Creating Pondscapes for Avian Communities: An Artificial Neural Network Experience Beyond Urban Regions

  • Wei-Ta Fang
Part of the Ecological Research Monographs book series (ECOLOGICAL)


Farm ponds are unique geographic features of the Taoyuan Tableland. Besides irrigation, they provide refuges for wintering birds. The issue at hand is that these features are disappearing, and this brings with it the loss of this refuge function. It is ecologically significant because one-fifth of all the bird species in Taiwan find a home on these ponds. This study aims at characterizing the diversity of bird species associated with these ponds, whose likelihood of survival was assessed along the gradient of land development intensities. Such characterization helps establish the decision criteria needed for designating certain ponds for habitat preservation and developing their protection strategies. A holistic model was developed by incorporating logistic regression with error back-propagation into the paradigm of artificial neural networks (ANN). The model considers pond shape, size, neighboring farmlands, and developed areas when calculating parameters pertaining to their respective and interactive influences on avian diversity, among them the Shannon–Wiener diversity index (H′). Results indicate that ponds with a regular shape or ones of larger size possess a strong positive correlation with H′. Farm ponds adjacent to farmland benefited the diversity of waterside birds. On the other hand, urban development was shown to cause a reduction of farmland and pond numbers, which in turn reduced the diversity of waterside birds. By running the ANN model with four neurons, the resulting H′ index shows a good-fit prediction of bird diversity against pond size, shape, neighboring farmlands, and neighboring developed areas with a correlation coefficient (r) of 0.72, in contrast to the results from a linear regression model (r  <  0.28).


Artificial Neural Network Model Vernal Pool Bird Diversity Farm Pond Wiener Diversity Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2011

Authors and Affiliations

  1. 1.Chung Hua UniversityHsinchuRepublic of China

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