Seasonality and Species Specificity of Submerged Macrophyte Biomass in Shallow Lakes Under the Influence of Climate Warming and Eutrophication

Climate warming and eutrophication caused by anthropogenic activities strongly affect aquatic ecosystems. Submerged macrophytes usually play a key role in shallow lakes and can maintain a stable clear state. It is extremely important to study the effects of climate warming and eutrophication on the growth of submerged macrophytes in shallow lakes. However, the responses of submerged macrophytes to climate warming and eutrophication are still controversial. Additionally, the understanding of the main pathways impacting submerged macrophytes remains to be clarified. In addition, the influence of seasonality on the growth responses of submerged macrophytes to climate warming and eutrophication requires further elucidation. In this study, we conducted a series of mesocosm experiments with four replicates across four seasons to study the effects of rising temperature and nutrient enrichment on the biomass of two submerged macrophytes, Potamogeton crispus and Elodea canadensis. Our results demonstrated the seasonality and species specificity of plant biomass under the influence of climate warming and eutrophication, as well as the main explanatory factors in each season. Consistent with the seasonal results, the overall results showed that E. canadensis biomass was directly increased by rising temperature rather than by nutrient enrichment. Conversely, the overall results showed that P. crispus biomass was indirectly reduced by phosphorus enrichment via the strengthening of competition among primary producers. Distinct physiological and morphological traits may induce species-specific responses of submerged macrophytes to climate warming and eutrophication, indicating that further research should take interspecies differences into account.

Preliminary Assessment of Heavy Metal Concentration in Water, Sediment and Macrophytes in Shallabugh Wetland, Kashmir Himalaya

The present study analyzed the levels of Fe, Cu, Mn, Zn, Ni, Pb and Cd in water, sediment and macrophytes (viz Potamogeton crispus and Trapa natans) of Shallabugh wetland from December 2017 to November 2018 at three selected sites. The values of heavy metals in water and sediment reflected higher concentrations in winter at site II and lower in summer at site I. However, macrophytes accumulated high concentrations of heavy metals during summer at site II and lower values during autumn at site I. In water, all heavy metals were found within EPA limits except Fe and Mn and sediments were classified as non-polluted. In macrophytes Cu, Zn and Ni were found below WHO limits, whereas, Pb and Cd were above the limits. The contamination factor for all heavy metals was < 1 except for Cd which show moderate degree of contamination (CF < 3). The pollution load index values were < 1 and geoaccumulation index values were < 0 indicates unpolluted sediment. The bioconcentration factor for both the macrophytes was > 1, hence both the species can be regarded as potential phytoremediators. The correlation analysis indicates a significant positive dependency between water and sediment whereas negative correlation was observed between heavy metals in water and sediment with macrophytes (p < 0.01). The study suggests continuous assessment of water, sediment and macrophytes in order to serve as a useful link for indicating pollution levels and to minimize the potential health hazards.

Abiotic Factors That Affect The Distribution of Aquatic Macrophytes In Shallow Lakes Located In Sibley County, Minnesota, USA: A Spatial Modeling Approach

Macrophytes are an integral component of lake communities, therefore understanding the factors that affect macrophyte community structure is important for conservation and management of lakes. In Sibley County, Minnesota, USA, five lakes were surveyed using the point-intercept method. At each point the presence of macrophytes were recorded, water depth was measured, and a sediment sample was collected. Sediment samples were quantified by determining soil particle size and percent organic matter. The richness of macrophytes in all lakes were modeled via generalized linear regression with six explanatory variables: water depth, distance from shore, percent sand, percent silt, percent clay, and percent sediment organic matter. If model residuals were spatially autocorrelated, then a geographically weighted regression was used. Water depth and distance from shore were negatively related to mean species richness, and silt was either negatively or positively related to species richness depending on the lake and macrophytes present. All species richness models had pseudo-R2 values between 0.25 and 0.40. Curlyleaf pondweed (Potamogeton crispus) was related to with water depth, percent silt, and percent sediment organic matter during early season surveys.

Approach to optimizing ecological monitoring of water quality using indicators of constancy of higher water vegetation (using example of upper course of white river in Bashkortostan republic)

The article provides an analysis of the dynamics of the constancy indicators of representatives of higher aquatic vegetation (VBR) in the upper reaches of the Belaya River (Republic of Bashkortostan) from 2005 to 2019. Constancy indicators of 11 species of BBP (Butomus umbellatus L., 1753; Elodea canadensis Michx., 1803; Najas marina L., 1753; Potamogeton natans L., 1753; Potamogeton perfoliatus L., 1753; Potamogeton crispus L., 1753; Stuckenia pectinata L., 1753; Myriophyllum spicatum L., 1753; Lemna minor L., 1753; Spirodela polyrhiza Schleid., 1839 and Ceratophyllum demersum L., 1753) were registered in the areas of 2 state water posts the Shushpa railway station and the Arsky Kamen recreation house. In the course of research, for the first time, lists of permanent, additional and random species of BBP were compiled, correlation-regression models of the relationships between the constancy indicators of representatives of higher aquatic vegetation with the content of chemicals were calculated, the pollutants most strongly affecting the constancy indicators of representatives of BBP were identified, determined in the water of the upper course of the Belaya River.

Predicting aquatic invasion in Adirondack lakes: a spatial analysis of lake and landscape characteristics

Invasive species continue to pose major challenges for managing coupled human-environmental systems. Predictive tools are essential to maximize invasion monitoring and conservation efforts in regions reliant on abundant freshwater resources to sustain economic welfare, social equity, and ecological services. Past studies have revealed biotic and abiotic heterogeneity, along with human activity, can account for much of the spatial variability of aquatic invaders; however, improvements remain. This study was created to (1) examine the distribution of aquatic invasive species richness (AISR) across 126 lakes in the Adirondack Region of New York; (2) develop and compare global and local models between lake and landscape characteristics and AISR; and (3) use geographically weighted regression (GWR) to evaluate non-stationarity of local relationships, and assess its use for prioritizing lakes at risk to invasion. The evaluation index, AISR, was calculated by summing the following potential aquatic invaders for each lake: Asian Clam (Corbicula fluminea), Brittle Naiad (Najas minor), Curly-leaf Pondweed (Potamogeton crispus), Eurasian Watermilfoil (Myriophyllum spicatum), European Frog-bit (Hydrocharis morsus-ranae), Fanwort (Cabomba caroliniana), Spiny Waterflea (Bythotrephes longimanus), Variable-leaf Milfoil (Myriophyllum heterophyllum Water Chestnut (Trapa natans), Yellow Floating Heart (Nymphoides peltata), and Zebra Mussel (Dreissena polymorpha). The Getis-Ord Gi_ statistic displayed significant spatial hot and cold spots of AISR across Adirondack lakes. Spearman’s rank (q) correlation coefficient test (rs) revealed urban land cover composition, lake elevation, relative patch richness, and abundance of game fish were the strongest predictors of aquatic invasion. Five multiple regression global Poisson and GWR models were made, with GWR fitting AISR very well (R2 = 76–83%). Local pseudo-t-statistics of key explanatory variables were mapped and related to AISR, confirming the importance of GWR for understanding spatial relationships of invasion. The top 20 lakes at risk to future invasion were identified and ranked by summing the five GWR predictive estimates. The results inform that inexpensive and publicly accessible lake and landscape data, typically available from digital repositories within local environmental agencies, can be used to develop predictions of aquatic invasion with remarkable agreement. Ultimately, this transferable modeling approach can improve monitoring and management strategies for slowing the spread of invading species.

Predicting aquatic invasion in Adirondack lakes: a spatial analysis of lake and landscape characteristics

Invasive species continue to pose major challenges for managing coupled human-environmental systems. Predictive tools are essential to maximize invasion monitoring and conservation efforts in regions reliant on abundant freshwater resources to sustain economic welfare, social equity, and ecological services. Past studies have revealed biotic and abiotic heterogeneity, along with human activity, can account for much of the spatial variability of aquatic invaders; however, improvements remain. This study was created to (1) examine the distribution of aquatic invasive species richness (AISR) across 126 lakes in the Adirondack Region of New York; (2) develop and compare global and local models between lake and landscape characteristics and AISR; and (3) use geographically weighted regression (GWR) to evaluate non-stationarity of local relationships, and assess its use for prioritizing lakes at risk to invasion. The evaluation index, AISR, was calculated by summing the following potential aquatic invaders for each lake: Asian Clam (Corbicula fluminea), Brittle Naiad (Najas minor), Curly-leaf Pondweed (Potamogeton crispus), Eurasian Watermilfoil (Myriophyllum spicatum), European Frog-bit (Hydrocharis morsus-ranae), Fanwort (Cabomba caroliniana), Spiny Waterflea (Bythotrephes longimanus), Variable-leaf Milfoil (Myriophyllum heterophyllum Water Chestnut (Trapa natans), Yellow Floating Heart (Nymphoides peltata), and Zebra Mussel (Dreissena polymorpha). The Getis-Ord Gi_ statistic displayed significant spatial hot and cold spots of AISR across Adirondack lakes. Spearman’s rank (q) correlation coefficient test (rs) revealed urban land cover composition, lake elevation, relative patch richness, and abundance of game fish were the strongest predictors of aquatic invasion. Five multiple regression global Poisson and GWR models were made, with GWR fitting AISR very well (R2 = 76–83%). Local pseudo-t-statistics of key explanatory variables were mapped and related to AISR, confirming the importance of GWR for understanding spatial relationships of invasion. The top 20 lakes at risk to future invasion were identified and ranked by summing the five GWR predictive estimates. The results inform that inexpensive and publicly accessible lake and landscape data, typically available from digital repositories within local environmental agencies, can be used to develop predictions of aquatic invasion with remarkable agreement. Ultimately, this transferable modeling approach can improve monitoring and management strategies for slowing the spread of invading species.