scholarly journals Multi-scale trend analysis of water quality using error propagation of generalized additive models

2022 ◽  
Vol 802 ◽  
pp. 149927
Author(s):  
Marcus W. Beck ◽  
Perry de Valpine ◽  
Rebecca Murphy ◽  
Ian Wren ◽  
Ariella Chelsky ◽  
...  
2018 ◽  
Author(s):  
David J. Phair ◽  
Johannes J. Le Roux ◽  
Cecile Berthouly-Salazar ◽  
Vernon Visser ◽  
Bettine Jansen van Vuuren ◽  
...  

AbstractSpecies undergoing range expansion frequently experience increased dispersal rates, especially among invasive alien species. Such increased dispersal rates have been attributed to ‘spatial sorting’, where traits enhancing dispersal assort towards the expanding range edge while traits enhacing competitiveness are favoured within the core range. To date no single study has compared patterns of spatial sorting across multiple continents for the same species. Here we compared patterns of spatial sorting in Sturnus vulgaris, the European starling (hereafter referred to as starlings), in its invasive ranges in South Africa and Australia. Starlings have experienced similar residence times in these two countries. Using multi-scale pattern analyses and generalized additive models, we determine whether dispersal and foraging traits (i.e. the morphological attributes of wings and bills) were sorted along the distance from introduction site. We found apparent patterns of spatial sorting in Australia, but not in South Africa. This difference may be attributed to differences in dispersal rates, clinal variation, environmental heterogeneity, and thus population demography on the two continents. Genetic data suggests that starlings in South Africa have experienced frequent long distance dispersal events, which could have diluted or overridden patterns of spatial sorting.


2013 ◽  
Vol 65 ◽  
pp. 111-116 ◽  
Author(s):  
Russell Richards ◽  
Lawrence Hughes ◽  
Daniel Gee ◽  
Rodger Tomlinson

2021 ◽  
Vol 8 ◽  
Author(s):  
Yoonja Kang ◽  
Chang-Ho Moon ◽  
Hyun-Jung Kim ◽  
Yang Ho Yoon ◽  
Chang-Keun Kang

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.


1981 ◽  
Author(s):  
Robert M. Hirsch ◽  
James Richard Slack ◽  
Richard A. Smith

Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.


Author(s):  
Mark David Walker ◽  
Mihály Sulyok

Abstract Background Restrictions on social interaction and movement were implemented by the German government in March 2020 to reduce the transmission of coronavirus disease 2019 (COVID-19). Apple's “Mobility Trends” (AMT) data details levels of community mobility; it is a novel resource of potential use to epidemiologists. Objective The aim of the study is to use AMT data to examine the relationship between mobility and COVID-19 case occurrence for Germany. Is a change in mobility apparent following COVID-19 and the implementation of social restrictions? Is there a relationship between mobility and COVID-19 occurrence in Germany? Methods AMT data illustrates mobility levels throughout the epidemic, allowing the relationship between mobility and disease to be examined. Generalized additive models (GAMs) were established for Germany, with mobility categories, and date, as explanatory variables, and case numbers as response. Results Clear reductions in mobility occurred following the implementation of movement restrictions. There was a negative correlation between mobility and confirmed case numbers. GAM using all three categories of mobility data accounted for case occurrence as well and was favorable (AIC or Akaike Information Criterion: 2504) to models using categories separately (AIC with “driving,” 2511. “transit,” 2513. “walking,” 2508). Conclusion These results suggest an association between mobility and case occurrence. Further examination of the relationship between movement restrictions and COVID-19 transmission may be pertinent. The study shows how new sources of online data can be used to investigate problems in epidemiology.


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