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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. 

Abstract Progress in the field of ecological stoichiometry has demonstrated that the outcome of ecological interactions can often be predicted a priori based on the nutrient ratios (e.g., carbon: nitrogen: phosphorus, C:N:P) of interacting organisms. However, the challenges of accurately measuring the nutrient content of active parasites within hosts has limited our ability to rigorously apply ecological stoichiometry to host–parasite systems. Traditional nutrient analyses require high parasite biomasses, often preventing individual‐level analyses. This prevents researchers from estimating variation in the nutrient content of individual parasites within a single host infrapopulation, a critical factor that could define how the ecology of the parasite affects the host–parasite interaction. Here, we explain how energy dispersive technology, a technique currently used to measure the elemental content of free‐living microbes, can be adapted for parasitic microbial infrapopulations. We demonstrate the power of accurately quantifying the biomass stoichiometry of individual microbial parasites sampled directly from individual hosts. Using this approach, we show that the stoichiometric composition of two microbial parasites capable of infecting the same host are stoichiometrically distinct and respond to host diet quality differently. We also demonstrate that characteristics of the stoichiometric trait distributions of these infrapopulations were important predictors of host fecundity, a proxy for virulence in this system, and better predictors of parasite load than the mean parasite stoichiometry or our parasite and diet treatments alone. EDS provides a rigorous tool for applying ecological stoichiometry to host–parasite systems and enables researchers to explore the nutritional physiology of host–parasite interactions at a scale that is more relevant to the ecology and evolution of the system than traditional nutrient analyses. Here we demonstrate that this level of resolution provides useful insights into the diet‐dependent physiology of microbial parasites and their hosts. We anticipate that this improved level of resolution has the potential to elucidate a range of eco–evo interactions in host–parasite systems that were previously unobservable.