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      Decline in wild bee species richness associated with honey bee ( Apis mellifera L.) abundance in an urban ecosystem

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          Abstract

          The spatial heterogeneity of urban landscapes, relatively low agrochemical use, and species-rich floral communities often support a surprising diversity of wild pollinators in cities. However, the management of Western honey bees ( Apis mellifera L.) in urban areas may represent a new threat to wild bee communities. Urban beekeeping is commonly perceived as an environmentally friendly practice or a way to combat pollinator declines, when high-density beekeeping operations may actually have a negative influence on native and wild bee populations through floral resource competition and pathogen transmission. On the Island of Montréal, Canada there has been a particularly large increase in beekeeping across the city. Over the years following a large bee diversity survey ending in 2013, there was an influx of almost three thousand honey bee colonies to the city. In this study, we examined the wild bee communities and floral resources across a gradient of honey bee abundances in urban greenspaces in 2020, and compared the bee communities at the same sites before and after the large influx of honey bees. Overall, we found a negative relationship between urban beekeeping, pollen availability, and wild bee species richness. We also found that honey bee abundance had the strongest negative effect on small (inter-tegular span <2.25 mm) wild bee species richness. Small bee species may be at higher risk in areas with abundant honey bee populations as their limited foraging range may reduce their access to floral resources in times of increased competition. Further research on the influence of urban beekeeping on native and wild pollinators, coupled with evidence-based beekeeping regulations, is essential to ensure cities contain sufficient resources to support wild bee diversity alongside managed honey bees.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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              U1 snRNP regulates cancer cell migration and invasion in vitro

              Stimulated cells and cancer cells have widespread shortening of mRNA 3’-untranslated regions (3’UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in introns and last exons. U1 snRNP (U1), vertebrates’ most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread premature transcription termination and mRNA shortening. Here we show that low U1 AMO doses increase cancer cells’ migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3’UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                3 February 2023
                2023
                : 11
                : e14699
                Affiliations
                [1 ]Biology Department, Concordia University , Montreal, Quebec, Canada
                [2 ]Institut de Recherche en Biologie Végétale, University of Montreal , Montreal, Quebec, Canada
                Article
                14699
                10.7717/peerj.14699
                9901307
                36755869
                3f10ca16-9329-4c14-9437-67c51e61d3c0
                ©2023 MacInnis et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 14 June 2022
                : 14 December 2022
                Funding
                Funded by: MITACS and Bayer Crop Science
                Funded by: Concordia University and an NSERC Discovery Grant
                This research was supported by a post-doctoral fellowship awarded by MITACS and Bayer Crop Science to Gail MacInnis, Concordia University and an NSERC Discovery Grant to Carly D. Ziter. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Biodiversity
                Conservation Biology
                Ecology
                Entomology
                Zoology

                wild bees,pollinators,exploitative competition,beekeeping,urban,biodiversity,native bees,honey bees

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