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Article

Zooplankton Index for Shallow Lakes’ Assessment: Elaboration of a New Classification Method for Polish Lakes

by
Agnieszka Ochocka
Department of Freshwater Protection, Institute of Environmental Protection–National Research Institute, Słowicza 32, 02-170 Warsaw, Poland
Water 2024, 16(19), 2730; https://doi.org/10.3390/w16192730
Submission received: 4 September 2024 / Revised: 20 September 2024 / Accepted: 23 September 2024 / Published: 25 September 2024
(This article belongs to the Section Water Quality and Contamination)
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Abstract

:
Due to its fundamental position in the aquatic food chain linking primary producers (phytoplankton) to higher trophic levels (fish), zooplankton has a crucial influence on the structure and function of lakes. The scientific literature shows that zooplankton is an effective indicator of eutrophication. However, according to the requirements of the Water Framework Directive, zooplankton is still not included as one of the biological components for assessing the ecological status of lakes. In Poland, the zooplankton-based method (ZIPLAs) has been developed to assess the ecological status of deep stratified lakes. Shallow lakes function differently from deep lakes, and literature data show that the response of zooplankton indices to eutrophication parameters is much weaker than in deep lakes. This paper presents the Zooplankton Index for Shallow Lakes’ Assessment (ZISLA), a new method for assessing ecological status based on zooplankton community structure. The ZISLA includes the body size index of Daphnia cucullata (BSI), the percentage share of high trophy-indicating rotifer species (IHTROT), the number of rotifer species (NROT), and the Margalef index (D). The ZISLA shows a strong, significant correlation with total phosphorus and total nitrogen (Spearman’s coefficient (R = −0.77, R = −0.74; p < 0.0001) and slightly weaker with Secchi disk visibility (R = 0.72; p < 0.0001). The ZISLA index shows a statistically significant good/moderate distinction for all water quality parameters.

1. Introduction

Increasing eutrophication, particularly by human activities, remains a significant pressure on European surface waters [1,2]. The Water Framework Directive (WFD), implemented by the European Commission in 2000, introduced the requirement to assess the ecological status of water bodies using biological components as well as supporting physico-chemical and hydromorphological elements. The achievement of the WFD objective is closely linked to the prevention of eutrophication caused by excessive nutrient inputs. Lake ecosystems form a closed area where externally introduced organic matter and pollutants circulate through their biological food chains [3]. Therefore, assessing variations in the interactions between lake organisms within these chains can be used to represent the overall health of the lake ecosystem [4]. The position of zooplankton in the lake trophic web is crucial because, on the one hand, it is the main food for planktivorous fish as well as for juveniles of most fish species and, on the other hand, it controls the structure of phytoplankton and bacterial communities [5,6,7]. Therefore, the identification of changes in zooplankton community is important for the efficient management of lakes. Eutrophication significantly affects the composition and density of zooplankton species. One of the most significant consequences of eutrophication is an increase in the intensity of cyanobacteria blooms, which significantly reduce the amount and availability of edible food for zooplankton communities [8]. The results demonstrated by Jeppesen et al. [5] indicated that the number and biomass of zooplankton increase with eutrophication. However, the proportion of Daphnia spp. in the total biomass of cladocerans decreased, while the share of cyclopoids in the biomass of copepods increased. Filamentous cyanobacteria can reduce the food intake of daphnia by clogging their filtering apparatus [9]. In the presence of high levels of nutrients in the water, there are noticeable changes in the size structure of the zooplankton. The average size of individual organisms decreases and the community becomes dominated by small individuals with a high reproductive rates [10]. Haberman [11,12] showed that zooplankton species diversity decreases with increasing phosphorus concentration in water, while zooplankton biomass increases. Researchers have demonstrated for many years the usefulness of zooplankton as an indicator of trophic status associated with nutrient inputs [5,13,14,15,16,17,18,19]. The response of zooplankton to nutrient enrichment is straightforward and directly visible in changes in community structure. Among other factors, this can be an early warning signal of water eutrophication. Despite appeals and recommendations from many scientific authorities [5,15,20,21], zooplankton is still absent from the list of biological components used for the assessment of the ecological status of lakes. Some researchers have developed a zooplankton index for water quality assessment [22,23,24], but most of them are concerned with reservoirs. In Poland, the ZIPLAs index—the Zooplankton Index of Polish Lakes’ Assessment—has been developed for deep, stratified lakes [25].
Shallow lakes function differently than deep lakes and are characterized by different conditions than those found in stratified lakes. The availability of nutrients, light, and oxygen in lakes is strongly influenced by the type of mixing and the thermal structure of the lakes [26,27]. The process of permanent mixing ensures a constant alternation of light and dark conditions throughout the water column, resulting in increased heterotrophic activity and stimulation of primary production. [28]. Shallow lakes are particularly susceptible to eutrophication and are at high risk of internal loading due to the significant impact on water quality of biogenic substances accumulated in their sediments [29]. This impact is much greater than in deeper, stratified lakes [30]. In shallow, polymictic lakes, even low wind speeds can lead to significant resuspension of the lake bottom sediments [31], which is further amplified by boating activities (especially motor boats) [32]. In these lakes, internal phosphorus release from the sediments is often the primary source of phosphorus during the summer, significantly affecting both primary production and algal nutrient availability [33,34]. In addition, permanent mixing increases the availability of dissolved organic matter, allochthonous matter and bacteria, which can serve as a food base for zooplankton. Compared to stratified lakes, polymictic lakes tend to have higher zooplankton species diversity due to the rich food base and higher number of microhabitats and refuges against predators [35,36].
The current literature [15,37] shows that zooplankton and trophic state relationships vary between stratified and polymictic lakes. Correlations of zooplankton indices with eutrophication parameters were much weaker in shallow lakes than in deep lakes, indicating more complex relationships.
The main objective of this paper is to develop a zooplankton index to assess the ecological status of shallow polymictic lakes in accordance with the requirements of the WFD.

2. Material and Methods

2.1. Study Sites

Zooplankton samples were collected during the summer period (between the end of July and the beginning of August) in 2021–2022. During the summer period, the abiotic and biotic factors fluctuate very little compared to the spring and autumn periods, when frequent water mixing causes rapid changes in the ranges of temperature, pH, conductivity, and dissolved oxygen. The structure of the summer plankton community is mainly determined by the trophy, therefore many authors consider the summer zooplankton communities to be the most useful for assessing lake water quality [17,37]. According to the PEG model, which describes the seasonal succession of plankton communities, the summer is characterized by stable conditions where nutrient levels, particularly nitrogen and phosphorus, play a crucial role in determining the structure of the zooplankton community [38,39]. The studies were conducted in 27 shallow, polymictic, lowland (<200 m a.s.l.) lakes, situated in the province of the Eastern Baltic-Belarusian Lowlands, north-eastern Poland (Figure 1). According to the Polish typology of lakes [40], polymictic lakes are considered those where the vast majority of the bottom (more than 75%) lays within the permanently mixed epilimnion (the active bottom), and where the hypolimnion is not formed. The surface area of the studied lakes ranged from 53 to 680 ha, the mean depth ranged from 0.6 to 5.1 m, and the maximum depth ranged from 2.5 to 10.5 m. In lakes where the maximum depth exceeded 10 m, the mean depth was less than 5 m and the metalimnion, if present, did not exceed 15% of the total lake volume (Supplementary Table S1).
Sampling stations were usually located near the deepest point in each lake, although morphologically diverse lakes with large surface areas were sampled at several stations. In both cases, the sampling point was located far from vegetation and the littoral zone. In the case of two lakes, Łuknajno and Stromek, where the average depth was between 0.6 and 0.9 m, the zooplankton community was dominated by species typical of the littoral zone and ponds. Therefore, these lakes were not included in the further analysis. In total, 31 samples from 25 lakes were analysed.

2.2. Sampling Method and Laboratory Analysis

Water samples for zooplankton and chemical analyses were collected using a Limnos-type sampler with a capacity of 2.6 L. Samples were taken at 1 m intervals from the surface to the bottom and pooled. Zooplankton samples were concentrated by filtering through a 25 μm mesh size plankton net and then preserved in Lugol’s solution and 4% formalin. Water temperature and oxygen concentration were measured in the field using a multi-parametric probe YSI 650 MDS (Yellow Springs, OH, USA). Secchi disk visibility (SD) was measured at the place of sampling. Laboratory analyses included total phosphorus (TP) and total nitrogen (TN), which were determined using standard methods [41]. Total phosphorus and total Kjeldahl nitrogen were determined after mineralization of unfiltered water with concentrated sulphuric acid [42] followed by spectrophotometric analyses of resulting phosphates and ammonium ions, respectively. Soluble reactive phosphorus was determined with the standard molybdenum blue method [42] and nitrates with phenyldisulphonic acid [43]. Total nitrogen was a sum of total Kjeldahl nitrogen and nitrates concentration. Water for chlorophyll analyses was filtered through Whatman GF/F filters. The filters were extracted in 90% acetone [42], and the chlorophyll a concentration was determined using a spectrophotometric method adopted from Nusch [44].
Identification of Cladocera and copepods was performed using the identification key of Błędzki and Rybak [45], while Rotifera taxa were identified based on Bielańska-Grajner et al. [46]. Approximately 10–15 randomly selected individuals of each taxon were measured to determine the body length. All adult zooplankton species were identified to the lowest taxonomic level (species) where possible, while in the copepod group, larval stages (nauplii and copepodit) were distinguished. Crustacea biomass was estimated based on the size of 15 individuals of all taxa and calculated according to the formula proposed by Balushkina and Vinberg [47]. The standard biomass of Rotifera was determined from the individual body weights proposed by Ejsmont-Karabin [48]. To calculate the body size index, 408 individuals of Daphnia cucullata were measured. In order to identify sensitive zooplankton species in shallow lakes, the trophy of the studied lakes was determined based on TP, TN, chlorophyll a (Chl-a), and the visibility of the Secchi disc (SD), according to the formulas proposed by Carlson [49] and Kratzer and Brezonik [50].

2.3. Establishing Reference Conditions

The establishment of reference conditions is required for the development of a WFD-compliant ecological status assessment system. Following the guidelines for establishing reference conditions for inland surface waters recommended by the WFD and used in other studies, e.g., Birk et al. [51] and Lyche Solheim [52], the main option for establishing reference conditions is a spatial approach, “the best of existing”, using data on point and spatial sources of pollution in the catchment area.
The main problem with this approach in shallow lakes is the lack of reference sites in Poland, corresponding to a full range of the REFCOND guidelines [53] pressures, since unimpacted conditions for shallow lakes no longer exist. The Directive provides several options for establishing type-specific reference conditions. These may include specific disturbances, indicating that anthropogenic pressures are acceptable as long as there are no or only very minimal, ecological consequences. Therefore, sites subject to a greater anthropogenic disturbance can be used as reference points, provided the relevant biological quality parameters do not deviate from true biological reference conditions. The spatial database CORINE Land Cover 2018 [54] was used to analyse the land use of the catchment areas of the studied lakes in order to find reference lakes according to the criteria developed by Birk et al. [51] and Lyche Solheim [52]. The catchment areas, together with their land use, are presented in Supplementary Table S1. Due to the lack of such lakes, the reference conditions for the parameters used in the new zooplankton index were set at the 90th percentile of the value of each parameter distribution, as these index values decrease with increasing trophy. The reference value represents the reference point for determining the boundaries between ‘high’ and ‘good’ ecological status.

2.4. Testing Zooplankton Indices

Based on the extensive scientific literature [13,15,17,37,55,56,57], 31 possible candidate zooplankton indices, calibrated to the trophic conditions of the lake assessment, were selected. The values of these indices were calculated using the data from shallow lakes investigated in 2021–2022. Tested indices were subdivided into four groups based on the following characteristics: the composition and abundance of fauna, the diversity of the zooplankton community, the abundance of sensitive taxa, and functional metrics that address the ecological function of the taxa (see Table 1). The ZIPLAs index [25] was also applied to assess the ecological status of stratified lakes. Selected indices were tested against trophic parameters (TP, TN, SD, and chl-a) to determine their sensitivity to eutrophication pressure. Zooplankton indices most strongly correlated with proxies of eutrophication (Spearman rank correlation) were identified and selected to form a multimetric index. This approach allowed for the selection of indices that together provide a robust assessment of the impact of eutrophication, ensuring a more accurate assessment of environmental conditions.
Based on Hering et al. [58], the zooplankton metrics, which have different units, were standardised into Ecological Quality Ratios (EQRs) in ZISLA. These ratios range from 0 (indicating the worst condition) to 1 (indicating the best condition), and were calculated using the following equation:
For indices decreasing with increasing pressure:
E Q R = I n d e x   r e s u l t − L o w e r   A n c h o r U p p e r   a n c h o r − L o w e r   A n c h o r
For indices increasing with increasing pressure:
E Q R = 1 − I n d e x   r e s u l t − L o w e r   A n c h o r U p p e r   a n c h o r − L o w e r   A n c h o r
Values > 1 were set to 1, while values < 0 were set to 0.

2.5. Boundary Setting of Ecological Status Classes

The purpose of the newly developed zooplankton index is to classify water bodies into one of five status classes: high (H), good (G), moderate (M), poor (P), and bad (B). The delineation of these classes is based on predetermined boundaries, which are determined by the percentile approach applied to the zooplankton index values. Specifically, the boundary between the high and good classes (H/G) is set at the 25th percentile of the zooplankton index values observed in the reference lakes, following the recommendation by Hering et al. [58]. Subsequent class boundaries are set as a percentage of the H/G limit value:
  • the boundary between good and moderate classes (G/M) is set at 75%;
  • the boundary between moderate and poor classes (M/P) is set at 50%;
  • the boundary between poor and bad classes (P/B) is set at 25%.

2.6. Statistical Analysis

The eutrophication parameters and zooplankton indices did not follow a normal distribution, therefore non-parametric statistical tests were used for all analyses. The relationship between zooplankton indices and water quality measures was tested using Spearman’s rank correlation coefficient to select indices the most sensitive to eutrophication. The performance of ZISLA along nutrient gradients was also tested using the Spearman rank correlation test. The non-parametric Mann–Whitney U test was used to demonstrate the statistical differentiation of ZISLA between ecological classes. The p-value was set at a significance level of 0.1. The above statistical analyses were performed in STATISTICA 12.0 PL software [59].
Species discriminating between different trophic status classes were identified using the indicator value (IndVal), which is based on the species’ relative abundance compared to the relative frequency of occurrence of the species occurrence in each group [60], and Spearman’s rank correlation. IndVal were calculated using the labdsv package [61] in the R software (version 4.3.1) in the RStudio environment [62].

3. Results

3.1. Physicochemical Characteristics and Trophic Variables

In the studied lakes, the temperature ranged from 18.4 to 25.7 °C, while the dissolved oxygen ranged from 0.4 to 11.5 mg L−1. The total phosphorus varied from 0.034 to 0.337 mg L−1, the total nitrogen values ranged between 0.44 and 1.63 mg L−1, the chlorophyll a concentration varied in the range of 1.6 to 102.7 µg L−1, and the water transparency (SD) ranged from 0.5 to 4.2 m. Based on the Carlson trophic index, the studied lakes represent a wide trophic gradient, from meso- to hypertrophy. The values of temperature, dissolved oxygen, trophic parameters (chl-a, SD, TP, and TN), and trophic status of all investigated lakes are presented in Supplementary Table S1.

3.2. Reference Conditions

The total catchment areas of the analysed lakes varied from 3.5 to 804.5 km2. In 20 lakes, arable land predominated, covering more than 50% of the area. The percentage share of forests and seminatural areas ranged from 5.9% to 45%. Catchment area forests dominated only four lakes, and their contribution constituted more than 70% of the area. The waters of two of them, Piłwąg and Świętajno Łąckie, were highly eutrophicated and characterized by high concentrations of chlorophyll a, ranging from 30.3 to 80.5 μg L−1. Due to the lack of lakes located in the forest catchment with low water trophy, the spatially based method of selecting reference lakes using data from minimally disturbed sites (“best of existing”) could not be applied. Therefore, the reference values of the newly developed index were established as median values of the parameter distribution in lakes classified as the 90th percentile of the value of each component of the index.

3.3. The Structure of the Zooplankton Community

In all studied lakes, 80 species belonging to Rotifera and Crustacea were identified. The Rotifera were the most species-rich group with 57 species; 17 species belonged to the Cladocera, 4 to Cyclopoida, and the presence of 2 representatives of the Calanoid was confirmed. In the Rotifera community, the most frequent species, representing about or more than 10% of the community, were Trichocerca pusilla (9%), Anuraeopsis fissa (10%), Polyarthra major (11%), Keratella tecta (12%), and Keratella cochlearis (13%). Most of them are indicator species of high trophic waters. The most frequent species among crustaceans were Bosmina longirostris (10%), Mesocyclops leuckarti (11%), Diaphanosoma brachyurum (11%), Daphnia cucullata (20%), and Chydorus sphaericus (21%), which are indicators of high-trophic lakes. Based on microscopic observations, confirmed by the Indicator Value analysis (Table 2) and Spearman’s rank correlation between zooplankton species biomass and TP (Table 3), potentially indicative species for low- and high-trophy waters in shallow, polymictic lakes were identified.

3.4. Development of Multimetric Index

The four indices showing the strongest correlation to TP, TN, and SD were selected (Table 4). Due to different ranges of values and different reaction directions of these metrics, their values were normalized to EQRs, ranging from 0 to 1. The relationships between values of tested indices and TP concentration are shown in Figure 2.

3.4.1. NROT—Rotifera Numbers

The regression analysis for Rotifer numbers and TP showed a positive and highly significant relationship (Figure 2a, Table 4). The lowest abundance (135 ind. L−1) was found in Lake Mój, while the highest abundance accounted for 4184 ind. L−1 was found in Lake Symsar. Low numbers of Rotifera individuals, <200 ind. L−1, were also found in lakes Dowcień and Marksoby, which are characterized by mesotrophic waters. Meanwhile, the highest numbers of Rotifer, more than 2000 ind. L−1, were noticed in eutrophic and hypertrophic lakes—Blanki, Piłwąg, Rekąty, Tajno, Symsar, and Świętajno Łąckie. NROT values gradually increased with increasing TP concentration from 37 to 239 μg L−1.

3.4.2. IHTROT—Percentage Share of the Rotifer Species Indicating High Trophic State

The percentage share of rotifer species that increased with lake eutrophication—Anuraeopsis fissa, Brachionus spp, Filinia longiseta, Keratella tecta, Pompholyx sulcata, and Trichocerca pusilla—in the total number of Rotifera, indicating both high and low trophic states (i.e., Ascomorpha ecaudis, A. ovalis, and Gastropus stylifer) was the second very sensitive index of the trophic state of lakes (Figure 2b). Polyarthra major, known from the literature [15] to be characteristic of the low trophic state, was found in all sampled lakes, had a significant proportion in high trophic lakes, and was removed from the list of indicator species. Also, the abundance of Conochilus hippocrepis showed strong and positive correlations with TP concentration, and this species was excluded from the list of indicator species. The values of IHTROT increased rapidly from 0 to 100% with increasing TP concentration from 37 to 105 μg L−1, and then the curve flattened out.

3.4.3. BSI—The Body Size Index of Daphnia cucullata

Daphnia cucullata was found in all the studied lakes. The mean size of D. cucullata ranged from 484 to 865 μm (Figure 2c), while the body length of individuals ranged from 223 to 1145 μm. The values of BSI decreased with increasing TP concentration from 37 to 151 μg L−1, and then the curve flattened out.

3.4.4. D—Margalef Index

Margalef’s index values showed a consistent decrease (from 5.2 to 2.2) over the TP gradient (2d).

3.4.5. ZISLA—Zooplankton Index for Shallow Lakes’ Assessment

The normalised values of the selected metrics were integrated to obtain the multimetric index ZISLA (arithmetic mean),
ZISLA = NROT + BSI + IHTROT + D 4
The ZISLA showed strong correlations with all tested pressure parameters. The strongest and most negative correlation was observed with Chl-a (R = −0.81; p < 0.0001), and slightly weaker correlations with TP and TN (R = −0.77, R = −0.74; p < 0.0001). A positive and strong correlation was found between ZISLA and SD (R = 0.72; p < 0.0001). The relationships between the multimetric ZISLA and the eutrophication parameters (TP, TN, and SD) are presented in Figure 3.

3.5. ZISLA Index Ecological Status Class Boundaries

The analysis of the ZISLA values in the studied lakes focused on establishing thresholds for different ecological status categories. As a result, the boundary between the high and good (H/G) classes was set at 0.779. For the remaining classes, the range of ZISLA values from the H/G boundary was divided into four equal parts to establish their respective limits (Table 5).
Three lakes were classified as high ecological status, six as good, seven as moderate, five as poor, and four as bad, based on the developed ZISLA class boundaries. The distribution of ZISLA values among the ecological status classes differs significantly (Figure 4). For all water quality parameters, ZISLA differs between good and moderate classes (Figure 4a–c). In the poorer classes (moderate and poor), differentiation was found for TP and TN (Figure 4a,b). The distribution of ZISLA differs significantly between poor and bad classes for TP (Figure 4a) and SD (Figure 4c). However, for all eutrophication parameters (TP, TN, and SD), there was a clear overlap between good and high classes.

4. Discussion

Shallow, polymictic lakes differ significantly from deep lakes, so different components to those used in the ZIPLAs index were selected to create a multimetric index to assess ecological status. In developing the ZISLA index, the metrics that were most highly correlated with all the pressure parameters and that represented different characteristics of the zooplankton community were selected. Finally, four indices were selected to form the ZISLA multimetric index.
Increasing trophic pressure leads to changes in the zooplankton community: larger macro-filters disappear from the community due to poor feeding conditions, while micro-filters—rotifers and small cladocerans that feed mainly on bacteria and detritus—become dominant. Jeppesen et al. [63] pointed out that one of the most obvious effects of eutrophication is the replacement of large cladoceran species by smaller ones. The attainment of smaller body sizes with increasing trophy size by individuals within a given species is observed in the case of D. cucullata [55]. This species is considered to be one of the most abundant crustacean zooplankton species inhabiting the pelagic zone of temperate lakes [45,64] and occurs in a variety of trophic ranges, from oligo- to hypertrophy. Due to its transparent body, D. cucullata is often overlooked as a food source for visually foraging planktivorous fish compared to other Daphnia species [65]. Therefore, the body size of this particular Daphnia species may be a promising and valuable indicator of the ecological status of the lake, which is mainly dependent on the quality of the food base (bottom-up effect). The body size index (BSI) of Daphnia cucullata showed the strongest correlation with total phosphorus in this study. Previously, reductions in D. cucullata body length have been observed under more eutrophic conditions [55,66]. D. cucullata feeds by filtering on small suspended particles and bacteria. During eutrophication, the cyanobacteria that dominate the phytoplankton community foul their filtering apparatus [67], resulting in reduced filtering efficiency [68], which leads to a reduction in the ingestion rate [69]. This phenomenon leads to inefficient feeding of individuals and limits their growth.
The relationship between zooplankton structure and lake trophy varies between polymictic and dimictic lakes. Rotifera are more responsive to trophic changes in both lake types than Crustacea species [15,17]. Their small body size exempts them from the pressure of planktivorous fish, and their numbers and biomass are mainly regulated by bottom-up forces [70].
IHTROT was the second most sensitive index to changes in TP concentrations. One of the most commonly observed effects of nutrient enrichment is increased Rotifera abundance in eutrophic waters [15,17]. Ejsmont-Karabin [15] suggested several Rotifera species that thrive well in high trophic levels due to their food preferences (detritus, bacteria, and also small blue-green algae), making them indicative of such conditions: Anuraeopsis fissa, Brachionus angularis, Brachionus diversicornis, Filinia longiseta, Keratella tecta, K. quadrata, Pompholyx sulcata, and Trichocerca pusilla. The results of Indicator Value analysis and Spearman rank correlations between species abundance and TP concentration confirmed that these species are also indicative of eutrophic conditions in shallow lakes. Ejsmont-Karabin [15]. also pointed out indicative species of low trophic lakes: Ascomorpha ecaudis, A. ovalis, Conochilus hippocrepis, Gastropus stylifer, and Polyarthra major. According to statistical analysis, two of them, A. ovalis and Gastropus stylifer, were also indicative in shallow lakes. Surprisingly, Conochilus hippocrepis and Polyarthra major abundance showed strong and positive correlations with TP concentration. They were therefore excluded from the list of indicator species. These species are commonly found in low trophy lakes, and their high share in abundance and biomass is indicative of low water trophy. Their life strategy probably differs in shallow lakes compared to deep ones. This mechanism has been observed previously (Ejsmont-Karabin, pers. comm.), but to my best knowledge, has not yet been published in the scientific literature.
It is well widely recognized that the abundance of Rotifera and Crustacea is positively correlated with an increasing trophic level [15,17,70,71,72]. Shallow lakes are habitats for a wide variety of zooplankton [73] due to the coverage of macrophytes, which create favourable conditions for their coexistence [74,75], e.g., by providing a variety of habitat conditions, food sources [74], and daytime refuge from predators [76,77]. However, submerged macrophytes can provide a refuge for zooplankton and juvenile fish, which are their potential predators [78]. This is a particular threat to large-bodied zooplankton such as Daphnia. Rotifera are less affected by fish predation due to their small size. Therefore, they play a crucial role in shaping zooplankton communities in shallow lakes [73,74,79]. In addition, shallow lakes are rich in microinvertebrates, particularly Rotifera, which are associated with the macrophytes that are washed off them into the open water zone [80]. The results showed a positive correlation between Rotifera abundance (NROT; ind. L−1), TP concentration (R = 0.42; p < 0.01), and other eutrophication parameters (Table 4). Therefore, this index, which is easy to calculate, was chosen as a component of the multimetric index ZISLA. As mentioned above, Rotifera are on the one hand independent of fish pressure while at the same time feeding on a variety of food, including bacteria, phytoplankton, protozoa, organic detritus, and other rotifers, as well as small crustaceans. Therefore, the indicator based on their abundance provides information on the trophic conditions in the lakes.
The Margalef index (D), which quantifies the relationship between the number of species and the total number of individuals, shows a statistically significant relationship with the eutrophication proxies. This implies that a higher index value indicates a higher ecological status. Various environmental abiotic and biotic factors can influence zooplankton diversity in both shallow and deep lakes.
One of these is the effect of temperature on the metabolic rates, reproduction, and distribution of zooplankton species. Warmer temperatures can increase growth rates, but can also favour certain species over others [81]. Hydrological factors like water level fluctuations and residence time can affect habitat structure, connectivity, and nutrient distribution, which in turn affect zooplankton communities [82]. Fish and invertebrate predators strongly influence zooplankton communities by reducing zooplankton size diversity and altering community composition [83]. The presence of aquatic plants provides shelter and breeding grounds for zooplankton, influencing their diversity and abundance [36]. Kuczyńska-Kippen [74] noticed that a wide variety of Rotifera and Crustacea are often found in polymictic lakes. However, Sługocki and Czerniawski [79] observed that higher species diversity of rotifers occurs in shallow waters, while crustaceans, especially copepods, are more abundant in deep waters. Food supply is crucial in determining zooplankton biodiversity in shallow lakes, and phytoplankton is not the only food source, as mixing increases the availability of dissolved organic matter, allochthonous matter, and bacteria [84]. These factors were not considered in this work. However, they interact in complex ways to shape zooplankton biodiversity in shallow lakes. Because zooplankton respond quickly to changes in the environment, they can provide an early warning signal of deteriorating conditions, therefore zooplankton monitoring is important for the effective management and conservation of lakes. The newly developed zooplankton ZISLA index combines a few different aspects of the zooplankton community, allowing the ecological status of shallow lakes to be assessed more comprehensively than their individual physico-chemical parameters. Zooplankton sampling is straightforward and can usually be carried out at the same time as phytoplankton sampling, therefore it could be easy to incorporate into monitoring programmes. However, this method has some limitations and should not be applied to lakes where macrophytes fill the water column. Experience from this work shows that in such lakes the zooplankton community is dominated by species typical of the littoral zone and ponds. In these lakes, the main predictor of zooplankton diversity is macrophyte biomass, which depends on different macrophyte species [85].

5. Conclusions

Zooplankton is still not included in the legal acts concerning the monitoring programmes of lakes in Europe, and efforts by lake ecologists to include this element in the lake assessment system have been unsuccessful. The new zooplankton index, ZISLA, evaluates four aspects of zooplankton communities inhabiting shallow lakes, namely taxonomic composition and abundance, diversity of the zooplankton community, stressor-sensitive species, and the ecological function of taxa, which are combined into a multimetric index. The ZISLA responds to eutrophication pressures and therefore can provide a valuable tool for assessing the ecological status of shallow, polymictic lakes. Due to its simplicity and cost-effectiveness, this method could be relatively easy to implement in many European countries.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16192730/s1, Supplementary Table S1: The main parameters of investigated lakes.

Funding

This research was supported by the Internal Research Fund of the Institute of Environmental Protection-National Research Institute.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

Special thanks go to Karol Przeździecki and Sebastian Kutyła for their technical support in using statistical analysis.

Conflicts of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

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Figure 1. Distribution of the studied lakes in the province of the Eastern Baltic-Belarusian Lowlands (grey circles). The grey line shows the largest rivers in Poland. The numbers refer to the lake names: 1—Symsar, 2—Blanki, 3—Jełmuń, 4—Stromek, 5—Rańskie, 6—Walpusz, 7—Marksoby, 8—Świętajno Łąckie, 9—Kołowin, 10—Inulec, 11—Łuknajno, 12—Iławki, 13—Mój, 14—Kirsajty, 15—Pozezdrze, 16—Brożówka, 17—Łękuk, 18—Piłwąg, 19—Szwałk Mały, 20—Zawadzkie, 21—Rekąty, 22—Haleckie—Ołówka, 23—Tajno, 24—Rospuda Augustowska, 25—Tobołowo, 26—Dowcień, and 27—Gremzdel.
Figure 1. Distribution of the studied lakes in the province of the Eastern Baltic-Belarusian Lowlands (grey circles). The grey line shows the largest rivers in Poland. The numbers refer to the lake names: 1—Symsar, 2—Blanki, 3—Jełmuń, 4—Stromek, 5—Rańskie, 6—Walpusz, 7—Marksoby, 8—Świętajno Łąckie, 9—Kołowin, 10—Inulec, 11—Łuknajno, 12—Iławki, 13—Mój, 14—Kirsajty, 15—Pozezdrze, 16—Brożówka, 17—Łękuk, 18—Piłwąg, 19—Szwałk Mały, 20—Zawadzkie, 21—Rekąty, 22—Haleckie—Ołówka, 23—Tajno, 24—Rospuda Augustowska, 25—Tobołowo, 26—Dowcień, and 27—Gremzdel.
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Figure 2. Relationship between zooplankton indices selected to develop ZISLA multimetric: NROT (a), IHTROT (b), BSI (c), D (d), and total phosphorus concentrations (TP), lines represent the lowess smoothed models.
Figure 2. Relationship between zooplankton indices selected to develop ZISLA multimetric: NROT (a), IHTROT (b), BSI (c), D (d), and total phosphorus concentrations (TP), lines represent the lowess smoothed models.
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Figure 3. Relationships between ZISLA and TP, TN, and SD in studied lakes. The lines represent the distance weight least squares smoothing fitted model.
Figure 3. Relationships between ZISLA and TP, TN, and SD in studied lakes. The lines represent the distance weight least squares smoothing fitted model.
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Figure 4. Distribution of TP (a), TN (b), and SD (c) in lakes according to one of the five classes of ecological status based on the ZISLA index. Boxplots: 25–75th percentiles with median; whiskers: range; circles: outliers. The table shows level of confidence in comparison of distribution of nutrients between subsequent classes obtained in Mann–Whitney U test.
Figure 4. Distribution of TP (a), TN (b), and SD (c) in lakes according to one of the five classes of ecological status based on the ZISLA index. Boxplots: 25–75th percentiles with median; whiskers: range; circles: outliers. The table shows level of confidence in comparison of distribution of nutrients between subsequent classes obtained in Mann–Whitney U test.
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Table 1. Overview of zooplankton indices tested to develop ZISLA multimetric.
Table 1. Overview of zooplankton indices tested to develop ZISLA multimetric.
Index TypeAcronymDescriptionCrustacea/RotiferaReferences
Composition/abundance indexNCRUCrustacea numbers (ind./L)CrustaceaKarabin, 1985 [17]; Ejsmont-Karabin and Karabin, 2013 [37]
BCLBiomass of CladoceraCrustacea
BCYBiomass of CyclopoidaCrustacea
B CABiomass of CalanoidaCrustacea
BCRUBiomass of CrustaceaCrustacea
CBPercentage of cyclopoid biomass in total biomass of CrustaceaCrustacea
CY/CLRatio of Cyclopoida biomass to the biomass of CladoceraCrustacea
CL/CYRatio of Cladocera biomass to the biomass of CyclopoidaCrustacea
CA/CYRatio of Calanoida to Cyclopoida individual numbersCrustacea
CY/CARatio of Cyclopoida to Calanoida individual numbersCrustacea
B/NCRURatio of biomass to numbersCrustacea
ND/NCRURatio of Daphnia to Crustacea numbersCrustaceaOwn elaboration
CL/CopRatio of Cladocera to Copepoda (Cyclopoida + Calanoida) numbersCrustaceaAndronikova, 1996 [13]
NROTRotifera numbers (ind./L)RotiferaEjsmont-Karabin, 2012 [15]
BROTBiomass of RotiferaRotifera
B/NROTRatio of biomass to numbersRotifera
BMAMacrozooplankton biomassCrustacea/RotiferaKarabin, 1985 [17]
BMEMesozooplankton biomassCrustacea/Rotifera
BMIMicrozooplankton biomassCrustacea/Rotifera
NCRU/NROTRatio of Crustacea to Rotifera numbersCrustacea/RotiferaOwn elaboration
BCRU/BROTRatio of Crustacea to Rotifera biomassCrustacea/RotiferaAndronikova, 1996 [13]
NZOOZooplankton abundanceCrustacea/Rotifera
NspSpecies numberCrustacea/Rotifera
BZOOZooplankton biomassCrustacea/Rotifera
Sensitivity indexIHTCRUPercentage of species indicative of high trophy in the indicative group’s numbersCrustaceaKarabin, 1985 [17]; Ejsmont-Karabin and Karabin 2013 [37]
TECTAPercentage of Keratella tecta in the population of Keratella cochlearisRotiferaEjsmont-Karabin, 2012 [15]
IHTROTPercentage of species indicative of high trophy in the indicative group’s numberRotifera
Functional indexBSID. cucullata body lengthCrustaceaKarpowicz et al., 2020 [55]
BACPercentage of bacterivorous in total rotifer numbersRotiferaEjsmont-Karabin, 2012 [15]
Diversity indexdMargales indexCrustacea/RotiferaMargalef, 1958 [56]
H’Shannon–Weaver diversity indexCrustacea/RotiferaShannon and Weaver, 1963 [57]
Multimetric indexZIPLAsZooplankton Index for Polish Lakes’ AssessmentCrustacea/RotiferaOchocka, 2021 [25]
Table 2. Indicator Value (IndVal) of Crustacea and Rotifera species of the low vs. high-trophy lakes.
Table 2. Indicator Value (IndVal) of Crustacea and Rotifera species of the low vs. high-trophy lakes.
Species IndValp ValueLow/High Trophy
Keratella tecta0.9900.001high trophy
Mesocyclops leuckarti0.8080.005high trophy
Filinia longiseta0.6140.047high trophy
Brachionus angularis0.5900.012high trophy
Ceriodaphnia quadrangula0.6810.034low trophy
Gastropus stylifer0.6710.011low trophy
Ascomorpha ovalis0.5480.007low trophy
Bosmina (Eubosmina) coregoni0.3330.014low trophy
Table 3. Spearman’s rank correlation coefficients between the biomass of zooplankton species and TP (in order of statistical significance against TP).
Table 3. Spearman’s rank correlation coefficients between the biomass of zooplankton species and TP (in order of statistical significance against TP).
Taxon Namerp Value
Anuraeopsis fissa0.7300.011
Chydorus sphaericus0.3840.044
Pompholyx sulcata0.6570.000
Trichocerca pusilla0.6230.017
Table 4. Spearman’s rank correlation coefficients between proxies of eutrophication (TP—total phosphorus, TN—total nitrogen, SD—Secchi disc visibility) as well as chlorophyll a (Chl-a) and selected metrics.
Table 4. Spearman’s rank correlation coefficients between proxies of eutrophication (TP—total phosphorus, TN—total nitrogen, SD—Secchi disc visibility) as well as chlorophyll a (Chl-a) and selected metrics.
Index TypeAcronymCorrelations with Trophy Parameters
TPTNSDChl-a
rprprprp
Composition/abundance indexNROT0.420.0140.360.042−0.460.0090.490.004
Sensitivity indexIHTROT0.670.0000.470.006−0.700.0000.700.000
Functional indexBSI−0.690.000−0.440.0130.450.010−0.630.000
Diversity indexD−0.480.005−0.610.0000.560.001−0.460.008
Table 5. Boundary values of ZISLA for ecological status classes.
Table 5. Boundary values of ZISLA for ecological status classes.
Ecological StatusRange of ZISLA Values
High≥0.779
Good0.584–0.778
Moderate0.389–0.583
Poor0.196–0.388
Bad≤0.195
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Ochocka, A. Zooplankton Index for Shallow Lakes’ Assessment: Elaboration of a New Classification Method for Polish Lakes. Water 2024, 16, 2730. https://doi.org/10.3390/w16192730

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