A classification-occupancy model based on automatically identified species data

IF 4.3 2区 环境科学与生态学 Q1 ECOLOGY
Ecology Pub Date : 2025-05-07 DOI:10.1002/ecy.70086
Ryo Ogawa, Frédéric Gosselin, Kevin F. A. Darras, Stephanie Roilo, Anna F. Cord
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引用次数: 0

Abstract

Occupancy models estimate a species' occupancy probability while accounting for imperfect detection, but often overlook the issue of false-positive detections. This problem of false positives has gained attention recently with the rapid advancement of automated species detection tools using artificial intelligence (AI), which generate continuous confidence scores for each species detection. Novel occupancy models have been introduced that integrate these confidence scores to identify false positives, but these models require thorough assessments of diagnosis and validation. Here, we propose a new occupancy model based solely on AI-detected species data. We conducted simulations to examine the inferential and predictive accuracies with known true parameters and analyzed AI-detected species data to test the practical usefulness through goodness-of-fit tests and evaluation with external data. Our proposed model mostly outperformed alternative models that ignore imperfect detection or false-positive error probabilities in terms of accuracy in simulation analyses and goodness-of-fit tests in the case study, but not in terms of discrimination metrics based on external data. The proposed occupancy model aids in understanding species–habitat relationships and developing automated biodiversity monitoring workflows by accounting for both false-negative and false-positive errors.

Abstract Image

基于自动识别物种数据的分类-占用模型
占用模型估计一个物种的占用概率,同时考虑不完善的检测,但往往忽略了假阳性检测的问题。最近,随着使用人工智能(AI)的自动物种检测工具的快速发展,假阳性问题引起了人们的关注,这些工具可以为每个物种检测生成连续的置信度分数。已经引入了新的占用模型,整合这些置信度分数来识别假阳性,但这些模型需要对诊断和验证进行彻底的评估。在这里,我们提出了一个新的基于人工智能检测物种数据的占用模型。我们进行了模拟,以检查已知真实参数的推断和预测准确性,并分析了人工智能检测到的物种数据,通过拟合优度测试和外部数据评估来测试实际有用性。在案例研究中,我们提出的模型在模拟分析的准确性和拟合优度检验方面大多优于忽略不完美检测或假阳性误差概率的替代模型,但在基于外部数据的判别指标方面则不然。所提出的占用模型通过考虑假阴性和假阳性误差,有助于理解物种-栖息地关系和开发自动化生物多样性监测工作流程。
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来源期刊
Ecology
Ecology 环境科学-生态学
CiteScore
8.30
自引率
2.10%
发文量
332
审稿时长
3 months
期刊介绍: Ecology publishes articles that report on the basic elements of ecological research. Emphasis is placed on concise, clear articles documenting important ecological phenomena. The journal publishes a broad array of research that includes a rapidly expanding envelope of subject matter, techniques, approaches, and concepts: paleoecology through present-day phenomena; evolutionary, population, physiological, community, and ecosystem ecology, as well as biogeochemistry; inclusive of descriptive, comparative, experimental, mathematical, statistical, and interdisciplinary approaches.
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