scholarly journals Water Quality Improvement Shifts the Dominant Phytoplankton Group From Cryptophytes to Diatoms in a Coastal Ecosystem

2021 ◽  
Vol 8 ◽  
Author(s):  
Yoonja Kang ◽  
Chang-Ho Moon ◽  
Hyun-Jung Kim ◽  
Yang Ho Yoon ◽  
Chang-Keun Kang
Keyword(s):  
Water Quality ◽  
River Discharge ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Industrial Complex ◽  
Quality Improvements ◽  
Dominant Group ◽  
Study Region ◽  
Phytoplankton Group

We investigated long-term variations in the dominant phytoplankton groups with improvements in water quality over 11 years in the Yeongil Bay on the southeastern coast of Korea. River discharge declined during the study period but TN from river discharge remained stable, indicating the input of enriched nutrients to the bay was fairly consistent. NH4+ levels decreased with a decrease in TN from the POSCO industrial complex. While the study region was characterized by the P-limited and deficient environment, cryptophytes dominated with the intensified P-limitations. The relative abundance of cryptophytes declined from 70% in 2010 to 10% in 2016, but that of diatoms increased from 70% in 2009 to 90% in 2016. Correlation analysis showed a positive correlation of cryptophytes with NH4+ and a negative correlation with photic depth. Generalized additive models also exhibited an increase in diatom dominance and a decrease in cryptophyte dominance with an increase in water quality, indicating that a decrease in NH4+ and increase in light favored the diatom growth but suppressed the cryptophyte growth. Thus, water quality improvements shift the dominant group in the coastal ecological niche from cryptophytes to diatoms.

Environmental and geographical effects on fog occurrence

2021 ◽  
Author(s):  
Iva Hunova ◽  
Marek Brabec ◽  
Marek Malý ◽  
Alexandru Dumitrescu ◽  
Jan Geletič
Keyword(s):  
Air Pollution ◽  
Environmental Factors ◽  
Tensor Product ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Low Rank ◽  
Seasonal Component ◽  
Explanatory Variables ◽  
The Individual

<p>Fog is a very complex phenomenon (Gultepe et al., 2007). In some areas it can contribute substantially to hydrological and chemical inputs and is therefore of high environmental relevance (Blas et al., 2010). Fog formation is affected by numerous factors, such as meteorology, air pollution, terrain (geomorphology), and land-use.</p><p>In our earlier studies we addressed the role of meteorology and air pollution on fog occurrence (Hůnová et al., 2018) and long-term trends in fog occurrence in Central Europe (Hůnová et al., 2020). This study builds on earlier model identification of year-to-year and seasonal components in fog occurrence and brings an analysis of the deformation of the above components due to the individual explanatory variables. The aim of this study was to indicate the geographical and environmental factors affecting the fog occurrence.</p><p>       We have examined the data on fog occurrence from 56 meteorological stations of various types from Romania reflecting different environments and geographical areas. We used long-term records from the 1981–2017 period. </p><p>       We considered both the individual explanatory variables and their interactions. With respect to geographical factors, we accounted for the altitude and landform. With respect to environmental factors,   we accounted for proximity of large water bodies, and proximity of forests. Geographical data from Copernicus pan-European (e.g. CORINE land cover, high resolution layers) and local (e.g. Urban Atlas) projects were used. Elevation data from EU-DEM v1.1 were source for morphometric analysis (Copernicus, 2020).</p><p>        We applied a generalized additive model, GAM (Wood, 2017; Hastie & Tibshirani, 1990) to address nonlinear trend shapes in a formalized and unified way. In particular, we employed penalized spline approach with cross-validated penalty coefficient estimation. To explore possible deformations of annual and seasonal components with various covariates of interest, we used (penalized) tensor product splines to model (two-way) interactions parsimoniously, Wood (2006).</p><p>       The fog occurrence showed significant decrease over the period under review. In general the selected explanatory variables significantly affected the fog occurrence and their effect was non-linear. Our results indicated that, the geographical and environmental variables affected primarily the seasonal component of the model. Of the factors which were accounted for, it was mainly the altitude showing the clear effect on seasonal component deformation (Hůnová et al., in press).</p><p>      </p><p> </p><p>References:</p><p>Blas, M, Polkowska, Z., Sobik, M., et al. (2010). Atmos. Res. 95, 455–469.</p><p>Copernicus Land Monitoring Service (2020). Accessed online at: https://land.copernicus.eu/.</p><p>Gultepe, I., Tardif, R., Michaelidis, S.C., Cermak, J., Bott, A. et al. (2007). Pure Appl Geophys, 164, 1121-1159.</p><p>Hastie, T.J., Tibshirani, R.J. (1990). Generalized Additive Models. Boca Raton, Chapman & Hall/CRC.</p><p>Hůnová, I., Brabec, M., Malý, M., Dumitrescu, A., Geletič, J. (in press) Sci. Total Environ. 144359.</p><p>Hůnová, I., Brabec, M., Malý, M., Valeriánová, A. (2018) Sci. Total Environ. 636, 1490–1499.</p><p>Hůnová, I., Brabec, M., Malý, M., Valeriánová, A. (2020) Sci. Total Environ. 711, 135018.</p><p>Wood, S.N. (2006) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036</p><p>Wood, S.N. (2017). Generalized Additive Models: An Introduction with R (2nd ed). Boca Raton, Chapman & Hall/CRC.</p><p> </p>

Evidence for temperature-dependent shifts in spawning times of anadromous alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis)

2020 ◽  
Vol 77 (4) ◽  
pp. 741-751 ◽  
Author(s):  
Steven M. Lombardo ◽  
Jeffrey A. Buckel ◽  
Ernie F. Hain ◽  
Emily H. Griffith ◽  
Holly White
Keyword(s):  
Generalized Additive Models ◽  
Additive Models ◽  
Alosa Pseudoharengus ◽  
Spawning Habitat ◽  
Temperature Dependent ◽  
Blueback Herring ◽  
River Herring ◽  
Alosa Aestivalis ◽  
Timing Of Spawning

We analyzed four decades of presence–absence data from a fishery-independent survey to characterize the long-term phenology of river herring (alewife, Alosa pseudoharengus; and blueback herring, Alosa aestivalis) spawning migrations in their southern distribution. We used logistic generalized additive models to characterize the average ingress, peak, and egress timing of spawning. In the 2010s, alewife arrived to spawning habitat 16 days earlier and egressed 27 days earlier (peak 12 days earlier) relative to the 1970s. Blueback herring arrived 5 days earlier and egressed 23 days earlier (peak 13 days earlier) in the 2010s relative to the 1980s. The changes in ingress and egress timing have shortened the occurrence in spawning systems by 11 days for alewife over four decades and 18 days for blueback herring over three decades. We found that the rate of vernal warming was faster during 2001–2016 relative to 1973–1988 and is the most parsimonious explanation for changes in spawning phenology. The influence of a shortened spawning season on river herring population dynamics warrants further investigation.

Stock-recruitment models for Pacific sardine (Sardinops sagax)

1995 ◽  
Vol 52 (3) ◽  
pp. 566-577 ◽  
Author(s):  
Larry D. Jacobson ◽  
Alec D. MacCall
Keyword(s):  
Reproductive Success ◽  
Environmental Conditions ◽  
Statistical Power ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Cold Temperatures ◽  
Pacific Sardine ◽  
Sardinops Sagax ◽  
Spawning Biomass

We used generalized additive models to study the recruitment of Pacific sardine (Sardinops sagax) along western North America and to compare the effects of fishing and environmental factors on the stock. We found significant relationships between the logarithm of sardine reproductive success (recruits per unit of spawning biomass) and average sea surface temperature (SST), as well as between sardine recruitment, spawning biomass, and average SST. Simulation and time series analyses were used to evaluate bias, variance, and statistical power for one of our models. Correlation with log reproductive success was highest when average SST data included temperatures after the period of larval development, indicating that recruitment estimates were affected by environmentally driven changes in availability of older sardine to the fishery. Predicted equilibrium biomass and MSY for sardine are lower under environmental conditions that prevail when temperatures are colder. Long-term fluctuations in the environment may cause long-term fluctuations in sardine productivity, and little or no sardine harvest may be sustainable during periods of adverse environmental conditions and cold temperatures.

scholarly journals Evaluation of the Performance of Smoothing Functions in Generalized Additive Models for Spatial Variation in Disease

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17300 ◽  
Author(s):  
Umaporn Siangphoe ◽  
David C. Wheeler
Keyword(s):  
Spatial Variation ◽  
Disease Risk ◽  
Generalized Additive Models ◽  
Elevated Risk ◽  
Additive Models ◽  
Risk Area ◽  
Study Region ◽  
Risk Levels ◽  
Permutation Methods ◽  
Smoothing Functions

Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of locally weighted scatterplot smoothing (loess). We tested the null hypothesis of constant risk and detected areas of elevated risk using analysis of deviance with permutation methods and assessed the performance of the smoothing methods based on the spatial detection rate, sensitivity, accuracy, precision, power, and false-positive rate. The results showed that all methods had a higher sensitivity and a consistently moderate-to-high accuracy rate when the true disease risk was higher. The models generally performed better in detecting elevated risk areas than detecting overall spatial variation. One of the loess methods had the highest precision in detecting overall spatial variation across scenarios and outperformed the other methods in detecting a linear elevated risk area. The TPRS methods outperformed loess in detecting elevated risk in two circular areas.

Calcium declines in northeastern Ontario lakes

2001 ◽  
Vol 58 (10) ◽  
pp. 2011-2020 ◽  
Author(s):  
W Keller ◽  
S S Dixit ◽  
J Heneberry
Keyword(s):  
Water Quality ◽  
20Th Century ◽  
Acid Deposition ◽  
Forest Harvesting ◽  
Climatic Changes ◽  
Quality Improvements ◽  
Sulphur Deposition ◽  
Lakewater Chemistry

Thousands of lakes in northeastern Ontario, Canada, have been acidified by sulphur deposition associated with emissions from the Sudbury area metal smelters. However, water quality improvements including increased pH and reduced sulphate concentrations have followed large reductions in Sudbury emissions that were implemented, beginning in the 1970s. Substantial decreases in Ca concentrations accompanied these other changes in lakewater chemistry. Monitoring of 38 lakes 20–128 km from Sudbury showed declines in Ca concentrations, averaging 2.7 µeq·L–1·year–1, over the period 1981–1999. Declines were particularly apparent during the 1990s, averaging 3.8 µeq·L–1·year–1. Paleolimnological reconstructions of the long-term Ca patterns in six lakes suggest that general lakewater Ca declines occurred through much of the 20th century. Comparison of recent measured Ca concentrations in 16 lakes with diatom-inferred pre-industrial Ca concentrations indicates that overall decreases in Ca have been large, averaging 74.6 µeq·L–1 or 46%. Long-term Ca patterns may reflect a combination of factors including climatic changes, forest harvesting activities, and leaching by acid deposition, the effects of which we can not separate. Calcium declines have biological implications that will need to be considered in the development of appropriate targets as these lakes continue to recover from acidification.

Modelling the occurrence of convective hazards using ERA5 reanalysis data

2021 ◽  
Author(s):  
Francesco Battaglioli ◽  
Pieter Groenemeijer ◽  
Tomas Pucik ◽  
Uwe Ulbrich ◽  
Henning Rust ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Generalized Additive Models ◽  
Reanalysis Data ◽  
Severe Weather ◽  
Additive Models ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Additive Regression

<p>Convective hazards such as large hail, severe wind gusts, tornadoes, and heavy rainfall cause high economic damages, fatalities, and injuries across Europe. There are insufficient observations to determine whether trends in such local phenomena exist, however recent studies suggest that the conditions supporting such hazards have become more frequent across large parts of Europe in recent decades.</p><p>We model the occurrence of these hazards using Generalized Additive Models (GAM) to investigate the existence of such long-term trends, and to enable objective probabilistic forecasts of these hazards. The models are trained with storm reports from the European Severe Weather Database (ESWD), lightning observations from the EUCLID network, and predictor parameters derived from the ERA5 reanalysis. Our work is based on the framework AR-CHaMo (Additive Regression Convective Hazard Models), previously developed at ESSL.</p><p>Preliminary results include a spatial depiction of the environmental conditions giving rise to convective hazards at a higher resolution than was possible before. The skill of hail models developed using AR-CHaMo has been shown to be superior to composite parameters used by weather forecasters for the occurrence of large hail, such as the Supercell Composite Parameter (SCP) and the Significant Hail Parameter (SHP). Likewise, for tornadoes, more skillful models can be constructed using the AR-CHaMo framework than predictors such as the Significant Tornado Parameter (STP).</p><p>The developed models have use both in climate studies and in medium-range severe weather forecasting. We will report on initial efforts to detect long term (1979-2019) trends of convective hazards and present how these models can be used to support severe weather forecasting using medium-range ensemble forecasts.</p>

Warming in the upper San Francisco Estuary: Patterns of water temperature change from 5 decades of data

2021 ◽  
Author(s):  
Samuel M. Bashevkin ◽  
Brian Mahardja ◽  
Larry R. Brown
Keyword(s):  
Water Temperature ◽  
Temperature Change ◽  
San Francisco ◽  
Generalized Additive Models ◽  
San Francisco Estuary ◽  
Additive Models ◽  
Spatiotemporal Variability ◽  
Northern Regions ◽  
Migration Corridor

Temperature is a key controlling variable from subcellular to ecosystem scales. Thus, climatic warming is expected to have broad impacts, especially in economically and ecologically valuable systems such as estuaries. The heavily managed upper San Francisco Estuary (SFE) supplies water to millions of people and is home to fish species of high conservation, commercial, and recreational interest. Despite a long monitoring record (> 50 years), we do not yet know how water temperatures have already changed or how trends vary spatially or seasonally. We fit generalized additive models on an integrated database of discrete water temperature observations to estimate long-term trends with spatio-seasonal variability. We found that water temperatures have increased 0.017 °C/year on average over the past 50 years. Rates of temperature change have varied over time, but warming was predominant. Temperature increases were most widespread in the late-fall to winter (November to February) and mid-spring (April to June), coinciding with the winter development of juvenile Chinook Salmon and spring spawning window of the endangered Delta Smelt. Warming was fastest in the northern regions, a key fish migration corridor with important tidal wetland habitat. However, no long-term temperature trends were detected in October and were only observed in some regions in May, July, and August. These results can help identify optimal areas for restoration or refugia to buffer the effects of a warming climate, and the methods can be leveraged to understand the spatiotemporal variability in climate warming patterns in other aquatic systems.

The effect of Budapest on the water quality of the Danube

1994 ◽  
Vol 30 (5) ◽  
pp. 147-155
Author(s):  
Pál Benedek ◽  
Atilla Darázs ◽  
Veronika Major ◽  
Károly Oszkó
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Wastewater Treatment ◽  
Pollution Control ◽  
Storm Water ◽  
Industrial Complex ◽  
Polluted River ◽  
Planning Concept

The Danube flowing across Hungary is a moderately polluted river, at least in its middle stretch, where its greatest polluter is Budapest with 2 million inhabitants and a large industrial complex. Serving the river for drinking water supply, as well, it is obvious that the pollution control for the capital is of paramount importance. There is a 20 year planning concept for the improvement of the sewerage, wastewater treatment and storm water outfall. This long term plan, together with its impact on the water quality of the river and the review of the present conditions, are investigated in this paper.

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