Modelling Vertical Migration:Combined Effects of Environment and Animal

Vertical migration is one of the most important types of animal migrations, encompassing most zooplankton and many fish species in the world’s oceans

Modellering av fordeling, vekst og overlevelse av fisk i Barentshavet

Ved hjelp av modeller for strøm, temperatur, plankton og fisk i Barentshavet skal fiskenes fordeling, vekst og overlevelse beregnes fra biologiske (evolusjonære, økologiske) og fysiske drivkrefter. Målet er å utvikle modeller der forståelse (teori) danner grunnlaget for prediksjonene. Klassiske forvaltningsmodeller er basert på observasjoner og statistikk, som gjør dem i stand til å håndtere konstante situasjoner, men svakere under variable forhold. Prosjektet har tre stadier: (1) utvikle modeller for vandring, vekst og overlevelse, (2) teste modellene på historiske data og (3) evaluere deres nytte i forvaltning og diskutere sammenkoplinger med eksisterende forvaltningsmodeller

Computational animal welfare: Towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.publishedVersio

Hormones as adaptive control systems in juvenile fish

Growth is an important theme in biology. Physiologists often relate growth rates to hormonal control of essential processes. Ecologists often study growth as a function of gradients or combinations of environmental factors. Fewer studies have investigated the combined effects of environmental and hormonal control on growth. Here, we present an evolutionary optimization model of fish growth that combines internal regulation of growth by hormone levels with the external influence of food availability and predation risk. The model finds a dynamic hormone profile that optimizes fish growth and survival up to 30 cm, and we use the probability of reaching this milestone as a proxy for fitness. The complex web of interrelated hormones and other signalling molecules is simplified to three functions represented by growth hormone, thyroid hormone and orexin. By studying a range from poor to rich environments, we find that the level of food availability in the environment results in different evolutionarily optimal strategies of hormone levels. With more food available, higher levels of hormones are optimal, resulting in higher food intake, standard metabolism and growth. By using this fitness-based approach we also find a consequence of evolutionary optimization of survival on optimal hormone use. Where foraging is risky, the thyroid hormone can be used strategically to increase metabolic potential and the chance of escaping from predators. By comparing model results to empirical observations, many mechanisms can be recognized, for instance a change in pace-of-life due to resource availability, and reduced emphasis on reserves in more stable environments.publishedVersio

Adaptive host responses to infection can resemble parasitic manipulation

Using a dynamic optimisation model for juvenile fish in stochastic food environments, we investigate optimal hormonal regulation, energy allocation and foraging behaviour of a growing host infected by a parasite that only incurs an energetic cost. We find it optimal for the infected host to have higher levels of orexin, growth and thyroid hormones, resulting in higher activity levels, increased foraging and faster growth. This growth strategy thus displays several of the fingerprints often associated with parasite manipulation: higher levels of metabolic hormones, faster growth, higher allocation to reserves (i.e. parasite-induced gigantism), higher risk-taking and eventually higher predation rate. However, there is no route for manipulation in our model, so these changes reflect adaptive host compensatory responses. Interestingly, several of these changes also increase the fitness of the parasite. Our results call for caution when interpreting observations of gigantism or risky host behaviours as parasite manipulation without further testing.publishedVersio

Modellering av fordeling, vekst og overlevelse av fisk i Barentshavet

Ved hjelp av modeller for strøm, temperatur, plankton og fisk i Barentshavet skal fiskenes fordeling, vekst og overlevelse beregnes fra biologiske (evolusjonære, økologiske) og fysiske drivkrefter. Målet er å utvikle modeller der forståelse (teori) danner grunnlaget for prediksjonene. Klassiske forvaltningsmodeller er basert på observasjoner og statistikk, som gjør dem i stand til å håndtere konstante situasjoner, men svakere under variable forhold. Prosjektet har tre stadier: (1) utvikle modeller for vandring, vekst og overlevelse, (2) teste modellene på historiske data og (3) evaluere deres nytte i forvaltning og diskutere sammenkoplinger med eksisterende forvaltningsmodeller

Making predictions in a changing world: The benefits of individual-based ecology

Ecologists urgently need a better ability to predict how environmental change affects biodiversity. We examine individual-based ecology (IBE), a research paradigm that promises better a predictive ability by using individual-based models (IBMs) to represent ecological dynamics as arising from how individuals interact with their environment and with each other. A key advantage of IBMs is that the basis for predictions-fitness maximization by individual organisms-is more general and reliable than the empirical relationships that other models depend on. Case studies illustrate the usefulness and predictive success of long-term IBE programs. The pioneering programs had three phases: conceptualization, implementation, and diversification. Continued validation of models runs throughout these phases. The breakthroughs that make IBE more productive include standards for describing and validating IBMs, improved and standardized theory for individual traits and behavior, software tools, and generalized instead of system-specific IBMs. We provide guidelines for pursuing IBE and a vision for future IBE research

Decision-Making From the Animal Perspective: Bridging Ecology and Subjective Cognition

Organisms have evolved to trade priorities across various needs, such as growth, survival, and reproduction. In naturally complex environments this incurs high computational costs. Models exist for several types of decisions, e.g., optimal foraging or life history theory. However, most models ignore proximate complexities and infer simple rules specific to each context. They try to deduce what the organism must do, but do not provide a mechanistic explanation of how it implements decisions. We posit that the underlying cognitive machinery cannot be ignored. From the point of view of the animal, the fundamental problems are what are the best contexts to choose and which stimuli require a response to achieve a specific goal (e.g., homeostasis, survival, reproduction). This requires a cognitive machinery enabling the organism to make predictions about the future and behave autonomously. Our simulation framework includes three essential aspects: (a) the focus on the autonomous individual, (b) the need to limit and integrate information from the environment, and (c) the importance of goal-directed rather than purely stimulus-driven cognitive and behavioral control. The resulting models integrate cognition, decision-making, and behavior in the whole phenotype that may include the genome, physiology, hormonal system, perception, emotions, motivation, and cognition. We conclude that the fundamental state is the global organismic state that includes both physiology and the animal's subjective “mind”. The approach provides an avenue for evolutionary understanding of subjective phenomena and self-awareness as evolved mechanisms for adaptive decision-making in natural environments

Individual based spatial models with evolved fish behaviour

Due to spatial heterogeneity, the survival and growth of fishes depend upon their habitat. For mobile pelagic fishes, it hence becomes important to analyse the factors responsible for spatial dynamics in order to understand population dynamics better. The current work is an attempt to approach fisheries assessment from a theoretical ecological perspective. We here present individual based single species and multispecies models where spatial behaviour and life history strategies are evolved. Spatial movement for each fish is calculated using an artificial neural network with weights evolved by a genetic algorithm. Behaviour relies on sensory information about food, temperature, light and predators. A concept for modelling life history strategies and migration in fish using this method is presented along with an application of this concept for the Barents Sea capelin. A predator-prey model based on the same principle is also presented. Potential areas of application for these kinds of models are discussed