Identification of species’ Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence‐only data are increasingly being used to develop SDMs to aid the conservation decision‐making process. The application of presence‐only SDMs for marine species’ is particularly attractive due to often logistical and economic costs of obtaining systematic species’ distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species’ occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross‐validation (CV) approach to validate a MaxEnt presence‐only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003–2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line‐transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost‐effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non‐calf groups) was obtained that the presence‐only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence‐only models within certain conservation management applications, such as spatial risk assessments.

中文翻译：

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验证仅存在模型的保护规划以及在多用途海洋公园中对鲸鱼的应用。

识别物种的生物重要区域 (BIA) 是保护规划的基础，而物种分布模型 (SDM) 是常用的强大工具。仅存在的数据越来越多地用于开发 SDM，以帮助保护决策过程。由于获取系统物种分布数据通常会产生后勤和经济成本，因此仅存在的 SDM 在海洋物种中的应用特别有吸引力。然而，稳健的模型验证对于需要准确可靠的物种发生数据（例如，空间明确的风险评估）的保护管理应用非常重要。这通常是使用数据的随机子集来完成的，不太常见的是使用完全独立的测试数据。在这里，我们应用空间块交叉验证（CV）方法来验证 MaxEnt 仅存在模型，使用独立的存在/不存在调查数据来验证大堡礁（GBR）中高度流动的海洋物种（座头鲸，Megaptera novaengliae ））。MaxEnt 模型是利用机会鲸鱼目击事件（2003-2007 年）开发的，然后用于识别栖息地适宜性不同的区域（低、中、高），以进行系统的线横断面航空调查（2012 年）并得出密度表面模型。采用空间块 CV 缓冲策略来验证 MaxEnt 模型，使用机会目击事件作为训练数据，独立航测目击数据作为测试数据。中等的性能测量表明 MaxEnt 在识别移动鲸鱼物种在其繁殖地的分布模式方面是可靠的，高密度区域与高栖息地适宜性区域相一致。此外，我们证明 MaxEnt 模型对于设计抽样方案以进行系统调查非常有用且具有成本效益，可显着减少抽样工作量。在这项研究中，仅对大堡礁世界遗产区域的 18% 进行了采样，但获得了有关鲸鱼繁殖等级（幼鲸与非幼鲸群体）的更高质量信息，而仅存在的数据则缺乏。使用完全独立数据的验证方法为 MaxEnt 模型提供了更大的信心，这表明与座头鲸的主要繁殖地和内部运输路线存在显着重叠。在评估某些保护管理应用（例如空间风险评估）中的仅存在模型时，这一点非常重要。