SAMPLING AND ANALYSIS OF GUT CONTENTS IN RELATION TO ENVIRONMENTAL VARIABILITY AND DIEL VERTICAL MIGRATION BY HERBIVOROUS ZOOPLANKTON

The accuracy of standard sampling and analysis procedures for estimati ng ingestion by herbivorous zooplankton was assessed using models. Art ificial environments were created in a computer model, allowing for de pth-dependent variability in temperature, chlorophyll and primary prod uction. Model zooplankton were simulated within these artificial envir onments using individual-based models. The model zooplankton feed and defaecate at rates determined by temperature and food concentrations, and also exhibit diel vertical migration (DVM) according to a variety of migration models. The computer model was run for different combinat ions of these nine environmental and five DVM models. Data were 'sampl ed' from the model output, similar to field sampling of mesozooplankto n grazing. Daily ingestion was calculated from the gut 'samples' using standard procedures for analysing gut fluorescence. The sample result s were compared with the actual ingestion values in the model, and som e causes of discrepancies were noted. (i) If incorrect temperatures we re assumed when calculating the gut evacuation rate (K),then estimates of ingestion were wrong by up to 40%. (ii) Non-uniform food environme nts gave errors of up to 30% because of the large variability of measu red gut contents among individuals. (iii) Sampling from only part of t he total depth range (e.g. at the chlorophyll maximum) resulted in est imates of ingestion being only 5% of the real value. This sampling pra ctice should be discouraged, because the sample is not random. (iv) If sampling is not frequent enough, errors can be as large as 45%, but m ore usually were similar to 10% for realistic sampling frequencies. We describe an analysis procedure that uses Monte Carlo-type simulations in a computer spreadsheet to estimate population consumption. These c alculations take into account natural variability due to populations, samples and assumptions. We urge that results should be presented as r anges of possible values, rather than as single 'mean' values, to allo w for easier recognition of meaningful differences among samples and s ystems.