期刊
ECOLOGICAL MODELLING
卷 299, 期 -, 页码 51-63出版社
ELSEVIER
DOI: 10.1016/j.ecolmodel.2014.12.005
关键词
Spatial pattern; Rare species; Eigenfunction; Spatial filter; Delta model; Zero-inflated data
类别
资金
- USDA Cooperative State Research, Education and Extension Service, Hatch project [0210510]
- National Oceanic and Atmospheric Administration (NOAA)
- National Marine Fisheries Service (NMFS) Southeast Fisheries Science Center (SEFSC), as part of the NOAA Fisheries National Seabird Program
Data of rare species usually contain a high percentage of zero observations due to their low abundance. Such data are generally referred as zero-inflated data. Modeling spatial patterns in such data has been challenging, especially when large datasets are involved and intensive computing are required. The eigenfunction-based spatial filtering provides a flexible tool that allows the existing modeling approaches that can handle zero-inflated data such as the delta model to be applied in the presence of spatial dependence. With a real dataset, the longline seabird bycatch data, the present study demonstrated a modification of delta model with the spatial filters to investigate spatial patterns in zero-inflated data for rare species. We explored a total of 108 spatial weighting matrices, and modified the-delta model by incorporating the spatial filters generated from the best spatial weighting matrix. We applied the five-fold cross-validation to compare performance of the modified delta model with other three candidate models based on the mean absolute error and the mean bias. The three candidate models included the baseline model without spatial dependence considered, the trend-surface generalized additive model and the random areal effect model. The delta model modified with spatial filters showed superior performance over the other three candidate models in the seabird bycatch example. With the seabird bycatch example, we illustrated a modification of delta model with the eigenfunction-based spatial filters to investigate spatial patterns. This study provides an alternative to incorporate spatial dependence in the existing approaches for modeling spatial patterns in zero-inflated data for rare species. (C) 2014 Elsevier B.V. All rights reserved.
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。
推荐
暂无数据