气候变化领域集成服务门户(CCPortal): Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming

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DOI	10.1111/2041-210X.14295
	Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming
	MacNeil, Liam; Joly, Lea J.; Ito, Maysa; Steinmann, Anna; Mehler, Knut; Scotti, Marco
发表日期	2024
ISSN	2041-210X
EISSN	2041-2096
起始页码	15
结束页码	3
卷号	15 期号:3
英文摘要	1. The expansion of scientific image data holds great promise to quantify individuals, size distributions and traits. Computer vision tools are especially powerful to automate data mining of images and thus have been applied widely across studies in aquatic and terrestrial ecology. Yet marine benthic communities, especially infauna, remain understudied despite their dominance of marine biomass, biodiversity and playing critical roles in ecosystem functioning. 2. Here, we disaggregated infauna from sediment cores taken throughout the spring transition (April-June) from a near-natural mesocosm setup under experimental warming (Ambient, +1.5 degrees C, +3.0 degrees C). Numerically abundant mudsnails were imaged in batches under stereomicroscopy, from which we automatically counted and sized individuals using a superpixel-based segmentation algorithm. Our segmentation approach was based on clustering superpixels, which naturally partition images by low-level properties (e.g., colour, shape and edges) and allow instance-based segmentation to extract all individuals from each image. 3. We demonstrate high accuracy and precision for counting and sizing individuals, through a procedure that is robust to the number of individuals per image (5-65) and to size ranges spanning an order of magnitude (<750 mu m to 7.4 mm). The segmentation routine provided at least a fivefold increase in efficiency compared with manual measurements. Scaling this approach to a larger dataset tallied >40k individuals and revealed overall growth in response to springtime warming. 4. We illustrate that image processing and segmentation workflows can be built upon existing open-access R packages, underlining the potential for wider adoption of computer vision tools among ecologists. The image-based approach also generated reproducible data products that, alongside our scripts, we have made freely available. This work reinforces the need for next-generation monitoring of benthic communities, especially infauna, which can display differential responses to average warming.
英文关键词	automatic size measurement; Hydrobiidae; image segmentation; mesocosm experiment; superpixels; Wadden Sea
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:001158625700001
来源期刊	METHODS IN ECOLOGY AND EVOLUTION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303586
作者单位	Helmholtz Association; GEOMAR Helmholtz Center for Ocean Research Kiel; Helmholtz Association; Alfred Wegener Institute, Helmholtz Centre for Polar & Marine Research; Consiglio Nazionale delle Ricerche (CNR); Istituto di Bioscienze e Biorisorse (IBBR-CNR)
推荐引用方式 GB/T 7714	MacNeil, Liam,Joly, Lea J.,Ito, Maysa,et al. Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming[J],2024,15(3).
APA	MacNeil, Liam,Joly, Lea J.,Ito, Maysa,Steinmann, Anna,Mehler, Knut,&Scotti, Marco.(2024).Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming.METHODS IN ECOLOGY AND EVOLUTION,15(3).
MLA	MacNeil, Liam,et al."Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming".METHODS IN ECOLOGY AND EVOLUTION 15.3(2024).