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      Honeybees prefer novel insect-pollinated flower shapes over bird-pollinated flower shapes

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          Abstract

          Plant–pollinator interactions have a fundamental influence on flower evolution. Flower color signals are frequently tuned to the visual capabilities of important pollinators such as either bees or birds, but far less is known about whether flower shape influences the choices of pollinators. We tested European honeybee Apis mellifera preferences using novel achromatic (gray-scale) images of 12 insect-pollinated and 12 bird-pollinated native Australian flowers in Germany; thus, avoiding influences of color, odor, or prior experience. Independent bees were tested with a number of parameterized images specifically designed to assess preferences for size, shape, brightness, or the number of flower-like shapes present in an image. We show that honeybees have a preference for visiting images of insect-pollinated flowers and such a preference is most-likely mediated by holistic information rather than by individual image parameters. Our results indicate angiosperms have evolved flower shapes which influence the choice behavior of important pollinators, and thus suggest spatial achromatic flower properties are an important part of visual signaling for plant–pollinator interactions.

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          Mixed effects models and extensions in ecology with R

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            MCMC Methods for Multi-Response Generalized Linear Mixed Models: TheMCMCglmmRPackage

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              Regularization Paths for Generalized Linear Models via Coordinate Descent.

              We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.
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                Author and article information

                Contributors
                Role: Handling Editor
                Journal
                Curr Zool
                Curr Zool
                czoolo
                Current Zoology
                Oxford University Press
                1674-5507
                2396-9814
                August 2019
                13 December 2018
                13 December 2018
                : 65
                : 4
                : 457-465
                Affiliations
                [1 ]Bio-inspired Digital Sensing (BIDS) Lab, School of Media and Communication, RMIT University, Melbourne, Victoria 3000, Australia
                [2 ]Faculty of Information Technology, Monash University, Melbourne, Victoria 3800, Australia
                [3 ]Institute of Developmental Biology and Neurobiology (iDn), Johannes Gutenberg University, Mainz 55122, Germany
                [4 ]Centre de Recherches sur la Cognition Animale, Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, Toulouse 31400, France
                [5 ]ARC Centre of Excellence for Nanoscale BioPhotonics, School of Science, RMIT University, Melbourne, Victoria 3000, Australia
                [6 ]Department of Physiology, Monash University, Clayton, Victoria 3800, Australia
                Author notes
                Address correspondence to Scarlett R. Howard. E-mail: scarlett.howard@ 123456rmit.edu.au .
                Article
                zoy095
                10.1093/cz/zoy095
                6688580
                31413718
                6da82766-24f6-4d01-ad92-236cc7b57711
                © The Author(s) (2018). Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 11 September 2018
                : 04 December 2018
                Page count
                Pages: 10
                Categories
                Special Column: Behavioural and Cognitive Plasticity in Foraging Pollinators
                Guest Editors: David Baracchi, Dipartimento di Biologia, Università degli Studi di Firenze, Italy
                Articles

                angiosperm,apis mellifera (european honeybee),bird-pollinated,flower,insect-pollinated,pollinator

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