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@article{csillery_abc_2012,
title = {abc: an {R} package for approximate {Bayesian} computation ({ABC})},
volume = {3},
copyright = {© 2012 The Authors. Methods in Ecology and Evolution © 2012 British Ecological Society},
issn = {2041-210X},
shorttitle = {abc},
url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.2041-210X.2011.00179.x},
doi = {10.1111/j.2041-210X.2011.00179.x},
abstract = {1. Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian computation (ABC) is devoted to these complex models because it bypasses the evaluation of the likelihood function by comparing observed and simulated data. 2. We introduce the R package ‘abc’ that implements several ABC algorithms for performing parameter estimation and model selection. In particular, the recently developed nonlinear heteroscedastic regression methods for ABC are implemented. The ‘abc’ package also includes a cross-validation tool for measuring the accuracy of ABC estimates and to calculate the misclassification probabilities when performing model selection. The main functions are accompanied by appropriate summary and plotting tools. 3. R is already widely used in bioinformatics and several fields of biology. The R package ‘abc’ will make the ABC algorithms available to a large number of R users. ‘abc’ is a freely available R package under the GPL license, and it can be downloaded at http://cran.r-project.org/web/packages/abc/index.html.},
language = {en},
number = {3},
urldate = {2019-07-21},
journal = {Methods in Ecology and Evolution},
author = {Csilléry, Katalin and François, Olivier and Blum, Michael G. B.},
year = {2012},
keywords = {coalescent, model-based inference, neural networks, population genetics},
pages = {475--479},
file = {Full Text PDF:C\:\\Users\\Sean.Lucey\\Zotero\\storage\\ER3DFAE5\\Csilléry et al. - 2012 - abc an R package for approximate Bayesian computa.pdf:application/pdf;Snapshot:C\:\\Users\\Sean.Lucey\\Zotero\\storage\\SAXEMWYA\\j.2041-210X.2011.00179.html:text/html}
}
@misc{link_EMAX_2006,
title = {Documentation for the {Energy} {Modeling} and {Analysis} {eXercise} ({EMAX})},
abstract = {The Northeast U.S. (NEUS) Continental Shelf Ecosystem is a dynamic environment. In
order to evaluate the response of this ecosystem to numerous human-induced perturbations and
to explore possible future scenarios, the Northeast Fisheries Science Center (NEFSC) instituted
the Energy Modeling and Analysis eXercise (EMAX). The primary goal of EMAX was to
establish an ecological network model (i.e., a nuanced energy budget) of the entire NEUS
Ecosystem food web. The highly interdisciplinary EMAX work focused on four contemporary
(1996-2000) subregions of the ecosystem; designated 36 network nodes (biomass state variables)
across a broad range of the biological hierarchy; and incorporated a wide range of key rate
processes. The emphasis of EMAX was to explore the particular role of small pelagic fishes
in the ecosystem, and various model configurations were constructed and psuedo-dynamic
scenarios evaluated to explore how potential changes to this group can affect the rest of the food
web. Preliminary results show that small pelagic fishes are clearly keystone species in the
ecosystem. There are some differences across the four EMAX regions reflective of the local
biology, but major patterns of network properties are similar over space. EMAX will continue to
play a critical role in the further development of an ecosystem approach to fisheries (EAF) by
acting as a catalogue of information and data; identifying major fluxes among biotic components
of the ecosystem; serving as a basis for further analytical models; developing a way to evaluate
biomass tradeoffs; and acting as a backdrop for a suite of other relevant management and
research questions.},
publisher = {US Department of Commerce},
author = {Link, Jason S. and Griswold, Carolyn A. and Methratta, Elizabeth T. and Gunnard, Jessie},
year = {2006},
file = {crd0615.pdf:C\:\\Users\\Sean.Lucey\\Zotero\\storage\\BMKWG3FA\\crd0615.pdf:application/pdf}
}
@article{link_prebal_2010,
title = {Adding rigor to ecological network models by evaluating a set of pre-balance diagnostics: {A} plea for {PREBAL}},
volume = {221},
issn = {0304-3800},
shorttitle = {Adding rigor to ecological network models by evaluating a set of pre-balance diagnostics},
url = {http://www.sciencedirect.com/science/article/pii/S0304380010001468},
doi = {10.1016/j.ecolmodel.2010.03.012},
abstract = {The widespread use of ecological network models (e.g., Ecopath, Econetwrk, and related energy budget models) has been laudable for several reasons, chief of which is providing an easy-to-use set of modeling tools that can present an ecosystem context for improved understanding and management of living marine resources (LMR). Yet the ease-of-use of these models has led to two challenges. First, the veritable explosion of the use and application of these network models has resulted in recognition that the content and use of such models has spanned a range of quality. Second, as these models and their application have become more widespread, they are increasingly being used in a LMR management context. Thus review panels and other evaluators of these models would benefit from a set of rigorous and standard criteria from which the basis for all network models and related applications for any given system (i.e., the initial, static energy budget) can be evaluated. To this end, as one suggestion for improving network models in general, here I propose a series of pre-balance (PREBAL) diagnostics. These PREBAL diagnostics can be done, now, in simple spreadsheets before any balancing or tuning is executed. Examples of these PREBAL diagnostics include biomasses, biomass ratios, vital rates, vital rate ratios, total production, and total removals (and slopes thereof) across the taxa and trophic levels in any given energy budget. I assert that there are some general ecological and fishery principles that can be used in conjunction with PREBAL diagnostics to identify issues of model structure and data quality before balancing and dynamic applications are executed. I humbly present this PREBAL information as a simple yet general approach that could be easily implemented, could be considered for further incorporation into these model packages, and as such would ultimately result in a straightforward way to evaluate (and perhaps identify areas for improving) initial conditions in food web modeling efforts.},
number = {12},
urldate = {2019-06-07},
journal = {Ecological Modelling},
author = {Link, Jason S.},
month = jun,
year = {2010},
keywords = {Econetwrk, Ecopath, Energy budgets, Error traps, Food web models, Quality assurance, Quality control, Rules of thumb},
pages = {1580--1591},
file = {ScienceDirect Full Text PDF:/home/slucey/.zotero/zotero/b2yilhtq.default/zotero/storage/YWM7VRRD/Link - 2010 - Adding rigor to ecological network models by evalu.pdf:application/pdf;ScienceDirect Snapshot:/home/slucey/.zotero/zotero/b2yilhtq.default/zotero/storage/E4ZZSZ9A/S0304380010001468.html:text/html}
}
@article{nelson_cluster_2014,
title = {Cluster {Sampling}: {A} {Pervasive}, {Yet} {Little} {Recognized} {Survey} {Design} in {Fisheries} {Research}},
volume = {143},
issn = {0002-8487},
shorttitle = {Cluster {Sampling}},
url = {https://doi.org/10.1080/00028487.2014.901252},
doi = {10.1080/00028487.2014.901252},
abstract = {Cluster sampling is a common survey design used pervasively in fisheries research to sample fish populations, but it is not widely recognized by researchers. Because fish collected via cluster sampling are not independent of each other, standard simple random sampling estimators and statistical tests that assume independence cannot be used to make inferences about fish populations. If the clustered nature of fisheries data is ignored, the main consequence is that the type I error rate of common statistical tests will be severely inflated and significant differences will often be found in group comparisons where none exist. The goal of this paper is to provide an introduction to the estimation of population attributes and analysis of fisheries data collected via cluster sampling. This article addresses the nature of clustered fisheries data, reviews the random cluster sampling estimators of population attributes, explores the implications of violating the assumption of independence in hypothesis testing, and reviews current statistical approaches that can be used to analyze appropriately clustered data.Received November 8, 2013; accepted February 27, 2014},
number = {4},
urldate = {2019-07-28},
journal = {Transactions of the American Fisheries Society},
author = {Nelson, Gary A.},
month = jul,
year = {2014},
pages = {926--938},
file = {Full Text PDF:/home/slucey/.zotero/zotero/b2yilhtq.default/zotero/storage/34QCDFYN/Nelson - 2014 - Cluster Sampling A Pervasive, Yet Little Recogniz.pdf:application/pdf;Snapshot:/home/slucey/.zotero/zotero/b2yilhtq.default/zotero/storage/9N3DUNZS/00028487.2014.html:text/html}
}
@article{trites_mammal_size_1998,
title = {Estimating mean body masses of marine mammals from maximum body lengths},
volume = {76},
abstract = {Generalized survival models were applied to growth curves published for 17 species of cetaceans (5 mysticetes, 12 odontocetes) and 13 species of pinnipeds (1 odobenid, 4 otariids, 8 phocids). The mean mass of all individuals in the population was calculated and plotted against the maximum body length reported for each species. The data showed strong linearity (on logarithmic scales), with three distinct clusters of points corresponding to the mysticetes (baleen whales), odontocetes (toothed whales), and pinnipeds (seals, sea lions, and walruses). Exceptions to this pattern were the sperm whales, which appeared to be more closely related to the mysticetes than to the odontocetes. Regression equations were applied to the maximum lengths reported for 76 species of marine mammals without published growth curves. Estimates of mean body mass were thus derived for 106 living species of marine mammals.},
author = {Trites, Andrew W and Pauly, Daniel},
year = {1998},
pages = {11},
file = {Trites and Pauly - 1998 - Estimating mean body masses of marine mammals from.pdf:/home/slucey/.zotero/zotero/b2yilhtq.default/zotero/storage/TZISG2AB/Trites and Pauly - 1998 - Estimating mean body masses of marine mammals from.pdf:application/pdf}
}
@techreport{buchheister_NWACS_2017,
type = {University of {Maryland} {Center} for {Environmental} {Sciences} {Report}},
title = {Technical {Documentation} of the {Northwest} {Atlantic} {Continental} {Shelf} ({NWACS}) {Ecosystem} {Model}. {Report} to the {Lenfest} {Ocean} {Program}, {Washington}, {D}.{C}},
language = {en},
number = {TS -694 -17},
author = {Buchheister, Andre and Miller, Thomas J and Houde, Edward D and Loewensteiner, David A},
month = apr,
year = {2017},
pages = {58},
file = {Buchheister et al. - TECHNICAL DOCUMENTATION OF THE NORTHWEST ATLANTIC .pdf:/home/slucey/.zotero/zotero/b2yilhtq.default/zotero/storage/SP8HZKFK/Buchheister et al. - TECHNICAL DOCUMENTATION OF THE NORTHWEST ATLANTIC .pdf:application/pdf}
}