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      Elevational and seasonal patterns of butterflies and hawkmoths in plant-pollinator networks in tropical rainforests of Mount Cameroon

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          Abstract

          Butterflies and moths are conspicuous flower visitors but their role in plant-pollinator interactions has rarely been quantified, especially in tropical rainforests. Moreover, we have virtually no knowledge of environmental factors affecting the role of lepidopterans in pollination networks. We videorecorded flower-visiting butterflies and hawkmoths on 212 plant species (> 26,000 recorded hrs) along the complete elevational gradient of rainforests on Mount Cameroon in dry and wet seasons. Altogether, we recorded 734 flower visits by 80 butterfly and 27 hawkmoth species, representing only ~ 4% of all flower visits. Although lepidopterans visited flowers of only a third of the plant species, they appeared to be key visitors for several plants. Lepidopterans visited flowers most frequently at mid-elevations and dry season, mirroring their local elevational patterns of diversity. Characteristics of interaction networks showed no apparent elevational or seasonal patterns, probably because of the high specialisation of all networks. Significant non-linear changes of proboscis and forewing lengths were found along elevation. A positive relationship between the lengths of proboscis of hesperiid butterflies and tube of visited flowers was detected. Differences in floral preferences were found between sphingids and butterflies, revealing the importance of nectar production, floral size and shape for sphingids, and floral colour for butterflies. The revealed trait-matching and floral preferences confirmed their potential to drive floral evolution in tropical ecosystems.

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          ImageJ2: ImageJ for the next generation of scientific image data

          Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users.
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            Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks

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              Patterns of Mutualistic Interactions in Pollination and Seed Dispersal: Connectance, Dependence Asymmetries, and Coevolution

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                Author and article information

                Contributors
                robert.tropek@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                6 May 2021
                6 May 2021
                2021
                : 11
                : 9710
                Affiliations
                [1 ]Department of Ecology, Faculty of Science, Charles University, Viničná 7, 12843 Prague, Czechia
                [2 ]Department of Biology of Organisms and Populations, Faculty of Fundamental and Applied Science, University of Poitiers, 5 rue Albert Turpain, 86000 Poitiers, France
                [3 ]Institute of Entomology, Biology Centre, Czech Academy of Sciences, Branišovská 31, 37005 České Budějovice, Czechia
                [4 ]Faculty of Science, University of South Bohemia, Branišovská 1760, 37005 České Budějovice, Czechia
                [5 ]Nicholas School of the Environment, Duke University, 9 Circuit Dr., Durham, NC 27710 USA
                [6 ]Institute of Silviculture and Forest Protection, Faculty of Forestry, University of West Hungary, Bajcsy-Zsilinszky utca 4, 9400 Sopron, Hungary
                [7 ]Department of Invertebrate Evolution, Institute of Zoology and Biomedical Research, Jagiellonian Univeristy, Gronostajowa 9, 30-387 Kraków, Poland
                Article
                89012
                10.1038/s41598-021-89012-x
                8102585
                33958665
                2c01a3ab-1f6e-404f-8eb2-e02a3a6a11dc
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 February 2021
                : 20 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001824, Grantová Agentura České Republiky;
                Award ID: 16‐11164Y
                Funded by: FundRef http://dx.doi.org/10.13039/100007543, Grantová Agentura, Univerzita Karlova;
                Award ID: PRIMUS/17/SCI/8
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                ecology,biodiversity,community ecology,ecological networks
                Uncategorized
                ecology, biodiversity, community ecology, ecological networks

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