Size-dependant mortality influences the recolonization success of juvenile corals transplanted for reef restoration and assisting juvenile corals attain a refuge size would thus improve post-transplantation survivorship. To explore colony size augmentation strategies, recruits of the scleractinian coral Pocillopora damicornis were fed with live Artemia salina nauplii twice a week for 24 weeks in an ex situ coral nursery. Fed recruits grew significantly faster than unfed ones, with corals in the 3600, 1800, 600 and 0 (control) nauplii/L groups exhibiting volumetric growth rates of 10.65±1.46, 4.69±0.9, 3.64±0.55 and 1.18±0.37 mm³/week, respectively. Corals supplied with the highest density of nauplii increased their ecological volume by more than 74 times their initial size, achieving a mean final volume of 248.38±33.44 mm³. The benefits of feeding were apparent even after transplantation to the reef. The corals in the 3600, 1800, 600 and 0 nauplii/L groups grew to final sizes of 4875±260 mm³, 2036±627 mm³, 1066±70 mm³ and 512±116 mm³, respectively. The fed corals had significantly higher survival rates than the unfed ones after transplantation (63%, 59%, 56% and 38% for the 3600, 1800, 600 and 0 nauplii/L treatments respectively). Additionally, cost-effectiveness analysis revealed that the costs per unit volumetric growth were drastically reduced with increasing feed densities. Corals fed with the highest density of nauplii were the most cost-effective (US$0.02/mm³), and were more than 12 times cheaper than the controls. This study demonstrated that nutrition enhancement can augment coral growth and post-transplantation survival, and is a biologically and economically viable option that can be used to supplement existing coral mariculture procedures and enhance reef restoration outcomes.

Predicting the bleaching responses of corals is crucial in light of frequent heat stress events to manage further losses of biodiversity and ecosystem functioning, especially for reefs impacted by urbanisation. We examined if the coral cover and community at various Singapore sites changed during the 2016 global coral bleaching event. Bleaching prevalence varied widely among sites in June 2016, and was best explained by site and coral species. While some sites were minimally impacted, others registered significant decreases in coral cover and community changes persisting till March 2017, when normal colouration was mostly regained by corals. Bleaching susceptibility was associated with larger corallites in hermaphrodites and smaller corallites in gonochores (probably due to the cost of maintaining dual sexual functions in hermaphrodites), and with increasing proximity between polyps (likely because thermal damage would be less contained among polyps with greater physiological integration). However, bleaching resilience-the capacity to regain baseline pigmentation-was poorly explained by the traits studied. Our findings suggest that the interplay between local conditions and species composition strongly affects bleaching outcomes on urbanised reefs, and underscore the utility of coral traits for predicting bleaching responses to help in formulating appropriate management strategies.

Count data commonly arise in natural sciences but adequately modeling these data is challenging due to zero-inflation and over-dispersion. While multiple parametric modeling approaches have been proposed, unfortunately there is no consensus regarding how to choose the best model. In this article, we propose a ordinal regression model (MN) as a default model for count data given that this model is shown to fit well data that arise from several types of discrete distributions. We extend this model to allow for automatic model selection (MN-MS) and show that the MN-MS model generates superior inference when compared to using the full model or more traditional model selection approaches. The MN-MS model is used to determine how human biting rate of mosquitoes, known to be able to transmit malaria, are influenced by environmental factors in the Peruvian Amazon. The MN-MS model had one of the best fit and out-of-sample predictive skill amongst all models. WhileA. darlingiis strongly associated with highly anthropized landscapes, all the other mosquito species had higher mean biting rates in landscapes with a lower fraction of exposed soil and urban area, revealing a striking shift in species composition. We believe that the MN and MN-MS models are valuable additions to the modelling toolkit employed by environmental modelers and quantitative ecologists.

Background: Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modelling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision-makers as they scale up community-based accessibility, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities. Methods: Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria. Results: Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria. Conclusions: This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.