ABSTRACT
Crown-of-Thorns Starfish (CoTS) population outbreaks contribute to coral cover decline on Indo-Pacific reefs. On the Great Barrier Reef (GBR), enhanced catchment nutrient loads are hypothesised to increase phytoplankton food for CoTS larvae in the outbreak initiation zone. This study examines whether catchment load reductions will improve water quality in this zone during the larval period. We defined the i) initiation zone's spatial extent; ii) larval stage's temporal extent; and iii) water quality thresholds related to larval food, from published information. We applied these to model simulations, developed to quantify the effect of catchment load reductions on GBR water quality (Baird et al., 2021), and found a consistently weak response of chlorophyll-a, total organic nitrogen and large zooplankton concentrations in the initiation zone. Model results indicate marine and atmospheric forcing are more likely to control the planktonic biomass in this zone, even during major flooding events purported to precede CoTS outbreaks.
ABSTRACT
Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18-24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichiacoli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R(2) were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R(2) were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical predictive models appropriate for assessment of both recreational and shellfish harvesting water quality in estuarine waters.
