A dynamic foraging habitat distribution estimate for green turtles in the Great Barrier Reef

A detailed understanding of how protected species use their habitats can
guide management interventions in areas of high human use. For marine
turtles, different food availability and physical habitat characteristics
can underpin turtle presence at anthropogenically modified compared to
unmodified sites. We develop telemetry-based habitat models with boosted
regression trees to identify the environmental characteristics
underpinning foraging habitat suitability for green turtles in the Great
Barrier Reef region. We fit models to green turtle Fastloc GPS tracks from
both modified and unmodified inshore foraging sites and using
 pseudo-absences (simulated correlated random walks). We assess
model performance by the ability to predict known foraging areas, true
skill statistic, explanatory power (percent deviance explained) and
predictive skill (AUC) of the models. We then predict potentially suitable
foraging areas for green turtles in the Great Barrier Reef region using
the model for unmodified habitats. Between 2010 and 2022, the total area
of suitable foraging habitat declined by 41.2%, and nearshore habitat
suitability retracted. These areas are likely affected by floods,
development and increased turbidity. In 2022, 50% of predicted suitable
habitat fell within habitat protection zones, and 19.4% in Marine National
Park Zones of the Great Barrier Reef Marine Park. A detailed foraging
distribution of the species has not previously been compiled at this
regional scale. Identifying biophysical drivers of habitat suitability can
inform identification of possible foraging habitat in less data rich
regions in Australia and overseas. Evaluating changes over time in habitat
distribution provides insights into the degree to which broad-scale
environmental changes and anthropogenic activities influence the condition
and function of habitats, even within protected area boundaries.

深入了解受保护物种对栖息地的利用模式，可为人类活动密集区域的管理干预措施提供科学指引。对于海龟而言，相较于未受干扰的栖息地，食物可获得性与栖息地物理特征的差异，是决定其在人为改造区域出现与否的核心因素。我们采用基于遥测技术（telemetry）的提升回归树（Boosted Regression Trees）构建栖息地模型，以识别大堡礁区域绿海龟觅食栖息地适宜性的关键环境特征。我们基于改造与未改造近岸觅食区域的绿海龟Fastloc GPS追踪数据，并结合伪缺席点（pseudo-absences，即模拟关联随机游走样本）对模型进行拟合。我们通过多项指标评估模型性能：对已知觅食区域的预测能力、真实技能统计量（True Skill Statistic）、解释能力（偏差解释百分比）以及模型预测效能（AUC，受试者工作特征曲线下面积）。随后我们基于未受干扰栖息地的模型，预测大堡礁区域绿海龟的潜在适宜觅食区域。2010年至2022年间，适宜觅食栖息地的总面积减少了41.2%，近岸栖息地的适宜性也出现退缩。此类区域或受洪水、开发活动以及水体浊度升高的影响。2022年，预测得到的适宜栖息地中，50%位于栖息地保护区范围内，另有19.4%位于大堡礁海洋公园的海洋国家公园分区内。此前尚未在该区域尺度上系统整理过该物种的详细觅食分布数据。识别栖息地适宜性的生物物理驱动因子，可为澳大利亚及海外数据匮乏区域的潜在觅食栖息地甄别提供参考。评估栖息地分布的时间动态变化，可帮助我们理解大范围环境变化与人为活动对栖息地状态与功能的影响程度，即便在保护区边界范围内亦是如此。