Applied hierarchical modeling in ecology : analysis of distribution, abundance and species richness in R and BUGS
著者
書誌事項
Applied hierarchical modeling in ecology : analysis of distribution, abundance and species richness in R and BUGS
Academic Press, 2015
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
内容説明・目次
内容説明
Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management.
This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields.
目次
Preface
Part 1: Prelude
1. Distribution, abundance and species richness in ecology
2. What are hierarchical models and how do we analyse them ?
3. Linear models, generalized linear models (GLMs), and random-effects: the components of hierarchical models
4. Introduction to data simulation
5. The Bayesian modeling software BUGS and JAGS
Part 2: Models for static systems
6. Modeling abundance using binomial N-mixture models
7. Modeling abundance using multinomial N-mixture models
8. Modeling abundance using hierarchical distance sampling
9. Advanced hierarchical distance sampling
10. Modeling distribution and occurrence using site-occupancy models
11. Community models (incidence- and abundance-based)
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