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.

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.This article has an associated First Person interview with the first author of the paper.

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.

The global potential for carbon capture and storage from forestry.

BACKGROUND: Discussions about limiting anthropogenic emissions of CO[Formula: see text] often focus on transition to renewable energy sources and on carbon capture and storage (CCS) of CO[Formula: see text]. The potential contributions from forests, forest products and other low-tech strategies are less frequently discussed. Here we develop a new simulation model to assess the global carbon content in forests and apply the model to study active annual carbon harvest 100 years into the future. RESULTS: The numerical experiments show that under a hypothetical scenario of globally sustainable forestry the world's forests could provide a large carbon sink, about one gigatonne per year, due to enhancement of carbon stock in tree biomass. In addition, a large amount of wood, 11.5 GT of carbon per year, could be extracted for reducing CO[Formula: see text] emissions by substitution of wood for fossil fuels. CONCLUSION: The results of this study indicate that carbon harvest from forests and carbon storage in living forests have a significant potential for CCS on a global scale.

What difference does it make if viruses are strain-, rather than species-specific?

Theoretical work has suggested an important role of lytic viruses in controlling the diversity of their prokaryotic hosts. Yet, providing strong experimental or observational support (or refutation) for this has proven evasive. Such models have usually assumed "host groups" to correspond to the "species" level, typically delimited by 16S rRNA gene sequence data. Recent model developments take into account the resolution of species into strains with differences in their susceptibility to viral attack. With strains as the host groups, the models will have explicit viral control of abundance at strain level, combined with explicit predator or resource control at community level, but the direct viral control at species level then disappears. Abundance of a species therefore emerges as the combination of how many strains, and at what abundance, this species can establish in competition with other species from a seeding community. We here discuss how species diversification and strain diversification may introduce competitors and defenders, respectively, and that the balance between the two may be a factor in the control of species diversity in mature natural communities. These models can also give a dominance of individuals from strains with high cost of resistance; suggesting that the high proportion of "dormant" cells among pelagic heterotrophic prokaryotes may reflect their need for expensive defense rather than the lack of suitable growth substrates in their environment.

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.

The emotion system promotes diversity and evolvability.

Studies on the relationship between the optimal phenotype and its environment have had limited focus on genotype-to-phenotype pathways and their evolutionary consequences. Here, we study how multi-layered trait architecture and its associated constraints prescribe diversity. Using an idealized model of the emotion system in fish, we find that trait architecture yields genetic and phenotypic diversity even in absence of frequency-dependent selection or environmental variation. That is, for a given environment, phenotype frequency distributions are predictable while gene pools are not. The conservation of phenotypic traits among these genetically different populations is due to the multi-layered trait architecture, in which one adaptation at a higher architectural level can be achieved by several different adaptations at a lower level. Our results emphasize the role of convergent evolution and the organismal level of selection. While trait architecture makes individuals more constrained than what has been assumed in optimization theory, the resulting populations are genetically more diverse and adaptable. The emotion system in animals may thus have evolved by natural selection because it simultaneously enhances three important functions, the behavioural robustness of individuals, the evolvability of gene pools and the rate of evolutionary innovation at several architectural levels.

Optimal defense strategies in an idealized microbial food web under trade-off between competition and defense.

Trophic mechanisms that can generate biodiversity in food webs include bottom-up (growth rate regulating) and top-down (biomass regulating) factors. The top-down control has traditionally been analyzed using the concepts of "Keystone Predation" (KP) and "Killing-the-Winner" (KtW), predominately occuring in discussions of macro- and micro-biological ecology, respectively. Here we combine the classical diamond-shaped food web structure frequently discussed in KP analyses and the KtW concept by introducing a defense strategist capable of partial defense. A formalized description of a trade-off between the defense-strategist's competitive and defensive ability is included. The analysis reveals a complex topology of the steady state solution with strong relationships between food web structure and the combination of trade-off, defense strategy and the system's nutrient content. Among the results is a difference in defense strategies corresponding to maximum biomass, production, or net growth rate of invading individuals. The analysis thus summons awareness that biomass or production, parameters typically measured in field studies to infer success of particular biota, are not directly acted upon by natural selection. Under coexistence with a competition specialist, a balance of competitive and defensive ability of the defense strategist was found to be evolutionarily stable, whereas stronger defense was optimal under increased nutrient levels in the absence of the pure competition specialist. The findings of success of different defense strategies are discussed with respect to SAR11, a highly successful bacterial clade in the pelagic ocean.

A theoretical analysis of how strain-specific viruses can control microbial species diversity.

Pelagic prokaryote communities are often dominated by the SAR11 clade. The recent discovery of viruses infecting this clade led to the suggestion that such dominance could not be explained by assuming SAR11 to be a defense specialist and that the explanation therefore should be sought in its competitive abilities. The issue is complicated by the fact that prokaryotes may develop strains differing in their balance between competition and viral defense, a situation not really captured by present idealized models that operate only with virus-controlled "host groups." We here develop a theoretical framework where abundance within species emerges as the sum over virus-controlled strains and show that high abundance then is likely to occur for species able to use defense mechanisms with a low trade-off between competition and defense, rather than by extreme investment in one strategy or the other. The J-shaped activity-abundance community distribution derived from this analysis explains the high proportion low-active prokaryotes as a consequence of extreme defense as an alternative to explanations based on dormancy or death due to nutrient starvation.

Effects of the emotion system on adaptive behavior.

A central simplifying assumption in evolutionary behavioral ecology has been that optimal behavior is unaffected by genetic or proximate constraints. Observations and experiments show otherwise, so that attention to decision architecture and mechanisms is needed. In psychology, the proximate constraints on decision making and the processes from perception to behavior are collectively described as the emotion system. We specify a model of the emotion system in fish that includes sensory input, neuronal computation, developmental modulation, and a global organismic state and restricts attention during decision making for behavioral outcomes. The model further includes food competition, safety in numbers, and a fluctuating environment. We find that emergent strategies in evolved populations include common emotional appraisal of sensory input related to fear and hunger and also include frequency-dependent rules for behavioral responses. Focused attention is at times more important than spatial behavior for growth and survival. Spatial segregation of the population is driven by personality differences. By coupling proximate and immediate influences on behavior with ultimate fitness consequences through the emotion system, this approach contributes to a unified perspective on the phenotype, by integrating effects of the environment, genetics, development, physiology, behavior, life history, and evolution.

Selective consequences of catastrophes for growth rates in a stream-dwelling salmonid.

Optimal life histories in a fluctuating environment are likely to differ from those that are optimal in a constant environment, but we have little understanding of the consequences of bounded fluctuations versus episodic massive mortality events. Catastrophic disturbances, such as floods, droughts, landslides and fires, substantially alter the population dynamics of affected populations, but little has been done to investigate how catastrophes may act as a selective agent for life-history traits. We use an individual-based model of population dynamics of the stream-dwelling salmonid marble trout (Salmo marmoratus) to investigate how trade-offs between the growth and mortality of individuals and density-dependent body growth can lead to the maintenance of a wide or narrow range of individual variation in body growth rates in environments that are constant (i.e., only demographic stochasticity), variable (i.e., environmental stochasticity), or variable with catastrophic events that cause massive mortalities (e.g., flash floods). We find that occasional episodes of massive mortality can substantially reduce persistent variability in individual growth rates. Lowering the population density reduces density dependence and allows for higher fitness of more opportunistic strategies (rapid growth and early maturation) during the recovery period.

Quantifying the adaptive value of learning in foraging behavior.

The value of acquiring environmental information depends on the costs of collecting it and its utility. Foragers that search for patchily distributed resources may use experiences in previous patches to learn the habitat quality and adjust their behavior. We map the ecological landscape for the evolution of learning under a range of conditions, including both spatial and temporal heterogeneity. We compare the learning strategy with genetically fixed patch-leaving rules and with strategies of foragers that have free and perfect information about their environment. The model reveals that the efficiency of learning is highest when low encounter stochasticity results in reliable estimates of patch quality, when there is no or little temporal change, and when there is little spatial variability. This partially contrasts with the value of learning, which is highest when there is temporal change, because flexible strategies may track the environmental trend, and when there is spatial variability, because there is a need to distinguish between good and bad patches. Learning rules with short-term memory are beneficial when patch information is accurate and when there is temporal change, whereas learning rules that update slowly are generally more robust to spatial variability.

Artificial evolution of life history and behavior.

We present an individual-based model that uses artificial evolution to predict fit behavior and life-history traits on the basis of environmental data and organism physiology. Our main purpose is to investigate whether artificial evolution is a suitable tool for studying life history and behavior of real biological organisms. The evolutionary adaptation is founded on a genetic algorithm that searches for improved solutions to the traits under scrutiny. From the genetic algorithm's "genetic code," behavior is determined using an artificial neural network. The marine planktivorous fish Müller's pearlside (Maurolicus muelleri) is used as the model organism because of the broad knowledge of its behavior and life history, by which the model's performance is evaluated. The model adapts three traits: habitat choice, energy allocation, and spawning strategy. We present one simulation with, and one without, stochastic juvenile survival. Spawning pattern, longevity, and energy allocation are the life-history traits most affected by stochastic juvenile survival. Predicted behavior is in good agreement with field observations and with previous modeling results, validating the usefulness of the presented model in particular and artificial evolution in ecological modeling in general. The advantages, possibilities, and limitations of this modeling approach are further discussed.