Describing sizerelated mortality and size distribution by nonparametric estimation and model selection using the Akaike Bayesian Information Criterion
 Kenichiro Shimatani,
 Satoko Kawarasaki,
 Tohru Manabe
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When we calculate mortality along a gradient such as size, dividing into size classes and calculating rates for every class often involves a tradeoff: fine class intervals produce fluctuating rates along the gradient, whereas broad ones may miss some trends within an interval. The same tradeoff occurs when we want to illustrate size distribution by a histogram. This paper introduces nonparametric methods, published in a statistical journal, into forest ecology, in which the fineclass strategy is used in an extreme way: (1) a smoothly changing pattern is approximated by a fine step function, (2) the goodnessoffit to the data and the smoothness along the gradient are formulated as a weighting sum within a Bayesian framework, (3) the Akaike Bayesian Information Criterion (ABIC) selects the weighting system that most appropriately balances the two demands, and (4) the values of the step function are optimized by the maximum likelihood method. The nonparametric estimates enable us to represent various patterns visually and, unlike parametric modeling, calculations do not demand the determination of a functional form. Mortality and size distribution analyses were conducted on 12year forest tree monitoring data from a 4 ha permanent plot in an oldgrowth warm–temperate evergreen broadleaved forest in Japan. From trees of 11 evergreen species with a diameter at breast height (DBH) greater than 5 cm, we found three types of trend with increasing DBH: decreasing, ladleshaped and constant mortality. These patterns reflect variations in life history particular to each species.
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 Title
 Describing sizerelated mortality and size distribution by nonparametric estimation and model selection using the Akaike Bayesian Information Criterion
 Journal

Ecological Research
Volume 23, Issue 2 , pp 289297
 Cover Date
 20080301
 DOI
 10.1007/s112840070375y
 Print ISSN
 09123814
 Online ISSN
 14401703
 Publisher
 Springer Japan
 Additional Links
 Topics
 Keywords

 Akaike Bayesian Information Criterion
 Akaike Information Criterion
 Diameter at breast height
 Evergreen forest
 Population dynamics
 Authors

 Kenichiro Shimatani ^{(1)}
 Satoko Kawarasaki ^{(2)}
 Tohru Manabe ^{(3)}
 Author Affiliations

 1. The Institute of Statistical Mathematics, 467 MinamiAzabu, Minato, Tokyo, 1068569, Japan
 2. Transdisciplinary Research Integration Center, Research Organization of Information and Systems, 467 MinamiAzabu, Minato, Tokyo, 1068569, Japan
 3. Kitakyushu Museum and Institute of Natural History, Kitakyushu, 8050071, Japan