Indicator Framework for assessing the condition of coral reef habitats in the Great Barrier Reef

At present, there are multiple data streams available from existing coral reef monitoring programs that measure aspects of condition and recovery. However, a holistic assessment of condition and recovery requires a multidimensional approach acknowledging complex ecological processes varying across the reef. Adding to this ecological complexity is the practical complexity in applying such knowledge to define a high-level metric that informs management and evaluates the efficacy of management strategies towards proposed conservation targets. The latter requires heuristic, measurable, sensitive, and scalable indicators to facilitate conservation planning and management. 

A project was set in collaboration between the Australian Institute of Marine Science (AIMS), James Cook University (JCU) and the University of Queensland (UQ) funded by the partnership between the Australian Government&rsquo;s Reef Trust and the Great Barrier Reef Foundation. This project developed an integrative suite of indicators towards streamlining the decision-making processes and enabling tracking progress toward achieving the objectives of the Reef 2050 Long-term Sustainability plan. A critical component of this project is making data, information, and knowledge (provided by the ongoing monitoring programs that form the core of the RIMReP) available in a relevant and integrated form that enables high-level reporting and supports decision-making at various management scales. This includes high-level reporting on Reef 2050 objectives across the GBR management scales and sea-country-focused reporting on TUMRA objectives and issues of concern to Traditional Owner co-managers.

This data record includes source code, documentation and parameter files for the calculation and integrated interpretation of indicator scores derived from coral reef monitoring programs in the GBR.

Parameter files V2

<code>Coral Cover Baseline (CC__baseline.RData,  CC_baseline.RData): Spatiotemporal model and posterior distributions of coral cover values per Bioregion. This serves as a baseline reference from historical observations of coral cover (Maintenance Threshold) to evaluate monitoring observations and derive the coral cover indicator scores.

Juvenile Density Baseline (JU__baseline_mod.RData, JUV_baseline.RData): Spatiotemporal model and posterior distributions of juvenile density values per Bioregion. This serves as a baseline reference from historical observations  (Maintenance Threshold) to evaluate monitoring observations and derive the Juvenile density indicator scores.

Macroalgae Baseline (MA__baseline_deep.RData, MA__baseline_offshore.RData,  MA__baseline_shallow.RData): Spatiotemporal model for generating posterior distributions of macroalgae proportion values per reef and survey depth. This serves as a baseline reference from historical observations  (Maintenance Threshold) to evaluate monitoring observations and derive the Macroalgae indicator scores.

Juvenile Density Contextual Threshold files (IPM_juv.RData,JuvCover_Acropora_NA_Inshore_NA_ResJuv_FALSE_Morph_NA.Rdata, JuvCover_Acropora_NA_Offshore_NA_ResJuv_FALSE_Morph_NA.Rdata,JuvCover_Mound_growth_NA_Inshore_NA_ResJuv_FALSE_Morph_Merulinidae.Rdata, JuvCover_Mound_growth_NA_Inshore_NA_ResJuv_FALSE_Morph_Poritidae.Rdata, JuvCover_Mound_growth_NA_Offshore_NA_ResJuv_FALSE_Morph_Merulinidae.Rdata, JuvCover_Mound_growth_NA_Offshore_NA_ResJuv_FALSE_Morph_Poritidae.Rdata, JuvCover_Pocilloporidae_NA_Inshore_NA_ResJuv_FALSE_Morph_NA.Rdata, JuvCover_Pocilloporidae_NA_Offshore_NA_ResJuv_FALSE_Morph_NA.Rdata, newdata_grid.RData): These files contain parameters for the estimation of the minimal density of Acropora juvenile colonies required yearly to ensure rapid recovery of benthic communities following severe disturbance. Values were derived from Integral Population Models (provided here) and numerical optimisation of the density of colonies against coral cover trajectories.

Lookup tables for Synoptic text (Synoptic_text_reef.csv, Synoptic_text_region.csv, taxaLookup.csv): Descriptive text is generated as part of the framework to assist in the interpretation of indicator scores. These files are lookup tables for the conditional statements to generate the synopsis.
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Parameter files V1

<code>Coral Cover Baseline (CC__baseline.RData,  CC_baseline.RData, CC_baseline_posteriors.RData): Spatiotemporal model and posterior distributions of coral cover values per Bioregion. This serves as a baseline reference from historical observations of coral cover (Maintenance Threshold) to evaluate monitoring observations and derive the coral cover indicator scores.

Juvenile Density Baseline (JU__baseline.RData, JU__baseline_mod.RData, JUV_baseline.RData): Spatiotemporal model and posterior distributions of juvenile density values per Bioregion. This serves as a baseline reference from historical observations  (Maintenance Threshold) to evaluate monitoring observations and derive the Juvenile density indicator scores.

Macroalgae Baseline (MA__baseline_deep.RData, MA__baseline_offshore.RData,  MA__baseline_shallow.RData): Spatiotemporal model and posterior distributions of macroalgae proportion values per reef and survey depth. This serves as a baseline reference from historical observations  (Maintenance Threshold) to evaluate monitoring observations and derive the Macroalgae indicator scores.

Juvenile Density Contextual Threshold files (IPM_juv.RData,JuvCover_Acropora_NA_Inshore_NA_ResJuv_FALSE_Morph_NA.Rdata, JuvCover_Acropora_NA_Offshore_NA_ResJuv_FALSE_Morph_NA.Rdata,JuvCover_Mound_growth_NA_Inshore_NA_ResJuv_FALSE_Morph_Merulinidae.Rdata, JuvCover_Mound_growth_NA_Inshore_NA_ResJuv_FALSE_Morph_Poritidae.Rdata, JuvCover_Mound_growth_NA_Offshore_NA_ResJuv_FALSE_Morph_Merulinidae.Rdata, JuvCover_Mound_growth_NA_Offshore_NA_ResJuv_FALSE_Morph_Poritidae.Rdata, JuvCover_Pocilloporidae_NA_Inshore_NA_ResJuv_FALSE_Morph_NA.Rdata, JuvCover_Pocilloporidae_NA_Offshore_NA_ResJuv_FALSE_Morph_NA.Rdata, newdata_grid.RData): These files contain parameters for the estimation of the minimal density of Acropora juvenile colonies required yearly to ensure rapid recovery of benthic communities following severe disturbance. Values were derived from Integral Population Models (provided here) and numerical optimisation of the density of colonies against coral cover trajectories.

Lookup tables for Synoptic text (Synoptic_text_reef.csv, Synoptic_text_region.csv, taxaLookup.csv): Descriptive text is generated as part of the framework to assist in the interpretation of indicator scores. These files are lookup tables for the conditional statements to generate the synopsis.
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Source code:

<code>Indicator_Framework_preprint_v2: R source code for generating indicator scores from monitoring data.

Indicator Framework_v1: R source code for generating indicator scores from monitoring data.

Data visualisation example_v1: A R shiny app to deploy a dashboard with functions to provide an example for visualisation of data products derived from the Indicator framework.

Singularity Container for Indicator Framework source code: While the source code repository includes instructions to generate the singularity container, this data record also includes access to a pre-generated image of this singularity container.
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Documentation:

<code>Guidelines for interpretation of the framework_v1: A detailed introduction to the indicator framework and guiding points to assist in the combine interpretation of indicator scores.

Methods_v1: A detailed methodological description detailing the approach to calculate the indicator scores within the framework. For each indicator, this document also includes required data inputs, expected outputs and guidelines for interpretation
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Acknowledgements:

The project &ldquo;Indicator framework for assessing the condition and recovery capacity of coral reef habitats in the Great Barrier Reef&rdquo; is funded by the partnership between the Australian Government&rsquo;s Reef Trust and the Great Barrier Reef Foundation with support from the Australian Institute of Marine Science, The University of Queensland and James Cook University. The authors are grateful for the support from the Great Barrier Reef Marine Park Authority in guiding the design of the framework throughout the project.

Organization: Australian Ocean Data Network