Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys.

This study tested the hypothesis that the response of corals to temperature and pCO 2 is consistent between taxa. Juvenile massive Porites spp. and branches of P. rus from the back reef of Moorea were incubated for 1 month under combinations of temperature (29.3 °C and 25.6 °C) and pCO 2 (41.6 Pa and 81.5 Pa) at an irradiance of 599 μmol quanta m−2 s−1. Using microcosms and CO2 gas mixing technology, treatments were created in a partly nested design (tanks) with two between‐plot factors (temperature and pCO 2), and one within‐plot factor (taxon); calcification was used as a dependent variable. pCO 2 and temperature independently affected calcification, but the response differed between taxa. Massive Porites spp. was largely unaffected by the treatments, but P. rus grew 50% faster at 29.3 °C compared with 25.6 °C, and 28% slower at 81.5 Pa vs. 41.6 Pa CO2. A compilation of studies placed the present results in a broader context and tested the hypothesis that calcification for individual coral genera is independent of pH, [HCO3 −], and [CO3 2−]. Unlike recent reviews, this analysis was restricted to studies reporting calcification in units that could be converted to nmol CaCO3 cm−2 h−1. The compilation revealed a high degree of variation in calcification as a function of pH, [HCO3 −], and [CO3 2−], and supported three conclusions: (1) studies of the effects of ocean acidification on corals need to pay closer attention to reducing variance in experimental outcomes to achieve stronger synthetic capacity, (2) coral genera respond in dissimilar ways to pH, [HCO3 −], and [CO3 2−], and (3) calcification of massive Porites spp. is relatively resistant to short exposures of increased pCO 2, similar to that expected within 100 y.

Ocean acidification (OA) has important implications for the persistence of coral reef ecosystems, due to potentially negative effects on biomineralization. Many coral reefs are dynamic with respect to carbonate chemistry, and experience fluctuations in pCO2 that exceed OA projections for the near future. To understand the influence of dynamic pCO2 on an important reef calcifier, we tested the response of the crustose coralline alga Porolithon onkodes to oscillating pCO2. Individuals were exposed to ambient (400 µatm), high (660 µatm), or variable pCO2 (oscillating between 400/660 µatm) treatments for 14 days. To explore the potential for coralline acclimatization, we collected individuals from low and high pCO2 variability sites (upstream and downstream respectively) on a back reef characterized by unidirectional water flow in Moorea, French Polynesia. We quantified the effects of treatment on algal calcification by measuring the change in buoyant weight, and on algal metabolism by conducting sealed incubations to measure rates of photosynthesis and respiration. Net photosynthesis was higher in the ambient treatment than the variable treatment, regardless of habitat origin, and there was no effect on respiration or gross photosynthesis. Exposure to high pCO2 decreased P. onkodes calcification by >70%, regardless of the original habitat. In the variable treatment, corallines from the high variability habitat calcified 42% more than corallines from the low variability habitat. The significance of the original habitat for the coralline calcification response to variable, high pCO2 indicates that individuals existing in dynamic pCO2 habitats may be acclimatized to OA within the scope of in situ variability. These results highlight the importance of accounting for natural pCO2 variability in OA manipulations, and provide insight into the potential for plasticity in habitat and species-specific responses to changing ocean chemistry.

Single-lake studies offer an opportunity for understanding, predicting, and mitigating local or regional threats to lake ecosystems. Our goal was to understand how concurrent environmental stressors such as climate change, eutrophication, and salinization affect long-term lake water quality. We report epilimnetic changes in 18 waterquality parameters collected at seven sites from 1980 to 2016 in Lake George, a large oligotrophic lake in the Adirondack Park, New York, USA. Improvements and deteriorations in water quality occurred over 37 years. We observed a 32% increase in chlorophyll a associated with an increase in orthophosphate, but not total phosphorus or a warming epilimnion (0.05 C/year). Salinization from road deicing salts contributed to the largest deterioration in water quality. However, chloride concentrations and the current rate of increase are low enough that few ecological impacts are likely to occur over the next few decades. Increasing calcium concentrations were not high enough to facilitate the persistence of invasive species in the lake such as zebra mussels (Dreissena polymorpha) but are sufficient for Asian clams (Corbicula fluminea) and the spiny water flea (Bythotrephes longimanus). Similar to other lakes, environmental legislation has supported recovery from acidification, indicated by reduced sulfate and nitrate, and increased alkalinity and pH. Declines in water quality were minor relative to other lakes, suggesting that decades of tourism and development occurred without major deterioration in water quality, but management efforts are needed to curb salinization in the Lake George watershed, particularly as it relates to sodium concentrations to prevent a loss of drinking water quality.

In vivo fluorometers use chlorophyll a fluorescence (Fchl) as a proxy to monitor phytoplankton biomass. However, the fluorescence yield of Fchl is affected by photoprotection processes triggered by increased irradiance (nonphotochemical quenching; NPQ), creating diurnal reductions in Fchl that may be mistaken for phytoplankton biomass reductions. Published correction methods are mostly designed for pelagic oceans and are ill suited for inland waters or for high‐frequency data collection. A machine learning‐based method was developed to correct vertical profiler data from an oligotrophic lake. NPQ was estimated as a percent reduction in Fchl by comparing daytime values to mean, unquenched values from the previous night. A random forest regression was trained on sensor data collected coincident with Fchl; including solar radiation, water temperature, depth, and dissolved oxygen saturation. The accuracy of the model was assessed using a grouped 10‐fold cross validation (mean absolute error [MAE]: 7.6%; root mean square error [RMSE]: 10.2%), which was then used to correct Fchl profiles. The model also predicted NPQ and corrected unseen Fchl profiles from a future period with excellent results (MAE: 9.0%; RMSE: 14.4%). Fchl profiles were then correlated to laboratory results, allowing corrected profiles to be compared directly to collected samples. The correction reduced error (RMSE) due to NPQ from 0.67 μg L−1 to 0.33 μg L−1 when compared to uncorrected Fchl data. These results suggest that the use of machine learning models may be an effective way to correct for NPQ and may have universal applicability.