Predicting the probability of avian reproductive success and its components at a nesting site

Sinchan Ghosh, A. Banerjee, Soumalya Mukhopadhyay, S. Bhattacharya, S. Ray
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Abstract

Avian reproduction has three chronological components: nesting, mating, hatching, and fledging. Predicting the probability of individual components helps to identify the period of reproduction that needs the most aid, increasing the conservation efficiency. This prediction requires identification of biotic, abiotic, and sociological variables of a bird’s environment responsible for these componentwise success probabilities. There is also no standard methodology to estimate these probability values separately. This study estimates the absolute success probability of each component, identifies correlated environmental predictors and gives a modeling framework to accurately predict the success probabilities using Merops Philippines as a test bed. The result using surveyed data and proposed methodology indicates the corridor between nesting and mating is most vulnerable to the environment. Social structure is the key to all reproductive components but nesting. Both biotic and abiotic factors are crucial determinants of nesting success. Mating, hatching, and fledging success depend more on biotic factors than abiotic ones. Linear modeling frameworks are helpful to explore which types of environment are a better determinant of the success of a reproductive component. Artificial neural networking is more useful to predict the successes of a new site. Although developed using Merops philippinus data, the proposed methodology and modeling framework are also applicable for other birds.
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预测鸟类在筑巢地点繁殖成功的概率及其组成部分
鸟类的繁殖按时间顺序有三个组成部分:筑巢、交配、孵化和羽化。预测单个组成部分的概率有助于确定最需要帮助的繁殖时期,从而提高保护效率。这种预测需要识别鸟类环境中影响这些组成部分成功概率的生物、非生物和社会学变量。也没有标准的方法来分别估计这些概率值。本研究估计了每个组成部分的绝对成功概率,确定了相关的环境预测因素,并给出了一个建模框架,以Merops菲律宾作为测试平台准确预测成功概率。利用调查数据和提出的方法得出的结果表明,筑巢和交配之间的走廊最容易受到环境的影响。除了筑巢,社会结构是所有繁殖要素的关键。生物和非生物因素都是筑巢成功的关键决定因素。交配、孵化和羽化的成功更多地取决于生物因素而不是非生物因素。线性建模框架有助于探索哪种类型的环境对繁殖成分的成功有更好的决定作用。人工神经网络在预测新网站的成功方面更有用。虽然是利用菲律宾Merops的数据开发的,但所提出的方法和建模框架也适用于其他鸟类。
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