ADRIA.jl

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Disturbances ​

Heat Stress ​

Heat stress in ADRIA is represented using Degree Heating Weeks (DHW), a standard oceanographic metric that accumulates positive thermal anomalies above a bleaching threshold over time. DHW data is supplied as part of the domain package as a three-dimensional array (timesteps x locations x scenarios). The dhw_scenario parameter in the scenario table selects which projection is applied in a given run.

Bleaching mortality ​

Bleaching mortality is computed using a population-level probabilistic model. Each combination of functional group, size class, and location has a critical DHW tolerance distribution – a truncated normal distribution describing the spread of thermal tolerances within that sub-population. The lower bound of the distribution is fixed at HEAT_LB = 1.0 DHW-week; the mean cannot increase more than HEAT_UB = 24.0 DHW-weeks above its initial value.

At each timestep, locations where DHW exceeds HEAT_LB are considered thermally active. For each active location the proportion of the sub-population that bleaches is given by the cumulative distribution function (CDF) of the tolerance distribution evaluated at the current DHW value. Bleaching mortality is then depth-adjusted:

mortality = bleaching_fraction * depth_coefficient(depth)

The depth coefficient follows Baird et al. (2018):

depth_coeff = exp(-0.07551 * (depth - 2.0))

clamped to [0, 1], so that deeper reefs experience proportionally less bleaching mortality.

Functional group and size class differences ​

Each functional group and size class carries its own mean critical DHW threshold and standard deviation, initialised from observational data (Hughes et al. 2018, Bairos-Novak et al. 2021). Bleaching sensitivity therefore differs between functional groups, with tabular Acropora being more sensitive than, for example, massive corals.

Thermal adaptation ​

Population mean tolerance shifts each timestep through two mechanisms:

  • Survival selection: After a bleaching event, individuals with higher tolerances are preferentially retained, shifting the surviving population mean upward. This is formalised via the Breeder's equation using a fixed heritability parameter.

  • Settler tolerance: Newly seeded corals can carry a user-specified thermal tolerance offset (a_adapt), representing a given level of enhanced thermal tolerance, through assisted gene flow, adaptation or other enhancement process.

The mean tolerance is hard-capped at initial_mean + HEAT_UB to represent a biological ceiling on adaptation.

Fogging and Solar Radiation Management (SRM) ​

Interventions that reduce light and heat reaching corals (fogging and SRM) are modelled as a direct multiplicative reduction of the DHW experienced at selected locations:

effective_DHW = DHW * (1 - fogging_effectiveness)

This reduction is applied before bleaching mortality is computed, so treated locations experience reduced bleaching proportional to the intervention intensity.

Cyclones ​

Each cyclone mortality scenario is defined as a series of cyclone mortality rates for each timestep, location and functional group. They are the result of applying a set of cyclone stochastic generated category projections (Bozec et al., 2025) for each location and timestep, converted to windspeed (Bureau of Meteorology, 2025), to a regression that provides a coral mortality rate as a function of wind speed. The cyclone categories go from 0 (no cyclone) to 5 (maximum wind speed cyclone). The three regression models, for massive corals, branching corals deeper than 5 meters and branching corals shallower than 5 meters, were adjusted for a dataset extract from Fabricius et al. (2008). When the model is run, a cyclone mortality scenario is used, meaning that at each timestep, a mortality rate is applied to each location and functional group.

References ​

  1. Baird, A., Madin, J., Alvarez-Noriega, M., Fontoura, L., Kerry, J., Kuo, C., Precoda, K., Torres-Pulliza, D., Woods, R., Zawada, K., & Hughes, T. (2018). A decline in bleaching suggests that depth can provide a refuge from global warming in most coral taxa. Marine Ecology Progress Series, 603, 257-264. https://doi.org/10.3354/meps12732

  2. Bairos-Novak, K. R., Hoogenboom, M. O., van Oppen, M. J., & Connolly, S. R. (2021). Coral adaptation to climate change: Meta-analysis reveals high heritability across multiple traits. Global Change Biology, 27, 5694-5710. https://doi.org/10.1111/gcb.15829

  3. Bozec, Y.-M., Hock, K., Mason, R. A. B., Baird, M. E., Castro-Sanguino, C., Condie, S. A., Puotinen, M., Thompson, A., & Mumby, P. J. (2022). Cumulative impacts across Australia's Great Barrier Reef: A mechanistic evaluation. Ecological Monographs, 92(1), e01494. https://doi.org/10.1002/ecm.1494

  4. Bozec, Y. M., Adam, A. A., Nava, B. A., Cresswell, A. K., Haller-Bull, V., Iwanaga, T., ... & Mumby, P. J. (2025). A rapidly closing window for coral persistence under global warming. bioRxiv, 2025-01.

  5. Bureau of Meteorology. (2025). Tropical cyclone categories. Australian Government. http://www.bom.gov.au/cyclone/tropical-cyclone-knowledge-centre/understanding/categories/

  6. Fabricius, K. E., De'Ath, G., Puotinen, M. L., Done, T., Cooper, T. F., & Burgess, S. C. (2008). Disturbance gradients on inshore and offshore coral reefs caused by a severe tropical cyclone. Limnology and Oceanography, 53(2), 690-704.

  7. Hughes, T. P., Kerry, J. T., Baird, A. H., Connolly, S. R., Dietzel, A., Eakin, C. M., Heron, S. F., Hoey, A. S., Hoogenboom, M. O., Liu, G., McWilliam, M. J., Pears, R. J., Pratchett, M. S., Skirving, W. J., Stella, J. S., & Torda, G. (2018). Global warming transforms coral reef assemblages. Nature, 556, 492-496. https://doi.org/10.1038/s41586-018-0041-2