Seasonal and diel patchiness of a Daphnia population: An acoustic analysis

Abstract

Detailed information about the location and extent of zooplankton patches is fundamental to understand how abiotic and biotic forces interact to structure the spatial distribution of zooplankton. We mapped zooplankton patchiness in a Minnesota lake during spring, summer, and autumn with high-frequency (192-kHz) single-beam sonar. Conventional plankton samples of aggregations detected acoustically revealed that Daphnia pulicaria (mean body length 1.6 mm, mean target strength Ϫ120 dB) scattered most (ϳ63%) of the sound. Other taxa were smaller (Ͻ½ the length of D. pulicaria) and were usually less abundant and therefore scattered much less sound than D. pulicaria. Our acoustic estimates of Daphnia concentrations illustrate extreme patchiness, with concentrations varying by as much as four orders of magnitude over vertical distances of less than 1 m. Seasonal patterns of patchiness were related to predation by rainbow trout and to abiotic factors associated with stratification. Daphnia concentrations were highest from June to October in a deep-water ''refuge zone'' where oxygen concentrations were between 3 and 5 mg L Ϫ1 . These oxygen levels are suitable for Daphnia but are lower than those required by rainbow trout. Heterogeneity in Daphnia concentration along the lake's long axis was highest in May and June, when the population resided primarily in the oxic hypolimnion during the daytime. From July to October, as oxygen concentrations declined in the hypolimnion, the population became more metalimnetic and more uniformly distributed in the horizontal dimension. A diel study of the population in October indicated that the patchiness of population also changed dramatically between day and night. During the day the population aggregated densely in a thin layer (ϳ2 m thick) in the thermocline. After sunset the population dispersed into the epilimnion, where concentrations were ϳ100,000 m Ϫ3 less than they were during the day in the thermocline.Patchy spatial distributions and the low resolution of conventional sampling methods have impeded analyses of zooplankton populations. Zooplankton concentrations have been shown to vary by a factor of 1,000 within distances of meters horizontally or vertically, and the sampling resolution of conventional plankton nets and hoses is usually too coarse to identify the spatial limits of aggregations precisely (Coyle 2000). Variation in population density estimates due to sampling often cannot be distinguished from real changes of population density, and the effects of biological processes are difficult to distinguish from those of advective transport (Megard et al. 1997). Acoustic and optical plankton samplers developed during recent decades are major advances. They have very high sampling rates and spatial resolution, comparable to modern instruments (e.g., CTD profilers) used to measure environmental variables. Large numbers of plank-

Variability inCalanusspp. abundance on fine- to mesoscales in an Arctic fjord: implications for little auk feeding

“…For this particular potential feeding ground a sampling grid of less than 2 km would be necessary to cover the variation in the area. Given the logistical demands associated with sampling at such resolution, modern high-resolution sampling equipment such as continuous net-plankton pumping (Molinero et al 2008), optical plankton recorder (Pinel-Alloul 1995;Currie et al 1998) and acoustics (ADCP) (Pinel-Alloul 1995;Tokarev et al 1998;Coyle 2000;Hembre & Megard 2003) are necessary complements to traditional plankton net tows. Furthermore, the heterogeneity in horizontal distribution also raises questions about the extent and nature of vertical patchiness.…”

“…Characteristic horizontal length scales of plankton patches range from less than 1 m to more than 10 4 m (Legendre et al 1986;Tsuda et al 1993;Currie et al 1998;Mann & Lazier 2006, see Table I for details). Such variability can complicate interpretations of ecological data sets obtained from a limited number of sampling stations as such data may not adequately capture relevant meso-and fine-scale spatial variability (Hembre & Megard 2003). Yet ecological studies often extrapolate data based on only a few sampling locations that at best reflect variability on one or a restricted number of spatial scales.…”

“…Such investigations may produce over-smoothed data sets that render the detection of ecologically significant variability unlikely and may potentially lead to biased interpretations (Young et al 2009). It is therefore of importance to design the sampling scheme in a way that captures the variability relevant to the processes of interest (Tsuda et al 1993;Hembre & Megard 2003;Molinero et al 2008;Young et al 2009). …”

Vertical distribution of zooplankton in a shallow peatland pond: the limiting role of dissolved oxygen

“…A similar phenomenon was found by Blinn and Green (1986) in a senescent, high altitude crater pond in Arizona, when the highest densities of zooplankton were seasonally most frequently restricted to the upper, well-oxygenated 30-cmlayer. The phenomenon that zooplanktons are restricted to the meta-and epilimnion is typical in thermally stratified larger lakes with hypoxia in the hypolimnion (Sell, 1998;Hembre and Megard, 2003). Under these conditions, even benthic species migrate upwards to the oxygenated layers (Tinson and Laybourn-Parry, 1985).…”

Active and passive vertical motion of zooplankton in a lake

“…Acoustic measurements are widely used for the observation of zooplankton distributions in lakes and oceans. The high spatial and temporal resolution offered by this method, combined with the possibility for autonomous deployment or ship-based operation, can be used for longterm measurements of DVM (Pinot and Jansá 2001;Cottier et al 2006;Record and de Young 2006) and spatial distribution of zooplankton (Greene et al 1998;Hembre and Megard 2003), respectively. Such high-resolution data allow the spectral distribution of zooplankton variability to be determined and thus provide a direct comparison to spectral characteristics obtained from theoretical arguments or numerical models (Abraham 1998;Lovejoy et al 2001).…”