植物群落间物种共现模式的迁移学习

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2024-09-19 DOI:10.1016/j.ecoinf.2024.102826

Johannes Hirn , Verónica Sanz , José Enrique García , Marta Goberna , Alicia Montesinos-Navarro , José Antonio Navarro-Cano , Ricardo Sánchez-Martín , Alfonso Valiente-Banuet , Miguel Verdú

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引用次数: 0

摘要

目的神经网络（NN）的使用正在生活的各个领域蔓延，生态学也不例外。然而，神经网络的数据饥渴特性可能会遗漏许多有价值的小型数据集。方法基于西班牙东部半干旱地区植被斑块中植物物种共生的大量样本，我们拟合了一个生成式人工智能（AI）模型，该模型能正确再现这些斑块中哪些物种与哪些物种共生。随后，我们在两个只有较小数据集的群落（西班牙东部的另一个半干旱群落和墨西哥的一个热带群落）上训练了相同类型的模型。至于较不相似的社区，要提高知识转移的准确性，需要根据本地数据进一步调整模型。特别是，转移的知识主要与物种频率有关，其次与它们的系统发育关系有关，众所周知，系统发育关系是物种相互作用模式的决定因素。主要结论这种基于人工智能的方法可用于与参考群落相似或不太相似的群落，为在小型数据集上进行准确预测的系统转移学习打开了大门。有趣的是，这种迁移是通过在原数据集和目标数据集之间匹配不相关的物种来实现的，这意味着可以将任意数据集迁移到甚至组合起来以相互增强，而不管涉及的物种是什么，从而有可能将此类模型应用到不同气候条件下的各种植物群落中。

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Transfer learning of species co-occurrence patterns between plant communities

Aim

The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence.

Location

Semiarid plant communities in Spain and México.

Time period

2016–2022.

Major taxa studied

Angiosperms.

Methods

Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico).

Results

When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns.

Main conclusions

This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.