The many paths to ecological network inference: reconciling machine learning, empirical data, and ecological knowledge

生态网络推理的多种途径：协调机器学习、经验数据和生态知识

• 批准号: RGPIN-2021-03112
• 负责人: Poisot, Timothée
• 金额: $ 2.4万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765231
• 关键词: many; paths; ecological; network; inference

Ecology is rapidly becoming a science tasked with delivering actionable predictions to a wide variety of stakeholders. In complex adaptive systems, like ecological networks, this is a difficult taksk, both for lack of data, and for lack of robust predictive movels. Being able to formulate these predictions requires to simultaneously increase our understanding of the dynamics of complex ecological systems, and to translate this understanding into predictive models that can deliver data-driven predictions. Technical and methodological innovations from the field of machine learning and artificial intelligence are promising in this regard, as far as their successes in other disciplines reveals. In this proposal, I suggest three research questions, all aiming at predicting the structure of ecological networks, that will result in a systematic exploration of the potential of machine learning approaches to formulate predictions on biodiversity. First, I will develop models to predict, and then forecast, the structure of food webs over space, and investigate to which environmental drivers this structure responds. Second, I will develop a suite of Essential Biodiversity Variables for the structure of ecological networks, thereby examining which aspects of network structure have the highest information content. Finally, I will assess the potential to reconcile data from current and past sampling techniques, to evaluate whether hindcasting can be supported by better data integration. All of these projects will result in the development of free and open source software that will be re-usable by other initiatives, in addition to increasing the amount of existing open data; finally, they provide HQPs with a superb opportunity to acquire marketable skills in data science, in addition to furthering their ecological expertise.

生态学正在迅速成为一门科学，其任务是向各种利益相关者提供可操作的预测。在复杂的自适应系统中，如生态网络，这是一个困难的任务，既缺乏数据，也缺乏强大的预测移动。能够制定这些预测需要同时增加我们对复杂生态系统动态的理解，并将这种理解转化为可以提供数据驱动预测的预测模型。机器学习和人工智能领域的技术和方法创新在这方面很有前途，因为它们在其他学科中取得了成功。在这个提议中，我提出了三个研究问题，所有这些问题都旨在预测生态网络的结构，这将导致系统地探索机器学习方法在制定生物多样性预测方面的潜力。首先，我将开发模型来预测，然后预测，空间食物网的结构，并调查这种结构对哪些环境驱动因素的反应。其次，我将开发一套生态网络结构的生物多样性基本变量，从而研究网络结构的哪些方面具有最高的信息含量。最后，我将评估从当前和过去的采样技术协调数据的潜力，以评估后报是否可以通过更好的数据集成来支持。所有这些项目都将导致自由和开源软件的开发，这些软件除了增加现有的开放数据量外，还将被其他计划重用;最后，它们为HQP提供了一个极好的机会，除了进一步发展他们的生态专业知识外，还可以获得数据科学方面的市场技能。