4.3 Article

Mixed-stock and discriminant models use for assessing recruitment sources of estuarine fish populations in La Plata Basin (South America)

出版社

CAMBRIDGE UNIV PRESS
DOI: 10.1017/S002531541900016X

关键词

LA-ICP-MS; maximum likelihood; otolith microchemistry; quadratic discriminant; recruitment; stock composition

资金

  1. Universidad de Buenos Aires [UBACYT 20020150100052BA]
  2. ANPCyT [PICT 2015-1823]
  3. Administrative Commission of the River Uruguay (CARU, 2010-2014)
  4. Government of the Principality of Asturias [FC-15-GRUPIN14-040]

向作者/读者索取更多资源

The objective of this study was to identify potential recruitment sources of Prochilodus lineatus from freshwater areas (Parana and Uruguay rivers) to estuarine population of the Rio de la Plata Estuary (La Plata Basin, South America), considering young (age-1) and adult (age-7) fish. LA-ICP-MS chemical analysis of the otolith core (nine element:Ca ratios) of an unknown mixed sample from Rio de la Plata Estuary (2011 and 2017) was compared with a young-of-year baseline data set (same cohort) and classified into freshwater nurseries (Parana or Uruguay river) by using maximum classification-likelihood models (MLE and MCL) and quadratic discriminant analysis (QDA). Considering the three models used, the Uruguay River was the most important contributor for both young and adult populations. The young population (2011) was highly mixed with contributions between 31.7 to 68.3%, while the degree of mixing was found to decrease in 2017 (adult fish) from 97.1 to 100% contributions. The three employed methods showed comparable estimates, however, the QDA showed a high similarity with the MCL model, suggesting sensitivity to evaluate small contributions, unlike the MLE method. Our results show the potential application of maximum likelihood mixture models and QDA for determining the relative importance of recruitment sources of fish in estuarine waters of the La Plata Basin.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据