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      Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off

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          Abstract

          Internal states can profoundly alter the behavior of animals. A quantitative understanding of the behavioral changes upon metabolic challenges is key to a mechanistic dissection of how animals maintain nutritional homeostasis. We used an automated video tracking setup to characterize how amino acid and reproductive states interact to shape exploitation and exploration decisions taken by adult Drosophila melanogaster. We find that these two states have specific effects on the decisions to stop at and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient-deprived flies focus on specific patches while satiated flies explore more globally. Finally, we show that olfaction mediates the efficient recognition of yeast as an appropriate protein source in mated females and that octopamine is specifically required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Internal states therefore modulate specific aspects of exploitation and exploration to change nutrient selection.

          DOI: http://dx.doi.org/10.7554/eLife.19920.001

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          When making decisions, animals, including humans, do not always choose the same option. One reason for this is that their “internal state” changes the value of different options. This is particularly evident when deciding what type of food to eat. Depending on which nutrients the animal needs, it will choose to eat different foods.

          Amino acids are key nutrients that affect health, lifespan and reproduction. Female fruit flies that have recently mated, for example, eat more amino acids in order to obtain the raw materials required to produce eggs. Despite the importance of amino acids, little was known about how animal behavior changes in response to a lack of this nutrient.

          Corrales-Carvajal et al. used a video tracking system to measure the time that fruit flies – some of which had a need for amino acids – spent feeding on patches of yeast (which are rich in amino acids) versus patches of sucrose. Recently mated females – and virgins that had been fed a diet lacking in amino acids – consumed more yeast than sucrose, whereas virgin females that were not amino acid deficient showed the opposite pattern. To bias the fly toward eating the right food for their needs, several aspects of the fly’s behavior changed, including the number and length of individual feeding bouts. These different behaviors did not all change at the same time.

          The pattern of exploration taken by the flies also depended on their need for amino acids. Amino acid deficient flies spent most of their time near known yeast patches. By contrast, fully fed flies adopted a riskier foraging strategy, moving away from known sources of food to explore their environment more widely. In common with humans, the flies relied upon their sense of smell to efficiently identify different types of food.

          Overall, the results presented by Corrales-Carvajal et al. provide us with a detailed understanding about how changes to the internal state of the fly affect its behavior. The next step will be to use the powerful genetic tools available for studying fruit flies to reveal the neural circuits and molecular mechanisms that help animals find the types of food that they need.

          DOI: http://dx.doi.org/10.7554/eLife.19920.002

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          Most cited references59

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          Power-law distributions in empirical data

          Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution -- the part of the distribution representing large but rare events -- and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data while in others the power law is ruled out.
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            Behavioural reaction norms: animal personality meets individual plasticity

            Recent studies in the field of behavioural ecology have revealed intriguing variation in behaviour within single populations. Increasing evidence suggests that individual animals differ in their average level of behaviour displayed across a range of contexts (animal 'personality'), and in their responsiveness to environmental variation (plasticity), and that these phenomena can be considered complementary aspects of the individual phenotype. How should this complex variation be studied? Here, we outline how central ideas in behavioural ecology and quantitative genetics can be combined within a single framework based on the concept of 'behavioural reaction norms'. This integrative approach facilitates analysis of phenomena usually studied separately in terms of personality and plasticity, thereby enhancing understanding of their adaptive nature. Copyright 2009 Elsevier Ltd. All rights reserved.
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              High-throughput Ethomics in Large Groups of Drosophila

              We present a camera-based method for automatically quantifying the individual and social behaviors of fruit flies, Drosophila melanogaster, interacting within a planar arena. Our system includes machine vision algorithms that accurately track many individuals without swapping identities and classification algorithms that detect behaviors. The data may be represented as an ethogram that plots the time course of behaviors exhibited by each fly, or as a vector that concisely captures the statistical properties of all behaviors displayed within a given period. We found that behavioral differences between individuals are consistent over time and are sufficient to accurately predict gender and genotype. In addition, we show that the relative positions of flies during social interactions vary according to gender, genotype, and social environment. We expect that our software, which permits high-throughput screening, will complement existing molecular methods available in Drosophila, facilitating new investigations into the genetic and cellular basis of behavior.
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                Author and article information

                Contributors
                Role: Reviewing editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                22 October 2016
                2016
                : 5
                : e19920
                Affiliations
                [1 ]deptChampalimaud Neuroscience Programme , Champalimaud Centre for the Unknown , Lisbon, Portugal
                [2 ]deptDepartment of Bioengineering , Imperial College London , London, United Kingdom
                [3 ]deptDepartment of Computing , Imperial College London , London, United Kingdom
                [4 ]deptIntegrative Biology Division , MRC Clinical Sciences Centre , London, United Kingdom
                [5]Max Planck Institute for Ornithology , Germany
                [6]Max Planck Institute for Ornithology , Germany
                Author notes
                Author information
                http://orcid.org/0000-0002-3813-5790
                http://orcid.org/0000-0002-9542-7335
                Article
                19920
                10.7554/eLife.19920
                5108593
                27770569
                38e9f937-03c6-4293-938c-8fc33dac42f6
                © 2016, Corrales-Carvajal et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 22 July 2016
                : 20 October 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001871, Fundação para a Ciência e a Tecnologia;
                Award ID: PTDC/BIA-BCM/118684/2010
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000854, Human Frontier Science Program;
                Award ID: RGP0022/2012
                Award Recipient :
                Funded by: Champalimaud Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001871, Fundação para a Ciência e a Tecnologia;
                Award ID: Graduate Student Fellowship, SFRH/BD/51113/2010
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Neuroscience
                Research Article
                Custom metadata
                2.5
                A quantitative video tracking analysis reveals that to gain the nutrients they need, flies change their decisions to exploit foods with different nutrient contents and explore the environment according to their internal amino acid and reproductive states.

                Life sciences
                behavior,internal states,nutrition,neurogenetics,feeding,homeostasis,d. melanogaster
                Life sciences
                behavior, internal states, nutrition, neurogenetics, feeding, homeostasis, d. melanogaster

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