Machine Learning for the Fast and Accurate Assessment of Fitness in Coral Early Life History

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.

Experimental considerations of acute heat stress assays to quantify coral thermal tolerance

“…When considering capital costs and accessibility, tissue colour change is by far the most cost-effective measure captured, further reducing processing and analysis time investment through the development of automated approaches 62 . Recent technological advances also allow scaling of automated bleaching assessments with the implementation of new technologies such as hyperspectral imaging 63 , despite additional and significant capital costs.…”

Antifouling coatings can reduce algal growth while preserving coral settlement

“…The analysis was adapted from the “Trainable Weka Segmentation User Manual” published as Supplementary data in Arganda-Carreras et al 78 , but other studies have previously applied similar approaches to quantify biofouling with the TWS 79 – 82 . More recently, Macadam et al 83 successfully used a similar machine-learning tool to measure coral spat survival, size, and color. In this study, each plug image was segmented into 5 different classes: crustose coralline algae (CCA), green algae, brown algae, bare substrate and background.…”

Experimental considerations of acute heat stress assays to quantify coral thermal tolerance

“…When considering capital costs and accessibility, tissue colour change is by far the most cost-effective measure captured, further reducing processing and analysis time investment through the development of automated approaches 52 . Recent technological advances also allow scaling of automated bleaching assessments with the implementation of new technologies such as hyperspectral imaging 53 , although this carries additional and signi cant capital costs.…”