Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management (data and code)

This repository contains data and code corresponding to the following manuscript, published in the <i>Journal of Applied Ecology</i>, 2021:<b><br></b><b>Planning ahead: Dynamic models forecast blue whale distribution with applications for spatial management</b><br>Authors: Dawn R. Barlow¹* and Leigh G. Torres¹^<br>¹Geospatial Ecology of Marine Megafauna Lab, Marine Mammal Institute, Department of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Newport, Oregon, USA<br>*[e-mail]<br>Abstract:1. 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. 2. 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 three 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. 3. Boosted regression tree models skillfully 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. 4. The spatial location and extent of forecasted blue whale habitat was variable, with the proportion of petroleum and mineral permit areas that overlapped with daily suitable habitat ranging from 0-70%. 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. 5. 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 three 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.<br>

本代码仓库包含对应2021年发表于《应用生态学杂志》（*Journal of Applied Ecology*）的研究手稿的数据与代码：<br><b>《前瞻规划：动态模型预测蓝鲸分布及其在空间管理中的应用》</b><br>作者：Dawn R. Barlow¹* 与 Leigh G. Torres¹<br>¹美国俄勒冈州立大学渔业、野生动物与保护科学系海洋哺乳动物研究所海洋巨型动物地理空间生态学实验室，新港，俄勒冈州，美国<br>*通讯作者邮箱：[e-mail]<br>摘要：1. 海洋资源具有瞬时性，因此有效的海洋管理必须充分考量受关注生态系统与物种的动态时空分布格局。本研究聚焦新西兰南塔拉纳基湾（South Taranaki Bight, STB），该区域的上升流活动可催生高生产力与猎物资源，形成重要的蓝鲸觅食场，但其同时与工业活动带来的人为干扰压力存在空间重叠。2. 我们将上升流动态、物理-生物耦合作用及相关滞后效应的区域生态知识纳入模型，以提前最多3周的预报提前量预测海表温度（sea surface temperature, SST）与净初级生产力（net primary productivity, NPP）。随后将预测得到的环境图层应用于物种分布模型，以预测南塔拉纳基湾内的适宜蓝鲸栖息地。模型以2009-2019年南半球夏季的观测数据进行校准，并通过对预留测试数据集进行预测来评估生态预测性能。3. 提升回归树（Boosted Regression Tree）模型可精准预测海表温度（交叉验证偏差解释率=0.969~0.970）与净初级生产力（交叉验证偏差解释率=0.738~0.824）。后续的蓝鲸分布预测模型同样具备优异的预测性能（受试者工作特征曲线下面积AUC=0.889），可在提前1~3周的尺度上逐日预测适宜栖息地。4. 预测得到的蓝鲸适宜栖息地的空间位置与范围存在动态变化，每日适宜栖息地与油气矿产许可作业区的重叠比例介于0~70%之间。因此，南塔拉纳基湾区域与本研究的预测模型非常适用于动态空间管理：相较于传统静态保护区，该管理模式既可降低对鲸鱼的人为威胁，又能减轻工业用户的监管负担。5. 综合与应用：我们开发并验证了生态预测模型，可在新西兰南塔拉纳基湾区域提前最多3周预测海表温度、净初级生产力与蓝鲸适宜栖息地。由于这些模型基于物理强迫与生物响应之间的量化关联及滞后效应构建，因此其预测的鲸鱼分布可有效应用于动态空间管理。目前团队正在开发一个通过用户驱动的应用程序来落地这些预测的框架，以主动为保护管理决策提供依据。该框架通过利益相关方参与推进，可根据管理目标灵活调整，且可在获取新知识与反馈后持续优化。