Three species of invasive carp—Grass Carp Ctenopharyngodon idella, Silver Carp Hypophthalmichthys molitrix, and Bighead Carp H. nobilis—are rapidly spreading throughout North America. Monitoring their reproduction can help to determine establishment in new areas but is difficult due to challenges associated with identifying fish eggs. Recently, random forest models provided accurate identification of eggs based on morphological traits, but the models have not been validated using independent data. Our objective was to evaluate the predictive performance of egg identification models developed by Camacho et al. (2019) for classifying invasive carp eggs by using an independent data set. When invasive carp were grouped as one category, predictive accuracy was high at the following levels: family (89%), genus (90%), species (91%), and species with reduced predictor variables (94%). Invasive carp predictive accuracy decreased when we only considered observations from newly sampled locations (family: 9%; genus: 22%; species: 30%; species with reduced predictor variables: 70%), suggesting potential differences in egg characteristics among locations. Random forest models using a combination of previous and new data resulted in high predictive accuracy for invasive carp (96–98%) when invasive carp were grouped as one class for all models at the family, genus, and species levels. The two most influential predictor variables were average membrane diameter and average embryo diameter; the probability of predicting an invasive carp egg increased with these metrics. High predictive accuracy metrics suggest that these trained and validated random forest models can be used to identify invasive carp eggs based on morphometric variables. However, decreased performance at new locations suggests that more research would be beneficial to determine the models’ applicability to a larger spatial region.

中文翻译：

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基于形态学特征识别入侵鲤鱼卵的随机森林模型评价

三种入侵鲤鱼——草鱼Ctenopharyngodon idella、鲢鱼Hypophthalmichthys molitrix和鳙鱼H. nobilis——正在整个北美迅速传播。监测它们的繁殖有助于确定新地区的定居点，但由于与识别鱼卵相关的挑战而变得困难。最近，随机森林模型提供了基于形态特征的鸡蛋准确识别，但该模型尚未使用独立数据进行验证。我们的目标是评估 Camacho 等人开发的鸡蛋识别模型的预测性能。(2019) 使用独立数据集对入侵的鲤鱼卵进行分类。当入侵的鲤鱼被归为一类时，预测准确性在以下水平上很高：科 (89%)、属 (90%)、物种 (91%) 和预测变量减少的物种 (94%)。当我们仅考虑来自新采样地点的观察结果时（科：9%；属：22%；物种：30%；预测变量减少的物种：70%），入侵鲤鱼的预测准确性会降低，这表明不同地点的卵特征存在潜在差异。当入侵鲤鱼在科、属和物种水平上被归为一个类别时，结合使用以前和新数据的随机森林模型导致对入侵鲤鱼的预测准确性很高 (96–98%)。两个最有影响力的预测变量是平均膜直径和平均胚胎直径；随着这些指标的增加，预测入侵鲤鱼卵的可能性增加了。高预测准确性指标表明，这些经过训练和验证的随机森林模型可用于根据形态变量识别入侵的鲤鱼卵。