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      Artificial light at night bans Chaoborus from vital epilimnetic waters

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          Abstract

          Artificial light at night (ALAN) is known to affect organisms in terrestrial ecosystems and adjacent litoral habitats. In the present study, we tested the effect of ALAN on the spatial distribution of organisms in open waters, using the insect larvae of Chaoborus flavicans as an example. During the day C. flavicans typically hide from visually hunting fish in deep, dark, anoxic waters. On safer nights, they forage in rich subsurface waters. Nighttime field tests revealed that light from an HPS street lamp mounted on a boat anchored in open water attracted planktivorous fish, but deterred planktonic Chaoborus from rich but risky surface waters. Chaoborus did not descend to the safest, anoxic hypolimnion, but remained in hypoxic mid-depth metalimnion, which does not appear to be a perfect refuge. Neither light gradient nor food distribution fully explained their mid-depth residence under ALAN conditions. A further laboratory test revealed a limited tolerance of C. flavicans to anoxia. Half of the test larvae died after 38 h at 9 °C in anoxic conditions. The trade-off between predation risk and oxygen demand may explain why Chaoborus did not hide in deep anoxic waters, but remained in the riskier metalimnion with residual oxygen under ALAN conditions.

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          Regression Models and Life-Tables

          D R Cox (1972)
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            Generalized linear mixed models: a practical guide for ecology and evolution.

            How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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              Modeling Survival Data: Extending the Cox Model

              This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals.
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                Author and article information

                Contributors
                j.cytan@student.uw.edu.pl
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 April 2024
                5 April 2024
                2024
                : 14
                : 7995
                Affiliations
                [1 ]Department of Hydrobiology, Institute of Functional Biology and Ecology, Faculty of Biology, Biological and Chemical Research Centre, University of Warsaw, ( https://ror.org/039bjqg32) Żwirki I Wigury 101, 02-089 Warsaw, Poland
                [2 ]Hydrobiological Station, Faculty of Biology, University of Warsaw, ( https://ror.org/039bjqg32) Pilchy 5, 12-200 Pisz, Poland
                Author information
                http://orcid.org/0000-0002-2311-5033
                http://orcid.org/0000-0002-9845-8866
                http://orcid.org/0000-0002-1554-6474
                http://orcid.org/0000-0002-8913-0105
                Article
                58406
                10.1038/s41598-024-58406-y
                10997633
                38580701
                285ae2dd-5952-4311-92a0-661905579ab9
                © The Author(s) 2024

                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
                : 30 December 2023
                : 28 March 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004442, Narodowym Centrum Nauki;
                Award ID: 2016/21/N/NZ8/00914
                Award Recipient :
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                © Springer Nature Limited 2024

                Uncategorized
                ecology,physiology
                Uncategorized
                ecology, physiology

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