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  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © Inter-Research, 2006. This article is posted here by permission of Inter-Research for personal use, not for redistribution. The definitive version was published in Marine Ecology Progress Series 310 (2006): 271-295, doi:10.3354/meps310271.
    Description: Cetacean–habitat modeling, although still in the early stages of development, represents a potentially powerful tool for predicting cetacean distributions and understanding the ecological processes determining these distributions. Marine ecosystems vary temporally on diel to decadal scales and spatially on scales from several meters to 1000s of kilometers. Many cetacean species are wide-ranging and respond to this variability by changes in distribution patterns. Cetacean–habitat models have already been used to incorporate this variability into management applications, including improvement of abundance estimates, development of marine protected areas, and understanding cetacean–fisheries interactions. We present a review of the development of cetacean–habitat models, organized according to the primary steps involved in the modeling process. Topics covered include purposes for which cetacean–habitat models are developed, scale issues in marine ecosystems, cetacean and habitat data collection, descriptive and statistical modeling techniques, model selection, and model evaluation. To date, descriptive statistical techniques have been used to explore cetacean–habitat relationships for selected species in specific areas; the numbers of species and geographic areas examined using computationally intensive statistic modeling techniques are considerably less, and the development of models to test specific hypotheses about the ecological processes determining cetacean distributions has just begun. Future directions in cetacean–habitat modeling span a wide range of possibilities, from development of basic modeling techniques to addressing important ecological questions.
    Description: Funding from the U.S. Navy and the Strategic Environmental Research and Development Program (SERDP) supported this research under Projects CS-1390 and CS-1391.
    Keywords: Cetacean–habitat modeling ; Predictive models ; Regression models ; Cross validation ; Spatial autocorrelation ; Classification models ; Ordination ; Environmental envelope models
    Repository Name: Woods Hole Open Access Server
    Type: Article
    Format: application/pdf
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  • 2
    Publication Date: 1901-04-26
    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: 2011-08-19
    Description: Digitized agricultural field boundary taken in the United States and Canada during the LACIE and AgRISTARS programs, in 1977 through 1980, were used to construct histograms showing the distributions of field area, width, and length for crops for which there were data for 700 or more fields per state. The observed distributions of area and width for fields of 10 crops grown in 13 states of the United States and Canada were compared with best-fit inverse Gaussian distributions and with log-normal distributions. For 28 distributions of area and 16 distributions of width there was found to be a probability of greater than .01 of their being inverse Gaussian. There were 10 distributions of area for which there was probability of greater than .005 of their being log-normal. Distributions of area and width stratified by state and crop type appear to be unique. The inverse Gaussian, which represents a wide range of statistical distributions from skewed to almost symmetrical, can provide a useful model for distributions of field area.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: Remote Sensing of Environment (ISSN 0034-4257); 19; 25-45
    Format: text
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  • 4
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    In:  CASI
    Publication Date: 2019-06-28
    Description: Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.
    Keywords: EARTH RESOURCES AND REMOTE SENSING
    Type: E83-10349 , NASA-TM-85347 , SR-J1-04157 , JSC-17428 , NAS 1.15:85347 , LEMSCO-17285
    Format: application/pdf
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