Calibrated estimates of fisheries population abundance are vital in the development and appraisal of management actions. Capture–recapture (CR) experiments are invaluable monitoring tools for estimating abundance of biological populations in general. Many researchers, including those studying out-migrating juvenile Chinook Salmon Oncorhynchus tshawytscha in the Klamath Basin of Oregon and California, have attempted to employ Bayesian B-splines to smooth temporal variation in abundance estimates. However, concerns about overfitting and reduced precision are common with this approach. We conducted a simulation study assessing the relative fit of the standard Bayesian B-spline implementation—a B-spline model of abundance with a random effects (RE) binomial model of recapture probabilities—in comparison with that of three alternative model options: a B-spline model of abundance with an autoregressive (AR) model of recapture; an AR model of abundance with an RE model of recapture, and an AR model of abundance with an AR model of recapture. We analyzed 1–3 years of CR data from three rotary screw trap sites and simulated data sets of variable completeness to assess each model’s strengths and weaknesses in estimating the underlying data. Results demonstrated that in general, an AR model of abundance coupled with an RE binomial model of recapture was the least biased model, consistently exhibiting no greater than 5% bias in abundance estimates across data sets. The B-spline abundance models were able to produce narrower 95% credible intervals (CRIs), ranging from 15% to 30% of the parameterized annual abundance value, compared to CRI widths between 23% and 54% from the AR models. However, these narrower CRI widths were commonly errant, with coverage probability rates as low as 50–70% in half of the simulated data set collections compared to over 90% coverage for the AR models. These analyses provide valuable insight and caution for researchers employing modern methods of modeling abundance in natural populations.

中文翻译：

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评估用于估计鲑鱼幼鱼丰度的替代分层建模方法

渔业种群丰度的校准估计对于管理行动的制定和评估至关重要。捕获-再捕获 (CR) 实验是用于估计一般生物种群丰度的宝贵监测工具。许多研究人员，包括那些研究迁徙幼鱼Oncorhynchus tshawytscha 的研究人员在俄勒冈州和加利福尼亚州的克拉马斯盆地，已经尝试使用贝叶斯 B 样条来平滑丰度估计的时间变化。然而，这种方法普遍存在对过度拟合和精度降低的担忧。我们进行了一项模拟研究，评估标准贝叶斯 B 样条实现的相对拟合 - 丰度的 B 样条模型与重新捕获概率的随机效应 (RE) 二项式模型 - 与三个替代模型选项的比较：B - 丰度样条模型与自回归 (AR) 重新捕获模型；一个 AR 丰度模型和一个 RE 重新捕获模型，一个 AR 丰度模型和一个 AR 重新捕获模型。我们分析了来自三个旋转螺旋捕集器站点的 1-3 年 CR 数据和变量完整性的模拟数据集，以评估每个模型在估计基础数据方面的优势和劣势。结果表明，一般而言，AR 丰度模型与 RE 二项式再捕获模型相结合是偏差最小的模型，在跨数据集的丰度估计中始终表现出不超过 5% 的偏差。与 AR 模型的 CRI 宽度在 23% 和 54% 之间，B 样条丰度模型能够产生更窄的 95% 可信区间 (CRI)，范围为参数化年度丰度值的 15% 到 30%。然而，这些较窄的 CRI 宽度通常是错误的，与 AR 模型的覆盖率超过 90% 相比，一半的模拟数据集集合的覆盖率低至 50-70%。