Knowing how many fishers can fish from a particular reef and understanding the specific impact of changes in habitat quality would be very useful to people managing coastal environments across the East Asia-Pacific.
“It’s the dream,” says Dr Alice Rogers from the University of Queensland, who is leading the food web model project for CCRES.
The aim of the project is to develop a model that can estimate a reef’s productivity, that is how many fish it can support, based on its structure and health. This will assist marine spatial planners and coastal resource managers from government and community agencies to estimate the value of coral reef fisheries and their potential to change over time, given different habitat impacts.
“For many planners and resource managers, a reef is a reef, and its quality is not taken into account in the decision-making process,” says Dr Rogers.
“However reefs can vary widely in habitat quality and this has a direct correlation with how many fish they can support. For example, a healthy reef with a complex structure provides more refuges from predators than a reef damaged by bleaching or bombing. The productivity of a reef with many hiding places is higher, as more fish survive, grow and reproduce.”
Knowing the likely productivity of particular reefs can lead to better policy and planning, and therefore better management outcomes. Predicting the consequences of certain actions on a fishery will also be useful.
In 2017, the team will begin testing the project’s outputs with end users. These will potentially inform the answers to questions such as:
Development of the model is well underway, informed by fisheries and habitat data collected for specific reefs at both CCRES pilot sites – El Nido in the Philippines and Selayar in Indonesia.
“Models need a lot of data, and we have excellent information from the pilot sites which we have used to build the model,” says Dr Rogers.
“However a key aim of CCRES is to develop tools and models that can be used more broadly across the East Asia-Pacific. Our team is working with GIS experts on how to scale up our model so that it can be used in different ways, for example to identify productivity ‘hotspots’.”
The food web model will be communicated using a ‘Baysian Belief Network’ which predicts the probability of outcomes, based on different variables and dependencies. It will show the probability that a reef will have a high, medium or low density of fish based on a set of variables such as refuge density or level of fishing. A user-friendly interface which summarises the results of the model will allow users to play with the values for variables to predict outcomes.
For more information, contact Dr Alice Rogers, The University of Queensland