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Integr Environ Assess Manag
2024 Mar 01;202:401-418. doi: 10.1002/ieam.4871.
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Evaluating the effects of climate change and chemical, physical, and biological stressors on nearshore coral reefs: A case study in the Great Barrier Reef, Australia.
Mentzel S
,
Nathan R
,
Noyes P
,
Brix KV
,
Moe SJ
,
Rohr JR
,
Verheyen J
,
Van den Brink PJ
,
Stauber J
.
FIGURE 2. Conceptual steps used in this study from knowledge acquisitions, including a detailed overview of the approach used to develop the modeling chain to derive projections of climate stressors and effects on a near shore reef system. AOP, adverse outcome pathway; BN, Bayesian network
FIGURE 3. Projections of average annual (A) air temperature, (B) rainfall, and (C) runoff in Blacks Creek catchment derived from a 16-member ensemble based on four climate models and four downscaling methods. Historical projections are provided for the period 1976–2005, and those for future scenarios extend from 2006 to 2100. Hollow symbols represent the estimates from each individual ensemble member and the filled diamond symbols are the ensemble average. (D) Projected distributions of the frequency of bleaching events in a 30-year period, obtained from individual climate models (open circle symbols) and the mean of the ensemble (solid diamond symbol)
FIGURE 4. Illustrative AOP network of potential effects of PSII herbicide and sedimentation interactions with climate change parameters, ocean warming, and ocean acidification on GBR coral reef systems. Toxicant-induced climate sensitivities are presented involving PSII herbicides exacerbating thermal stress, showing differing but related molecular initiation events that proceed through shared key events and adverse outcomes. Climate-induced toxicant sensitivities are depicted by PSII herbicide, potentially worsening the effects of ocean acidification on downstream calcification and growth by pathways of photosynthetic inhibition that perturb electron flow, leading to reductions in ATP and NADPH and impeded carboxylation and calcification. Additional interactions are shown by climate-related increases in sedimentary runoff that increase the effects of PSII herbicides. Red highlighted variables were implemented as nodes in the Bayesian network. ΔF/Fm′, effective quantum yield in light-adapted samples (measure of open reaction centers and proportional to energy conversion in PSII); AOP, adverse outcome pathway; Fv/Fm, maximum quantum yield in dark-adapted samples (indicator of potential energy conversion at PSII; reductions indicate photodamage; Jones, 2005); GBR, Great Barrier Reef; PSII, photosystem II; ROS, reactive oxygen species
FIGURE 5. (A) Conceptual model used for construction of the BN and (B) an example parameterized BN for an RCP8.5 scenario in 2080. BN, Bayesian network
FIGURE 6. Probability distribution for some of the selected endpoints (output nodes) depending on the climate scenarios for (A) coral bleaching, (B) total suspended sediments (TSS), (C) cyclone category, (D) coral cover influenced by macroalgal cover and TSS, (E) Coral mortality influenced by cyclone and bleaching, and (F) coral bleaching uder various scenarios.