1. Cancer is an understudied but important process in wildlife. Cancerous cells are proposed to have had significant effect on the evolution of metazoan species due to their negative effect on host fitness. However, gaining knowledge on the impact of cancer on species and ecosystems is currently relatively slow as it requires expertise in both ecology and oncology. The field can greatly benefit from automation to reduce the need of excessive manpower and analyse complex ecological datasets.
2. In this commentary, we examine how machine learning has been used to gain knowledge on oncogenic processes in wildlife. Using a landscape ecology approach, we explore spatial scales ranging from the size of a molecule up to whole ecosystems and detail, for each level, how machine learning has been used, or could contribute to obtain insights on cancer in wildlife populations and ecosystems.
3. We illustrate how machine learning is a powerful toolbox to conduct studies at the interface of ecology and oncology. We provide guidance for the readers of both fields on how to implement machine learning tools in their research and identify directions to move the field forward using this promising technology. We demonstrate how applying machine learning to complex ecological datasets will (a) contribute to quantitating the effect of cancer at different life stages in wildlife; (b) allow the mining of long-term datasets to understand the spatiotemporal variability of cancer risk factors and (c) contribute to mitigating cancer risk factors and the conservation of endangered species.
4. With this study, we aim to facilitate the use of machine learning to wildlife species and to encourage discussion between the scientists of the fields of oncology and ecology. We highlight the importance of international and pluridisciplinary collaborations to collect high-quality datasets on which efficient machine learning algorithms can be trained.

中文翻译：

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机器学习是研究癌症对物种和生态系统影响的有力工具

1. 癌症是野生动物中一个未被充分研究但很重要的过程。癌细胞被认为对后生动物物种的进化有显着影响，因为它们对宿主健康有负面影响。然而，目前获得关于癌症对物种和生态系统影响的知识相对较慢，因为它需要生态学和肿瘤学方面的专业知识。该领域可以极大地受益于自动化，以减少对过多人力的需求并分析复杂的生态数据集。
2. 在这篇评论中，我们研究了如何使用机器学习来获取有关野生动物致癌过程的知识。使用景观生态学方法，我们探索从分子大小到整个生态系统和细节的空间尺度，对于每个级别，如何使用机器学习，或者可能有助于深入了解野生动物种群和生态系统中的癌症。
3. 我们说明了机器学习如何成为在生态学和肿瘤学的界面上进行研究的强大工具箱。我们为这两个领域的读者提供有关如何在他们的研究中实施机器学习工具的指导，并确定使用这种有前途的技术推动该领域向前发展的方向。我们展示了如何将机器学习应用于复杂的生态数据集将 (a) 有助于量化癌症在野生动物不同生命阶段的影响；(b) 允许挖掘长期数据集以了解癌症风险因素的时空变异性和 (c) 有助于减轻癌症风险因素和保护濒危物种。
4. 通过这项研究，我们旨在促进机器学习对野生动物物种的使用，并鼓励肿瘤学和生态学领域的科学家之间的讨论。我们强调了国际和多学科合作的重要性，以收集可以训练高效机器学习算法的高质量数据集。