Scaling-up assisted evolution on coral reefs: an empirical modelling approach

Lead Research Organisation: Newcastle University

Abstract

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People

ORCID iD

Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/S007512/1 30/09/2019 29/09/2027
2271884 Studentship NE/S007512/1 30/09/2019 30/12/2023
 
Description Coral reefs have an inherent capacity for adaptation that has not previously been found. It is unlikey that selecting for corals with higher heat tolerance will have negative tradeoffs with other important traits like growth and reproductive output, for the case study of Palau. Mass bleaching can be predicting more accurately using a revised global algorithm. Restorative interventions like assisted evolution can rely on this scientific information to improve the chance of success.
Exploitation Route Adoption of heat stress algorithm amendments by researchers and NOAA CRW, which is happening in the near future.
Sectors Environment
 
Description My results on improving heat stress algorithms to predict mass belaching have contributed to improving the global bleaching risk monitoring product by NOAA Coral Reef Watch. This tool is used in academia, but also by reef managers to monitor coral bleaching risk.
First Year Of Impact 2020
Sector Environment
Impact Types Policy & public services
 
Description Scaling Up Coral Preservation in Palau
Amount $1,200 (USD)
Funding ID LACHPALA1219 
Organisation Idea Wild 
Sector Charity/Non Profit
Country United States
Start 06/2020 
End 12/2023
 
Description Scaling-up restorative assisted evolution on Anthropocene coral reefs
Amount £13,366 (GBP)
Funding ID NE/T014547/1 
Organisation Natural Environment Research Council 
Sector Public
Country United Kingdom
Start 03/2020 
End 03/2022
 
Title SizeExtractR - a workflow for extracting object size metrics from scaled images 
Description Here, we present SizeExtractR, an open-source workflow that enables faster extraction of size metrics from scaled images (e.g., each image includes a ruler) using semi-automated protocols. It comprises a set of ImageJ macros to speed up size extraction and annotation, and an R-package for the quality control of annotations, data collation, calibration, and visualization. 
Type Of Material Data analysis technique 
Year Produced 2022 
Provided To Others? Yes  
Impact Being used by researchers in University of Leeds, University of Hull, Oxford, and the Australian Institute of Marine Science. 
URL https://doi.org/10.5281/zenodo.5997934