Deep Sea Research Part II: Topical Studies in Oceanography
Estimates of oceanic mesozooplankton production: a comparison using the Bermuda and Hawaii time-series data
Introduction
In situ copepod growth estimates have been made in restricted water bodies such as lakes, mesocosms, lagoons and bays where emigration and immigration are zero or minimal. In situ growth measurements in open waters have been conducted while following drogues (i.e. Cushing and Tungate, 1963), but the measurements are extremely time-consuming and it is difficult/impossible to follow the same population/water mass over enough time to measure copepod growth rates. Shipboard incubation techniques have been used for growth estimates for individual copepod species based on molting frequency (e.g., Miller et al., 1984) and egg production (e.g., Kiørboe and Johansen, 1986; Berggreen et al., 1988), but these techniques are subject to a variety of containment effects and are of limited value for overall copepod community growth estimates in tropical and sub-tropical seas where the species diversity of copepods is great (e.g., Grice and Hart, 1962; Timonin, 1971). In addition, in some circumstances weight-specific reproductive growth (i.e. egg production) is not the same as weight-specific somatic (increase in body mass) growth (McKinnon, 1996; Hopcroft and Roff, 1998), so that the egg production method may not accurately represent the growth of the whole copepod population.
Another approach to estimating copepod metabolism is based on regression models that use temperature (e.g. Huntley and Lopez, 1992) or temperature and body size (e.g. McLaren, 1965; Ikeda and Motoda, 1975; Hirst and Sheader, 1997; Hirst and Lampitt, 1998) to predict copepod growth rates. These empirical models are based on laboratory, and in some cases, field measurements of copepod growth rates (somatic growth) over a range of temperatures, food conditions, copepod body sizes and copepod species. The predictive equations assume that all species (sizes of copepods) grow at the same rate at a particular temperature. In some cases we know that this is not true. For example, cyclopoid copepods may grow slower than the same size calanoid copepods (Lampitt and Gamble, 1982; Kiørboe and Sabatini, 1995; Hopcroft et al., 1998). This regression approach also has been criticized (Kleppel et al., 1996; Calbet and Agusti, 1999) because it assumes that growth rate is not food limited. The egg production rates of copepods have been shown to be food-limited in a variety of marine waters (e.g. Checkley, 1980; Durbin et al., 1983; Saiz and Kiørboe, 1995). It is interesting to note, however, that spatial and temporal estimates of copepod biomass can vary by several orders of magnitude whereas published copepod growth rates at similar temperature varies by 2–4 times (e.g. Huntley and Lopez, 1992; Hopcroft et al., 1998). Thus for estimates of copepod production (growth rate×biomass) the errors associated with estimates of growth rate are probably less compared to those of biomass.
We have used an empirical regression model (Hirst and Lampitt, 1998) to estimate mesozooplankton production and assumed rates of ingestion and egestion at the Hawaiian and Bermuda ocean time-series stations, both part of the US Joint Global Ocean Flux Study (US JGOFS) program. At these study sites, seasonal measurements of mesozooplankton size fractions and species composition can be used for size-based copepod growth models. JGOFS core measurements allow these mesozooplankton rate estimates to be compared to rates of primary production and export flux to deduce the role of mesozooplankton in the cycling of biogenic material. Repeated mesozooplankton biomass measurements over the year can provide the necessary data for crude estimates of annual mesozooplankton production, which can be compared to annual estimates of primary production and the gravitational flux of material from the euphotic zone.
Section snippets
Mesozooplankton sampling
Mesozooplankton samples were collected at the Hawaiian ocean time-series (HOT; 22°45′N, 158°00′W; Karl and Lukas, 1996) and Bermuda Atlantic time-series (BATS; 31°50′N, 64°10′W; Michaels and Knap, 1996) stations. The details of mesozooplankton sampling and processing have been described for both HOT (Landry et al., 2001) and BATS (Madin et al., 2001). Briefly, oblique tows were taken with a 1 m-diameter (BATS) or 1-m2 (HOT), 200-μm mesh net equipped with a General Oceanics flowmeter and
Mesozooplankton biomass
The seasonal cycles of mesozooplankton biomass at HOT (Landry et al., 2001) and BATS (Madin et al., 2001) have been described previously. In general, there was over twice as much mesozooplankton at HOT compared to BATS (Fig. 1; Table 1), with the average being 23.84 mmol C m−2 at HOT and 11.38 mmol C m−2 at BATS for samples collected between 1994 and 1997. There were significant (P<0.05) seasonal differences in mesozooplankton biomass at HOT, with the highest mean values found in the fall and the
Discussion
Our estimates of mesozooplankton growth, production, mortality, egestion and ingestion will be overestimates if the actual in situ mesozooplankton growth rates were food-limited. The 935 published growth rates used to generate the Hirst and Lampitt (1998) equation covered a range of food conditions, some of which were food-limited growth. The average estimated mesozooplankton growth rates, weighted by the distribution of biomass size fractions, were 0.09 and 0.08 d−1 at HOT and BATS,
Acknowledgments
This research was support by Grant OCE-9725976 from the National Science Foundation as part of the US JGOFS Synthesis and Modeling study. We are grateful to the scientists involved in the HOT and BATS time-series programs for collecting and analyzing the data. Scott Doney and two anonymous reviewers made helpful comments on an earlier version of the manuscript. This is University of Maryland Center for Environmental Sciences Contribution No. 3486; and US JGOFS Contribution No. 693.
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