Abstract
As coral reefs continue to degrade globally due to climate change, considerable effort and investment is being put into coral restoration. The production of coral offspring via asexual and sexual reproduction are some of the proposed tools for restoring coral populations and will need to be delivered at scale. Simple, inexpensive, and high-throughput methods are therefore needed for rapid analysis of thousands of coral offspring. Here we develop a machine learning pipeline to rapidly and accurately measure three key indicators of coral juvenile fitness: survival, size, and color. Using machine learning, we classify pixels through an open-source, user-friendly interface to quickly identify and measure coral juveniles on two substrates (field deployed terracotta tiles and experimental, laboratory PVC plastic slides). The method’s ease of use and ability to be trained quickly and accurately using small training sets make it suitable for application with images of species of sexually produced corals without existing datasets. Our results show higher accuracy of survival for slides (94.6% accuracy with five training images) compared to field tiles measured over multiple months (March: 77.5%, June: 91.3%, October: 97.9% accuracy with 100 training images). When using fewer training images, accuracy of area measurements was also higher on slides (7.7% average size difference) compared to tiles (24.2% average size difference for October images). The pipeline was 36× faster than manual measurements. The slide images required fewer training images compared to tiles and we provided cut-off guidelines for training for both substrates. These results highlight the importance and power of incorporating high-throughput methods, substrate choice, image quality, and number of training images for measurement accuracy. This study demonstrates the utility of machine learning tools for scalable ecological studies and conservation practices to facilitate rapid management decisions for reef protection.
Highlights
Introduction published maps and institutional affilThe continued increase in sea surface temperatures due to climate change has been the major driver in the loss of up to 50% of the world’s coral reefs [1,2,3,4,5,6]
We present a novel machine learning (ML) pipeline, for the high-throughput data acquisition of coral juveniles from images, that is accessible to coral restoration practitioners with little training (Figure 1)
The machine learning (ML) image analysis pipeline was assessed using coral juveniles settled onto two types of substrates: (1) field deployed terracotta tiles and (2) laboratory-maintained PVC plastic slides
Summary
Introduction published maps and institutional affilThe continued increase in sea surface temperatures due to climate change has been the major driver in the loss of up to 50% of the world’s coral reefs [1,2,3,4,5,6]. The occurrence of mass bleaching and mortality events, in which corals lose their symbiotic dinoflagellates (Symbiodiniaceae) en-masse, has become more frequent [7]. An increase in the frequency of these mass bleaching events impedes a reef’s ability to potentially recover to previous levels of coral cover before the disturbance event [8]. Coral reefs show some potential to endure anthropogenic impacts through rapid acclimation and adaptation [9,10,11]. There has been a rapid increase in investment in intervention and restoration initiatives focused on improving coral survival [12,13,14], especially in the large-scale production of iations
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