AIM: Cetaceans are inherently difficult to study due to their elusive, pelagic and often highly migratory nature. New Zealand waters are home to 50% of the world's cetacean species, but their spatial distributions are poorly known. Here, we model distributions of 30 cetacean taxa using an extensive at‐sea sightings dataset (n > 14,000) and high‐resolution (1 km²) environmental data layers. LOCATION: New Zealand's Exclusive Economic Zone (EEZ). METHODS: Two models were used to predict probability of species occurrence based on available sightings records. For taxa with <50 sightings (n = 15), Relative Environmental Suitability (RES), and for taxa with ≥50 sightings (n = 15), Boosted Regression Tree (BRT) models were used. Independently collected presence/absence data were used for further model evaluation for a subset of taxa. RESULTS: RES models for rarely sighted species showed reasonable fits to available sightings and stranding data based on literature and expert knowledge on the species' autecology. BRT models showed high predictive power for commonly sighted species (AUC: 0.79–0.99). Important variables for predicting the occurrence of cetacean taxa were temperature residuals, bathymetry, distance to the 500 m isobath, mixed layer depth and water turbidity. Cetacean distribution patterns varied from highly localised, nearshore (e.g., Hector's dolphin), to more ubiquitous (e.g., common dolphin) to primarily offshore species (e.g., blue whale). Cetacean richness based on stacked species occurrence layers illustrated patterns of fewer inshore taxa with localised richness hotspots, and higher offshore richness especially in locales of the Macquarie Ridge, Bounty Trough and Chatham Rise. MAIN CONCLUSIONS: Predicted spatial distributions fill a major knowledge gap towards informing future assessments and conservation planning for cetaceans in New Zealand's extensive EEZ. While sightings datasets were not spatially comprehensive for any taxa, these two best available approaches allow for predictive modelling of both more common, and of rarely sighted, cetacean species with limited available information.

中文翻译：

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新西兰水域鲸类空间分布建模

目的：鲸类动物由于难以捉摸、浮游且通常具有高度迁徙性，因此天生就难以研究。新西兰水域拥有世界上 50% 的鲸类物种，但它们的空间分布知之甚少。在这里，我们使用广泛的海上目击数据集 (n > 14,000) 和高分辨率 (1 km²) 环境数据层对 30 种鲸类分类群的分布进行建模。位置：新西兰的专属经济区（EEZ）。方法：根据现有的目击记录，使用两种模型来预测物种出现的概率。对于 <50 个目击事件 (n = 15)、相对环境适宜性 (RES) 和≥50 个目击事件 (n = 15) 的分类群，使用了增强回归树 (BRT) 模型。独立收集的存在/不存在数据用于对分类群子集的进一步模型评估。结果：罕见物种的 RES 模型显示出与现有的目击数据和搁浅数据的合理拟合，这些数据基于有关物种自有生态学的文献和专家知识。BRT 模型显示出对常见物种的高预测能力（AUC：0.79-0.99）。预测鲸类分类群发生的重要变量是温度残差、测深、到 500 m 等深线的距离、混合层深度和水浊度。鲸类的分布模式从高度本地化的近岸（例如赫氏海豚）到更普遍存在的（例如普通海豚）再到主要是近海物种（例如蓝鲸）。基于叠加物种发生层的鲸类丰富度说明了具有局部丰富度热点的近海类群较少的模式，以及较高的近海丰富度，尤其是在麦夸里海岭地区，赏金谷和查塔姆崛起。主要结论：预测的空间分布填补了一个主要的知识空白，为新西兰广泛的专属经济区的鲸类动物的未来评估和保护计划提供信息。虽然目击数据集对于任何分类群在空间上都不全面，但这两种最佳可用方法允许对更常见和很少见的鲸类物种进行预测建模，可用信息有限。