Considering the rates of growth in two taxa of coral across Pacific islands

Closing the gap between existing large-area imaging research and marine conservation needs

Emerging technology has immense potential to increase the scale and efficiency of marine conservation. One such technology is large-area imaging (LAI), which relies on structure-from-motion photogrammetry to create composite products, including 3-dimensional (3-D) environmental models, that are larger in spatial extent than the individual images used to create them. Use of LAI has become widespread in certain fields of marine science, primarily to measure the 3D structure of benthic ecosystems and track change over time. However, the use of LAI in the field of marine conservation appears limited. We conducted a review of the coral reef literature on the use of LAI to identify research themes and regional trends in applications of this technology. We also surveyed 135 coral reef scientists and conservation practitioners to determine community familiarity with LAI, evaluate barriers practitioners face in using LAI, and identify applications of LAI believed to be most exciting or relevant to coral conservation. Adoption of LAI was limited primarily to researchers at institutions based in advanced economies and was applied infrequently to conservation, although conservation practitioners and survey respondents from emerging economies indicated they expect to use LAI in the future. Our results revealed disconnect between current LAI research topics and conservation priorities identified by practitioners, highlighting the need for more diverse, conservation-relevant research using LAI. We provide recommendations for how early adopters of LAI (typically Global North scientists from well-resourced institutions) can facilitate access to this conservation technology. These recommendations include developing training resources, creating partnerships for data storage and analysis, publishing standard operating procedures for LAI workflows, standardizing methods, developing tools for efficient data extraction from LAI products, and conducting conservation-relevant research using LAI.

Post-settlement demographics of reef building corals suggest prolonged recruitment bottlenecks

For many organisms, early life stages experience significantly higher rates of mortality relative to adults. However, tracking early life stage individuals through time in natural settings is difficult, limiting our understanding of the duration of these ‘mortality bottlenecks’, and the time required for survivorship to match that of adults. Here, we track a cohort of juvenile corals (1–5 cm maximum diameter) from 12 taxa at a remote atoll in the Central Pacific from 2013 to 2017 and describe patterns of annual survivorship. Of the 537 juveniles initially detected, 219 (41%) were alive 4 years later, 163 (30%) died via complete loss of live tissue from the skeleton, and the remaining 155 (29%) died via dislodgement. The differing mortality patterns suggest that habitat characteristics, as well as species-specific features, may influence early life stage survival. Across most taxa, survival fit a logistic model, reaching > 90% annual survival within 4 years. These data suggest that mortality bottlenecks characteristic of ‘recruitment’ extend up to 5 years after individuals can be visually detected. Ultimately, replenishment of adult coral populations via sexual reproduction is needed to maintain both coral cover and genetic diversity. This study provides key insights into the dynamics and time scales that characterize these critical early life stages.

Automated 2D, 2.5D, and 3D Segmentation of Coral Reef Pointclouds and Orthoprojections

Enabled by advancing technology, coral reef researchers increasingly prefer use of image-based surveys over approaches depending solely upon in situ observations, interpretations, and recordings of divers. The images collected, and derivative products such as orthographic projections and 3D models, allow researchers to study a comprehensive digital twin of their field sites. Spatio-temporally located twins can be compared and annotated, enabling researchers to virtually return to sites long after they have left them. While these new data expand the variety and specificity of biological investigation that can be pursued, they have introduced the much-discussed Big Data Problem: research labs lack the human and computational resources required to process and analyze imagery at the rate it can be collected. The rapid development of unmanned underwater vehicles suggests researchers will soon have access to an even greater volume of imagery and other sensor measurements than can be collected by diver-piloted platforms, further exacerbating data handling limitations. Thoroughly segmenting (tracing the extent of and taxonomically identifying) organisms enables researchers to extract the information image products contain, but is very time-consuming. Analytic techniques driven by neural networks offer the possibility that the segmentation process can be greatly accelerated through automation. In this study, we examine the efficacy of automated segmentation on three different image-derived data products: 3D models, and 2D and 2.5D orthographic projections thereof; we also contrast their relative accessibility and utility to different avenues of biological inquiry. The variety of network architectures and parameters tested performed similarly, ∼80% IoU for the genus Porites, suggesting that the primary limitations to an automated workflow are 1) the current capabilities of neural network technology, and 2) consistency and quality control in image product collection and human training/testing dataset generation.