Model for deriving benthic irradiance in the Great Barrier Reef from MODIS satellite imagery

We demonstrate a simple, spectrally resolved ocean color remote sensing model to estimate benthic photosynthetically active radiation (bPAR) for the waters of the Great Barrier Reef (GBR), Australia. For coastal marine environments and coral reefs, the underwater light field is critical to ecosystem health, but data on bPAR rarely exist at ecologically relevant spatio-temporal scales. The bPAR model presented here is based on Lambert-Beer’s Law and uses: (i) sea surface values of the downwelling solar irradiance, Es(λ); (ii) high-resolution seafloor bathymetry data; and (iii) spectral estimates of the diffuse attenuation coefficient, Kd(λ), calculated from GBR-specific spectral inherent optical properties (IOPs). We first derive estimates of instantaneous bPAR. Assuming clear skies, these instantaneous values were then used to obtain daily integrated benthic PAR values. Matchup comparisons between concurrent satellite-derived bPAR and in situ values recorded at four optically varying test sites indicated strong agreement, small bias, and low mean absolute error. Overall, the matchup results suggest that our benthic irradiance model was robust to spatial variation in optical properties, typical of complex shallow coastal waters such as the GBR. We demonstrated the bPAR model for a small test region in the central GBR, with the results revealing strong patterns of temporal variability. The model will provide baseline datasets to assess changes in bPAR and its external drivers and may form the basis for a future GBR water-quality index. This model may also be applicable to other coastal waters for which spectral IOP and high-resolution bathymetry data exist

Reef state and performance as indicators of cumulative impacts on coral reefs

Coral bleaching, cyclones, outbreaks of crown-of-thorns seastar, and reduced water quality (WQ) threaten the health and resilience of coral reefs. The cumulative impacts from multiple acute and chronic stressors on “reef State” (i.e., total coral cover) and “reef Performance” (i.e., the deviation from expected rate of total coral cover increase) have rarely been assessed simultaneously, despite their management relevance. We evaluated the dynamics of coral cover (total and per morphological groups) in the Central and Southern Great Barrier Reef over 25 years, and identified and compared the main environmental drivers of State and Performance at the reef level (i.e. based on total coral cover) and per coral group. Using a combination of 25 environmental metrics that consider both the frequency and magnitude of impacts and their lagged effects, we find that the stressors that correlate with State differed from those correlating with Performance. Importantly, we demonstrate that WQ metrics better predict Performance than State. Further, inter-annual dynamics in WQ (here available for a subset of the data) improved the explanatory power of WQ metrics on Performance over long-term WQ averages. The lagged effects of cumulative acute stressors, and to a lesser extent poor water quality, correlated negatively with the Performance of some but not all coral groups. Tabular Acropora and branching non-Acropora were the most affected by water quality demonstrating that group-specific approaches aid in the interpretation of monitoring data and can be crucial for the detection of the impact of chronic pressures. We highlight the complexity of coral reef dynamics and the need of evaluating Performance metrics in order to prioritise local management interventions

Unique sequence of events triggers manta ray feeding frenzy in the Southern Great Barrier Reef, Australia

Manta rays are classified as Vulnerable to Extinction on the IUCN Red List for Threatened Species. In Australia, a key aggregation site for reef manta rays is Lady Elliot Island (LEI) on the Great Barrier Reef, ~7 km from the shelf edge. Here, we investigate the environmental processes that triggered the largest manta ray feeding aggregation yet observed in Australia, in early 2013. We use MODIS sea surface temperature (SST), chlorophyll-a concentration and photic depth data, together with in situ data, to show that anomalous river discharges led to high chlorophyll (anomalies: 10–15 mg·m−3) and turbid (photic depth anomalies: −15 m) river plumes extending out to LEI, and that these became entrained offshore around the periphery of an active cyclonic eddy. Eddy dynamics led to cold bottom intrusions along the shelf edge (6 °C temperature decrease), and at LEI (5 °C temperature decrease). Strongest SST gradients (>1 °C·km−1) were at the convergent frontal zone between the shelf and eddy-influenced waters, directly overlying LEI. Here, the front intensified on the spring ebb tide to attract and shape the aggregation pattern of foraging manta rays. Future research could focus on mapping the probability and persistence of these ecologically significant frontal zones via remote sensing to aid the management and conservation of marine species

DbRDA ordination relating environmental variables to <i>Symbiodinium</i> ITS-type data of all host species combined.

<p>Presence/absence data of <i>Symbiodinium</i> ITS-types (A) and presence/absence data of <i>Symbiodinium</i> summed by clade (B) showing biplot projections (r >0.3) for environmental data (red; including the host variation expressed as HPCO1 and HPCO2) and symbiont types (blue). Sites are designated a sea surface temperature (SST) group (indicated by symbols) and turbidity (TUR) group (indicated with different shades of grey). The ‘% of fitted’ indicates the variability in the original data explained by the fitted model and ‘% of total variation’ indicates the variation in the fitted matrix.</p

MODIS satellite images for the Great Barrier Reef.

<p>Long-term sea surface temperature climatology (SST, °C) (A) and Secchi depth climatology (Z_SD, m) (B). Sites are indicated with crosses. Pie charts represent shelf position (inner, mid and outer) of collection sites for each section of the Great Barrier Reef.</p

Accumulation curve of <i>Symbiodinium</i> ITS-types versus host genera.

<p>Database sites (circles) and reference data (crosses) are included. The shaded area indicates the 90% confidence interval and the circles increasing grey intensity indicates percentage of local hosts sampled.</p

DbRDA ordination relating environmental variables to <i>Symbiodinium</i> ITS-types per host species.

<p>Presence/absence data of <i>Symbiodinium</i> ITS-types in (A) <i>Acropora millepora</i>, (B) <i>Acropora tenuis</i>, (C) <i>Turbinaria reniformis,</i> (D) <i>Stylophora pistillata</i>, (E) <i>Pocillopora damicornis</i> and (F) <i>Seriatopora hystrix</i> at different sites are shown in biplot projections (log transformed and normalized environmental data is shown in red and symbiont ITS-types in blue). For illustrative purposes only, sites are designated a sea surface temperature (SST) group (indicated by symbols) and turbidity (TUR) group (indicated with different shades of grey). The ‘% of fitted’ indicates the variability in the original data explained by the fitted model and ‘% of total variation’ indicates the variation in the fitted matrix.</p

Flowchart of statistical analyses performed in PRIMER with PERMANOVA add-on.

<p>Flowchart of statistical analyses performed in PRIMER with PERMANOVA add-on.</p

Summary of distLM analyses.

<p>Table shows output for model selection of the relationship between <i>Symbiodinium</i> communities, host and/or environmental variables: per host species (<i>Acropora millepora</i>, <i>A. tenuis</i>, <i>Turbinaria reniformis, Stylophora pistillata</i>, <i>Pocillopora damicornis</i> and <i>Seriatopora hystrix</i>), of all host species combined at ITS2-type level and at clade level.</p>*<p>Significant values.</p