Morten Omholt Alver

An individual-based population model for rotifer (Brachionus plicatilis) cultures

An individual-based dynamic energy budget model is used in a Lagrangian simulation to compute population dynamics for the rotifer strain Brachionus plicatilis. This model structure allows description of transient as well as stationary conditions, making the model useful for a variety of applications. It also has the advantage of allowing the use of dynamic energy budged (DEB) theory in describing rotifer energetics. The model is developed with aquaculture-related applications in mind, including planning, monitoring and control of rotifer production systems and first feeding of marine fish larvae. Simulations show acceptable agreement with measured data on the population level, with regard to steady-state egg ratio, yields of daily diluted cultures, and maximum net growth rate. Further refinement of the model can enable its application for processes such as essential nutrient enrichment of rotifers for first feeding of larval fish.

Modelling growth performance and feeding behaviour of Atlantic salmon (Salmo salar L.) in commercial-size aquaculture net pens: Model details and validation through full-scale experiments

We have developed a mathematical model which estimates the growth performance of Atlantic salmon in aquaculture production units. The model consists of sub-models estimating the behaviour and energetics of the fish, the distribution of feed pellets, and the abiotic conditions in the water column. A field experiment where three full-scale cages stocked with 120,000 salmon each (initial mean weight 72.1 ± SD 2.8 g) were monitored over six months was used to validate the model. The model was set up to simulate fish growth for all the three cages using the feeding regimes and observed environmental data as input, and simulation results were compared with the experimental data. Experimental fish achieved end weights of 878, 849 and 739 g in the three cages respectively. However, the fish contracted Pancreas Disease (PD) midway through the experiment, a factor which is expected to impair growth and increase mortality rate. The model was found able to predict growth rates for the initial period when the fish appeared to be healthy. Since the effects of PD on fish performance are not modelled, growth rates were overestimated during the most severe disease period. This work illustrates how models can be powerful tools for predicting the performance of salmon in commercial production, and also imply their potential for predicting differences between commercial scale and smaller experimental scales. Furthermore, such models could be tools for early detection of disease outbreaks, as seen in the deviations between model and observations caused by the PD outbreak. A model could potentially also give indications on how the growth performance of the fish will suffer during such outbreaks. Statement of relevance We believe that our manuscript is relevant for the aquaculture industry as it examines the growth performance of salmon in a fish farm in detail at a scale, both in terms of number of fish and in terms of duration, that is higher than usual for such studies. In addition, the fish contracted a disease (PD) midway through the experiment, thus resulting in a detailed dataset containing information on how PD affects salmon growth, which can serve as a foundation to understanding disease effects better. Furthermore, the manuscript describes an integrated mathematical model that is able to predict fish behaviour, growth and energetics of salmon in response to commercial production conditions, including a dynamic model of the distribution of feed pellets in the production volume. To our knowledge, there exist no models aspiring to estimate such a broad spectre of the dynamics in commercial aquaculture production cages. We believe this model could serve as a future tool to predict the dynamics in commercial aquaculture net pens, and that it could represent a building block that can be utilised in a future development of knowledge-driven decision-support tools for the salmon industry.

An individual-based population model for the prediction of rotifer population dynamics and resting egg production

Most species of rotifers have a combination of sexual and asexual reproduction, with sexual reproduction resulting in resting eggs, which can lay dormant for long periods. The occurrence of sexual reproduction affects population dynamics through the temporary presence of male rotifers, and a reduction in the growth of the number of female rotifers. A previously published, individual-based model used dynamic energy budget theory to describe rotifer food intake, growth, egg production, and mortality, but assumed asexual reproduction only. In the current study, we have expanded the model to describe the entire reproductive cycle of the rotifers, making it usable for investigating relationships, such as those between the signal triggering mictic egg production, and the timing and number of resting eggs produced. The model is intended for use in predicting the specific future development of cultures, for instance, as a process model in rotifer or resting egg production for aquaculture.

A system for model-based biomass estimation of larvae in intensive cod larvicultures

High and unpredictable mortality rates are observed in the larval rearing of cod (Gadus morhua). As a means of addressing this problem, we present a model-based estimator system which can be used to indirectly measure the larval density through monitoring the live food dynamics and larval growth. The estimator has been evaluated in a conceptual context using a preliminary model formulation, and the observability of the process has been investigated. It was found that the two parameters, live food dynamics and larval growth, contain enough information for the larval density to be estimated under noisy conditions, given the correct model. When the system is applied practically, the estimation error will depend on the measurement and model accuracy; this is especially true with respect to the predictability of the feed intake rate of the fish.

Modelling spatial surface pellet distribution from rotary pneumatic feed spreaders

This paper presents a combined robotic and external ballistic model to predict the feed pellet distribution pattern across the water surface generated by a pneumatic rotary feed spreader commonly used in sea cage aquaculture. Results from experimental studies have been used to parameterize and validate the model. The model can be applied to evaluate spreader performance under varying operational conditions as well as exploring alternative spreader designs and configurations in order to optimize pellet distribution and feed utilization with respect to fish growth and welfare.

A computer vision approach for detection and quantification of feed particles in marine fish farms

In the realm of marine fish farming, there is increased focus on employing numerical models and tools to optimize production. A model describing the distribution of pelleted fish feed in time and space within a sea cage, a process which is essential for proper fish growth and welfare, has been established, but proper data for model validation have been scarce. A device based on computer vision which is able to accurately quantify the feed density within a specified volume of the sea cage as a function of time was thus developed. This paper describes the physical design of the device, as well as the application and combination of well-established algorithms to reliably detect and quantify feed pellets. Results from tests using realistic feed densities showed that the device was capable of detecting and quantifying with an error of 1.3 %.

Effects of wind on surface feed distribution in sea cage aquaculture: A simulation study

This study examines the simulated effects of wind on the spatial surface distribution of pelletized feed from rotary pneumatic spreaders which are used extensively in sea cage aquaculture. A robotic model of the spreader and external ballistic description of the pellet trajectories have been extended to include wind forces, and the spatial distribution is compared for different sized pellets in various wind fields. The results show that overall effects of wind on spatial pellet distribution is limited, however, there is a negative correlation between wind and surface coverage.

Feed spreaders in sea cage aquaculture – Motion characterization and measurement of spatial pellet distribution using an unmanned aerial vehicle

Abstract Pneumatic rotary feed spreaders represent essential equipment in the feeding system of present day industrial-scale sea cage aquaculture. This study presents experimentally obtained attitude measurements and corresponding surface distribution patterns of pellets in order to characterize the dynamic behavior and performance of such spreaders. Spreader attitude and direction were measured by employing an attitude and heading reference system along with a rotary encoder. In addition, an unmanned aerial vehicle (UAV) was used to record pellet surface impacts from the air, and the position and direction of the spreader was obtained by applying computer vision methods to the recorded video. The proposed UAV method was fast to deploy, requires minimal equipment installation and presents a viable alternative to the approach of collecting pellets manually using Styrofoam boxes as reported in earlier studies. The findings from this study may be used as a base for further development and refinement with respect to using an UAV to observe the performance and spatial pellet distribution from various feed spreaders used in aquaculture. Such a tool may be valuable for farmers and equipment producers which may easily evaluate the performance of various spreader designs. In addition, the results serve as valuable input for parametrization and validation of mathematical feed spreader models.

Information-driven robotic sampling in the coastal ocean

Nansen Legacy Program, Grant/AwardNumber:27272; Senter for Autonome Marine Operasjoner og Systemer,Grant/Award Number: 223254; Norges Forskningsrad,Grant/Award Number: 255303/E40; European Unions Seventh Framework Programme(FP7/2007–2013), Grant/Award Number: 270180

Optimal in situ measurement networks for ocean data

This paper investigates four methods for calculation of fixed measurement positions for acquiring knowledge about an ocean area by observation and model based analysis. The qualities and limitations of the methods, applied to a rather complicated ocean area, is studied. We investigate two methods based on covariations in ocean data; (i) an extension of the data assimilation based method by [1] with the tabu search method by [2], and (ii) an extension of the footprint method by [3] by including time-lags and calculation of the percentage of coverage for each position in the ocean area that is investigated. Further, we study variance map and maps of mean values for the same purpose. The approach of using mean value maps has not been reported in the literature for ocean data. The importance of each measurement position is evaluated in order to decide how many sensors that are necessary to monitor an ocean area. The two methods based on current covariations show considerable differences in calculated positions due to the different backgrounds for the two methods. The SINTEF ocean model SINMOD, set up for an area at the Rataren marine installation close to Fr⊘ya, Norway, is used in a case study. This is a shallow area where the current pattern is rather complex due to the bathymetry.