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Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management
Journal of Applied Ecology ( IF 5.0 ) Pub Date : 2021-09-14 , DOI: 10.1111/1365-2664.13992 Dawn R. Barlow 1 , Leigh G. Torres 1
中文翻译：
提前规划：动态模型通过空间管理应用程序预测蓝鲸分布
更新日期：2021-11-12
Journal of Applied Ecology ( IF 5.0 ) Pub Date : 2021-09-14 , DOI: 10.1111/1365-2664.13992 Dawn R. Barlow 1 , Leigh G. Torres 1
Affiliation
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- Resources in the ocean are ephemeral, and effective management must therefore account for the dynamic spatial and temporal patterns of ecosystems and species of concern. We focus on the South Taranaki Bight (STB) of New Zealand, where upwelling generates productivity and prey to support an important foraging ground for blue whales that overlaps with anthropogenic pressure from industrial activities.
- We incorporate regional ecological knowledge of upwelling dynamics, physical–biological coupling and associated lags in models to forecast sea surface temperature (SST) and net primary productivity (NPP) with up to 3 weeks lead time. Forecasted environmental layers are then implemented in species distribution models to predict suitable blue whale habitat in the STB. Models were calibrated using data from the austral summers of 2009–2019, and ecological forecast skill was evaluated by predicting to withheld data.
- Boosted regression tree models skilfully forecasted SST (CV deviance explained = 0.969–0.970) and NPP (CV deviance explained = 0.738–0.824). The subsequent blue whale distribution forecast models had high predictive performance (AUC = 0.889), effectively forecasting suitable habitat on a daily scale with 1–3 weeks lead time.
- The spatial location and extent of forecasted blue whale habitat were variable, with the proportion of petroleum and mineral permit areas that overlapped with daily suitable habitat ranging from 0% to 70%. Hence, the STB and these forecast models are well-suited for dynamic management that could reduce anthropogenic threats to whales while decreasing regulatory burdens to industry users relative to a traditional static protected area.
- Synthesis and applications. We develop and test ecological forecast models that predict sea surface temperature, net primary productivity and blue whale suitable habitat up to 3 weeks in the future within New Zealand's South Taranaki Bight region. These forecasts of whale distribution can be effectively applied for dynamic spatial management due to model foundation on quantified links and lags between physical forcing and biological responses. A framework to operationalize these forecasts through a user-driven application is in development to proactively inform conservation management decisions. This framework is implemented through stakeholder engagement, allows flexibility based on management objectives, and is amenable to improvement as new knowledge and feedback are received.
中文翻译：
提前规划：动态模型通过空间管理应用程序预测蓝鲸分布
- 海洋中的资源是短暂的，因此有效的管理必须考虑到生态系统和相关物种的动态空间和时间模式。我们专注于新西兰的南塔拉纳基湾 (STB)，在那里上升流产生生产力和猎物，以支持蓝鲸的重要觅食地，与工业活动的人为压力重叠。
- 我们在模型中结合了上升流动力学、物理-生物耦合和相关滞后的区域生态知识，以预测海面温度 (SST) 和净初级生产力 (NPP)，提前期长达 3 周。然后在物种分布模型中实施预测的环境层，以预测 STB 中合适的蓝鲸栖息地。模型使用 2009-2019 年南方夏季的数据进行校准，并通过预测隐瞒数据来评估生态预测技能。
- 提升回归树模型巧妙地预测了 SST（解释的 CV 偏差 = 0.969–0.970）和 NPP（解释的 CV 偏差 = 0.738–0.824）。随后的蓝鲸分布预测模型具有较高的预测性能（AUC = 0.889），可以在 1 到 3 周的提前时间内有效地在每日范围内预测合适的栖息地。
- 预测的蓝鲸栖息地的空间位置和范围是可变的，石油和矿产许可区域与日常适宜栖息地重叠的比例从0%到70%不等。因此，STB 和这些预测模型非常适合动态管理，可以减少对鲸鱼的人为威胁，同时减少相对于传统静态保护区的行业用户的监管负担。
- 合成与应用。我们开发并测试了生态预测模型，这些模型可以预测未来 3 周内新西兰南塔拉纳基湾地区的海面温度、净初级生产力和蓝鲸的适宜栖息地。由于基于物理强迫和生物响应之间的量化联系和滞后的模型基础，这些鲸鱼分布预测可以有效地应用于动态空间管理。通过用户驱动的应用程序实施这些预测的框架正在开发中，以主动通知保护管理决策。该框架通过利益相关者的参与来实施，允许基于管理目标的灵活性，并且可以在收到新知识和反馈时进行改进。
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