Open Access
Issue
Knowl. Manag. Aquat. Ecosyst.
Number 418, 2017
Article Number 45
Number of page(s) 9
DOI https://doi.org/10.1051/kmae/2017036
Published online 26 September 2017

© E. Zębek and U. Szymańska, Published by EDP Sciences 2017

Licence Creative Commons
This is an Open Access article distributed under the terms of the Creative Commons Attribution License CC-BY-ND (http://creativecommons.org/licenses/by-nd/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. If you remix, transform, or build upon the material, you may not distribute the modified material.

1 Introduction

While biodiversity is mainly directed to nature protection areas under European Union guidelines, especially in Nature 2000 sites and reserves (Directive 92/43/EEC), ponds are very important because they contribute to increase of national aquatic biodiversity and catchment functions. Ponds also create links between existing aquatic habitats and provide ecosystem services such as nutrient interception, hydrological regulation and natural scenery enrichment (Carvalho et al., 1995; Biggs et al., 2005; Lombardo, 2005; Gligora et al., 2007; Céréghino et al., 2008a,b, 2014). Pond surface water quality is largely dependent on different land use in agriculture and village life and consequent catchment environments. These small water bodies differ greatly in origin and in morphological conditions such as surface area, depth and volume, and natural and anthrophogenically altered ponds experience different precipitation, insolation, water temperature and catchment nutrient inflow. This great variability therefore contributes to forming different habitats and increasing organism species diversity (Wiliams et al., 2008).

Phytoplankton with different environmental requirements profit from changes in water physicochemical parameters and their assemblages therefore differ. While phytoplankton are critical to the pond food chain, providing food for the multiplicity of animals, fish and invertebrates, they occasionally form blooms (Celewicz-Gołdyn et al., 2008). Phillips et al. (2008) and Teissier et al. (2012) also consider that nutrients are the main determinants of phytoplankton biomass levels in lakes and ponds.

The hypothesis asserts that pond catchment character affects the physicochemical water parameters and subsequent development and differentiation of phytoplankton assemblages. The purpose of this study was to determine the differences in phytoplankton abundance, biomass, structure, species diversity and environmental requirements of dominant species related to the physicochemical water parameters in village, mid-meadow, mid-field and mid-forest pond catchment areas.

2 Materials and methods

2.1 Study area

The phytoplankton study concentrated on ponds; small water bodies located in the Jonkowo village adjacent to Warmińskie Buczyny Nature 2000 sites and Kamienna Góra reserve in the Warmia Mazury Region of North-East Poland. The study area has 8 ponds with different catchment character: 2 in each of the village (VIP), mid-meadow (MEP), mid-forest (FRP) and mid-field (FLP) environments (Fig. 1).

  • VIPs: The 3000 and 400 m2 Węgajty village ponds have 1.5 m maximum depth and the urbanized catchment areas (100%) close to a major road promote polluted inflow to the ponds. The pond banks are overgrown by macrophytes; dominated by Typha latifolia L. One pond is connected to fish ponds, and while one bank of the second pond has underground outflow with a surplus of spring waters flowing towards the village centre, the remaining banks are clay and sandy and the bottom of the pond is muddy.

  • MEPs: The 80 and 50 m2 mid-meadow ponds are natural small water bodies with maximum 0.5 and 0.8 m depths, have muddy bottoms and are surrounded by meadows (68%) and pastures (32%). They contain the following macrophytes: Myriophyllum spicatum L., Acorus calamus L., Iris pseudacorus L., Spirodela polyrhiza (L.) Schleid. and Lemna minor L.

  • FRPs: The mid-forest ponds have natural character with 2000 m2 and 1.5 ha areas at 1.5 and 1 m maximum depth, respectively. The first pond is based on peat and surrounded by coniferous forest, while the second lies in mixed forest near the Buczyny Warmińskie Nature 2000 sites. These ponds have 100% forest catchment areas. They contain the Ricciocarpos natans L. macrophyte characteristic of static acidic waters and peat pits, and also Spirodela polyrhiza (L.) Schleid., Lemna minor L., Calla palustris L., Iris pseudacorus L. and Menyanthes trifolia L.

  • FLPs: The two mid-field ponds are small water bodies with 7 and 200 m2 area and maximum 0.5 m depth. They have approximately 70% agricultural and 30% pasture catchment areas and form water-holes for cattle and horses. The first pond is overgrown by Typha latifolia L., Eriophorum vaginatum L., Lemna minor L., Lemna trisulca L. and Polygonum amphibium L., and the second by Acorus calamus L., Phragmites australis (Cav.) Trin. Ex Steud, Spirodela polyrhiza (L.) Schleid. and Lemna minor L.

thumbnail Fig. 1

Map of pond localization: 1, 9 – village (VLP), 4, 5 – mid-meadow (MEP), 6, 7 – mid-forest (FRP) and 8, 10 – mid-fields (FLP) ponds, N-E Poland. Black bar is distance reference of 1 km.

2.2 Materials and methods

Phytoplankton samples were collected at the same time in three seasons (April, July and October) in 2010 at 8 ponds of different catchments: village (VIP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds. The samples were taken from euphotic zone of the surface open water with a 10 L calibrated bucket (20 L at each site), sieved through a plankton net no. 25 and preserved with a Lugol’s solution and then with a 4% formaldehyde solution. In total, 24 phytoplankton samples were tested. The following physicochemical properties were measured directly at the phytoplankton sampling sites: water temperature with 0.1 °C precision and oxygen content exact to 0.01 mg O2 L−1 (HI 9143 oxygen meter), and pH and electrolytic conductivity at 1–1500 μS cm−1 (CONMET 1).

The following groups of phytoplankton, were analyzed in this study; cyanobacteria, diatoms, chlorophytes, dinoflagellates, chrysophytes, and cryptomonads. Qualitative and quantitative determinations of phytoplankton were performed with an Alphaphot YS2 optical microscope at magnifications of 100×, 200×, 400× and 1000×. Numbers in 1 mL samples of phytoplankton were determined in 5000 fields of vision with 200× magnification in each planktonic chamber to account for differences in organism densities and their abundance and biomass expressed in identical basic 1 mL volumes. Diatoms were prepared following the standard method in Battarbee (1979). Algal biomass for 10 individuals was calculated by comparing algae with their geometric shape (Rott, 1981). The scope of water analysis in the laboratory included: orthophosphates P-PO4, TN and N-NH4 concentrations using Spectroquant Merck tests with NOVA 400 spectrophotometer.

2.3 Statistical analysis

In the analysis, means were applied as the average values of phytoplankton abundance or biomass from the three months (April, July and October) separately in each from 8 studied ponds in 2010. The mean values of water physicochemical properties were calculated in the same way. The standard deviations were also calculated. In addition, the chi2-square test was used for comparing the differences in phytoplankton community structure. The species diversity for phytoplankton abundance was analyzed to calculate the Shannon–Wiener index (Shannon and Weaver, 1949; Maurer and McGill, 2011). The modified t-test (Hutcheson, 1970) was used to statistically comparison of the species diversity.

Total algal abundance and biomass, and abundance of taxonomic groups and dominant species were correlated with physical and chemical water parameters using non-parametric methods because these data are not normally distributed (test Shapiro-Wilk, STATISTICA version 10). To reduce the number of variables a forward selection procedure using the Monte Carlo test with 999 permutations. Relationships were confirmed by calculating Spearman’s rank correlation coefficient (significance at p < 0.05) with STATISTICA version 10. A canonical correspondence analysis CCA was performed to relate water chemistry variables to phytoplankton abundance and biomass, phytoplankton groups and dominant species. Finally, these relationships were presented on a biplots graph using Canoco for Windows 4.5 software.

3 Results

3.1 Physicochemical water parameters

The study recorded the following differences in the physicochemical properties (Tab. 1):

  • the lowest water temperature and oxygenation, and the highest conductivity (466 μS cm−1) and orthophosphates (2.22 mg PO4 L−1) in mid-meadow ponds (MEP);

  • the lowest N-NH4 and TN, and the highest water temperature of 18.8 °C and pH of 8.29 in village ponds (VLP);

  • the highest oxygen content of 7.35 mg O2 L−1 and the lowest conductivity of in mid-forest ponds (FRP);

  • the lowest pH and P-PO4, and the highest N-NH4 (0.22 mg L−1) and TN (1.5 mg L−1) in mid-field ponds (FLP).

Table 1

Physicochemical water parameters (mean ± standard deviations) in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

3.2 Differentiation of phytoplankton abundance and biomass

The lowest mean abundance and biomass was found in MEP (2672 ind. mL−1 and 0.02 mg mL−1) and the highest abundance in VLP (17 166 ind. mL−1) and biomass in FRP (0.16 mg mL−1) (Fig. 2).

The chi2-square tests exhibited significance level at 0.001 indicating significantly differences between community structure of phytoplankton abundance (174.1, p < 0.0001) and biomass (170.71, p < 0.0001). Phytoplankton abundance was dominated by chlorophytes in VLP and FLP (78.9% and 55.0%, respectively) and diatoms in the remaining ponds, MEP – 55.4% and FRP – 45.6%, while dinoflagellates in MEP and euglenins in FRP reached a significant proportion of total phytoplankton abundance. Cyanobacteria had the low 4.3% abundance in FLP. In the case of phytoplankton biomass, chlorophyte share amounted to 85.3% in FRP and diatom share amounted to 78.9% in MEP. Other algal groups contributed in less than 10.0% of the phytoplankton biomass (Fig. 3).

thumbnail Fig. 2

Mean abundance and biomass (M), and standard deviations (SD) of phytoplankton in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

thumbnail Fig. 3

Structure of phytoplankton in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

3.3 Phytoplankton diversity and dominant species

The highest Shannon–Wiener species diversity index was recorded for phytoplankton in FLP at 4.86 bit ind.−1 and the lowest in VLP at 2.49 bit ind.−1 at 60 and 96 taxa, respectively (Tab. 2). The t-tests were significant at 0.001 indicating differences between VLP and MEP, VLP and FRP, VLP and FLP, MEP and FRP but not between MEP and FRP (p = 0.6250, Tab. 3).

The Euclidean diagram demonstrated that the greatest similarity in species composition is shown by the smallest distance between algae in FLP and FRP, and the least similarity was between algae in FLP and VLP (Fig. 4). CCA analysis also highlights the greatest similarity between phytoplankton in MEP and FLP and the most difference in FRP.

These phytoplankton assemblages were dominated by chlorophytes and diatoms (Fig. 3). Cyanobacteria were represented by Aphanizomenon gracile and chlorophytes by the genera Chlamydomonas sp. in all ponds, and Spirogyra sp. (FRP), Closterium cynthia (FRP, FLP), Closterium echrenbergii (MEP, FLP), Pediastrum duplex and Monoraphidium concortum (VLP), Ulothrix tenuissima (MEP) and Pediastrum boryanum species (FLP). Diatoms were represented by Pinnularia sp. and Fragilaria capucina (MEP) and Diatoma vulgaris var. linearis (FRP) and Diatoma vulgaris (VLP, FLP), euglenins by Euglena viridis and Euglena acus, cryptomonads by Cryptomonas sp. and dinoflagellates by Peridinium sp. genera.

Table 2

Species diversity Shannon–Wiener’s indices based for the logarithms of phytoplankton abundance in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

Table 3

Comparison species diversity expressed as Shannon–Wiener’s between of phytoplankton in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010. The values of the t-test, corresponding to F statistic value and associated p-values (p) are displayed for each comparison.

thumbnail Fig. 4

Euclidean diagram of species phytoplankton similarity in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

3.4 Relationships between phytoplankton and physicochemical water parameters

The Monte Carlo test revealed that the correlation between phytoplankton abundance and biomass, phytoplankton groups and dominant species and physicochemical properties from the canonical correspondence analysis was significant both for Axis 1 and Axis 2 (eigenvalue 0.032 and 0.022, 0.295 and 0.185, and 0.801 and 0.628, respectively). CCA analysis shows that N-NH4 and TN most commonly promoted algal growth in MEP and FLP, P-PO4 in FRP (Fig. 5). Chlorophytes were mainly correlated with temperature and P-PO4 in FLP, cyanobacteria and euglenins with O2 in VLP, while dinophytes with pH in FRP, and dinophytes and diatoms with TN in VLP and MEP (Fig. 6). The data in Figure 7 show correlation between phytoplankton species and physicochemical variables confirming these tendencies. Similarly, chlorophyte and cyanobacterial species correlated with temperature, P-PO4 and pH in MEP and FLP and diatoms with O2 and N-NH4 in FLP, FRP, VIP, and with TN in MEP. The following correlations were determined between phytoplankton species and physicochemical water parameters at p < 0.05:

  • Positive correlations: Diatom species Amphora veneta correlated with oxygen content (r = 0.46), Diatoma vulgaris, Cocconeis placentula, Fragilaria ulna, Navicula gregaria, Pinularia sp. and Nitzschia palea with N-NH4 (r = 0.47, r = 0.48, r = 0.56, r = 0.54, r = 0.62 and r = 0.51, respectively) and the following correlated with pH; Amphora veneta at r = 0.49 and Fragilaria delicatissima at r = 0.55 and euglenin species Euglena acus at r = 0.46.

  • Negative correlations

    • diatom species: Diatoma vulgaris and C. placentula correlated with conductivity (r = −0.73 and r = −0.54, respectively);

    • diatoms: F. delicatissima and F. ulna (r = −0.56 and r = −0.47, respectively), and the genera Trachelomonas sp. (r = −0.49) and Cryptomonas sp. (r = −0.56) correlated with P-PO4;

    • chlorophytes: the genus Spirogyra sp. and species Pediastrum boryanum correlated with N-NH4 (r = −0.53 and r = −0.47, respectively);

    • genus Trachelomonas sp. correlated with water temperature (r = −0.50).

thumbnail Fig. 5

CCA analysis physicochemical water parameters forming phytoplankton growth (AA – algal abundance, BA – algal biomass) in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

thumbnail Fig. 6

CCA analysis between phytoplankton taxonomic groups (Cyan – cyanobacteria, Diat – diatoms, Chlor – chlorophytes, Dinoph – dinophytes, Eugl – euglenins, Chrys – chrysophytes, Crypt – cryptomonads) and physicochemical water parameters in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

thumbnail Fig. 7

CCA analysis between dominant species of taxonomic phytoplankton groups (Agr – Aphanizomenon gracile, Aug – Aulacoseira granulata, Aven – Amphora veneta, Chl – Chlamydomonas spp., Cplac – Cocconeis placentula, Cast – Coleastrum astroideum, Cryp – Cryptomonas sp., Dvul – Diatoma vulgaris, Eac – Euglena acus, Evir – Euglena viridis, Fcap – Fragilaria capucina, Fdel – Fragilaria delicatissima, Ful – Fragilaria ulna, Gom – Gomphonema spp., Mpus – Micratinium pusillum, Mcon – Monoraphidium concortum, Ngreg – Navicula gregaria, Ntrip – Navicula tripunctata, Npal – Nitzschia palea, Pin – Pinnularia sp., Pbor – Pediastrum boryanum, Pdup – Pediastrum duplex, Phac – Phacus sp., Plsp – Planctoccoccus sphaerocystiformis, Scom – Scenedesmus communis, Sgr – Staurastrum gracile, Spir – Spirogyra sp., Trach – Trachelomonas sp.) and physicochemical water parameters (cond – conductivity) in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

4 Discussion

The limited water depth and volume and type of catchment area ensured great variability in the environmental conditions of the studied ponds. Moreover, the biotic and abiotic parameters can be very seasonally unstable (Joniak et al., 2006). These especially related to the density and quality of fish and invertebrate communities and physical factors including temperature and available nutrients. In this study, high conductivity and orthophosphates recorded in mid-meadow ponds (MEP) and the highest N-NH4 and TN in mid-field ponds (FLP) agree with similar investigation into field ponds by Kocarkova et al. (2004) and Celewicz-Gołdyn et al. (2008). This is related to land use practices in the ponds’ vicinity affecting the catchments by nutrient loading and fertilizer and pesticide contamination (Declerck et al., 2006). Moreover, intensified agriculture has far more influence at pond catchment scale than in larger river and lake catchment areas (Céréghino et al., 2008a). Similar to Owsianny and Gąbka’s (2006) and Celewicz-Gołdyn et al. (2008) results in dystrophic small water bodies, the highest oxygen content and the lowest conductivity were recorded in mid-forest ponds (FRP).

While some authors reported clear associations between algal assemblages and a variety of environmental factors such as surface area, catchment character and nutrient loading (Boix et al., 2008; Céréghino et al., 2008b; Gascon et al., 2008; Oertli et al., 2008), this study determined differences in pond algal abundance and biomass based on catchment area type. Although Kocarkova et al. (2004) found a low abundance of algae communities in mid-forest ponds and higher algal densities in mid-field ponds due to high nutrient concentrations, despite high P-PO4 and N-NH4. In this study, the lowest mean abundance and biomass was found in MEP, and the highest abundance in VLP and biomass in FRP at the highest water temperature and pH, and oxygen content, respectively.

CCA analysis illustrated that high N-NH4 in MEP and FLP and low P-PO4 in FRP determined phytoplankton growth. These water parameters influenced particular phytoplankton group growth. Similar to Celewicz-Gołdyn et al. (2008) findings, high percentages of chlorophytes and diatoms composed the taxonomical structure in the studied ponds. This was especially noticeable in the positive relationships of high N-NH4 with the highest proportion of chlorophytes in total FLP phytoplankton abundance and high oxygenation with diatoms in the FRP. In addition, nutrient-rich MEP and FLP waters enhanced dinoflagellates growth and high water temperature stimulated cryptomonad development in the VIP. Moreover, low percentage share of cyanobacteria in the study ponds suggests that nutrient availability (especially P) was probably insufficient to trigger cyanobacteria blooms (Downing et al., 2001).

Della Bella et al. (2008) reported that high phytoplankton species richness is characteristic of small water bodies, and this was especially evident in nutrient-rich FLP waters. Moreover, the lowest diversity (Shannon–Wiener index) in VLP is most likely related to high chlorophyte domination; especially by genera Chlamydomonas spp. In addition to differences in species diversity, there was also differentiation in algal species composition. The greatest similarity in species composition is shown between algae composition in FLP and FRP, and the least similarity was between algae in FLP and VLP. CCA analysis also confirmed similarity between phytoplankton in MEP and FLP, while the most distinctive were in FRP. Phytoplankton in the studied ponds was characteristic for small water bodies, with chlorophyte and diatom domination accompanied by cyanobacteria and euglenin presence (Kuczyńska-Kippen and Nagengast, 2006; Napiórkowska-Krzebietke et al., 2011; Asha Nair et al., 2015).

This study results agree with the findings in agricultural ponds reported by various authors (Sahin, 2000; Kuczyńska-Kippen and Nagengast, 2006; Asaduzzaman et al., 2010; Napiórkowska-Krzebietke et al., 2011), where the diatoms Diatoma, Fragilaria, Pinularia, Melosira, Navicula, Nitzschia, Pediastrum and Scenedesmus dominated together with Euglena and Phacus. Diatom genera Pinnularia sp., Diatoma vulgaris and Fragilaria capucina dominated MEP and FLP ponds with chlorophytes Closterium echrenbergii, Ulothrix tenuissima and Pediastrum boryanum. Euglenins Euglena viridis and Euglena acus, genus Phacus sp. and Trachelomonas sp. were present in these and other pond types with Peridinium sp. dinoflagellates and Aphanizomenon gracile cyanobacteria. Dinoflagellates were often noted in mesotrophic waters and cyanobacteria in eutrophic waters, with euglenophytes common and abundant in the natural shallow water bodies and cryptomonads ubiquitous (Hašler et al., 2008). In addition, the presence of Euglena, Phacus, and dinoflagellates in some ponds suggests a high availability of dissolved organic matter (DOM), as these algae can complement photosynthesis with organic matter uptake from the water column (Taylor and Pollingher, 1987). Moreover, the greatest abundance of the genus Chlamydomonas in nutrient-rich water was registered in VLP, accompanied by Monoraphidium concortum and Diatoma vulgaris and Cryptomonas sp. The eutrophic environments especially contained large unicellular elongate Closterium sp., the filamentous Mougeotia sp., Spirogyra sp. and Chlamydomonas sp. flagellates (Naselli-Flores and Barone, 2000Hašler et al., 2008; Asha Nair et al., 2015). In addition, Closterium cynthia, Pediastrum duplex and Spirogyra sp. chlorophytes and Diatoma vulgaris var. linearis dominated in FRP; corresponding with Owsianny and Gąbka (2006) mid-forest pond results.

Differentiation between diatoms, chlorophytes and others algal groups is closely allied to the availability of their preferred physicochemical parameters in the particular pond-types. This further explains the environmental differences determined by CCA analysis for the correlations between diatom dominant species and oxygen (Amphora veneta), N-NH4 (Diatoma vulgaris, Cocconeis placentula, Fragilaria ulna, Navicula gregaria, Pinularia sp. Nitzschia palea); and pH (Apmhora veneta, Fragilaria delicatissima). Asaduzzaman et al. (2010) also reported that Diatoma, Fragilaria, Pinularia, Nitzschia often correlated with nitrogenous compounds; especially in agricultural ponds. In addition, this study highlighted low water temperature favoured Trachelomonas sp. growth, chlorophyte Spirogyra sp. and Pediastrum boryanum abundance increased at low N-NH4 and Euglena acus at high pH.

Phytoplankton-nutrient relationships are widely used by lake managers to asses eutrophication and set nutrient targets (Teissier et al., 2012), and phytoplankton can remove phosphorous and nitrogen pollutants from ponds (Céréghino et al., 2014). This is related with the following nutrient uptake resulting in negative correlations between algal species and N-NH4 (Spirogyra sp., Pediastrum boryanum), conductivity (D. vulgaris, C. placentula) and P-PO4 (F. delicatissima, F. ulna, Trachelomonas sp. and Cryptomonas sp.).

5 Conclusion

Their catchment area type and small surface area and volume make these ponds susceptible to accelerated eutrophication from their particular environments, and phytoplankton type is an evident coefficient of this phenomenon. Anthrophogenic activity also exerts impact through agricultural field fertilization and village proximity. Our results confirm these phenomena, where the highest phytoplankton abundance was recorded in the nutrient-rich village ponds. Moreover, phytoplankton assemblages were characterized by high biodiversity, differentiated structure and differences in environmental requirements for dominant species. The studied assemblages have great biodiversity and their consequent ecological role warrants special protection. Moreover, our results highlight important future directions for conservation of these ponds and the protection of their biodiversity; especially for those water bodies located in close proximitylized to the natural protected areas of Nature 2000 sites.

Acknowledgement

This study was supported by the Grant No. 00092/10/62011/OP-PO/D of the WFOŚiGW in Olsztyn, Poland.

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Cite this article as: Zębek E, Szymańska U. 2017. Abundance, biomass and community structure of pond phytoplankton related to the catchment characteristics. Knowl. Manag. Aquat. Ecosyst., 418, 45.

All Tables

Table 1

Physicochemical water parameters (mean ± standard deviations) in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

Table 2

Species diversity Shannon–Wiener’s indices based for the logarithms of phytoplankton abundance in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

Table 3

Comparison species diversity expressed as Shannon–Wiener’s between of phytoplankton in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010. The values of the t-test, corresponding to F statistic value and associated p-values (p) are displayed for each comparison.

All Figures

thumbnail Fig. 1

Map of pond localization: 1, 9 – village (VLP), 4, 5 – mid-meadow (MEP), 6, 7 – mid-forest (FRP) and 8, 10 – mid-fields (FLP) ponds, N-E Poland. Black bar is distance reference of 1 km.

In the text
thumbnail Fig. 2

Mean abundance and biomass (M), and standard deviations (SD) of phytoplankton in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

In the text
thumbnail Fig. 3

Structure of phytoplankton in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

In the text
thumbnail Fig. 4

Euclidean diagram of species phytoplankton similarity in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

In the text
thumbnail Fig. 5

CCA analysis physicochemical water parameters forming phytoplankton growth (AA – algal abundance, BA – algal biomass) in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

In the text
thumbnail Fig. 6

CCA analysis between phytoplankton taxonomic groups (Cyan – cyanobacteria, Diat – diatoms, Chlor – chlorophytes, Dinoph – dinophytes, Eugl – euglenins, Chrys – chrysophytes, Crypt – cryptomonads) and physicochemical water parameters in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

In the text
thumbnail Fig. 7

CCA analysis between dominant species of taxonomic phytoplankton groups (Agr – Aphanizomenon gracile, Aug – Aulacoseira granulata, Aven – Amphora veneta, Chl – Chlamydomonas spp., Cplac – Cocconeis placentula, Cast – Coleastrum astroideum, Cryp – Cryptomonas sp., Dvul – Diatoma vulgaris, Eac – Euglena acus, Evir – Euglena viridis, Fcap – Fragilaria capucina, Fdel – Fragilaria delicatissima, Ful – Fragilaria ulna, Gom – Gomphonema spp., Mpus – Micratinium pusillum, Mcon – Monoraphidium concortum, Ngreg – Navicula gregaria, Ntrip – Navicula tripunctata, Npal – Nitzschia palea, Pin – Pinnularia sp., Pbor – Pediastrum boryanum, Pdup – Pediastrum duplex, Phac – Phacus sp., Plsp – Planctoccoccus sphaerocystiformis, Scom – Scenedesmus communis, Sgr – Staurastrum gracile, Spir – Spirogyra sp., Trach – Trachelomonas sp.) and physicochemical water parameters (cond – conductivity) in village (VLP), mid-meadow (MEP), mid-forest (FRP) and mid-fields (FLP) ponds in 2010.

In the text

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