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      Spatial fidelity of workers predicts collective response to disturbance in a social insect

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          Abstract

          Individuals in social insect colonies cooperate to perform collective work. While colonies often respond to changing environmental conditions by flexibly reallocating workers to different tasks, the factors determining which workers switch and why are not well understood. Here, we use an automated tracking system to continuously monitor nest behavior and foraging activity of uniquely identified workers from entire bumble bee ( Bombus impatiens) colonies foraging in a natural outdoor environment. We show that most foraging is performed by a small number of workers and that the intensity and distribution of foraging is actively regulated at the colony level in response to forager removal. By analyzing worker nest behavior before and after forager removal, we show that spatial fidelity of workers within the nest generates uneven interaction with relevant localized information sources, and predicts which workers initiate foraging after disturbance. Our results highlight the importance of spatial fidelity for structuring information flow and regulating collective behavior in social insect colonies.

          Abstract

          How do social insect colonies regulate tasks after the developmental stage and in response to changing environments? Here, Crall et al. use automated individual tracking to reveal that task switching after a major colony disturbance helps to maintain collective foraging performance in bumble bees.

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          Animal personalities: consequences for ecology and evolution.

          Personality differences are a widespread phenomenon throughout the animal kingdom. Past research has focused on the characterization of such differences and a quest for their proximate and ultimate causation. However, the consequences of these differences for ecology and evolution received much less attention. Here, we strive to fill this gap by providing a comprehensive inventory of the potential implications of personality differences, ranging from population growth and persistence to species interactions and community dynamics, and covering issues such as social evolution, the speed of evolution, evolvability, and speciation. The emerging picture strongly suggests that personality differences matter for ecological and evolutionary processes (and their interaction) and, thus, should be considered a key dimension of ecologically and evolutionarily relevant intraspecific variation. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            Ecological implications of behavioural syndromes.

            Interspecific trait variation has long served as a conceptual foundation for our understanding of ecological patterns and dynamics. In particular, ecologists recognise the important role that animal behaviour plays in shaping ecological processes. An emerging area of interest in animal behaviour, the study of behavioural syndromes (animal personalities) considers how limited behavioural plasticity, as well as behavioural correlations affects an individual's fitness in diverse ecological contexts. In this article we explore how insights from the concept and study of behavioural syndromes provide fresh understanding of major issues in population ecology. We identify several general mechanisms for how population ecology phenomena can be influenced by a species or population's average behavioural type, by within-species variation in behavioural type, or by behavioural correlations across time or across ecological contexts. We note, in particular, the importance of behavioural type-dependent dispersal in spatial ecology. We then review recent literature and provide new syntheses for how these general mechanisms produce novel insights on five major issues in population ecology: (1) limits to species' distribution and abundance; (2) species interactions; (3) population dynamics; (4) relative responses to human-induced rapid environmental change; and (5) ecological invasions. © 2012 Blackwell Publishing Ltd/CNRS.
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              Models of division of labor in social insects.

              Division of labor is one of the most basic and widely studied aspects of colony behavior in social insects. Studies of division of labor are concerned with the integration of individual worker behavior into colony level task organization and with the question of how regulation of division of labor may contribute to colony efficiency. Here we describe and critique the current models concerned with the proximate causes of division of labor in social insects. The models have identified various proximate mechanisms to explain division of labor, based on both internal and external factors. On the basis of these factors, we suggest a classification of the models. We first describe the different types of models and then review the empirical evidence supporting them. The models to date may be considered preliminary and exploratory; they have advanced our understanding by suggesting possible mechanisms for division of labor and by revealing how individual and colony-level behavior may be related. They suggest specific hypotheses that can be tested by experiment and so may lead to the development of more powerful and integrative explanatory models.
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                Author and article information

                Contributors
                jcrall@oeb.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 April 2018
                3 April 2018
                2018
                : 9
                : 1201
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Organismic and Evolutionary Biology, , Harvard University, ; 26 Oxford St., Cambridge, MA 02143 USA
                [2 ]ISNI 0000 0001 2107 4242, GRID grid.266100.3, Mechanical and Aerospace Engineering, , University of California San Diego, ; Engineer Ln, San Diego, CA 92161 USA
                [3 ]ISNI 0000 0004 0420 0595, GRID grid.252873.9, Department of Biology, , Bates College, ; 2 Andrews Road, Lewiston, ME 04240 USA
                [4 ]ISNI 0000 0001 2097 5006, GRID grid.16750.35, Lewis-Sigler Institute for Integrative Genomics, , Princeton University, ; Princeton, NJ 08540 USA
                [5 ]ISNI 0000 0001 2192 7145, GRID grid.167436.1, Department of Biological Sciences, , University of New Hampshire, ; 105 Main St., Durham, NH 03824 USA
                [6 ]ISNI 0000 0004 1936 9684, GRID grid.27860.3b, Department of Neurobiology, Physiology, and Behavior, , University of California Davis, ; Davis, CA 95616 USA
                Author information
                http://orcid.org/0000-0002-8981-3782
                http://orcid.org/0000-0001-8207-7519
                http://orcid.org/0000-0002-7204-266X
                http://orcid.org/0000-0001-5586-0727
                http://orcid.org/0000-0003-4846-654X
                http://orcid.org/0000-0003-3366-1625
                http://orcid.org/0000-0003-1586-6459
                Article
                3561
                10.1038/s41467-018-03561-w
                5882771
                29615611
                7c5c87c6-7001-437c-8b19-23519a1e72b9
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 11 July 2017
                : 22 February 2018
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