AIMS-LTMP and MMP Coral Reef Monitoring Modelled Output

The Australian Institute of Marine Science (AIMS) has been running coral reef monitoring programs since the 1980s, including both the Long-Term Monitoring Program (LTMP) and Marine Monitoring Program (MMP). These monitoring programs are designed to detect changes in coral reef communities at a sub-regional scale. Within this context, a subregion consists of inshore, mid-shelf, and outer shelf reefs across the continental shelf within one band of latitude (considered a sector).
Data are modelled for presentation on the AIMS Reef Reporting Dashboard https://apps.aims.gov.au/reef-monitoring/reefs.  
The Reef Monitoring Reporting (MonRep) platform displays modelled data collected by AIMS&#39; Long-Term Monitoring Program and Marine Monitoring Program at reef level, latitudinal Sector or Natural Resource Management (NRM)-region level in the Reef Monitoring Tool. How the data has been modelled for each graph is explained below for each data type.
Benthic community cover
Reef-level photo transect data.
Bayesian hierarchical models (INLA) to model the benthos over time.    Specifically, for each major benthic group (live hard coral, algae and soft corals) a model containing the population-level effects of year crossed with major taxonomic groups and the varying effects of transects nested within sites were fit to binomial photo point counts.
NRM-region/Sector photo transect data.
Bayesian hierarchical models (INLA) to model the benthos over time.    Specifically, for each NRM region/Sector and major benthic group (live hard coral, macroalgae and soft corals) a model containing the population-level effects of year and the varying effects of depth and transects nested within sites nested within reefs were fit to binomial photo point counts.
Manta tow surveys
Reef-level manta-tow data.
Bayesian hierarchical models (INLA) to model the benthos over time.    Specifically, for each major benthic group (live hard coral and soft corals) a model containing the population-level effects of year and the varying effects of tows were fit against a beta distribution to percentage cover data.
For NRM region//Sector level manta-tow data.
Bayesian hierarchical models (INLA) to model the benthos over time.   Specifically, for each NRM region/Sector major benthic group (live hard coral and soft corals) a model containing the population-level effects of year and the varying effects of tows nested within reef were fit against a beta distribution to percentage cover data.   
Juvenile hard corals
Reef-level data
Bayesian hierarchical models (INLA) were used to model the juvenile coral abundances (counts) over time.   Specifically, a model containing the population-level effects of year and the varying effects of sites were fit against a zero-inflated negative binomial and also included a (log-transformed) offset for available substrate.
NRM region/Sector level data
Bayesian hierarchical models (INLA) were used to model the juvenile coral abundances (counts) over time.   Specifically, for each NRM region/Sector a model containing the population-level effects of year and the varying effects of sites nested within reefs were fit against a zero-inflated negative binomial and also included a (log-transformed) offset for available substrate.
Reef fish
Reef-level data
Bayesian hierarchical models (INLA) to model the fish abundances (counts) over time.   Specifically, for each major fish group (Harvested, Herbivores, Coral Trout, Large fishes and Small fishes) a model containing the population-level effects of year and the varying effects of transects nested within sites were fit against zero-inflated negative binomials.
NRM region/Sector level data
Bayesian hierarchical models (INLA) to model the fish abundances (counts) over time.   Specifically, for each NRM region/Sector and for each major fish group (Harvested, Herbivores, Coral Trout, Large fishes and Small fishes) a model containing the population-level effects of year and the varying effects of transects nested within sites nested within reefs were fit against zero-inflated negative binomials.

Organization: Australian Ocean Data Network