Depth is a fundamental and universal driver of ocean biogeography but it is unclear how the biodiversity patterns of larger, more mobile organisms change as a function of depth. Here, we developed a predictive biogeography model to explore how information of mobile species' depth preferences influence biodiversity patterns. We employed a literature review to collate shark biotelemetry studies and used open‐access tools to extract 283 total records from 119 studies of 1133 sharks from 35 species. We then matched field guide reported depth ranges and IUCN habitat associations for each shark species to use as covariates in a hurdle variant of ensemble random forests. We successfully fit this model (R2 = 0.63) to the noisy time‐at‐depth observations and used it to predict the time budgets of the northeast Pacific shark regional pool (n = 52). We then assessed how occurrence diversity patterns, informed by minimum and maximum depth of occurrence, compared to time‐at‐depth weighted diversity patterns. Time‐at‐depth weighted richness was highest between 0 and 25 m and at the upper part of the mesopelagic zone, 250–300 m; resulting in little similarity to common depth or elevational biodiversity patterns while the occurrence‐weighted richness pattern was similar to the ‘low‐plateau' pattern. In the phylogenetic and functional dimensions of biodiversity and over three different distance metrics, we found strong but haphazard differences between the occurrence‐ and time‐at‐depth weighted biodiversity patterns. The strong influence of time budgets on biodiversity led us to conclude that occurrence data alone are likely insufficient or even misleading in terms of the depth‐driven biogeographic patterns in the open ocean. Utilizing the increasing amount of time‐at‐depth information from biotelemetry studies in predictive biogeographic models may be critical for capturing the preferences of pelagic, mobile species occupying the largest biome on the planet.

中文翻译：

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预测北太平洋鲨鱼的时间深度加权生物多样性模式

深度是海洋生物地理学的基本且普遍的驱动因素，但目前尚不清楚更大、更易移动的生物体的生物多样性模式如何随着深度的变化而变化。在这里，我们开发了一个预测生物地理学模型来探索移动物种的深度偏好信息如何影响生物多样性模式。我们采用文献综述来整理鲨鱼生物遥测研究，并使用开放获取工具从 35 个物种的 1133 条鲨鱼的 119 项研究中提取了 283 条总记录。然后，我们将现场指南报告的深度范围和每个鲨鱼物种的 IUCN 栖息地关联进行匹配，以用作整体随机森林的障碍变体中的协变量。我们成功地拟合了这个模型（R2= 0.63）到噪声深度时间观测，并用它来预测东北太平洋鲨鱼区域池的时间预算（n = 52）。然后，我们评估了由最小和最大发生深度告知的发生多样性模式与深度时间加权多样性模式的比较。深度时间加权丰富度在0~25 m之间最高，中深层上部250~300 m；导致与常见深度或海拔生物多样性模式几乎没有相似性，而发生加权丰富度模式与“低高原”模式相似。在生物多样性的系统发育和功能维度以及三个不同的距离度量中，我们发现发生加权和时间深度加权的生物多样性模式之间存在强烈但偶然的差异。时间预算对生物多样性的强烈影响使我们得出结论，就公海深度驱动的生物地理模式而言，仅发生数据可能是不够的，甚至具有误导性。在预测生物地理模型中利用来自生物遥测研究的越来越多的深度时间信息可能对于捕获占据地球上最大生物群落的中上层、移动物种的偏好至关重要。