Long-term dynamics and drivers of coral and macroalgal cover on inshore reefs of the Great Barrier Reef Marine Park.

Quantifying the role of biophysical and anthropogenic drivers of coral reef ecosystem processes can inform management strategies that aim to maintain or restore ecosystem structure and productivity. However, few studies have examined the combined effects of multiple drivers, partitioned their impacts, or established threshold values that may trigger shifts in benthic cover. Inshore fringing reefs of the Great Barrier Reef Marine Park (GBRMP) occur in high-sediment, high-nutrient environments and are under increasing pressure from multiple acute and chronic stressors. Despite world-leading management, including networks of no-take marine reserves, relative declines in hard coral cover of 40-50% have occurred in recent years, with localized but persistent shifts from coral to macroalgal dominance on some reefs. Here we use boosted regression tree analyses to test the relative importance of multiple biophysical drivers on coral and macroalgal cover using a long-term (12-18 yr) data set collected from reefs at four island groups. Coral and macroalgal cover were negatively correlated at all island groups, and particularly when macroalgal cover was above 20%. Although reefs at each island group had different disturbance-and-recovery histories, degree heating weeks (DHW) and routine wave exposure consistently emerged as common drivers of coral and macroalgal cover. In addition, different combinations of sea-surface temperature, nutrient and turbidity parameters, exposure to high turbidity (primary) floodwater, depth, grazing fish density, farming damselfish density, and management zoning variously contributed to changes in coral and macroalgal cover at each island group. Clear threshold values were apparent for multiple drivers including wave exposure, depth, and degree heating weeks for coral cover, and depth, degree heating weeks, chlorophyll a, and cyclone exposure for macroalgal cover, however, all threshold values were variable among island groups. Our findings demonstrate that inshore coral reef communities are typically structured by broadscale climatic perturbations, superimposed upon unique sets of local-scale drivers. Although rapidly escalating climate change impacts are the largest threat to coral reefs of the GBRMP and globally, our findings suggest that proactive management actions that effectively reduce chronic stressors at local scales should contribute to improved reef resistance and recovery potential following acute climatic disturbances.

Unravel biophysical factors on river water quality response in Chilean Central-Southern watersheds.

Identifying the key anthropogenic (land uses) and natural (topography and climate) biophysical drivers affecting river water quality is essential for efficient management of water resources. We tested the hypothesis that water quality can be predicted by different biophysical factors. Multivariate statistics based on a geographical information system (GIS) were used to explore the influence of factors (i.e., precipitation, topography, and land uses) on water quality (i.e., nitrate (NO 3 (-)), phosphate (PO 4 (3-)), silicate (Si(OH)4), dissolved oxygen (DO), suspended solids (TSS), biological oxygen demand (DO), temperature (T), conductivity (EC), and pH) for two consecutive years in the Itata and Biobío river watersheds, Central Chile (36° 00' and 38° 30'). The results showed that (NO 3 (-)), (PO 4 (3-)), Si(OH)4, TSS, EC, and DO were higher during rainy season (austral fall, winter, and spring), whereas BOD and temperature were higher during dry season. The spatial variation of these parameters in both watersheds was related to land use, topography (e.g., soil moisture, soil hydrological group, and erodability), and precipitation. Soil hydrological group and soil moisture were the strongest explanatory predictors for PO 4 (3-) , Si(OH)4 and EC in the river, followed by land use such as agriculture for NO 3 (-) and DO and silviculture for TSS and Si(OH)4. High-resolution water leaching and runoff maps allowed us to identify agriculture areas with major probability of water leaching and higher probability of runoff in silviculture areas. Moreover, redundancy analysis (RDA) revealed that land uses (agriculture and silviculture) explained in 60 % the river water quality variation. Our finding highlights the vulnerability of Chilean river waters to different biophysical drivers, rather than climate conditions alone, which is amplified by human-induced degradation.

Mercury levels in humpback whales, and other Southern Ocean marine megafauna.

Mercury is a known potent neurotoxin. The biogeochemical cycle of mercury in the remote Antarctic region is still poorly understood, with Polar climate change contributing added complexity. Longitudinal biomonitoring of mercury accumulation in Antarctic marine megafauna can contribute top-down insight into the bio-physical drivers of wildlife exposure. The bioaccumulative nature of organic mercury renders high trophic predators at the greatest risk of elevated exposure. Humpback whales represent secondary consumers of the Antarctic sea-ice ecosystem and an ideal biomonitoring species for persistent and bioaccumulative compounds due to their extended life-spans. This study provides the first results of mercury accumulation in humpback whales, and places findings within the context of mercury accumulation in both prey, as well as six other species of Antarctic marine megafauna. Combined, these findings contribute new baseline information regarding mercury exposure to Antarctic wildlife, and highlights methodological prerequisites for routine mercury biomonitoring in wildlife via non-lethally biopsied superficial tissues.

Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story?

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calibrated to an individual set of bloom observations, and few studies have assessed whether differences among observing platforms could lead to contrasting results in terms of relevant bloom predictors and their estimated influence on bloom severity. The aim of this study was to assess the degree of coherence of different monitoring methods in (1) capturing short- and long-term cyanobacteria bloom dynamics and (2) identifying environmental drivers associated with bloom variability. Using western Lake Erie as a case study, we applied boosted regression tree (BRT) models to long-term time series of cyanobacteria bloom estimates from multiple in-situ and remote sensing approaches to quantify the relative influence of physico-chemical and meteorological drivers on bloom variability. Results of BRT models showed remarkable consistency with known ecological requirements of cyanobacteria (e.g., nutrient loading, water temperature, and tributary discharge). However, discrepancies in inter-annual and intra-seasonal bloom dynamics across monitoring approaches led to some inconsistencies in the relative importance, shape, and sign of the modeled relationships between select environmental drivers and bloom severity. This was especially true for variables characterized by high short-term variability, such as wind forcing. These discrepancies might have implications for our understanding of the role of different environmental drivers in regulating bloom dynamics, and subsequently for the development of models capable of informing management and decision making. Our results highlight the need to develop methods to integrate multiple data sources to better characterize bloom spatio-temporal variability and improve our ability to understand and predict cyanobacteria blooms.