《生物多樣性公約》已有193個國家簽署，可見世界各國已正視生物多樣性之重要，因此研究生物間的交互行為亦是個重要的議題。互資訊 (mutual information) 是用來衡量兩隨機變數之間的關聯性，在生態上可以使用互資訊來量化物種之間的交互作用或是專一程度。互資訊可由熵 (entropy) 指標來表示，Chao, A., Wang, Y. T. and Jost, L. (2013) 將熵指標表示成發現新物種速率的一個函數，提出近乎不偏的熵指標估計量。本文研究Chao et al. (2013) 熵指標估計量套用在估計互資訊上的表現情形，亦即本文建議之估計量，並與其他五個估計量作比較，以及提出修正兩維度資料下拔靴方法之標準差估計。藉由電腦模擬的方式探討各個估計量的表現，結果顯示本文建議之估計量在所有實驗中有接近最小的偏差，可接受的變異程度，以及近乎一致的具有最小的均方根誤差，其收斂速度相較於其他估計量也更為迅速；當抽取個體數較少時，即可有非常出色的估計表現。同時搭配實例分析，更能了解互資訊對於生態上的實務應用。
此外介紹以R語言開發的互動式網頁ChaoEntropy Online，此軟體包含兩大部分「Shannon entropy」與「Mutual Information」，經由簡單地點選即可得到估計值、拔靴標準差與信賴區間。亦能在本機端執行，資料不外洩，希望對熵指標或互資訊估計有需求的人士，能藉由此軟體輕鬆分析。

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