4.3 Review

A review of ecological models for brown trout: towards a new demogenetic model

期刊

ECOLOGY OF FRESHWATER FISH
卷 20, 期 2, 页码 167-198

出版社

WILEY
DOI: 10.1111/j.1600-0633.2011.00491.x

关键词

brown trout; demogenetics; ecological model; population dynamics; population genetics

资金

  1. 'Fonds pour la formation a la Recherche dans l'Industrie et dans l'Agriculture' (F.R.I.A.)

向作者/读者索取更多资源

Ecological models for stream fish range in scale from individual fish to entire populations. They have been used to assess habitat quality and to predict the demographic and genetic responses to management or disturbance. In this paper, we conduct the first comprehensive review and synthesis of the vast body of modelling literature on the brown trout, Salmo trutta L., with the aim of developing the framework for a demogenetic model, i.e., a model integrating both population dynamics and genetics. We use a bibliometric literature review to identify two main categories of models: population ecology (including population dynamics and population genetics) and population distribution (including habitat-hydraulic and spatial distribution). We assess how these models have previously been applied to stream fish, particularly brown trout, and how recent models have begun to integrate them to address two key management and conservation questions: (i) How can we predict fish population responses to management intervention? and (ii) How is the genetic structure of fish populations influenced by landscape characteristics? Because salmonid populations tend to show watershed scale variation in both demographic and genetic traits, we propose that models combining demographic, genetic and spatial data are promising tools for improving their management and conservation. We conclude with a framework for an individual-based, spatially explicit demogenetic model that we will apply to stream-dwelling brown trout populations in the near future.

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