No abstract

Marine snow aggregates are sites of elevated biological activity. This activity depends on the exchange of solutes (O 2 , CO 2 , mineral nutrients, dissolved organic material, etc.) between the aggregate and the environment and causes heterogeneity in the distribution of dissolved substances in the ambient water. We described the fluid flow and solute distribution around a sinking aggregate by solving the Navier-Stokes' equations and the advection-diffusion equations numerically. The model is valid for Reynolds numbers characteristic of marine snow, up to Re = 20. The model demonstrates the importance of a correct flow environment when making biological rate-measurements on aggregates (e.g., oxygen consumption/production, growth rates of bacteria and phytoplankton) because both solute fluxes and internal solute concentrations depend strongly on the flow environment. Observations of flow and oxygen-concentration fields in the vicinity of both artificial and natural oxygen-consuming aggregates that are suspended in a flow compare well with model predictions, thus suggesting that our set-up is suitable for making biological rate measurements. The sinking aggregate leaves a long slender plume in its wake, where solute concentration is either elevated (leaking substances) or depressed (consumed substances) relative to ambient concentration. Such plumes may impact the nutrition of osmotrophs. For example, based on published solubilization rates of aggregates we describe the amino acid plume behind a sinking aggregate (0.1 to 1.0 cm radius). The volume of the plume with amino acid concentrations high enough to significantly affect bacterial uptake rates is ca 100 × the volume of the aggregate itself. Thus, sinking aggregates may create significant microniches also for free-living bacteria.

When geolocating fish based on archival tag data, a realistic assessment of uncertainty is essential. Here, we describe an application of a novel Fokker–Planck-based method to geolocate Atlantic cod ( Gadus morhua ) in the North Sea area. In this study, the geolocation relies mainly on matching tidal patterns in depth measurements when a fish spends a prolonged period of time at the seabed with a tidal database. Each day, the method provides a nonparametric probability distribution of the position of a tagged fish and therefore avoids enforcing a particular distribution, such as a Gaussian distribution. In addition to the tidal component of the geolocation, the model incoporates two behavioural states, either high or low activity, estimated directly from the depth data, that affect the diffusivity parameter of the model and improves the precision and realism of the geolocation significantly. The new method provides access to the probability distribution of the position of the fish that in turn provides a range of useful descriptive statistics, such as the path of the most probable movement. We compare the method with existing alternatives and discuss its potential in making population inference from archival tag data.

We present a process‐based approach to estimate residency and behavior from uncertain and temporally correlated movement data collected with electronic tags. The estimation problem is formulated as a hidden Markov model (HMM) on a spatial grid in continuous time, which allows straightforward implementation of barriers to movement. Using the grid to explicitly resolve space, location estimation can be supplemented by or based entirely on environmental data (e.g. temperature, daylight). The HMM method can therefore analyze any type of electronic tag data. The HMM computes the joint posterior probability distribution of location and behavior at each point in time. With this, the behavioral state of the animal can be associated to regions in space, thus revealing migration corridors and residence areas. We demonstrate the inferential potential of the method by analyzing satellite‐linked archival tag data from a southern bluefin tuna Thunnus maccoyii where longitudinal coordinates inferred from daylight are supplemented by latitudinal information in recorded sea surface temperatures.