Resolution changes relationships: Optimizing sampling design using small scale zooplankton data

Abstract

Marine research surveys are an integral tool in understanding the marine environment. Recent technological advances have allowed the development of automated or semi-automated methods for the collection of marine data. These devices are often easily implemented on existing surveys and can collect data at finer spatiotemporal resolutions than traditional devices. We used two automated instruments: the Plankton Imager and FerryBox, to collect information on zooplankton, temperature, salinity and chlorophyll in the Celtic Sea. The resulting data were spatiotemporally aligned and merged to decreasing spatial resolutions to explore how distribution patterns and the relationship between variables change across different spatial resolutions. Relative standard deviation was used to describe variability of merged data within grid cells. All variables displayed large, area-wide spatial patterns excluding copepod size which remained consistent across the study area. Copepod biomass and abundance displayed high variations across small spatial scales. Decreasing the sampling resolution changed the description of the data where small spatial changes (those that occur over scales < 3 km) were lost and area wide patterns were emphasized. Furthermore, we found that the choice of resolution can affect both the statistical strength and significance of relationships with high variability at lower resolutions due to the mismatch between the scales of ecological processes and sampling. Determining the optimum sampling resolution to answer a specific question will be dependent upon several factors, mainly the variable measured, season, location and scale of process, which all drive variation. These considerations should be a key element of survey design, helping move towards an integrated approach for an improved understanding of ecosystem processes and gaining a more holistic description of the marine environment.