeid et al., 2018). Although the correlation

these observables with low chemical reactivity at this time scale.

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coefficients were highest for the latter group, the results indicate rather weak temporal stability for

Plant coverage of different macrophyte species showed a higher variability between years

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compared to those of single water quality parameters, as observed in the lower correlation coefficients calculated for the different pairs of years. Even dominant competitive generalists such as Lemna

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minor L. or Ceratophyllum submersum L. revealed low correlation coefficients, pointing to high fluctuations in their coverage from year to year. This indicates that the macrophyte occurrence in the

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studied ponds depends on several drivers whose role might vary among years. Besides externally induced fluctuation in macrophyte occurrence, for example through water level changes, nutrient and

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light availability or prevalent wind conditions, their occurrence seems to be strongly determined by the species life cycle, overwintering strategy, dispersal traits and the resultant competition among species

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(Chesson, 2000; Paolacci et al., 2018 and references therein). This high temporal instability highlights the importance of relying on multiple-year data sets when using macrophytes as long-term indicators

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for the assessment of pond ecosystem health (Schaumburg et al., 2004). More information about the instability of single observables may help to better explain the large

share of unexplained variance identified by the PERMANOVA analysis and the low explanatory power of the factor “year”. The rather low correlation coefficients and high variance for the single water quality parameters and macrophyte species indicate strong differences between years for single ponds. This was also shown in the multivariate data set with large dispersion across years for the water quality and macrophyte composition of one pond, which indicates large differences between years for 16

one pond. Since the explanatory power of the factor “year” was low, the results indicate non-consistent patterns due to uncorrelated relationships between the entities. The explanatory power of the factor “year” was even lower for macrophytes, which showed high temporal variability for each single species and thus seemed to be slightly less structured across the years compared to water quality. Hence, the information obtained from the single entities supports the results obtained from the multivariate data set. Moreover, the different degree of year-to-year variability of single entities suggests that including or omitting highly temporally variable water quality parameters or species in a multivariate analysis

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changes the variance explained by the factor “year”. Although the different temporal behaviour of single water quality parameters is well known, we want to highlight that generalizations of results

from any multivariate spatiotemporal analysis should be strongly linked to the parameters or species

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included. This is especially important for highly dynamic ecosystems such as ponds.

Overall, the combined results of the correlation and the multivariate analyses (low predictive

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power of the factor “year” but large dispersion within single years and single ponds) emphasize that

macrophyte composition.

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5. Conclusions

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ponds are dynamic ecosystems at an annual scale for the ecological indicators, i.e. water quality and

The following conclusions are derived from glacially created ponds located in the young moraine

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landscape of north-eastern Germany. Thus, they are likely valid for other ponds in the agricultural Pleistocene landscapes of Europe and North America but need to be proven for other pond landscapes.

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Our systematic analysis of the extent of inter-annual changes in pond water quality and macrophyte composition generally supported the usual sampling design with high spatial replication but low temporal resolution (often just one sampling in a particular year), in ponds ranging from permanent to periodic during an hydrologically stable period. This was even true for our complete, but unbalanced data set of 20 ponds sampled in four to six years, while an analysis based on a subset of our data set including more years than ponds did not show the same patterns. In our complete, unbalanced data set “year” did not appear to be a sufficient factor for explaining the variability of 17

water quality and macrophytes. The low variance explained by the factor “year”, and the high dispersion within one year in the multivariate data sets indicated non-consistent inter-annual patterns in water quality and macrophyte composition for the single ponds. This was supported by the low correlation of single water quality parameters and macrophyte species among years that likewise point to high inter-annual variability. Hence, pond research would miss a substantial amount of information (7 to 10 % of the variance explained), when the sampling would generally be restricted to one year. Depending on the research question and the available resources reliable results can be expected when using an unbalanced sampling design that gives more weight to the number of ponds than the number

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of years. Choosing the right balance between the number of ponds and years would enable a more effective classification of ponds by water quality and macrophyte composition, but the number of

years should preferentially exceed one. Only if multiple years are considered do the results of a study

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not substantially depend on the sampled year, whose selection is often constrained by organisational

reasons. Furthermore, we expect that the importance of multiple-year sampling will likely increase in

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periods and regions of higher hydrological variability compared to the studied period under conditions of moderate hydrological fluctuations. This is of particular relevance in areas with more variable

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rainfall patterns, such as the Mediterranean and subtropical regions. The patterns of water quality and macrophyte composition of episodic ponds, the investigation of the macrophyte composition for the

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entire pond area, and especially the effects of intra-annual variability in comparison to the effects of

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inter-annual variability of single ponds on explained variance need to be tested in further studies.

Declaration of interests

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests

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Acknowledgement This study would not have been possible without the diligent work of several researchers and technical staff. We greatly appreciate their efforts. We especially would like to thank Christian Lehr for his great statistical support and Dorith Henning for technical assistance. Furthermore, we would like to thank Frank Dreger, Ursula Karlowski, Michael Ristow, Michael Succow and Bärbel Zander for their help with plant ecology and taxonomy. This research did not receive any specific grant from

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funding agencies in the public, commercial, or not-for-profit sectors.

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Appendix Data availability for water chemistry and macrophyte coverage per pond and year, created with the function ‘ezdesign’ from the R package ‘ez’ (Lawrence, 2016). Letters indicate the study regions with

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B = Bölkendorf, L = Lietzen and M = Müncheberg.

References:

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Lawrence, M.A., 2016. Package ‘ez’ Version 4.4-0: Easy analysis and visualization of factorial

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experiments. (Avaiblabe from: https://CRAN.R-project.org/package=ez).

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Figure captions Figure 1: Location of the 20 ponds from three regions in the young moraine landscape of the state of

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Brandenburg, Germany and position of the ZALF weather station in Müncheberg.

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Figure 2: Variance [%] assigned to the variability between years (dark grey), the variability between ponds (grey) and the residuals (light grey) for water quality (WQ) and macrophyte composition (MP)

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of the flooded pond area (N = 20 each).

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Figure 3: PCoA ordination representing the differences in dispersion of water quality parameters (A, B) and macrophyte composition (C, D) data for ponds (A, C) and years (B, D). Each symbol represents a group, e.g., samples of a single pond or a single year, group labels indicate group

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centroids, and coloured minimum convex polygons show potential group overlaps.

Figure 4: Uncertainty analysis comparing the portion of explained variance by ponds and years between the unbalanced complete data set (“cp”) and the three different variants of balanced data sets

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for water quality and macrophyte composition. Coding for the balanced variants on the x-Axis:

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number of years x number of ponds.

Figure 5: Boxplots of Spearman correlation coefficients rs between all possible pairwise year

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combinations for each water quality parameter or dominant macrophyte species. Boxes represent the 25% (Q1) and 75% (Q3) percentiles, black line in the box = median, ends of vertical lines show the

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lowest and highest values within the range of Q1 - 1.5*(Q3-Q1) to Q3 + 1.5*(Q3-Q1). Numbers next

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to species represent average coverage in % across ponds and years.

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No Code

Regio n

HGMtype

Hydroperiod

WD

N

CA

KA

MD

SW

SS

1

B10

BS-SO

semi-permanent

125-146

4

7.4

0.30

2.5

19

12.5

2

B12

BS-S

permanent

193-198

2

6.0

0.22

1.9

35

2.9

3

B13

BS-SO

semi-permanent

112-138

4

3.4

0.31

2.8

21

7.5

0.61

2.5

21

5.1

B18

BS-SO

semi-permanent

162-191

2

7.8

5

B40

SS-S

periodic

54-93

4

0.7

0.06

2.6

44

4.3

6

B43

SS-S

periodic

12-76

4

1.4

0.08

2.6

20

6.0

7

B53

BS-SO

semi-permanent

31-74

5

13.5

0.31

2.0

18

5.5

8

B54

BD-S

permanent

197-239

3

20.8

1.90

7.7

18

27.1

9

L01

BS-S

semi-permanent

25-131

5

15.3

0.89

2.0

22

14.5

10

L09

BS-SO

permanent

6.7

0.71

2.0

10

13.0

1.27

2.0

10

12.0

0.23

4.0

31

9.2

Bölkendorf

4

BS-SO

permanent

92-101

2

12

L14

BD-S

semi-permanent

20-172

5

7.2

13

L16

SW-S

periodic

0-53

3

3.8

0.14

1.4

27

3.8

14

L17

BD-S

permanent

102-194

6

7.9

0.19

4.3

47

4.8

15

L18

BD-S

permanent

17.8

0.55

5.0

26

8.0

16

L22

BS-S

semi-permanent

3.9

0.12

2.0

26

5.0

17

M03

SS-SO

semi-permanent

4

0.9

0.12

1.9

31

5.2

5

2.6

0.10

1.7

22

7.8

4

7.0

0.16

1.6

23

4.5

3

11.1

1.07

4.2

29

8.0

M18

20

M27

SS-S

periodic

7-47

SW-SO

semi-permanent

68-95

BD-S

semi-permanent

146-227

-p

19

50-135

re

M12

Müncheberg

18

ro of

L12

4.3

Lietzen

11

Table 1: Hydrogeomorphological characteristics of the selected 20 kettle holes. HGM-type =

lP

hydrogeomorphological type with BD=big & deep, BS=big & shallow, SS=small & shallow, SW= small & wadeable, SO = shore overflow, S=storage (Kalettka & Rudat, 2006). WD = range of water

na

depths between 1994-1999, N = number of years with one water level measurement between end of July and end of August, CA = catchment area [ha], KA = kettle hole area at top shoreline [ha], MD =

Jo

ur

maximum depth [m], SW = shore width [m] and SS = shore slope [%].

32

Min 6.4 132

Max 9.8 1376

CI 95% 0.15 54

n 93 93

% DL

µS cm

Mean 7.7 555

Temperature

°C

18.9

13.0

24.2

0.5

93

0

Oxygen

mg l-1

4.9

0.05

16.7

0.8

93

1.1

mg l

-1

40.3

0.5

106.2

5.5

92

1.1

mg l

-1

0.29

0.005

3.96

0.13

92

7.5

mg l

-1

69.6

0.25

507.9

19.9

92

4.3

mg l

-1

0.427

0.0015

1.760

0.090

92

8.6

0.66

0.06

2.06

0.10

93

0

Parameter pH pH EC Conductivity

Unit

Temp O2 Cl NH4-N SO4 SRP TP

-1

Chloride Ammonium nitrogen Sulphate Soluble reactive phosphorous Total phosphorous

mg l-1

0 0

re

-p

values. % DL = Percentage of values below detection limit.

ro of

Table 2: Summary of the water quality parameters. CI 95% = 95% confidence interval. n = number of

Londo scale

Coverage

<1

1

35 - <45

40

1 - <3

2

45 - <55

50

3 - <5

4

55 - <65

60

5 - <10

7

65 - <75

70

10 - <15

12

75 - <85

80

na

20

85 - <95

90

30

95 - 100

97.5

Jo

25 - <35

ur

15 - <25

Londo scale

lP

Coverage

Table 3: Conversion of percent coverage values to 14-part Londo scale in percent (Londo, 1976)

33