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Abstract Cyclones are a poorly described disturbance in tropical lakes, with the potential to alter ecosystems and compromise the services they provide. In November 2020, Hurricanes Eta and Iota made landfall near the Nicaragua-Honduras border, inundating the region with a large amount of late-season precipitation. To understand the impact of these storms on Lake Yojoa, Honduras, we compared 2020 and 2021 conditions using continuous (every 16 days) data collected from five pelagic locations. The storms resulted in increased Secchi depth and decreased algal abundance in December 2020, and January and February 2021, and lower-than-average accumulation of hypolimnetic nutrients from the onset of stratification (April 2021) until mixus in November 2021. Despite the reduced hypolimnetic nutrient concentrations, epilimnetic nutrient concentrations returned to (and in some cases exceeded) pre-hurricane levels following annual water column turnover in 2021. This response suggests that Lake Yojoa’s trophic state had only an ephemeral response to the disturbance imposed by the two hurricanes, likely due to internal input of sediment derived nutrients. These aseasonal storms acted as a large-scale experiment that resulted in nutrient dilution and demonstrated the resilience of Lake Yojoa’s trophic state to temporary nutrient reductions. 

Abstract Despite mounting evidence that tropical lakes may experience an array of phytoplankton nutrient limitation regimes, references to low‐latitude lakes being predominantly nitrogen limited remain common in the literature. To assess the current understanding of nutrient limitation regimes in tropical lakes, we performed a literature review with the following objectives: First, quantify and summarize observations of nitrogen (N), phosphorus (P), and colimitation in the collected studies. Second, identify common drivers of tropical nutrient limitation regimes. Third, provide literature‐informed recommendations for future research efforts into nutrient limitation of tropical lakes. We report that not only is N not the dominant limiting nutrient in the reviewed lakes, but tropical lakes exhibit a wide range of nutrient limitation regimes, and there is often considerable heterogeneity of the dominant limiting nutrient within an individual lake. This heterogeneity is driven largely by seasonal patterns of water column stratification and precipitation, land use and land cover (LULC), and the interaction of these system characteristics with lake morphology. To accurately characterize nutrient limitation in tropical aquatic ecosystems, assessments need to include sampling across space and time in order to capture within‐lake spatial heterogeneity and both inter‐ and intra‐annual temporal variation. Whenever possible, both inferential and experimental approaches should be used in concert to determine nutrient limitation due to methodological limitations. 

Abstract: Aim In this study, we present the results of a project which used Landsat Collection 2 Surface Reflectance data and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data to develop a machine learning model to estimate Secchi depth in Lake Yojoa, Honduras. Methods Satellite remote sensing data obtained within a 7-day window of an in situ measurement were matched with in situ Secchi depth measurements and were partitioned into train-test-validate data sets for model development. Results The machine learning model had good (R2= 0.57) agreement and reasonable uncertainty (MAE = 0.58 m) between remotely estimated and in situ observed Secchi depth. Application of the machine learning model increased the monitoring record of Lake Yojoa from 6 years of measured data to a 23-year record. Conclusions This model demonstrates the utility of coordinating in situ sampling schedules of short-term research projects with satellite imagery acquisition schedules in order to increase the temporal coverage of remote sensing derived estimates of water quality in understudied lakes. 

In aquatic ecosystems where primary productivity is limited by nitrogen (N), whether continuously, seasonally, or in concert with additional nutrient limitations, increased inorganic N availability can reshape ecosystem structure and function, potentially resulting in eutrophication and even harmful algal blooms. Whereas microbial metabolic processes such as mineralization and dissimilatory nitrate reduction to ammonium increase inorganic N availability, denitrification removes bioavailable N from the ecosystem. Therefore, understanding these key microbial mechanisms is critical to the sustainable management and environmental stewardship of inland freshwater resources. This study identifies and characterizes these crucial metabolisms in a warm, seasonally anoxic ecosystem. Results are contextualized by an ecological understanding of the study system derived from a multi-year continuous monitoring effort. This unique data set is the first of its kind in this largely understudied ecosystem (tropical lakes) and also provides insight into microbiome function and associated taxa in warm, anoxic freshwaters.