The Red Snapper Lutjanus campechanus is a structure‐associated species occurring across a wide depth range in the northern Gulf of Mexico. We used the random forest machine learning algorithm to understand which habitat and individual fish characteristics could predict reproductive parameters of female Red Snapper. We evaluated fish captured from 2016 to 2018 on three artificial structure types with various structure heights at depths of 100 m or less. Overall, we found that depth and month were important predictors for most reproductive parameters, but the type of structure (artificial reefs, oil platforms, and rigs‐to‐reefs structures) was not important. Maturity was correctly classified in 88.9% of the cases when using the random forest ensemble model, with important predictors including FL, depth, structure height, and month of collection. Spawning seasonality (measured as gonadosomatic index [GSI]) was correctly classified in 59.5% of the cases when using histology reproductive phase, FL, month, and depth variables. Reproductively active or inactive females were correctly classified in 89.3% of the cases using GSI, month, FL, and depth, while females in the developing versus spawning capable phases were correctly classified in 82.2% of the cases using GSI, FL, month, and depth. Histological indicators that show potential spawning within a 36‐h period were correctly classified 61.5% of the time, with the best predictors being depth, FL, GSI, and month. Stepwise regression indicated that month was the only factor that significantly predicted contrasts in relative batch fecundity, with significantly greater values in August compared to all other months. Our findings suggest that female Red Snapper reproductive effort is not consistently or well predicted by artificial structure type or height but that a combination of fish FL, month, and depth can predict reproductive characteristics of female Red Snapper.

中文翻译：

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深度和人工结构作为雌性红鲷鱼生殖参数预测指标的重要性

红鲷鱼Lutjanus campechanus是与结构相关的物种，分布在墨西哥湾北部的大范围深度内。我们使用随机森林机器学习算法来了解哪些栖息地和鱼类特征可以预测雌性红鲷鱼的繁殖参数。我们评估了从2016年到2018年捕获的三种结构高度在100 m或更小的人造结构类型的鱼类。总体而言，我们发现深度和月度是大多数生殖参数的重要预测指标，但结构类型（人工礁，石油平台和钻机礁结构）并不重要。在使用随机森林集成模型的情况下，成熟度在88.9％的情况下正确分类，并具有重要的预测因子，包括FL，深度，结构高度和收集月份。当使用组织学生殖期，FL，月份和深度变量时，在59.5％的病例中将产卵季节（以性腺体指数[GSI]衡量）正确分类。使用GSI，FL，月份和深度将89.3％的生殖活动或不活动女性正确分类，而使用GSI，FL，月份和月份将处于发育和产卵期的女性正确分类为82.2％深度。显示在36小时内可能产卵的组织学指标被正确分类为61.5％的时间，最佳预测指标为深度，FL，GSI和月份。逐步回归表明，月份是唯一能明显预测相对批次繁殖力差异的因素，八月份的值明显高于所有其他月份。