Zooplankton usually behaves in complex and dynamic ways on various temporal and spatial scales and its spatial distribution is usually highly aggregated, as it possesses a large-scale spatial variability similar to that observed in the physical environment in general. We assess here the suitability of the Optical Plankton Counter for estimating zooplankton abundance, as well as the day-to-day temporal and spatial patterns in its distribution during the open water season. The influence of certain environmental variables on zooplankton abundances is also examined.

Abundances of mesozooplankton-sized particles were measured with the Optical Plankton Counter monthly at five stations in a large mesohumic lake from May to September 2005 and agreed rather well with zooplankton abundances counted using a microscope. The poorest agreement was in autumn, when the Counter overestimated the zooplankton abundance, and for some samples in July, when it underestimated the abundance. Fluorescence and chlorophyll a had a significant effect on the Counter readings. Both the intercept and the slope of chlorophyll a varied randomly between depths, because the chlorophyll a decreased much more markedly in deeper waters than did the Counter particle counts. This emphasized the stratified nature of sampling.

In addition to seasonal variations, there were also marked short-interval variations (day-to-day) in zooplankton abundances in all the sampling periods. These patterns are highly dynamic and can on some occasions change rapidly in response to fluctuations in the weather. There were no clear differences in zooplankton abundance between the sampling stations. The horizontal heterogeneity was less pronounced than the seasonal and short-interval heterogeneity, possibly because there were no trophic gradients in the basin.

While the Optical Plankton Counter provided a rapid assessment of temporal and spatial patterns of zooplankton abundances, it does have limitations. At times it either overestimated abundances due to a large contribution from non-zooplankton particles, or underestimated abundances due to coincidence.

Introduction

Spatial heterogeneity in a lake ecosystem is a product of many interacting biological and physical processes. Zooplankton patchiness has been documented in earlier work on the Saimaa lake system (Viljanen and Karjalainen, 1993; Karjalainen et al., 1996a) and in many other studies (e.g. George, 1974; Malone and McQueen, 1983; Urabe, 1990; Mehner et al., 2005). Abiotic factors can have a dominant effect on abundance patterns over large spatial scales, while biotic factors are important on small spatial scales (e.g. Pinel-Alloul, 1995; Masson and Pinel-Alloul, 1998; Pinel-Alloul et al., 1999; Thackeray et al., 2004).

Besides the scale of sampling, the spatial and temporal heterogeneity of zooplankton has to be taken into account when designing a sampling protocol. Seasonal variation in zooplankton populations is well documented, and year-to-year changes in the weather have a profound effect on seasonal dynamics of plankton in lakes (George, 2000). In addition to seasonal variation, shorter term (day-to-day) variation can occur. The later is rarely studied due to the laborious and expensive nature of the sampling and analysing techniques, but spatial heterogeneity of zooplankton has been found to occur on both the vertical and horizontal axes of the pelagic habitats within lakes (Masson and Pinel-Alloul, 1998; George and Winfield, 2000). Horizontal zooplankton distribution patterns have been observed along various environmental gradients (e.g. Karjalainen et al., 1999; Pinel-Alloul et al., 1999; George and Winfield, 2000).

In open pelagial areas, wind-induced water movements can account for 29–47% of the basin-scale spatial variance in zooplankton (Thackeray et al., 2004). Rapid changes in wind directions can cause day-to-day differences in plankton distribution, but wind-driven horizontal transport processes also have an effect on the vertical distribution of plankton through upwelling and downwelling (George and Edwards, 1976) or the induction of internal waves (Rinke et al., 2007). Direct measurements of water currents combined with observations on plankton distributions are still fairly rare (see Rinke et al., 2007), and water temperature is more often used as indication of lake-wide circulation patterns (Pinel-Alloul et al., 1999; George and Winfield, 2000; Thackeray et al., 2004).

Spatial patterns can be determined by taking numerous short-interval samples using traditional methods (tube sampler, plankton pump or net), but the high costs of the laboratory analyses have generally become prohibitive. More rapid methods for assessing the abundance and size distribution of zooplankton would thus be highly desirable for routine work. Combined with less numerous microscopic analyses, such methods would offer powerful means for obtaining a better overview of both the spatial and temporal variability in zooplankton. Advanced technologies allow us to sample biological and physical parameters synoptically on the same spatial and temporal scales.

The Optical Plankton Counter (OPC) may be configured in a variety of ways for use in situ in the marine environment (Herman, 1988; Huntley et al,. 1995; Wieland et al., 1997; Grant et al., 2000), in freshwater lakes (Sprules et al., 1998), or in the laboratory to analyze preserved samples (Herman, 1992; Beaulieu et al., 1999; Zhang et al., 2000; Kessler and Lampert, 2003; González-Quirós and Checkley, 2006). The OPC was designed for assessing oceanic plankton, and is more commonly used for this purpose. By comparison, lake applications have so far been few in number (Sprules et al., 1998; Zhou et al., 2001; Yurista et al., 2005; Liebig et al., 2006; Patoine et al., 2006; Finlay et al., 2007).

This study is part of a multidisciplinary project aimed at gaining a better understanding of pelagic food web interactions, especially heterogeneous plankton distributions in relation to the hydrodynamic properties in a lake environment (Viljanen et al., 2009a and b). There were three main goals: (1) to assess the suitability of the OPC for estimating zooplankton abundance; (2) to document day-to-day temporal and spatial patterns in the distribution of zooplankton abundance during the open water season; (3) and to study the influence of certain environmental variables on zooplankton abundances.

Study area

Lake Pyhäselkä is a moderately large (263 km2) mesotrophic humic lake in eastern Finland (62°30'N, 29°43'E). The basin is open and almost without islands, and is thus exposed to winds and wind-induced currents (Figure 1). The mean depth of the lake is 9 metres, maximum depth 67 metres and volume 2.5 km3. The River Pielisjoki (mean discharge 228 m3 s− 1) flows into the northern part of the lake and alters its physicochemical characteristics (Huttula et al., 1996).

Only minor differences in water chemistry parameters with depth or stations were found in this lake in 2005 (Viljanen et al., 2009a, 2009b). The water was brown in colour (about 70–100 mg l− 1 Pt) and transparency values varied from 1.6 metres to 2.0 metres (Table 1). Phosphorus (mean 11–13 μ g l− 1) and chlorophyll a (mean 4.1–5.1 μ g l− 1) were characteristically moderately low.

Material and methods

Zooplankton was collected at five stations in Lake Pyhäselkä. Field sampling was performed on board R/V Muikku. Two stations (Kokonluoto and Pyhäsaari) were located in the deep southern and central parts of the lake, one (Kaskesniemi) in the shallower western part and two (Ylämylly and Noljakka) in the northern part (Figure 1).

The samples were taken at monthly intervals (from June to September) though the growing season of 2005. The sampling was repeated at fixed depths (two to five depths at each station). Sampling depths varied according to the total depth at the station. Five depths (1, 5, 10, 20 and 40 metres) were sampled at the two deep stations, but only two depths (1 and 5 m) at the shallowest station.

Short-interval variations in zooplankton were analysed in samples collected daily at all the five stations in Lake Pyhäselkä on four three-to-four-day sampling excursions. All the zooplankton samples representing successive days (total number of 247, volume 100 l) were taken at the same time of day, and always between 09.00 and 18.30, to avoid the question of diurnal variations in plankton distributions.

The laboratory version of the Optical Plankton Counter (OPC-1L) was installed in a specially built circulation system. The OPC is designed for counting and sizing zooplankton of diameters between 250 μ m and 2 cm, being capable of detecting and sizing particles by measuring the amount of light blocked, which is proportional to their projected areas as they pass through the sampling tunnel (Herman, 1988; Herman, 1992). The laboratory device (OPC-1L) has as its sensing zone a 2 × 2 cm glass cuvette, through which the water flows.

In our modified circulation system, lake water was transferred by means of a diaphragm pump to an upper reservoir, a method that ensures that the organism will survive intact. Any air bubbles were broken up in the upper reservoir before it entered the OPC. The flow rate (about 14 l min− 1) was regulated by a ball valve, placed after the OPC. The volume of water (100 l) that had passed through the OPC was measured with a single-jet water meter (Zenner D82) and the total abundance of the particles passing through was calculated by the OPC. The pump line was purged carefully by pumping a sufficient amount of water from the next depth before accepting a new sample. The samples were collected into a net (50 μ m), which was carefully washed and the samples were preserved with ethanol. Some of the samples (total 112) were also counted and analysed under a microscope.

A sufficient number of well-mixed subsamples were taken with a pipette, and the crustacean zooplankton was counted in a sedimentation cuvette using an inverted microscope. The precision of the counting procedure has been tested earlier (Karjalainen et al., 1996b). Each crustacean animal in every subsample was measured and counted (Rahkola et al., 1998). Animals in size classes under 300 μ m were excluded from further analysis because of the minimum detection limits of the OPC (250–300 μ m). A linear regression was calculated between the log-transformed the OPC counts and the log-transformed zooplankton abundances (over 300 μ m) obtained microscopically.

In order to estimate the variation at each of the five sampling sites, the coefficient of variation (CV%) was calculated from three measurements. The variations were tested by means of horizontal on-line sampling at five stations on two occasions, 7 and 16 September 2004. Three successive samples of volume 105 litres from a depth of 1.5 m were analysed with the OPC at each of the five stations. Since the speed of R/V Muikku was 2 knots, the length of the sampling line was about 500 metres. Spatial variation within the lake (CV%) was calculated for each month in 2005 on the basis of the surface layer samples (depths 1 and 5 m).

The spatial variation in total particle densities in the OPC samples was estimated for two cruises on Lake Pyhäselkä in 2004 and four cruises in 2005 giving the coefficients of variation between the replicate samples (5 stations and 3 replicates) that varied from 3% to 23%. The CV for the whole lake was 16% on 7 September 2004 (n = 15) and 45% on 16 September 2004 (n = 15). The variation in total particle density in the surface layer was high in spring (CV 119%, n = 30) and summer (CV 82%, n = 40), but decreased markedly in the late summer (CV 40%, n = 30) and autumn (CV 23%, n = 30). The variation between the replicate samples was lower than the spatial variation within lake.

The spatial differences in currents were studied with a 614 kHz Acoustic Doppler Current Profiler (ADCP; RD Instruments; www.rdinstruments.com) along three cross-sectional and three longitudinal lines, and at the five sampling stations. Daily wind direction and velocity measurements were made by the Finnish Meteorological Institute at automatic station located at Tuiskavanluoto in the middle of Lake Pyhäselkä. Water temperature, depth, conductivity and chlorophyll fluorescence in vivo (Self-Contained Underwater Fluorescence Apparatus, Scufa, Turner Design) were measured with a Sea-Bird Electronic 19-03 CTD meter, which has accuracies of 0.01°C for temperature, 0.01 μ S cm− 1 for conductivity, and ∓ 0.25% for depth. The coefficient of variation (CV%) between ten consecutive CDT measurements was 9% for the fluorometer values. Three replicate measurements were made at each sampling station in the form of vertical profiles from surface to bottom.

There was no clear stratification in May-June (Viljanen et al., 2009a). The northern shallow area was either very weakly stratified, in July, or not stratified in August 2005, but the deep central basin of Lake Pyhäselkä was stratified and the depth of the metalimnion varied by several metres on consecutive days at the same stations, and the temperature in the metalimnion also varied by several degrees (Viljanen et al., 2009a, 2009b). Lake Pyhäselkä was not stratified in September 2005.

Samples for the chemical analyses were taken at a depth of 1 metre, at a depth corresponding to the middle of the water layer and a point just above the bottom. Chemical water quality was analysed according to standard Finnish methods and quality assurance (Niemi et al., 2001; Mitikka and Ekholm, 2003).

Differences in the mean OPC counts were tested using Friedman's test. Friedman's test was used, because the assumption of normality was not met (Kolmogorov-Smirnov test for normality before and after transformation). This Chi-square test with a-1 degrees of freedom, where a is the number of repeated measures had as its hypothesis for the comparison Ho that the distributions of OPC particle counts should be the same across repeated measures (seasonal or day-to-day variations). Spatial variations (between the sampling stations) variations were tested only on the basis of the surface- layer samples (depths 1 and 5 m), because of the varying sampling depths at the stations.

The influence of certain environmental variables on the zooplankton abundances as measured by the OPC particle counts were studied using a generalized linear mixed model (GLMM) in which the hierarchical structure of the data was taken into account. Here the units at the highest level, 1, were a long time intervals (months), those at level 2 were short time intervals (day-to-day), those at level 3 were places and those at level 4 were depths. The environmental variables measured were: temperature, Secchi depth, turbidity, colour, Ptot, PO4-P, Ntot, NO3+NO2-N, chlorophyll a, fluorescence, PAR, conductivity and the rate and direction of flow of the water. The material for this analysis was collected from Lake Pyhäselkä in 2005 (June, July, August and September) and 2006 (July and August). The standard mixed model formulation for the observed data is:

formula
where X is the n x p matrix of explanatory variables, ß is the p x 1 vector of fixed parameters, d is a q x 1 vector of random parameters with expectation zero and with a dispersion matrix D, Z is an nxq matrix of explanatory variables associated with d, and ei is the n x 1 matrix of error terms with an n x n dispersion matrix R (Goldstein, 1986).

Results

Zooplankton abundances in Lake Pyhäselkä in 2005, as measured by the OPC particle counts, varied from 0.2 to 69 l− 1, with a mean of 17.4, while the density range based on the microscopically analysed samples was 0.5–149 crustaceans l− 1. Animals of size classes over 300 μ m were used in all the comparisons with the OPC because of the minimum detection limits of the OPC. The mean density of those animals was 11.4 l− 1 and the range was from 0.5 to 69 crustaceans l− 1. The zooplankton densities in the microscopic samples and OPC counts showed a significant linear correlation:

formula
where: x = log zooplankton densities (size classes over 300 μ m) in the microscope samples (Figure 2).

The seasonal differences in both particle counts and in crustacean densities were nevertheless large (Table 2, Figure 3) and the OPC clearly overestimated the zooplankton density in September and slightly underestimated it in July, when the differences between the particles counted by the OPC and the results obtained from the microscopically analysed samples were mainly due to the large proportion of nauplii of Copepoda (Table 2), which increase the density but are so small that the OPC device does not detect them. By September the proportion of Copepoda nauplii had decreased, but the amount of microscopically observable detritus and measured turbidity had increased (from 1.1 FNU in May–June to 2.5 FNU in September). Thus, where the OPC estimate varied seasonally, part of this variation may be said to have been real seasonal variation, but especially in autumn the OPC results did not corresponded to the microscopically estimated plankton abundance.

Seasonal variation

The most obvious differences in plankton abundance in Lake Pyhäselkä were seasonal ones, and these were statistically highly significant in the OPC material (Friedman statistics 127.2, p < 0.001, n = 56). Post hoc Dunn's multiple comparison tests showed the OPC counts to be significantly higher in July, August and September than in May-June (p < 0.001). Also, the OPC counted more particles in August and September than in July (p < 0.05 and p < 0.01, respectively). The density of counted particles did not differ between August and September, however. Plankton development follows the temperature curve, with the highest abundance normally occurring at a high temperature. The zooplankton abundance in springtime was low, with a mean density of only 0.9 counts l− 1, while the mean density in July was 10.7 l− 1 and the short-interval variation was also highest at this time (Figure 3). The mean counted density in August (27.9 l− 1) was three times higher than in July. The highest mean counted abundance was recorded in September (31.9 l− 1), which may be partly an inaccurate result (see above).

The water quality parameters (turbidity, colour, nutrients and chlorophyll a), and naturally temperature, also differed markedly between the months (Friedman, p < 0.001). The mean turbidity doubled in late summer (1.9 FNU) and autumn (2.4 FNU) compared with the values for May-June (1.1 FNU). The concentrations of total phosphorus (13.5 μ g l− 1) and nitrogen and dissolved nitrogen (143 μ g l− 1) were highest in springtime, while that of chlorophyll a (5.1–5.4 μ g l− 1) around the middle of summer (July and August).

Short-interval day-to-day variation

No significant day-to day variation was observed in springtime, May-June (Figure 4A) and the zooplankton was also fairly evenly distributed both horizontally and vertically during this first measurement period. In July there was a significant difference between successive days (Friedman statistics 9.4, p = 0.024, n = 19), and post hoc comparison tests revealed that the particle abundances on the third day were higher than on the fourth day (Figure 4B). Lake Pyhäselkä was stratified in terms of its plankton distribution in July, with a surface maximum detectable at all the stations. The short-interval variation was especially large in this period (Figures 3A and 4B).

In August the successive days differed slightly from one another (Friedman statistics 6.4, p = 0.040, n = 19), but the post hoc comparison tests revealed no significant differences (Figure 4C). The abundance of zooplankton in the northern part of the lake decreased on the third day relative to the first and second days, probably due to a change in wind direction and a drop in temperature, while neither the temperature difference nor that in density was so evident in the deep southern part of the lake. The shallow northern part and the deep southern part acted differently during this measurement period. The northern part was not stratified, but the deep southern part was and thus resisted the mixing effect of the wind-induced currents. Wind velocities were high during this sampling excursion (up to 14 m s− 1) and an increase in current velocity was measured at most of the stations (Table 3). Also, the temperature differences between the first and last days of the period in the shallow northern part of the lake were also statistically significant (p = 0.006), which is an indirect indication of powerful currents.

There were distinct differences between the sampling dates in September (Friedman statistics 24.1, p < 0.0001, n = 19), the particle abundances on the first day being much higher than those on the following days (Figure 4D) especially in the western and southern parts of the lake (Dunn's post hoc tests, 19 Sept vs 20 Sept p < 0.01 and 19 Sept vs 21 Sept p < 0.001) (Figure 5). The lake was not stratified and the strong winds had an efficient mixing effect. Current velocities were high at Kokonluoto on the first day (Table 3), leading to a horizontal displacement of particles of between 7 and 11 km per day. It seems that these currents caused by strong winds and a change in wind direction just before sampling period, caused the surface plankton in turn to move downwind, especially in the southern part of the lake (Table 3, Figure 5).

Horizontal differences

Zooplankton abundances were fairly evenly distributed horizontally in 2005, and there was no obvious trend between the sampling stations. The only statistically significant differences between the five stations were found in May-June (Friedman statistics p = 0.003, n = 5) and in August (p = 0.023, n = 6). The post hoc Dunn's multiple comparison tests showed the OPC counts to be significantly higher on Kokonluoto than on Pyhäsaari in May-June (p < 0.05) and higher at Ylämylly than at Kokonluoto in August (p < 0.01).

There were minor differences in water quality parameters between the stations. Turbidity was higher in the northern part of the lake, where the influence of the River Pielisjoki is greatest, and this difference was statistically significant from July to September (Friedman, p < 0.01).

Relationship between OPC abundances and environmental variables

The relationships between the analysed environmental variables here and OPC abundances showed that only fluorescence and chlorophyll a had a significant effect on the OPC values. Of the fixed effects, chlorophyll fluorescence in vivo (5.131, 1.623, 0.001) and chlorophyll a (3.030, 1.204, 0.0012) were statistically significant at 5 % level. The fixed effects are reported in order of magnitude with the most effective in terms of Wald's test stated in first. The figures quoted in the brackets are the estimated value, the standard error of the estimate, and the p-value, respectively.

The effect of chlorophyll a varies spatially (1.804, 0.373, 0.000) while both the intercept (25.315, 5.444, 0.000) and the slope of chlorophyll a (3.174, 0.899, 0.000) vary randomly with depth. The relationship varied from 1 to 40 m with depth, because the chlorophyll a decreased much more markedly in deeper waters than did the OPC particle counts (Figure 6). There is a negative correlation between the intercept and the slope for chlorophyll a (–0.88, covariance being –7.884, 2.321, 0.001), suggesting that depths with higher intercepts tend to have gentler slopes. In addition, the intercept varies between short time periods, i.e. on a day-to-day basis (9.713, 4.524, 0.032).

The results suggest that zooplankton abundances (OPC counts) were dependent on phytoplankton biomass and activity (chlorophyll a and fluorescence), which indicating bottom-up regulation in Lake Pyhäselkä. Temperature, nutrients, the other chemical parameters analysed and the speed and direction of flow of the water did not affect the variability in OPC abundance.

Discussion

The abundance of zooplankton as measured with the OPC device agreed well with the samples taken with a pump and analysed microscopically, explaining about 68% of the variability. Nevertheless, the unexplained variability still amounts to about 32%, which is relatively high and could be attributable to a large number of sources. Part of the variation must be due to seasonal and short-interval fluctuations in the zooplankton community and the rest may be attributed to methodological reasons, mainly the detection limits of the instrument (Herman 1988, 1992), coincidence of particles (Sprules et al., 1998) and the presence of non-zooplankton particles (Zhang et al., 2000; Liebig et al., 2006; Moore and Suthers, 2006), the last-mentioned being a factor which varies seasonally and depends on hydrodynamic events within the lake.

The zooplankton community in Lake Pyhäselkä is dominated by Cyclopoida and Copepoda nauplii, most of which, when occurring alone remain below the instrument's minimum detection limit of 250–300 μ m. It is also possible for larger animals to pass the light beam of an OPC without the device detecting them, depending on their orientation. The total abundance of the zooplankton community in Lake Pyhäselkä is underestimated in cases where the proportion of Copepoda nauplii exceeds 70% of total density; e.g. in July, in addition to which the OPC will underestimate the number of particles and overestimate their sizes when two or more particles pass through the light beam simultaneously (Sprules et al. 1998). Sprules et al. (1998) estimated that the precision of OPC measurements in freshwater systems is maximized and errors are minimized at zooplankton abundances of less than 30 individuals l− 1. The maximum abundance in our OPC measurements was 69 individuals l− 1, but the mean abundance was equal to figure recommended by Sprules et al. (1998).

The overestimation of the OPC plankton density in September was probably due to a high concentration of detritus particles. Lake Pyhäselkä is a humic lake (colour 80 mgl− 1 Pt), where the concentration of detritus particles is high, as was visible in the counted zooplankton samples during that time. Turbidity values (FNU) were high in September, especially in the northern part of the lake. Windy weather in autumn can cause a mixing of sediment and rapid changes in particle concentrations, which may in turn bias the measurements. When the abundance of detrital particles is high, many small particles may be counted by the OPC as a single large particle or a flat detritus particle under certain orientations could presumably produce a projected area that was large enough to be detected by the OPC (Zhang et al. 2000). Similar results have been observed elsewhere (Zhang et al., 2000; Liebig et al., 2006; Moore and Suthers, 2006).

The variation among the replicate OPC samples was lower than that within the lake in September 2004, which parallels an earlier result for total zooplankton biomass in the pelagic zone of Lake Paasivesi, where the variation was the highest (CV 34.4%) on the whole-lake scale and lowest (CV 15.8%) in the replicate samples (Viljanen and Karjalainen, 1993). The variation in total particle density in the surface layer was high in spring and summer, but decreased markedly in the late summer and autumn samples. Evans and Sell (1983) showed that variation was highest in summer and lower in spring and autumn in Lake Michigan. This may be due to the plankton population dynamics, as the zooplankton reproduction rate is fast in summer, meaning that variability at this time is a function of density and, given the partly hydrodynamic situation in summertime, the water is stratified, so that the mixing effect is less pronounced than in the unstratified period.

There were pronounced seasonal differences in both the OPC counts and zooplankton densities. In August, when the zooplankton community is fully developed, the OPC can provide a rapid, accurate picture of temporal and spatial patterns in zooplankton occurrence. The marked seasonal variability in both zooplankton densities obtained by microscopy and OPC particle densities emphasizes the need for regular calibration of an OPC with counted samples.

The results show that the short-interval variation in zooplankton abundance was highly dynamic. The rapid changes in daily particle density and also in phytoplankton and zooplankton biomass (Viljanen et al., 2009b) in Lake Pyhäselkä were assumed to indicate major alterations in the hydrological environment. The combined action of wind-induced water movements and the behaviour of organisms can result in large-scale spatial heterogeneity among crustaceans (Pinel-Alloul, 1995; Pinel-Alloul et al., 1999; Thackeray et al., 2004). The water circulation in Lake Pyhäselkä is mainly influenced by inflow from the River Pielisjoki and by wind-induced horizontal currents. Horizontal temperature variations between successive dates and changes in the depth of metalimnion in August (Viljanen et al., 2009b) and July (Viljanen et al., 2009a) confirmed the presence of wind-induced currents. The shallow northern part of the lake and the deep southern part acted differently both in August and in September. In August the northern part was not stratified, but the deep southern part was, and thus could resist better the mixing effect of the wind-induced currents. This could explain the fact that there were only slight differences in OPC particle counts between the successive days. The recent wind history may explain the higher particle density at the northernmost station, Ylämylly, than at the Kokonluoto station in the south. There were statistically significant daily differences in particle abundances in September when the stratification was discharged. The particle abundances on the first day were much higher than those on the following days, especially in the western and southern parts of the lake. Although the biological and physical data were collected synoptically during the field campaign, no statistical relationship emerged that could be used to predict plankton aggregations.

The observed water currents were complicated and did not directly explained the differences in particle concentration, because the surface flow in the northern part of Lake Pyhäselkä follows the wind on the shallow eastern side and the return flow is located near the western side, where the water is deep (Huttula et al., 1996). During windy periods, when the direction of the wind is along the main axis of the lake, a large, lake-wide circulation is formed and several big gyres are created along the transverse axis (Huttula et al., 1996).

Our results show that there were no permanent horizontal differences in plankton density, biomass (Viljanen et al., 2009b) or water quality parameters in Lake Pyhäselkä. The river inflow caused some significant differences in turbidity and transparency, and although the variability components cannot be directly compared, it seems that seasonal variability was high, whereas the horizontal differences were very minor ones. Similar results have been reported for Lake Stechlin, where the various components of the zooplankton biomass showed high heterogeneity in their vertical distribution and between the months but the diel differences and horizontal spatial variability were less decisive (Mehner et al., 2005). The nature of the spatial pattern and of the association between the physical environment and zooplankton heterogeneity can vary with time (Thackeray et al., 2004), as is clearly visible in the present findings.

This study, in common with several others (e.g. Thackeray et al., 2004; Rinke et al., 2007) has shown the large variability and lack of temporal persistence that is to be found in zooplankton abundance, which means that the results of single-survey sampling campaigns must be interpreted with caution. The present results also emphasize the importance of characterizing the hydrophysical processes taking place in the lakes studied.

The results regarding the influence of a set of environmental variables on the zooplankton abundances (OPC counts) showed that phytoplankton biomass and activity (chlorophyll a and fluorescence) best explained the variability in abundances, which in turn indicates bottom-up regulation of zooplankton in Lake Pyhäselkä. The relationship is a complicated one, however, and varies from one hierarchical temporal or spatial level to another. Both the intercept and the slope of the curve for chlorophyll a varied randomly between depths, which emphasized the need for stratified sampling rather than single integrated net hauls from the bottom to the lake surface. A similar complicated combination of environmental factors such as time of day, distance off- shore, season, year, chlorophyll a concentration and Brunt-Väisälä frequency explained the differences between particle abundances counted in situ using the OPC and net collections analysed afterwards by OPC in the California Current Region (González-Quirós and Checkley, 2006). On the other hand, this result supports the use of instruments such as an OPC or fluorometer for rapid and robust assessment of the condition of an ecosystem or trophic relationships, as suggested earlier by Yurista et al. (2005).

Conclusions

The zooplankton distribution in the pelagial part of the lake is highly dynamic and depends on both biological and abiotic processes. Optical and other automatic techniques can improve the temporal and spatial resolution of the measurements considerably, which may provide insights into ecosystem dynamics and regulation forces (see Sprules et al., 1998; Grant et al., 2000; Zhou et al., 2001; Vanderploeg and Roman, 2006; Rinke et al., 2007). Our results suggest that (1) OPC is fairly suitable for research of this kind, (2) seasonal and short-interval day-to-day variations in zooplankton abundance were large, but only minor spatial variation was detected between the stations, (3) OPC particles and chlorophyll a were associated, which indicates bottom-up regulation.

Acknowledgements

We thank the crew of R/V Muikku for their technical support and fieldwork assistance and the staff of the laboratory of the Ecological Research Institute for carrying out the chemical analyses. The English language of the paper was checked by Mr. Malcolm Hicks. This work was funded by the May and Tor Nessling Foundation and the University of Joensuu.

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