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      Proximity sensors on common noctule bats reveal evidence that mothers guide juveniles to roosts but not food

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          Abstract

          Female bats of temperate zones often communally rear their young, which creates ideal conditions for naive juveniles to find or learn about resources via informed adults. However, studying social information transfer in elusive and small-bodied animals in the wild is difficult with traditional tracking techniques. We used a novel ‘next-generation’ proximity sensor system (BATS) to investigate if and how juvenile bats use social information in acquiring access to two crucial resources: suitable roosts and food patches. By tracking juvenile–adult associations during roost switching and foraging, we found evidence for mother-to-offspring information transfer while switching roosts but not during foraging. Spatial and temporal patterns of encounters suggested that mothers guided juveniles between the juvenile and the target roost. This roost-switching behaviour provides evidence for maternal guidance in bats, a form of maternal care that has long been assumed, but never documented. We did not find evidence that mothers guide the offspring to foraging sites. Foraging bats reported brief infrequent meetings with other tagged bats that were best explained by local enhancement. Our study illustrates how this recent advance in automated biologging provides researchers with new insights into longstanding questions in behavioural biology.

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          Early development and fitness in birds and mammals.

          Conditions experienced during early development affect survival and reproductive performance in many bird and mammal species. Factors affecting early development can therefore have an important influence both on the optimization of life histories and on population dynamics. The understanding of these evolutionary and dynamic consequences is just starting to emerge.
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            Social Learning in Animals: Empirical Studies and Theoretical Models

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              Is Open Access

              A guide to null models for animal social network analysis

              Summary Null models are an important component of the social network analysis toolbox. However, their use in hypothesis testing is still not widespread. Furthermore, several different approaches for constructing null models exist, each with their relative strengths and weaknesses, and often testing different hypotheses. In this study, I highlight why null models are important for robust hypothesis testing in studies of animal social networks. Using simulated data containing a known observation bias, I test how different statistical tests and null models perform if such a bias was unknown. I show that permutations of the raw observational (or ‘pre‐network’) data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates. However, permutations of pre‐network data remain relatively uncommon in animal social network analysis because they are challenging to implement for certain data types, particularly those from focal follows and GPS tracking. I explain simple routines that can easily be implemented across different types of data, and supply R code that applies each type of null model to the same simulated dataset. The R code can easily be modified to test hypotheses with empirical data. Widespread use of pre‐network data permutation methods will benefit researchers by facilitating robust hypothesis testing.
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                Author and article information

                Journal
                Biology Letters
                Biol. Lett.
                The Royal Society
                1744-9561
                1744-957X
                February 28 2019
                February 28 2019
                : 15
                : 2
                : 20180884
                Affiliations
                [1 ]Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Invalidenstraße 43, 10115 Berlin, Germany
                [2 ]Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancón, Republic of Panama
                [3 ]Institute for Electronics Engineering, Friedrich-Alexander University of Erlangen-Nürnberg, Wetterkreuz 15, 91058 Erlangen-Tennenlohe, Germany
                [4 ]Carl-Friedrich-Gauß-Fakultät, Technische Universität Braunschweig, Mühlenpfordtstraße 23, 38106 Braunschweig, Germany
                [5 ]Chair for Electronics and Sensor Systems, Brandenburg University of Technology, Siemens-Halske-Ring 14, 03046 Cottbus, Germany
                [6 ]Berlin-Brandenburg Institute of Advanced Biodiversity Research, Altensteinstr. 34, 14195 Berlin, Germany
                Article
                10.1098/rsbl.2018.0884
                6405471
                30958135
                d212e38e-82be-4824-bd37-c360bccf70eb
                © 2019
                History

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