Free Access
Issue
Ann. Limnol. - Int. J. Lim.
Volume 56, 2020
Article Number 4
Number of page(s) 18
DOI https://doi.org/10.1051/limn/2020001
Published online 24 March 2020

© EDP Sciences, 2020

1 Introduction

Lentic ecosystems are inland surface freshwater ecosystems (such as lakes, ponds, pools, puddles, swamps) which have a key role in providing resources and habitats to aquatic and terrestrial organisms. These ecosystems can be classified as permanent or temporary waterbodies, and the main abiotic factors that control their dynamics are bedrock type, morphology, basin soil type, vegetation, nutrients content, light availability, oxygen levels, temperature, and pH conditions (e.g. Kamenik et al., 2001; Brönmark and Hansson, 2002). However, biotic interactions are important processes affecting the species diversity and composition, and therefore the ecosystems' dynamic and functioning. These factors may operate at different spatial and temporal scales (Brönmark and Hansson, 2002; Hoverman and Johnson, 2012).

Alpine ponds are an example of this situation. These water bodies are often generated by glacial processes, as the movement of glaciers erodes the loose soil and/or the bedrock, creating depressions which are later filled with water from precipitation or melted ice (Bengtsson, 2012). These ponds are typically located at high altitudes, above the local treeline (Tolotti et al., 2006). The prevailing environmental conditions affecting high altitude ponds include low rock weathering, poor soil development, highly diluted waters, thinner atmosphere (leading to higher UV radiation during the ice-free period) and light limited conditions during the winter season due to snow and ice accumulation (Catalan et al., 2006). At higher altitudes, air temperature is lower, and precipitation is higher, resulting in short growth periods, high water renewal rates and poor buffering capacity. Therefore, mountain ecosystems are very sensitive to external changes, even to small ones. On the other hand, anthropogenic pressure such as inadequate forest management, uncontrolled tourism, and acidic deposition and road de-icing are another group of factors that promote significant alterations on the ecosystem functioning (Céréghino et al., 2008; Oertli et al., 2008; Espinha Marques et al., 2019).

Species that occur in this type of habitat have adaptations that allow them to survive to such conditions. For instance, plant species have a great colonization ability, phytoplankton can be mixotrophic (i.e. both autotrophic and heterotrophic types of nutrition), may encyst when there are adverse conditions and zooplankton can enter diapause or overwinter as resting eggs (Bell, 2012). These traits make these species apt to fit these specific climatic conditions but the high degree of adaptation to alpine ecosystems can also make them very vulnerable to environmental changes. The rising temperature may lead to habitat loss and severe species ranges contractions, enhancing also biotic pressures, such as competition for resources (Bell, 2012). Alpine ponds are characterized by simple food webs and low resilience, which means that they are more likely to be negatively affected by small disturbances. Thus, alpine ponds are considered highly sensitive to environmental changes (Watson and Haeberli, 2004; Tolotti et al., 2006; Toro et al., 2006), therefore, are considered prime areas of environmental concern (Bell, 2012). The highly specific conditions of this environments, variable hydroperiods, remoteness, and stochastic events allow similar ponds to have distinct biological communities contributing to a higher regional diversity, making ponds important biodiversity hotspots (Williams et al., 2004).

It is known that phytoplankton assemblages alter rapidly in response to changes in water quality. These communities initially show quantitative changes and, later, qualitative changes in the community structure (Bouchard, 2005; Brettum and Andersen, 2005). Thus, phytoplankton is the only biological quality element for pond or lake classification, according to the Water Framework Directive, WFD (Directive 2000/60/CE), namely concerning their quantification in terms of biomass, abundance, and composition. In addition to biological criteria, hydromorphological elements, morphological conditions, and chemical and physical parameters are used to evaluate water quality and the ecological state of lakes and ponds (Directive 2000/60/CE). Likewise, zooplankton can be used as an indicator of trophic conditions of freshwater ecosystems (Caroni and Irvine, 2010; Jeppesen et al., 2011; Jensen et al., 2013). Yet, in the WFD metrics, this plankton community is not contemplated. Zooplankton is in a central position in food webs (primary consumer) and establishes the energy flow connection between primary producers (phytoplankton) and predators (planktivorous fish) (Jeppesen et al., 2011; Du et al., 2015), and is also important in nutrient cycling for microalgae (Jensen et al., 2013; Li et al., 2014). The main goal of WFD is to protect all water bodies. However, small water bodies such as pools and ponds remain unmonitored and usually are not considered in protection measures. The protection and management of alpine ponds are relevant for improving the implementation of the WFD as well of other EU water legislation and policies.

Lakes and ponds with long turnover time and low flow are waterbodies more susceptible to road salt contamination. The accumulation of salt in waterbody and in the groundwater supplies leads to an increase of biochemical oxygen demand, accelerated eutrophication, alteration of pH level and stratification of the waterbody (D'Itri, 1992). In addition to chemical changes, biological communities are affected by the increase of salt concentration and will potentially lead to a decrease in growth and reproductive output (Van Meter et al., 2011). Furthermore, the use of de-icing agents may present new conditions that exceed the species capacity for adaptation, surpassing their tolerances and thresholds (Van Meter et al., 2011). Several studies show that either phytoplankton and zooplankton are potentially vulnerable to de-icing salts (Dixit et al., 1999; Van Meter et al., 2011). However, the primary producers are more affected by these road salts than more complex organisms, and the chemical de-icers contamination in aquatic ecosystems is usually too low to be considered toxic for zooplankton (Judd et al., 2005; Langen et al., 2006).

The study site is located in Serra da Estrela (Central Portugal), and its territory corresponds to a protected area, a National Park, with an area of around 88,850 ha, from the Castelo Branco and Guarda districts. The Torre summit (1993 m a.s.l.) is the highest point of inland Portugal and is an important touristic point. Since the local economy is greatly dependant on tourism, mountain roads must remain free of snow and ice, especially after winter storms. For that reason, the local authorities carry out frequent de-icing operations, particularly above 1500 m a.s.l., producing changes on water, soil and on mountains ecosystems (Carvalho et al., 2012). As a result, ecosystems associated to water bodies located downstream from roads, namely, streams, aquifers and alpine ponds may experience hydrochemical changes which will probably affect the respective biota. The road de-icing agent typically used in Serra da Estrela is NaCl from rock salt, but CaCl2 is also applied after particularly intense winter storms, which result in the formation of greater amounts of ice (Rodrigues et al., 2010; Carvalho et al., 2012; Espinha Marques et al., 2019). The salt may reach streams, lakes and ponds, and may even infiltrate into groundwater, leading to a higher salt concentration that will probably affect the biota and water chemistry (Rodrigues et al., 2010).

This study intends to use a multidisciplinary approach to characterize the hydrology of five natural alpine ponds of Serra da Estrela, the water physical and chemical status, and biotic communities (phytoplankton and macrozooplankton), in order to assess the ecological status of these aquatic natural and extreme ecosystems.

2 Methods

2.1 Site description

Serra da Estrela (40° 15'-38'N; 7° 18'-47'W) is part of the Cordilheira Central, an ENE-WSW mountain range that crosses the Iberian Peninsula (Fig. 1). The regional climate has Mediterranean and Atlantic influence. The mean annual precipitation is around 2500 mm in the most elevated areas, the wet season ranges from October to May and the mean annual air temperature is below 7 °C in the highest part of the mountain, but may reach 4 °C at the Torre summit (Daveau et al., 1977; Miranda et al., 2006). The Köppen-Geiger climatic classification is Csb (warm temperate, with dry and warm summers), similarly to Northwestern Iberia (Kottek et al., 2006; Peel et al., 2007).

The Serra da Estrela hydrogeological units encompass sedimentary cover (mainly glacial, fluvioglacial and alluvial deposits), metasedimentary rocks and granitic rocks (e.g. Marques et al., 2013). Yet, at the study site only granitic rocks occur. The Serra da Estrela alpine ponds occur on depressions originated by glacial erosion during the Last Glacial Maximum (Daveau et al., 1997; Vieira, 2008).

This study was conducted in five shallow natural alpine ponds (numbered from P1 to P5) located in Serra da Estrela, in granitic terrain, with area ranging from 718 m2 to 18,672 m2 (Fig. 2 and Tab. 1). All ponds are located above 1700 m a.s.l. and present alpine conditions, freezing completely at mid-autumn, and the thaw occurs at the beginning of the spring season. All the ponds are permanent, except for P2 that was dry in two of the sampled months (Aug–Sep). There is a possible connection to groundwater flow in P2 and P5. These two ponds also have connection to the drainage network and have significant sediment infilling. The rest of the ponds do not present connections and have insignificant sediment infilling.

thumbnail Fig. 1

Serra da Estrela Natural Park geographical setting and location of the study area.

thumbnail Fig. 2

Drainage network of the studied ponds.

Table 1

Pond physical and morphometric features.

2.2 Sampling procedures

The study was conducted from June to November (2015) and water sampling was carried out monthly in all five ponds. In situ physical and chemical parameters (dissolved oxygen (mg/L and %), pH, conductivity (μS/cm), and temperature (°C)) were determined using a multiparametric probe. Zooplankton sampling was made by filtering 12 L of water collected sub-superficial through a 0.55 μm mesh in different habitats of each pond in order to make a compost sample that would represent all the pond's habitats, and then stored in ethanol at 96%. An additional water sample was collected on 1.5-L plastic bottles for later quantification of other physical, chemical and biological parameters (photosynthetic pigments and suspended solids), as well as for chemical analysis (nutrients and hardness). All bottles were immediately placed in the dark, at 4 °C and transported to the laboratory.

2.3 Laboratory procedures

2.3.1 Physical and chemical parameters

In the laboratory, water samples for chemical determinations were thawed and analyses were performed according to each parameter. Phosphorus (TP), nitrate (NO3), nitrites (NO2), ammonia (NH4+), turbidity (Turb), and water hardness quantifications followed the procedures described in APHA/AWWA/WEF (2012). Dissolved organic carbon in water samples was indirectly determined through the colour of water (CDOC) as described by Williamson et al. (1999). For the determination of suspended solids an aliquot of water from each pond was filtered through pre-weighed Whatman GF/C filters (1.2 µm porosity, 47 mm diameter). Total suspended solids were calculated after obtaining dry weights (dried at 60 °C for 24 h) and ash-free dry weights (ignited at 450 °C for 8 h) of the filtered residues (adapted from Lind, 1985; APHA et al., 1989).

For the determination of chlorophyll a concentration, an aliquot of water from each pond was filtered through a Whatman GF/C filter (1.2 µm porosity, 47 mm diameter). After this, filters were immersed in 90% acetone and stored in the dark at 4 °C for 24 h, to complete pigment extraction (Strickland and Parsons, 1972; Lind, 1985). After a short centrifugation, the absorbance of the extracts was read at 665 nm and 750 nm (in a Spectronic 20 Genesys spectrophotometer), before and after acidification with HCl. Chlorophyll a (Chl a) concentration was calculated according to Lorenzen's (1967) monochromatic equations. The Trophic State Index was obtained for each pond using the chlorophyll a content (Carlson, 1977).

2.4 Biological parameters

Phytoplankton quantification followed INAG's protocol (INAG, 2009a) and Lund et al. (1958) procedures. A sedimentation of 800 mL of water sample with 8 mL of lugol (1%, v/v) was conducted for 1 week. After this period, samples were decanted until a final volume of 2 mL was obtained. The identification was made by observation and quantification in a Neubauer camera through a light microscope to the genus level and was based on Bellinger and Sigee (2015) and Carter-Lund and Lund (1995). The enumeration was performed according to Lund et al. (1958) with at least 800 cells counted per Neubauer camera (each sample was recorded in 3 replicates).

Macrozooplankton quantification (Cladocera and Copepoda) was made by counting all adult individuals in each sample with a square grid. The identification was also taken to the genus level, based on Harding and Smith (1974), Amoros (1984), and Alonso (1996) guides.

2.5 Statistical analysis

Ecological data were analysed taking into account the phyto- and macrozooplankton communities (identification to genus level): diversity index (H'), richness (S), total abundance (N), relative abundance, evenness (J). The phytoplankton data was also used to assess the ecological quality ratio (EQR) as it is calculated for lakes and reservoirs (Van de Bund and Solimini, 2007). This ratio was calculated based on composition, abundance (% biovolume of cyanobacteria and IGA − Algae Group Index) and biomass ([Chlorophyll a and Total biovolume]) of phytoplankton community (INAG, 2009a). EQR represents the relation between the results obtained for the biological parameter used and the value expected for the same parameter in reference conditions of the waterbody. EQR use allows comparisons with ecological quality assessment methods applied in different countries and its values range between 0 (extreme degradation) and 1 (reference conditions).

To explore the relation between the planktonic communities and the spatial and environmental data, Detrended Correspondence Analysis (DCA) was performed. The gradient length of the first ordination axis was estimated to further decide what was the adequate ordination method for the multivariate analysis. For length gradients higher than 4 s.d., Canonical Correspondence Analysis (CCA) is recommended, while Redundancy Analysis (RDA) is more suited when the gradient length is less than 4 s.d. (ter Braak, 1995; ter Braak and Smilauer, 2002). According to this information, CCA (unimodal model) was considered the adequate multivariate analysis for the phytoplankton data (DCA axis length = 5.177). On the other hand, RDA (linear model) was chosen to analyse the zooplankton matrix once the length of gradient of the first ordination axis of the DCA was 1.645. After determination of the adequate ordination technique, Variance Inflation Factor (VIF) was calculated for all environmental variables in order to determine and exclude the redundant ones (VIF > 10, as suggested by Montgomery et al. (2012)). After this process, the CCA and RDA models were computed using the vegan package (Oksanen et al., 2019) in R-3.5.2 software (R Core Team, 2018) and graphed using the CANOCO 4.5® software. Permutations test were performed (999 permutations) to the CCA and RDA to assess the significance of the constrains (Legendre and Legendre, 1998) using the same package.

3 Results and discussion

The location of the study area in the Serra da Estrela Natural Park is shown in Figure 1. The studied ponds present different features, which result in distinct hydrological and ecological settings. Figure 2, along with Table 1, present the most relevant pond features: area, perimeter, length, width, elevation, connection to the local drainage network, connection to the groundwater flow, existence of sediment infilling and vegetation features. Regarding the surrounding vegetation, its main types according to Raunkiær life classification system (Raunkiaer, 1934) were hemicriptophytes (e.g. Festuca henriquesii Hack., an endemic species that occurred in all of the ponds), chamaeophytes, therophytes and phanerophytes. Although there was vegetation surrounding all ponds, P2 and P5 were the ones where plants where more abundant, with main species in common (Carex nigra (L.) Reichard, Juncus squarrosus L., Juniperus communis L., Nardus stricta L. and Potentilla erecta (L.) Raeusch).

P1, P3 and P4 do not have surface water or groundwater inlets, being hydrologically isolated. This means that the water level is mainly controlled by precipitation (as snow and rain) and evaporation, since the granitic bedrock is poorly weathered and fractured and, therefore, of very low permeability. P1 and P3 are also surrounded by undergrowth as woody plants (J. communis) and herbaceous vegetation (N. stricta, P. erecta, F. henriquesii, Leucanthemopsis pulverulenta (Lag.) Heywood, Rumex suffruticosus Gay ex Willk). P2 and P5 are characterized by abundant vegetation (helophytes) and coarse sediment filling. Water enters these ponds as runoff from several streams, groundwater flow and precipitation. During the drier months, namely, from July until September, when evaporation and evapotranspiration are higher, these ponds may dry out given their small depth and relatively large area, what happened to P2 in August and September (see Tab. 1).

Physical and chemical parameters data are presented in Table 2 and Figure 3. Dissolved oxygen was, in most cases, greater than 60% (of saturation) (Fig. 3b), as verified by several authors in similar alpine ecosystems (Girdner and Larson, 1995; Dumnicka and Galas, 2002; Martinez-Sanz et al., 2012). June and July were the months with the lowest pH and dissolved oxygen values, and higher water temperatures. The conductivity values are usually low in this type of ecosystems, nonetheless, the highest values in P5 may be explained by the impact of the use of de-icing salts in the local road. This practice can be responsible for soil and water salinization, for instance, P5 receives surface and underground flows from the road. Although this practice may have an environmental impact, in this study it does not seem to be a significant factor affecting the water ecological quality, neither its phytoplankton nor zooplankton diversity. Since this pond has water entry from four different points and has water run-off, this water renewal may dilute total salt content in the water during spring due to rain and ice melting. Nevertheless, it would be of interest the analysis of other compounds, other ponds without water renewal, and a continuous monitoring to prevent biodiversity loss and habitat degradation. Water temperature had different ranges according to the sampling month (Jun: 21.2–28.5 °C; Jul: 20.8–30.0 °C; Aug: 18.7–19.6 °C; Sep: 13.9–19.0 °C; Oct: 8.7–9.2 °C; Nov: 8.0–11.0 °C) and the turbidity was relatively low, ranging from 0 to 5.3 m−1. Phosphorous content was inferior to 0.04 mg/L (Fig. 3c). Regarding water hardness (3–8 mg/L CaCO3 in samples) (Tab. 2), it is considered to be very soft (<50 mg/L of CaCO3), which means that there is a very low dissolved mineral content (EPA, 2001), as expected in this type of alpine aquatic ecosystems. Overall ponds are in good chemical quality in most sampling periods, with values within the range of values for Good Ecological Potential in reservoirs (INAG, 2009a) (it is worthy of notice that there are no described values for small lentic waterbodies within the WFD for a better comparison). Besides, WFD recommends a five-class scale (bad, poor, moderate, good, high) but there is no national historical data and it is only possible to distinguish boundary values from Good to higher or lower (INAG, 2009b).

Trophic State Index (TSI) was calculated using chlorophyll a content for each pond in each sampled month (Tab. 2), and results were highly variable across the sampling months. However, when using the mean, values were close, with small variations between oligotrophic (TSI < 40; P2–34.59, P4–38.10 and P5–39.93) and slightly mesotrophic (40 < TST < 50; P1–41.78 and P3–40.18) (Carlson, 1977).

The results of the characterization of phytoplankton communities are presented in Table 3 and B1 from Appendix B. In general, the Shannon-Weaver diversity (H') was relatively low (0.87–2.47), as expected at high altitude ponds (Tab. 3). The richness of phytoplankton genera (S) ranged from 16 to 42, and evenness (J) values vary from 0.32 to 0.86. Although overall low, P2, P4 and P5 had higher diversity and richness values in comparison with the other sampled ponds. This was expected since these ponds had macrophytes, and it was already demonstrated that phytoplankton richness is higher in ponds with high abundance of macrophytes (Declerck et al., 2007). P2 had the greater richness and abundance what may be due to the high phosphorus content (Tab. 2), essential to phytoplankton proliferation. In August, P3 showed the highest Shannon-Weaver diversity value and evenness quantification, which means that the genera were similarly represented. The lowest value of diversity was recorded in November, also in P3 (Tab. 3).

The Ecological Quality Ratio (EQR) is an expression of biological assessments (phytoplankton in this study) that allows a comparison of ecological quality status independently from the ecological assessment method used. The ecological quality ratio, here calculated based on composition, abundance and biomass of phytoplankton community (INAG, 2009a), was higher in October and November and the poorest water quality was observed in June, except for P2 that was in July (Tab. 3). The lowest values of water potential ecological quality were very close to the limit value for good ecological quality; however, a moderate ecological quality was the final classification. It is worth reference that the EQR scale is supposed to be expressed using a numerical scale between zero and one (0–bad; 1–high). However, in Table 3 some values are higher than one. In fact, this method is described and used for heavily modified waterbodies but, in this study, it is being applied to alpine ponds. These results demonstrate that the current EQR scale is not fit to be applied to alpine ponds as expected. Since these are usually located in remote geographical areas with overall harsh environment and low anthropogenic disturbance (Körner, 2008), alpine ponds are normally in a pristine state although having harsh abiotic features, which originate overall unique biotic communities (Hinden et al., 2005; Boggero and Lencioni, 2006). Therefore, it is necessary to adapt the current ecological quality indexes to better fit ecosystems with extreme environmental conditions such as the alpine ponds here studied. Successful EQR reference values and class boundaries were already established and implemented in the WFD for alpine and peri-alpine lakes of Central Europe (Kaiblinger et al., 2009; Wolfram et al., 2009). Albeit not very precise, indexes as the Brettum Index for Austria and Slovenia and the German Index (PSI) are sufficient to roughly estimate the quality status of peri-alpine lakes accordingly to Kaiblinger et al. (2009). Class boundaries for chlorophyll a content were also established for those lakes by Wolfram et al. (2009). Although these class boundaries were not developed for Mediterranean alpine ponds, cross-checking the mean Chl a for the studied ponds with the boundaries established for shallow alpine lakes of Central Europe (Wolfram et al., 2009) allows a classification as “Good” or higher for all studied ponds. Further research should focus on adapting the indexes made for alpine lakes of Central Europe and make them applicable to Mediterranean alpine ponds.

Phytoplankton dynamics for each pond is presented in Figure 4. In early sampling months (mostly June and in some cases July), the phytoplanktonic community was dominated by Cyanobacteria, except in P1 where the community was dominated by Eustigmatophyceae. However, the CCA analysis (Fig. 5) shows a correlation between the groups Cyanobacteria and Eustigmatophyceae, and temperature values and ammonia content. Indeed, it has been already demonstrated that cyanobacteria species, in general, increase their growth and development at higher temperature when compared to other algal groups (Dokulil and Teubner, 2000; Reynolds, 2006; Vincent, 2009). Therefore, elevated water temperatures may be a factor driving the increase of cyanobacteria abundance that occurred in the studied ponds, especially during summer. Besides, some inorganic nitrogen forms, such as ammonia, are reported to also favour the increase of cyanobacteria abundance (Dokulil and Teubner, 2000; Dai et al., 2012), thus being also a factor contributing to the dynamics of cyanobacteria portrayed. Another factor for the increase of cyanobacteria in this period is their ability to produce UV-screening compounds, what may be a competitive advantage in alpine ponds that are under high UV radiation (McGowan et al., 2008).

In mid-summer (August and September) there was a general increase in the flagellated genera (Euglenophyceae) and in some ponds the green microalgae (Trebouxiophyceae) (Fig. 4). In alpine lakes and ponds, during ice-free periods, usually there are two phytoplankton abundance peaks: after de-icing in Spring and in Autumn, where Cyanobacteria (and some diatoms) and dinoflagellates dominate, respectively (McKnight et al., 1990; Winder et al., 2003; Tiberti et al., 2013). However, in the study, there was a relevant increase of Dinophyceae on P1 in August and September and there was no Autumn peak of dinoflagellates as could be expected for alpine ponds. Indeed, as the water temperature decreases, there is an increase in Diatom (Bacillariophyceae) and Cryptophyceae abundance, with exception of P3. These groups are usually found in colder waters and are expected to increase their abundance in October and November (see in Fig. 4 for P1, P2, P4 and P5), before the water freezes (Figueiredo et al., 2006; Bellinger and Sigee, 2015). Indeed, these taxa are able to grow under weak light and low water temperature conditions when compared to other microalgae groups (Kumar et al., 2012). The presence of phytoflagellates, as Cryptophyceae, is reported to be sporadic and intermittent with rapid changes in abundance (Dokulil, 1988). The phytoplanktonic community pattern of P3 appears to be the more distinct. In October and November, there was a growth in Chrysophyceae abundance that, along with coming green microalgae (Trebouxiophyceae), dominated the community in that period. This pattern is unusual and is not in accordance with the green algae ecology, where they are dominant or codominant in early or mid-summer (Bellinger and Sigee, 2015). Phytoplankton growth rates in alpine ecosystems respond rapidly to changes in nutrient availability and other environmental conditions (Gardner et al., 2008) and can explain the atypical community dynamics of P3. For example, P3 nitrate content was lower than in other ponds, and it has already been shown that nitrogen, as well as phosphorus, can be a limiting nutrient for primary productivity (Kratzer and Brezonik, 1981). Lower nitrogen concentrations may lead to a lower phytoplankton abundance and altered phytoplankton species dynamics (Maberly et al., 2002). It is known that chrysophytes typically do well in low nutrient environments (De Hoyos et al., 1998) and are able to flourish in low DOC systems (Vinebrooke and Leavitt, 1999; McMaster and Schindler, 2005), thus explaining the atypical pattern perceived.

Figure 5 shows a CCA analysis of the phytoplankton data against the environmental variables analysed. After analysis it is possible to see that June samples seem to be marked by increased CDOC, TP and NH4 (Fig. 5). Favoured by the increase of these values and in association with the summer period it is possible to see also that the communities differentiate, having higher concentrations of Xanthidium sp., Ceratoneis sp., Spirotaenia sp., Cocconeis sp., Glenodiniopsis sp., Nitzschia sp., Thalassiosira sp., Eudorina sp., Ulothrix sp., and Tetraedriela sp. and Cyanobacteria. In the right side of the plot, it is also possible to see an accumulation of the P2 samples, connected mainly with higher CDOC and with TSS for autumn samples. P5 had the higher amounts of total suspended solids for July, what was associated with noticeable higher presence of Gymnodinium sp. and Rhodomonas sp. For last, there seems to be an aggrupation of samples from P3 in association with higher BOD5, even though the values are not much different between ponds.

The macrozooplankton community analysis is displayed in Table 3 and B2 (Appendix B), Figures 6 and 7 for the five ponds along the sampling period. P4 shown the highest Shannon-Weaver diversity value (1.60) and P5 the highest evenness value (1.00) (Tab. 3). Although the development of macrophytes may have a positive effect on zooplankton biodiversity, as already shown for shallow lakes (Van den Berg et al., 1997) and shallow interconnected ponds (Cottenie et al., 2001), however not verified in our data, once diversity values were highly variable. Nonetheless, diversity, richness and evenness values were overall low, what is expected in this kind of high-altitude environments (Tavernini, 2008; Moreira et al., 2016) due to the adverse conditions. Indeed, the case-study ponds are isolated and, therefore, there is lower probability for colonization to occur (Scheffer et al., 2006; Moreira et al., 2016). However, it is possible to notice the usually lower diversity values in September. Higher summer temperatures increase water evaporation in the ponds (P2 was completely dry in August and September), and the instability of the waterbodies may have caused an impact in the overall communities composition (Wissinger et al., 2016). In fact, water volume may have a major influence in species composition in high-altitude waterbodies. Seasonal patterns for zooplankton communities are usually well defined and its turnover is often consistent from year to year (Kratz et al., 1987; Hu and Tessier, 1995; Rettig et al., 2006). Regarding the Cladocera order (Fig. 6), a predominance of Chydoridae genus (Acroperus, Alona and Chydorus) were observed in the samples. This is considered to be a constant pattern in shallow wetlands and high-altitude temporary ponds (Coronel et al., 2007; Kamenik et al., 2007; Moreira et al., 2016). There are several studies suggesting that Daphnia abundance is intrinsically correlated to an increase in water temperatures of lakes (Wolfinbarger, 1999; Benndorf et al., 2001), however, in this study was not possible to assess this seasonal trend. Moreover, in many other studies no correlation between the abundance of Daphnia and water temperature was recorded (Winder and Schindler, 2004; Rettig et al., 2006). For instance, the abundance peaks of Daphnia varied from pond to pond, occurring either in June and July, in P1 and P3, and in October, in P5. This distinct pattern in relation to temperature may be due to specific responses to local abiotic and biotic factors (Winder and Schindler, 2004) or even to water volume of the ponds, as already described. Nonetheless, temperature may influence the distribution of macroinvertebrates (Oertli et al., 2008) and amphibians (Newman, 1998) (top predators in fishless lakes and ponds), and the presence of these organisms may pressure large-bodied zooplankton species through predation, thus influencing overall zooplankton distribution and species richness (Anas et al., 2015). About Copepoda, Cyclopoida spp. was the dominant group throughout all sampling period for most ponds. However, the dominance of Calanoida in alpine lakes seems to be recognized worldwide (Jacobsen and Dangles, 2017). The dominance of copepods in almost all sample periods (except for October and November) may be due to their higher tolerance to the UV radiation, that is stronger in the summer. In fact, it was already described that in copepods, blue light screening carotenoid concentration seems to be higher with higher light intensity and lower temperatures (Byron, 1982). Besides, copepods that are under high UV radiation and at higher altitudes show higher concentration of mycosporines and mycosporines-like amino acids (Tartarotti et al., 2004; Persaud et al., 2007) and carotenoid pigmentation (Byron, 1982) in comparison with cladocerans, what thus may explain the higher dominance of copepods in the study sites, except for the autumn periods, with the decrease of UV radiation intensity. Several factors mentioned may explain the zooplanktonic dynamics shown, but one cannot exclude the possibility that heterogeneous distribution of zooplankton in the ponds due to wind, macrophytes distribution or even distribution in patches could influence some of the variations in zooplankton density registered, even though samples were taken in different sites in each pond to minimize this interference.

Regarding the macrozooplankton RDA analysis (Fig. 7), total phosphorus and chlorophyll a content were the only significant constrains assessed by the permutation test. There seems to be an association between most cladocerans genus found (Daphnia, Alona, Bosmina, Diaphanosoma and Simocephalus) with higher phosphorus content. In a study regarding 71 Danish lakes (mostly shallow), Jeppesen et al. (2000) found an increase in small-bodied cladocerans with an increase in TP content. Nonetheless, when cyprinids were removed from those lakes, the reduction of fish predation promoted a major abundance of average sized cladocerans. In the studied alpine ponds, such predation pressure was not so strong once the ponds are fishless, and the only predators were amphibians and macroinvertebrate larvae, thus leading to a dominance of normal sized cladocerans. Regarding to copepods, our data also suggests that cyclopoids dominate in environments with higher TP and chlorophyll a content. A similar shift from calanoids to cyclopoids along a TP gradient was also described for the same shallow lakes in Denmark (Jeppesen et al., 2000). Besides the relations with TP, there is not any other clear pattern connecting the environmental variables measured with the zooplankton communities sampled. This lack of clear influence of the environmental variables on the macrozooplankton communities' composition may indicate their inadequacy as biological indicators of water quality in Mediterranean alpine ponds. Nonetheless, zooplankton communities may respond to other water physical and chemical parameters not tested in the present work, not excluding them as possible bioindicators of specific pollutants in these extreme systems. Besides, as only macrozooplankton was tested, a necessity arises to test other specific groups of zooplankton as bioindicators for alpine ponds (e.g. rotifera).

Even though it was not possible to form distinct groups in the redundancy analysis, the lack of patterns was already expected since these ponds are very remote ecosystems, where even the smallest environmental variations may dictate a different biological pattern.

It is of agreement that cladocerans are usually the most dominant forms of macrozooplankton in alpine ponds (Rautio, 2001), what was not observed in this study (Fig. 6). In a study conducted by Rautio (2001) in treeline alpine ponds, it was possible to see that copepods appeared first in the ecological succession, right after ice melting, while cladoceran appearance was restricted do mid-late summer, when water temperatures reached 15-20 °C. However, this trend was not found as the limnetic conditions are highly variable, with cladocerans appearing at different months in different ponds. Besides, copepods highly dominated macrozooplanktonic communities in these ponds, contrarily to what was first expected. This may be due to the high ability of copepods to tolerate low food concentration and temperature when compared to cladoceran species (Muck and Lampert, 1980; Rautio, 2001). In addition, in limnetic systems, copepods are generally richer in nitrogen while cladocerans are richer in phosphorous (Sommer and Sommer, 2006). Thus, the overall limitation in phosphorous content of the studied ponds (Fig. 3) may be modulating the zooplanktonic community dynamics towards higher relative abundance of copepods over cladocerans.

Table 2

Physical and chemical parameters and Trophic State Index (TSI) values classification for chlorophyll a (TSI – Chl a), for each sampling period of each pond. *BDL – Below Detection Limit; O – Oligotrophic; M – Mesotrophic; E – Eutrophic. Pond 2 was dry during August and September sampling period.

thumbnail Fig. 3

Line graphs representing the evolution of a) pH; b) dissolved oxygen (%); c) ammonium (mg/L); d) nitrates (mg/L) and e) total phosphorus (mg/L) content for each sampling month. The lines represent the studied ponds. The grey limit represents range values for Good Ecological Potential: 6 ≤ pH ≤ 9; 60% ≤ O2% ≤ 120%; TP ≤ 0.13 mg/L ; NH4 + ≤ 1 mg/L; NO3 − ≤ 25 mg/L.

Table 3

Diversity Indices (DI): Shannon-Weaver Index (H' − diversity), Pielou (J − Evenness) and richness of taxa (S) for phytoplankton and zooplankton communities in the sampling period of each pond. EQR stands for Ecological Quality Ratio based on phytoplankton composition and abundance.

thumbnail Fig. 4

Relative abundance (%) of major algae groups for the sampling period of each pond. Phytoplankton richness values are represented in the secondary axis. For pond 2, there is no data for August and September due to the totally dryness of the pond. For density results, see Table B1 from Appendix B.

thumbnail Fig. 5

Canonical correspondence analysis between environmental variables and phytoplankton composition. CCA axis 1–8.04%, eigenvalue = 0.228; CCA axis 2–6.43%, eigenvalue = 0.182. For taxa abbreviation see Table A1 from Appendix A.

thumbnail Fig. 6

Relative abundance (%) of macrozooplankton − Cladocera and Copepoda groups for the sampling period of each pond. For pond 2, there is no data for August and September due to the totally dryness of the pond. For density results, see Table B2 from Appendix B.

thumbnail Fig. 7

Representation of a redundancy analysis between environmental variables and macrozooplankton composition. RDA axis 1–51.1%, eigenvalue = 658.43; RDA axis 2–1.90%, eigenvalue = 24.42. For taxa abbreviation see Table A2 from Appendix A.

4 Conclusion

Overall, the studied ponds presented a good chemical and physical state in almost sampling periods. These were also in good ecological state according to phytoplankton metrics from the Water Framework Directive approach to artificial water bodies (reservoirs). Trophic state was variable during the study period, but normally the ponds were classified by an oligotrophic system according to chlorophyll a concentration. Cyanobacteria, Euglenophyceae and Trebouxiophyceae were the most representative groups of phytoplankton in the studied alpine ponds and Cyclopoida was the most abundant from macrozooplankton group. The different seasonal dynamics of this study may be explained by the severe and extreme abiotic conditions of Serra da Estrela ponds. Although this study did not show a clear correlation between environmental variables and macrozooplankton besides the shifts caused by the total phosphorus content, it does not exclude the hypothesis of macrozooplankton community as bioindicator of water quality assessment and trophic state of alpine ponds and lakes. However, further studies should be carried out with the intention of perceiving which other environmental variables (such as sodium chloride, calcium chloride, depth, organic matter, ice cover, sediment type) may modulate the macrozooplankton distribution in extreme environments such as alpine ponds. In future research, it would be also important to study other biotic matrices (e.g. macroinvertebrates, aquatic plants; invertebrate predation; competition) to test the possible use as tools to assess water quality of these aquatic ecosystems. These factors are likely to be a source of variation in ecosystems' structure and composition and, therefore, in its food web. It would also be important to perform a continuous monitoring of these aquatic ecosystems in terms of anthropic threats and global climatic change.

Acknowledgements

Sara Antunes is hired through the Regulamento do Emprego Científico e Tecnológico − RJEC from the FCT program (CEECIND/01756/2017). This work was supported by National Funds (through the Portuguese Science Foundation) and by the European Regional Development Fund (through COMPETE2020 and PT2020) by means of the research project ReDEFine (POCI-01-0145-FEDER-029368), and by the Strategic Funding UID/Multi/04423/2019 through national funds provided by FCT − Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020, and by the project. J. Espinha Marques was supported by funds from the European Union through the European Regional Development Fund, framed in COMPETE, 2020 (Operational Programme for Competitiveness and Internationalization), through the project ICT (UID/GEO/04683/2013) with reference POCI-01-0145-FEDER-007690.

Appendix A

Table A1

Abbreviations of phytoplankton genus used in the phytoplankton communities CCA analysis.

Table A2

Abbreviations of zooplankton genus used in the zooplankton communities CCA analysis.

Appendix B

Table B1

Density in individuals per litre of phytoplankton families in each of the studied ponds for each of the periods sampled.

Table B2

Density in individuals per litre of macrozooplankton genus in each of the studied ponds for each of the period sampled.

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Cite this article as: Martins FS, Moutinho A, Espinha Marques J, Formigo N, Antunes SC. 2020. Plankton characterization of alpine ponds: a case of study for the assessment of water quality in Serra da Estrela (Portugal). Ann. Limnol. - Int. J. Lim. 56: 4

All Tables

Table 1

Pond physical and morphometric features.

Table 2

Physical and chemical parameters and Trophic State Index (TSI) values classification for chlorophyll a (TSI – Chl a), for each sampling period of each pond. *BDL – Below Detection Limit; O – Oligotrophic; M – Mesotrophic; E – Eutrophic. Pond 2 was dry during August and September sampling period.

Table 3

Diversity Indices (DI): Shannon-Weaver Index (H' − diversity), Pielou (J − Evenness) and richness of taxa (S) for phytoplankton and zooplankton communities in the sampling period of each pond. EQR stands for Ecological Quality Ratio based on phytoplankton composition and abundance.

Table A1

Abbreviations of phytoplankton genus used in the phytoplankton communities CCA analysis.

Table A2

Abbreviations of zooplankton genus used in the zooplankton communities CCA analysis.

Table B1

Density in individuals per litre of phytoplankton families in each of the studied ponds for each of the periods sampled.

Table B2

Density in individuals per litre of macrozooplankton genus in each of the studied ponds for each of the period sampled.

All Figures

thumbnail Fig. 1

Serra da Estrela Natural Park geographical setting and location of the study area.

In the text
thumbnail Fig. 2

Drainage network of the studied ponds.

In the text
thumbnail Fig. 3

Line graphs representing the evolution of a) pH; b) dissolved oxygen (%); c) ammonium (mg/L); d) nitrates (mg/L) and e) total phosphorus (mg/L) content for each sampling month. The lines represent the studied ponds. The grey limit represents range values for Good Ecological Potential: 6 ≤ pH ≤ 9; 60% ≤ O2% ≤ 120%; TP ≤ 0.13 mg/L ; NH4 + ≤ 1 mg/L; NO3 − ≤ 25 mg/L.

In the text
thumbnail Fig. 4

Relative abundance (%) of major algae groups for the sampling period of each pond. Phytoplankton richness values are represented in the secondary axis. For pond 2, there is no data for August and September due to the totally dryness of the pond. For density results, see Table B1 from Appendix B.

In the text
thumbnail Fig. 5

Canonical correspondence analysis between environmental variables and phytoplankton composition. CCA axis 1–8.04%, eigenvalue = 0.228; CCA axis 2–6.43%, eigenvalue = 0.182. For taxa abbreviation see Table A1 from Appendix A.

In the text
thumbnail Fig. 6

Relative abundance (%) of macrozooplankton − Cladocera and Copepoda groups for the sampling period of each pond. For pond 2, there is no data for August and September due to the totally dryness of the pond. For density results, see Table B2 from Appendix B.

In the text
thumbnail Fig. 7

Representation of a redundancy analysis between environmental variables and macrozooplankton composition. RDA axis 1–51.1%, eigenvalue = 658.43; RDA axis 2–1.90%, eigenvalue = 24.42. For taxa abbreviation see Table A2 from Appendix A.

In the text

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