Coral reefs are increasingly threatened by various disturbances, making it essential for managers to identify which reefs are more resilient and capable of retaining their biodiversity and ecosystem function. Resilience potential assessments evaluate coral reefs to determine their likelihood of withstanding disturbances, providing crucial information for reef management and conservation. Ecological resilience is the capacity of an ecosystem to maintain or recover state, functioning, and structure following a disturbance (see Box 1). This study evaluated 68 such assessments conducted between 2008 and 2022, using principles from decision science and indicator design theory. The study aimed to identify commonly used resilience indicators, assess the representation of key ecosystem components that confer resilience, and provide recommendations to enhance these assessments for management use.
The study developed a conceptual model of the features and processes that maintain ecological functioning and resilience in coral reef ecosystems. Nearly all assessments included indicators representing key ecosystem components such as coral, herbivory, competition, and reef structure. However, most assessments lacked at least one essential component, often excluding the abundance and diversity of non-herbivorous fish groups, competitive interactions with coral, coral predators, and bioeroders.
Few assessments used a structured process for selecting their indicators, such as a conceptual model or selection criteria. Additionally, only some assessments validated their indicators with real-world disturbances, leaving uncertainty about the accuracy of their predictions regarding reef resilience. Many assessments used multiple indicators and aggregated these into a composite score for simplicity and ease of communication. However, the methodological decisions involved in aggregating scores such as weighting affect their performance, and few assessments provided a detailed explanation or justification of their methods. The review also highlighted the importance of normalizing indicators to ensure assessments are meaningfully and reliably related to resilience. This can involve comparing the condition of reefs relative to ideal or historical states, for example.
Implications for managers
The authors provide recommendations for improving the reliability of resilience potential assessments. See Box 2 for more details.
Indicator selection:
- Specify the types of disturbances or threats the reef is experiencing.
- Create a conceptual model representing key local ecosystem components and resilience factors and choose indicators that cover a broad range of these components for a more holistic view of ecological resilience.
- List the chosen indicators and describe the selection process.
Indicator testing:
- Monitor reefs during and after disturbances to test whether the selected indicators accurately reflect the system’s resilience. For example, did reefs with higher resilience scores bleach less during a bleaching event?
- Ensure transparency around data quality including uncertainty, data gaps, and biases.
Normalization:
- Normalize data by scaling variables between 0 and 1 to convert them into meaningful indicators of resilience.
- Carefully select the type of reference (threshold) used for normalization to ensure accurate resilience indicators. Using locally or regionally relevant independent reference levels for indicators (e.g., pristine reefs or historical conditions), allows for a broader interpretation of results.
- Avoid normalizing indicators using only within-dataset references, as this can complicate the interpretation of final results
- Avoid a second normalization step of anchoring resilience scores against the highest score to rank sites
Composite indicators:
- Consider methodological decisions, uncertainties, and assumptions when aggregating indicators into composites to determine if it’s necessary.
- Track and present results for both individual indicators and composite indices.
- Explore alternative options to aggregating indicators, for example taking the highest (or lowest) value, or geometric means.
- Use robust methods for estimating the weighting of any variables, and justify the final weighting scheme even if it is equal weights
- Communicate all assumptions and decisions made in creating composite scores for repeatability and interpretation.
Management prioritization:
- Link assessment results to local management actions and prioritization.
- Consider making your data accessible for use in larger conservation planning efforts.
Authors: Gudka, M, D. Obura, E.A. Treml and E. Nicholson
Year: 2024
Methods in Ecology and Evolution 15: 612–627. doi: 10.1111/2041-210X.14303
