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  • 1
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    European Science Foundation
    In:  Bull., Polar Proj. OP-O3A4, First European Geotraverse Workshop: The Northern Segment, Strasbourg, European Science Foundation, vol. 20, no. Subvol. a, pp. 23-26, (ISBN 0080419208)
    Publication Date: 1984
    Keywords: Review article ; CRUST ; earth mantle ; Muller
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  • 2
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    American Association for the Advancement of Science (AAAS)
    Publication Date: 2002-07-06
    Description: Fishery management plans ignore the potential for evolutionary change in harvestable biomass. We subjected populations of an exploited fish (Menidia menidia) to large, small, or random size-selective harvest of adults over four generations. Harvested biomass evolved rapidly in directions counter to the size-dependent force of fishing mortality. Large-harvested populations initially produced the highest catch but quickly evolved a lower yield than controls. Small-harvested populations did the reverse. These shifts were caused by selection of genotypes with slower or faster rates of growth. Management tools that preserve natural genetic variation are necessary for long-term sustainable yield.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Conover, David O -- Munch, Stephan B -- New York, N.Y. -- Science. 2002 Jul 5;297(5578):94-6.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Marine Sciences Research Center, State University of New York, Stony Brook, NY 11794-5000, USA. dconover@notes.cc.sunysb.edu〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/12098697" target="_blank"〉PubMed〈/a〉
    Keywords: Animals ; *Biological Evolution ; Biomass ; Body Constitution ; Body Weight ; Conservation of Natural Resources ; *Fisheries ; Fishes/anatomy & histology/*genetics/*growth & development ; Genetic Variation ; Population Dynamics ; Regression Analysis ; *Selection, Genetic ; Time Factors
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
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  • 3
    Publication Date: 2017-07-28
    Description: Scientists and resource managers need to know life history parameters (e.g., average mortality rate, individual growth rate, maximum length or mass, and timing of maturity) to understand and respond to risks to natural populations and ecosystems. For over one-hundred years, scientists have identified “life history invariants” (LHI) representing pairs of parameters whose ratio is theorized to be constant across species. LHI then promise to allow prediction of many parameters from field-measurements of a few important traits. Using LHI in this way, however, neglects any residual patterns in parameters when making predictions. We therefore apply a multivariate model for eight variables (seven parameters and temperature) in all 32,000 marine fishes, and include taxonomic structure for residuals (with levels for class, order, family, genus, and species). We illustrate that this approach predicts variables probabilistically for taxa with many or few data. We then use this model to resolve three questions regarding life-history parameters in fishes. Specifically we show that: (1) on average there is a 1.24% decrease in the Brody growth coefficient for every 1% increase in maximum size; (2) the ratio of natural mortality rate and growth coefficient is not a LHI but instead varies systematically based on the timing of maturation, where movement along this life-history axis is predictably correlated with species taxonomy; and (3) three variables must be known per species to precisely predict remaining life-history variables. We distribute our predictive model as an R package to allow future life-history predictions for fishes to be conditioned on taxonomy and life-history data for fishes worldwide. This package also contains predictions (and predictive intervals) for mortality, maturity, size, and growth parameters for all described fishes. This article is protected by copyright. All rights reserved.
    Print ISSN: 1051-0761
    Electronic ISSN: 1939-5582
    Topics: Biology
    Published by Wiley on behalf of The Ecological Society of America (ESA).
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  • 4
    Publication Date: 2017-04-28
    Description: Climate change and ocean acidification are altering marine ecosystems and, from a human perspective, creating both winners and losers. Human responses to these changes are complex, but may result in reduced government investments in regulation, resource management, monitoring and enforcement. Moreover, a lack of peoples’ experience of climate change may drive some towards attributing the symptoms of climate change to more familiar causes such as management failure. Taken together, we anticipate that management could become weaker and less effective as climate change continues. Using diverse case studies, including the decline of coral reefs, coastal defences from flooding, shifting fish stocks and the emergence of new shipping opportunities in the Arctic, we argue that human interests are better served by increased investments in resource management. But greater government investment in management does not simply mean more of “business-as-usual.” Management needs to become more flexible, better at anticipating and responding to surprise, and able to facilitate change where it is desirable. A range of technological, economic, communication and governance solutions exists to help transform management. While not all have been tested, judicious application of the most appropriate solutions should help humanity adapt to novel circumstances and seek opportunity where possible. Using five diverse case studies, ranging from climate impacts on coral reefs to shifts in fisheries distributions to opening of the Arctic for shipping, we reveal the rational drivers that might generate reductions in management regardless of whether global change is providing benefits or costs to human society. We argue that better outcomes will follow from greater management investment. We then discuss the key challenges to make management more effective and flexible.
    Print ISSN: 1354-1013
    Electronic ISSN: 1365-2486
    Topics: Biology , Energy, Environment Protection, Nuclear Power Engineering , Geography
    Published by Wiley
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  • 5
    Publication Date: 2017-02-02
    Description: Niche-based approaches to community analysis often involve estimating a matrix of pairwise interactions among species (the “community matrix”), but this task becomes infeasible using observational data as the number of modeled species increases. As an alternative, neutral theories achieve parsimony by assuming that species within a trophic level are exchangeable, but generally cannot incorporate stabilizing interactions even when they are evident in field data. Finally, both regulated (niche) and unregulated (neutral) approaches have rarely been fitted directly to survey data using spatio-temporal statistical methods. We therefore propose a spatio-temporal and model-based approach to estimate community dynamics that are partially regulated. Specifically, we start with a neutral spatio-temporal model where all species follow ecological drift, which precludes estimating pairwise interactions. We then add regulatory relations until model selection favors stopping, where the “rank” of the interaction matrix may range from zero to the number of species. A simulation experiment shows that model selection can accurately identify the rank of the interaction matrix, and that the identified spatio-temporal model can estimate the magnitude of species interactions. A forty-year case study for the Gulf of St. Lawrence marine community shows that recovering grey seals have an unregulated and negative relation with demersal fishes. We therefore conclude that partial regulation is a plausible approximation to community dynamics using field data, and hypothesize that estimating partial regulation will be expedient in future analyses of spatio-temporal community dynamics given limited field data. We conclude by recommending ongoing research to add explicit models for movement, so that meta-community theory can be confronted with data in a spatio-temporal statistical framework. This article is protected by copyright. All rights reserved.
    Print ISSN: 0012-9658
    Electronic ISSN: 1939-9170
    Topics: Biology
    Published by Wiley on behalf of The Ecological Society of America (ESA).
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  • 6
    Publication Date: 1982-01-01
    Print ISSN: 0160-4120
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Elsevier
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  • 7
    Publication Date: 1986-01-01
    Print ISSN: 0160-4120
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Published by Elsevier
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