Incorporating Physiological Data into Environmental Resilience Models

A special issue of Diversity (ISSN 1424-2818). This special issue belongs to the section "Marine Diversity".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 2435

Special Issue Editor

Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, Miami, FL 33149, USA
Interests: marine biology; coral reefs; molecular biotechnology

Special Issue Information

Researchers typically concern themselves with abundance and diversity, with higher values normally deemed favorable. This interpretation prevails despite the fact that human population densities may just as commonly correlate inversely with individual metrics of health (e.g., lifespan). As a marine example, an aesthetically appealing reef with high coral abundance (Figures 1-2) may indeed draw more tourists and provide a number of ecosystem services; however, the respective corals are no healthier than conspecifics on marginalized, low-biodiversity reefs (& could actually be of diminished resilience). What this signifies is that, rather than exclusively counting the number of constituents during surveys, those interested in predicting how Earth’s ecosystems will respond to global climate change and other stressors need also consider the physiological condition of the resident organisms.

In this Special Issue of Diversity, I seek articles from anyone interested in applying what we know about organismal performance in the laboratory and in situ towards the development of tools that will allow us to triage ecosystems along a stress-susceptible to physiologically robust performance spectrum. Priority will be given to those articles that both develop diagnostic systems and exploit the data generated to construct models that can be used to delineate ecosystem health and resilience. A recent example of this is the work of Roach et al. (2021; Nat Ecol Evol), in which the authors linked metabolomic signatures to bleaching sensitivity in reef corals. This exciting article highlights the fact that, with the molecular and computational capacity now at our fingertips, it is time to move beyond recording temperature, counting organisms, and documenting habitat degradation and instead develop proactive, predictive management products that enable us to focus our efforts on those habitats and individuals of greatest conservation value.

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Figures 1-2.
 Although corals occupy 100% of the benthos on these Indonesian (upper image) and Filipino (lower image) coral reefs, these habitats are of low resilience to global climate change given the overall dominance of highly environmentally sensitive coral species.

Dr. Anderson Mayfield
Guest Editor

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Keywords

  • physiological data
  • environmental resilience models
  • coral reefs
  • global climate change
  • molecular biotechnology
  • marine biology

Published Papers (1 paper)

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Research

22 pages, 1188 KiB  
Article
Machine-Learning-Based Proteomic Predictive Modeling with Thermally-Challenged Caribbean Reef Corals
by Anderson B. Mayfield
Diversity 2022, 14(1), 33; https://doi.org/10.3390/d14010033 - 05 Jan 2022
Cited by 6 | Viewed by 1860
Abstract
Coral health is currently diagnosed retroactively; colonies are deemed “stressed” upon succumbing to bleaching or disease. Ideally, health inferences would instead be made on a pre-death timescale that would enable, for instance, environmental mitigation that could promote coral resilience. To this end, diverse [...] Read more.
Coral health is currently diagnosed retroactively; colonies are deemed “stressed” upon succumbing to bleaching or disease. Ideally, health inferences would instead be made on a pre-death timescale that would enable, for instance, environmental mitigation that could promote coral resilience. To this end, diverse Caribbean coral (Orbicella faveolata) genotypes of varying resilience to high temperatures along the Florida Reef Tract were exposed herein to elevated temperatures in the laboratory, and a proteomic analysis was taken with a subset of 20 samples via iTRAQ labeling followed by nano-liquid chromatography + mass spectrometry; 46 host coral and 40 Symbiodiniaceae dinoflagellate proteins passed all stringent quality control criteria, and the partial proteomes of biopsies of (1) healthy controls, (2) sub-lethally stressed samples, and (3) actively bleaching corals differed significantly from one another. The proteomic data were then used to train predictive models of coral colony bleaching susceptibility, and both generalized regression and machine-learning-based neural networks were capable of accurately forecasting the bleaching susceptibility of coral samples based on their protein signatures. Successful future testing of the predictive power of these models in situ could establish the capacity to proactively monitor coral health. Full article
(This article belongs to the Special Issue Incorporating Physiological Data into Environmental Resilience Models)
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