Item talk:Q308974

{

 "USGS Publications Warehouse": {
   "@context": "https://schema.org",
   "@type": "CreativeWork",
   "additionalType": "Conference Paper",
   "name": "Great Lakes prey fish populations: A cross-basin overview of status and trends in 2008",
   "identifier": [
     {
       "@type": "PropertyValue",
       "propertyID": "USGS Publications Warehouse IndexID",
       "value": "70162097",
       "url": "https://pubs.usgs.gov/publication/70162097"
     },
     {
       "@type": "PropertyValue",
       "propertyID": "USGS Publications Warehouse Internal ID",
       "value": 70162097
     }
   ],
   "inLanguage": "en",
   "datePublished": "2009",
   "dateModified": "2017-04-25",
   "abstract": "Assessments of prey fishes in the Great Lakes have been conducted annually since the 1970s by the Great Lakes Science Center, sometimes assisted by partner agencies. Prey fish assessments differ among lakes in the proportion of a lake covered, seasonal timing, bottom trawl gear used, sampling design, and the manner in which the trawl is towed (across or along bottom contours). Because each assessment is unique in one or more important aspects, a direct comparison of prey fish catches among lakes is problematic. All of the assessments, however, produce indices of abundance or biomass that can be standardized to facilitate comparisons of trends among lakes and to illustrate present status of the populations. We present indices of abundance for important prey fishes in the Great Lakes standardized to the highest value for a time series within each lake: cisco (Coregonus artedi), bloater (C. hoyi), rainbow smelt (Osmerus mordax), and alewife (Alosa pseudoharengus). We also provide indices for round goby (Neogobius melanostomus), an invasive fish presently spreading throughout the basin. Our intent is to provide a short, informal report emphasizing data presentation rather than synthesis; for this reason we intentionally avoid use of tables and cited references.For each lake, standardized relative indices for annual biomass and density estimates of important prey fishes were calculated as the fraction relative to the largest value observed in the times series. To determine whether basin-wide trends were apparent for each species, we first ranked standardized index values within each lake. When comparing ranked index values from three or more lakes, we calculated the Kendall coefficient of concordance (W), which can range from 0 (complete discordance or disagreement among trends) to 1 (complete concordance or agreement among trends). The P-value for W provides the probability of agreement across the lakes. When comparing ranked index values from two lakes, we calculated the Kendall correlation coefficient (\u03c4), which ranges from -1 (inverse association, perfect disagreement) to 1 (direct association, perfect agreement). Here, the P-value for \u03c4 provides the probability of either inverse or direct association between the lakes. First, we present trends in relative biomass of age-1 and older prey fishes to show changes in populations within each lake. Then, we present standardized indices of numerical abundance of a single age class to show changes in relative year-class strength within each lake. Indices of year-class strength reliably reflect the magnitude of the cohort size at subsequent ages. However, because of differences in survey timing across lakes, the age class that is used for each species to index year-class strength varies across lakes and, just as surveys differ among lakes, methods for determining fish age-class differ also. For Lake Superior cisco, bloater, smelt, and Lake Michigan alewife, year- class strengths are based on aged fish and age-length keys, and for all other combinations of lakes and species, age-classes are assigned based on fish length cut-offs. Our intent with this report is to provide a cross-lakes view of population trends but not to propose reasons for those trends.",
   "description": "9 p.",
   "publisher": {
     "@type": "Organization",
     "name": "Great Lakes Fishery Commission"
   },
   "author": [
     {
       "@type": "Person",
       "name": "Gorman, Owen T. otgorman@usgs.gov",
       "givenName": "Owen T.",
       "familyName": "Gorman",
       "email": "otgorman@usgs.gov",
       "identifier": {
         "@type": "PropertyValue",
         "propertyID": "ORCID",
         "value": "0000-0003-0451-110X",
         "url": "https://orcid.org/0000-0003-0451-110X"
       },
       "affiliation": [
         {
           "@type": "Organization",
           "name": "Great Lakes Science Center",
           "url": "https://www.usgs.gov/centers/great-lakes-science-center"
         }
       ]
     },
     {
       "@type": "Person",
       "name": "Bunnell, David B. dbunnell@usgs.gov",
       "givenName": "David B.",
       "familyName": "Bunnell",
       "email": "dbunnell@usgs.gov",
       "affiliation": [
         {
           "@type": "Organization",
           "name": "Great Lakes Science Center",
           "url": "https://www.usgs.gov/centers/great-lakes-science-center"
         }
       ]
     }
   ],
   "funder": [
     {
       "@type": "Organization",
       "name": "Great Lakes Science Center",
       "url": "https://www.usgs.gov/centers/great-lakes-science-center"
     }
   ],
   "spatialCoverage": [
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       "@type": "Place",
       "additionalType": "country",
       "name": "Canada",
       "url": "https://geonames.org/4269037"
     },
     {
       "@type": "Place",
       "additionalType": "country",
       "name": "United States",
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     },
     {
       "@type": "Place",
       "additionalType": "unknown",
       "name": "Great Lakes",
       "url": "https://geonames.org/4894474"
     }
   ]
 }

}