We used (1) water temperature (a proxy for water movement), (2) chlorophyll (a proxy for phytoplankton), and (3) zooplankton (bulk, small, and large size classes) to investigate the relationship between changing wind conditions and spatial patterns along linear transects (n 5 150) in two basins, South Arm and Annie Bay, of Lake Opeongo (Ontario, Canada). The basins have similar biological characteristics, but South Arm is larger and is oriented along the prevailing westerly wind direction. Large-scale patterns (.1 km) were described with an accumulation index, and wavelet analysis was used to describe small-scale patterns (,1 km). Spatial descriptors were correlated with five descriptors of wind conditions: (1) wind force, (2) scalar wind speed, (3) vector wind speed, (4) wind persistence, and (5) wind direction. Persistent westerly winds in South Arm resulted in more downwind accumulation of warm water and total and large-bodied zooplankton than in Annie Bay, while chlorophyll and small zooplankton did not show consistent downwind accumulation. The predominance of smallscale variability, particularly in large zooplankton, increased in the South Arm as the persistence and strength of westerly winds blowing parallel to the sampling transects increased. Only temperature showed such a pattern in the smaller Annie Bay. These patterns were not related to winds blowing at the time of sampling but rather to those blowing up to 12 h before sampling. Our observations provide a basis for future consideration of how simple surface winds may actually shape the nature of trophic interactions in lake ecosystems.

Error propagation is the calculation of statistical error in a quantity that comprises multiple components each with associated error. Such quantities constitute the end point of many ecological studies, but composite errors are rarely incorporated so that uncertainty in the final estimate is either unknown or underestimated. In this study we present the use of both parametric (observations are resampled from standard probability distributions) and nonparametric (raw observations are resampled) bootstrap techniques to propagate errors through the many steps involved in the egg‐ratio estimation of seasonal production for the freshwater zooplankter Bythotrephes longimanus. We first compute parametric and nonparametric bootstrap estimates of the standard deviation in seasonal production of B. longimanus, showing that it ranges from 21% to 27% of observed seasonal production. We demonstrate that our bootstrapping procedures are robust by developing a theoretical model and showing that the true variance of a parameter is included in 96% of the simulated 95% confidence intervals. We also show that the choice of probability distribution in the parametric bootstrap can change the standard deviation of seasonal production by up to 90%. We argue that ecologists should use such error propagation techniques more routinely than is currently the case.

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We measured spatial patterns of zooplankton and chlorophyll concentration (a proxy for phytoplankton) with continuous sensors along horizontal transects that were repeatedly sampled (n 5 150) under varying wind conditions throughout a growing season in two basins (South Arm and Annie Bay) of Lake Opeongo, Ontario, Canada. Spatially explicit in situ simulations that included activity costs associated with feeding were used to examine the effects of chlorophyll patchiness on the energy gain in different zooplankton communities. Simulations were repeated for several zooplankton size classes (small, large, and bulk) and two communities (all copepods and all cladocerans). For each simulated combination, a spatial energetic differential (SED) was estimated by contrasting the energy that zooplankton could gain using observed spatial patterns in chlorophyll and water temperature with the energy they could gain using uniform concentrations of chlorophyll and water temperature. Large zooplankton showed the greatest SED range across all communities, from a decrease of 8% to a maximum increase of 20%, assuming relatively low costs associated with feeding activity. Small zooplankton had the narrowest SED range. Zooplankton energy gain is sensitive to both the degree of zooplanktonchlorophyll spatial overlap and energetic costs associated with zooplankton feeding activity. SED values as high as 485% can occur under plausible estimates of activity costs. Wind-driven increases in spatial overlap between predator and prey can be large enough to substantially alter planktonic trophic interactions.In marine and freshwater ecosystems, both phytoplankton and zooplankton have patchy distributions that occur over a wide range of spatiotemporal scales. Although there is general agreement that the predominant drivers of spatial heterogeneity in phytoplankton distributions are physical (e.g., wind-driven currents), the relative importance of physical vs. biological drivers for zooplankton spatial distributions has been the subject of more debate (Martin 2003). Recent work is shifting this view by demonstrating that physical drivers, by themselves, are insufficient to explain the observed spatial structure across all scales (Martin 2003). The ''multiple driving forces hypothesis '' (Pinel-Alloul 1995) contends that physical drivers have strong control of zooplankton patchiness at large scales, but that the strength of biological drivers increases at small scales.Zooplankton play an important role in trophic interactions as they prey on phytoplankton and serve as food for fish. To gain a better understanding of such interactions, many researchers have used computer simulations (Martin 2003). Most simulations to date have estimated predator (zooplankton) consumption using statistical distributions, rather than observed data, to generate the predator patchiness, the prey (phytoplankton) patchiness, or both (Martin 2003). A few studies have computed consumption directly from observed data (Mullin and Brooks 1976; Sprules 2000). Mull...