Ecol Appl. 2014;24(8):2107-21. doi: 10.1890/13-2023.1.
Rakiura Māori (New Zealand's southernmost group of indigenous peoples) have harvested the chicks of burrow-nesting Sooty Shearwaters (Tītī; Puffinus griseus) for generations. As part of the harvest process, some families have maintained annual harvest diaries, some dating back to the 1950s. We used generalized boosted regression models, a machine-learning algorithm, to calculate a harvest index that takes into account factors that could impact the numbers of birds taken on any given hunt. For predicted vs. observed values, r2 was between 0.59 and 0.90 for the nanao (first half of the season, when chicks are harvested from burrows during the day) and 0.67 and 0.88 for the rama (second half of the season, during which chicks are harvested from the surface at night). Exploration of the controlling factors of the models revealed that “day of season” plays an important role in predicting daily harvest during the second half of the season (the rama). The nightly tally in the rama peaked approximately halfway through (10–15 days in), which is probably related to the timing of birds emerging from burrows to fledge. The models also suggested that data from the rama (when chicks are 100–120 days old) may be the most suitable for long-term monitoring of populations of Sooty Shearwaters due to consistencies in calculated harvest indices between diaries. Nanao harvest indices, although less consistent, showed patterns similar to those of the rama. When comparing these data to the harvest indices calculated by general linear models by Clucas and colleagues, we found that the agreement between both indices was r2 = 0.31 and r2 = 0.59 for the nanao and rama, respectively. The use of machine learning to correct for extraneous factors (e.g., hunting effort, skill level, or weather) and to create standardized measures could be applied to other systems such as fisheries or terrestrial resource management.
拉基乌拉毛利人（新西兰最南端的土著群体）世世代代都在捕捞穴居的斑海雀（Tītī；Puffinus griseus）的幼鸟。在捕捞过程中，一些家庭每年都有捕捞日记，有些甚至可以追溯到 20 世纪 50 年代。我们使用广义增强回归模型（一种机器学习算法）来计算一个收获指数，该指数考虑了可能影响任何给定狩猎中鸟类数量的因素。对于预测值与观测值，nanao（第一季，白天从洞穴中收获幼鸟）的 r2 在 0.59 到 0.90 之间，rama（第二季，晚上从表面收获幼鸟）的 r2 在 0.67 到 0.88 之间。对模型控制因素的探索表明，“季节中的天数”在预测第二季（rama）期间的每日收获中起着重要作用。rama 期间的夜间计数在中旬达到峰值（大约 10-15 天），这可能与幼鸟从洞穴中孵化出来的时间有关。模型还表明，rama 时期的数据（当幼鸟 100-120 天大时）可能最适合长期监测斑海雀的种群，因为不同日记之间的收获指数计算具有一致性。nanao 收获指数虽然不太一致，但显示出与 rama 相似的模式。当将这些数据与 Clucas 及其同事通过普通线性模型计算的收获指数进行比较时，我们发现两个指数之间的一致性 r2 分别为 0.31 和 0.59，分别适用于 nanao 和 rama。使用机器学习来纠正外部因素（例如狩猎努力、技能水平或天气）并创建标准化措施可以应用于其他系统，例如渔业或陆地资源管理。
