Coral bleaching events endangers the sustainability of major coral reef ecosystems around the world, such as the Great Barrier Reef (GBR), the Florida Keys and the Coral Triangle Area. The loss of symbiotic algae and/or their pigment lead the bleaching of corals [2]. Moreover, the mass bleaching phenomena degrade the valuable ecosystem services of coral reefs, which are home to 25% of all known marine species and provide approximately $375 billion per year in goods and services around the world [3, 4, 5]. Due the fact that corals tend to die in great numbers immediately following coral bleaching events [6], many scientists consider ‘bleaching’ as the problem per se, rather than as a symptomp to understanding the overall condition of coral reef
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For example, the NOOA Coral Reef Watch Program uses the Degree Heating Week (DHW) metric, which is a thermal stress algorithm based on satellite-derived sea surface temperature. The DHW higher than 4 °C predicts the occurrence of coral bleaching [10]. However, recent studies show that the use of the thermal tresshold variable alone is unable to predict the susceptibility of corals to bleaching [8, 11]. The high variability of thermal threshold had “relatively weak or no significant relationships with coral cover and susceptibility” [8]. In addition, researchs on coral bleaching model tends to ignore the significance of joint probability of multivariate dependent variables. Therefore, we propose the first effort ever to develop temporal and spatial models of coral bleaching susceptibility based on copula …show more content…
It expected that copula models in this research will perform well to forecast coral bleaching events and could help coral reef managers to predict its spatial susceptibility in local or regional scopes. Therefore, three major studies of the proposed research are 1) analysing the multivariate return period of coral bleaching, 2) developing the spatial susceptibility prediction for risk assessment and 3) evaluating the performance of the coral bleaching susceptibility models. The multivariate analysis shall follow the remarkable work by De Michele et al. in 2013 [25], which introduce the concept of dynamic return period based on copula and calculated via Survival Kendall’s approach. Meanwhile, the spatial susceptibility prediction will develop through the spatial vine copula procedure by Gräler and Pebesma [26, 27]. Any form of modification could be applied under the investigation. Both of studies require the determination of the suitable variables, the availability of a good time series data, as well as the selection of appropriate copula families and
For example, the NOOA Coral Reef Watch Program uses the Degree Heating Week (DHW) metric, which is a thermal stress algorithm based on satellite-derived sea surface temperature. The DHW higher than 4 °C predicts the occurrence of coral bleaching [10]. However, recent studies show that the use of the thermal tresshold variable alone is unable to predict the susceptibility of corals to bleaching [8, 11]. The high variability of thermal threshold had “relatively weak or no significant relationships with coral cover and susceptibility” [8]. In addition, researchs on coral bleaching model tends to ignore the significance of joint probability of multivariate dependent variables. Therefore, we propose the first effort ever to develop temporal and spatial models of coral bleaching susceptibility based on copula …show more content…
It expected that copula models in this research will perform well to forecast coral bleaching events and could help coral reef managers to predict its spatial susceptibility in local or regional scopes. Therefore, three major studies of the proposed research are 1) analysing the multivariate return period of coral bleaching, 2) developing the spatial susceptibility prediction for risk assessment and 3) evaluating the performance of the coral bleaching susceptibility models. The multivariate analysis shall follow the remarkable work by De Michele et al. in 2013 [25], which introduce the concept of dynamic return period based on copula and calculated via Survival Kendall’s approach. Meanwhile, the spatial susceptibility prediction will develop through the spatial vine copula procedure by Gräler and Pebesma [26, 27]. Any form of modification could be applied under the investigation. Both of studies require the determination of the suitable variables, the availability of a good time series data, as well as the selection of appropriate copula families and