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Freshwater ecosystems are being exposed to increasing salinisation, often because of pollution from road deicing salts, which is becoming more widely acknowledged. To address this issue, municipalities are turning towards the sodium salt alternatives of CaCl₂and MgCl₂, which are marketed as being safer for the environment. However, research into the actual safety of these salts on aquatic plants is lacking.

We investigated the effects of the most common road salt (NaCl) and two alternatives (MgCl₂and CaCl₂) on the productivity of a common freshwater plant (i.e.,Elodea canadensis) under three salt concentrations (control, 250, and 1,000 mg Cl⁻/L). Light‐bottle/dark‐bottle trials were performed to quantify net primary productivity, respiration, and gross primary productivity. These responses were tracked over time (1 vs. 3 weeks) to assess plant acclimation and lag effects under different levels of the three salts.

We discovered that NaCl and CaCl₂altered these measures of plant metabolism, but MgCl₂had no effects. We also observed instances of acclimation (i.e. salt effects after 1 week that disappeared after 3 weeks) and lag effects (i.e. no salt effect after 1 week, but salt effects after 3 weeks). These impacts are likely to be the results of plant responses to salt at the cell and molecular levels, including short‐ and long‐term changes in photosynthetic pigments. Therefore, the plant responses were salt‐specific, with instances of plant acclimation and lag effects.

This appears to be the first study of net primary productivity, respiration, and gross primary productivity in freshwater plants across a range of different salts, and it highlights how freshwater salinisation can have substantial effects on plant productivity. These effects will probably have an impact on the growth of macrophytes, which play key ecological roles in aquatic ecosystems.

 

Abstract The study of priority effects with respect to coinfections is still in its infancy. Moreover, existing coinfection studies typically focus on infection outcomes associated with exposure to distinct sets of parasite species, despite that functionally and morphologically similar parasite species commonly coexist in nature. Therefore, it is important to understand how interactions between similar parasites influence infection outcomes. Surveys at seven ponds in northwest Pennsylvania found that multiple species of echinostomes commonly co-occur. Using a larval anuran host ( Rana pipiens ) and the two most commonly identified echinostome species from our field surveys ( Echinostoma trivolvis and Echinoparyphium lineage 3), we examined how species composition and timing of exposure affect patterns of infection. When tadpoles were exposed to both parasites simultaneously, infection loads were higher than when exposed to Echinoparyphium alone but similar to being exposed to Echinostoma alone. When tadpoles were sequentially exposed to the parasite species, tadpoles first exposed to Echinoparyphium had 23% lower infection loads than tadpoles first exposed to Echinostoma . These findings demonstrate that exposure timing and order, even with similar parasites, can influence coinfection outcomes, and emphasize the importance of using molecular methods to identify parasites for ecological studies. 

Exposure to agrochemicals can drive rapid phenotypic and genetic changes in exposed populations. For instance, amphibian populations living far from agriculture (a proxy for agrochemical exposure) exhibit low pesticide tolerance, but they can be induced to possess high tolerance following a sublethal pesticide exposure. In contrast, amphibian populations close to agriculture exhibit high, constitutive tolerance to pesticides. A recent study has demonstrated that induced pesticide tolerance appears to have arisen from plastic responses to predator cues. As a result, we might expect that selection for constitutive pesticide tolerance in populations near agriculture (i.e., genetic assimilation) will lead to the evolution of constitutive responses to natural stressors. Using 15 wood frog (Rana sylvatica) populations from across an agricultural gradient, we conducted an outdoor mesocosm experiment to examine morphological (mass, body length, and tail depth) and behavioral responses (number of tadpoles observed and overall activity) of tadpolesexposedto three stressor environments (no‐stressor, competitors, or predator cues). We discovered widespread differences in tadpole traits among populations and stressor environments, but no population‐by‐environment interaction. Subsequent linear models revealed that population distance to agriculture (DTA) was occasionally correlated with tadpole traits in a given environment and with magnitudes of plasticity, but none of the correlations were significant after Bonferroni adjustment. The magnitudes of predator and competitor plasticity were never correlated with the magnitude of pesticide‐induced plasticity that we documented in a companion study. These results suggest that while predator‐induced plasticity appears to have laid the foundation for the evolution of pesticide‐induced plasticity and its subsequent genetic assimilation, inspection of population‐level differences in plastic responses show that the evolution of pesticide‐induced plasticity has not had a reciprocal effect on the evolved plastic responses to natural stressors.

 

In vivo fluorometers use chlorophyllafluorescence (F_chl) as a proxy to monitor phytoplankton biomass. However, the fluorescence yield ofF_chlis affected by photoprotection processes triggered by increased irradiance (nonphotochemical quenching; NPQ), creating diurnal reductions inF_chlthat 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 inF_chlby comparing daytime values to mean, unquenched values from the previous night. A random forest regression was trained on sensor data collected coincident withF_chl; 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 correctF_chlprofiles. The model also predicted NPQ and corrected unseenF_chlprofiles from a future period with excellent results (MAE: 9.0%; RMSE: 14.4%).F_chlprofiles 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⁻¹to 0.33 μg L⁻¹when compared to uncorrectedF_chldata. These results suggest that the use of machine learning models may be an effective way to correct for NPQ and may have universal applicability.