trend_analysis.bib

@misc{Mazerolle2017a,
abstract = {Functions to implement model selection and multimodel inference based on Akaike's in- formation criterion (AIC) and the second-order AIC (AICc), as well as their quasi- likelihood counterparts (QAIC, QAICc) from various model object classes. The package imple- ments classic model averaging for a given parameter of interest or predicted val- ues, as well as a shrinkage version of model averaging parameter estimates or ef- fect sizes. The package includes diagnostics and goodness-of-fit statistics for cer- tain model types including those of 'unmarkedFit' classes estimating demographic parameters af- ter accounting for imperfect detection probabilities. Some functions also allow the cre- ation of model selection tables for Bayesian models of the 'bugs' and 'rjags' classes. Func- tions also implement model selection using BIC. Objects following model selection and multi- model inference can be formatted to LaTeX using 'xtable' methods included in the package},
author = {Mazerolle, Marc J.},
booktitle = {R package version 2.1-1},
mendeley-groups = {trend{\_}assessment},
title = {{AICcmodavg: model selection and multimodel inference based on (Q)AIC(c)}},
url = {https://cran.r-project.org/package=AICcmodavg.},
year = {2017}
}

@misc{Nicholson2004,
abstract = {Community metrics describe aspects of community structure and are often calculated from species-size-abundance data collected during fish stock monitoring surveys. Several community metrics have been proposed as indicators to support ecosystem-based fishery management. These metrics should be sensitive to fishing impacts and respond rapidly to management action, so that managers can assess whether changes in the fish community are a desirable or undesirable response to management. It should also be possible to estimate metrics with sufficient precision so that changes in the community can be detected on management time scales of a year to a few years. Here, we test the power of a large-scale annual trawl survey (North Sea International Bottom Trawl Survey, IBTS) to detect trends in six community metrics: mean length, mean weight, mean maximum length, mean maximum weight, slope of the biomass size spectrum, and mean trophic level. Our analyses show that the power of the trawl survey to detect trends is generally poor. While community metrics do provide good long-term indicators of changes in fish community structure, they are unlikely to provide an appropriate tool to support short-term management decisions. If fish community metrics are to provide effective support for ecosystem-based management, and management time scales cannot be extended, then the power of many surveys to detect trends in fish community structure will need to be improved by increased replication and standardization. Crown Copyright {\textcopyright} 2003 Published by Elsevier Ltd on behalf of International Council for the Exploration of the Sea. All rights reserved.},
author = {Nicholson, Mike D. and Jennings, Simon},
booktitle = {ICES Journal of Marine Science},
doi = {10.1016/j.icesjms.2003.09.004},
isbn = {10543139},
issn = {10543139},
keywords = {Community metrics,Community structure,Ecosystem-based fishery management,Indicators,Power analysis},
mendeley-groups = {SOE simulations,trend{\_}assessment},
number = {1},
pages = {35--42},
title = {{Testing candidate indicators to support ecosystem-based management: The power of monitoring surveys to detect temporal trends in fish community metrics}},
volume = {61},
year = {2004}
}

@article{Pinheiro2017,
abstract = {(2017). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-131, https://CRAN.R-project.org/package=nlme},
author = {Pinheiro, J and Bates, D and DebRoy, S and Sarkar, D and {R Core Team}},
journal = {R package version 3.1-131, https://CRAN.R-project.org/package=nlme},
mendeley-groups = {trend{\_}assessment},
title = {{nlme: Linear and Nonlinear Mixed Effects Models}},
year = {2017}
}

@incollection{VonStorch1999a,
abstract = {The history of misuses of statistics is as long as the history of statistics itself. The following is a personal assessment about such misuses in our field, climate research. Some people might find my subjective essay of the matter unfair and not balanced. This might be so, but an effective drug sometimes tastes bitter. . The application of statistical analysis in climate research is methodologi-cally more complicated than in many other sciences, among others because of the following reasons: • In climate research only very rarely it is possible to perform real inde-pendent experiments (see Navarra's discussion in Chapter 1). There is more or less only one observational record which is analysed again and again so that the processes of building hypotheses and testing hypothe-ses are hardly separable. Only with dynamical models can independent Acknowledgments: I thank Bob Livezey for his most helpful critical comments, and Ashwini Kulkarni for responding so positively to my requests to discuss the problem of correlation and trend-tests.},
author = {von Storch, Hans},
booktitle = {Analysis of Climate Variability},
doi = {10.1007/978-3-662-03744-7_2},
isbn = {978-3-642-08560-4},
issn = {9783642085604},
mendeley-groups = {SOE simulations,trend{\_}assessment},
pages = {11--26},
title = {{Misuses of Statistical Analysis in Climate Research}},
url = {http://link.springer.com/10.1007/978-3-662-03744-7{\_}2},
year = {1999}
}

@article{Wagner2013,
abstract = {ABSTRACT Monitoring to detect temporal trends in biological and habitat indices is a critical component of fisheries management. Thus, it is important that management objectives are linked to monitoring objectives. This linkage requires a definition of what constitutes a management-relevant ?temporal trend.? It is also important to develop expectations for the amount of time required to detect a trend (i.e., statistical power) and for choosing an appropriate statistical model for analysis. We provide an overview of temporal trends commonly encountered in fisheries management, review published studies that evaluated statistical power of long-term trend detection, and illustrate dynamic linear models in a Bayesian context, as an additional analytical approach focused on shorter term change. We show that monitoring programs generally have low statistical power for detecting linear temporal trends and argue that often management should be focused on different definitions of trends, some of which can be better addressed by alternative analytical approaches.},
author = {Wagner, Tyler and Irwin, Brian J. and Bence, James R. and Hayes, Daniel B.},
doi = {10.1080/03632415.2013.799466},
isbn = {0363-2415},
issn = {0363-2415},
journal = {Fisheries},
mendeley-groups = {SOE simulations,trend{\_}assessment},
number = {7},
pages = {309--319},
title = {{Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives}},
url = {http://www.tandfonline.com/doi/abs/10.1080/03632415.2013.799466},
volume = {38},
year = {2013}
}