The relationship of chrysophycean stomatocysts to environmental variables in freshwater lakes in British Columbia

1995 ◽  
Vol 73 (7) ◽  
pp. 1097-1111 ◽  
Author(s):  
Katharine E. Duff ◽  
John P. Smol
Keyword(s):  
British Columbia ◽  
Environmental Variables ◽  
Secchi Depth ◽  
Predictive Ability ◽  
Environmental Variable ◽  
Distribution Data ◽  
Least Squares Regression ◽  
Freshwater Lakes ◽  
Inference Models ◽  
The Relationship

The relationships between the distributions of 82 chrysophycean cyst morphotypes and measured environmental variables in freshwater lakes in British Columbia were examined using ordination and regression statistics. After removal of unusual samples, 60 lakes were included in the analyses. Indirect and direct gradient analysis explained 23.2 and 14.0% of the variance in the cyst distribution data, and 31.4 and 53.7% of the variance in the cyst–environment relationship, respectively. Watershed area, Secchi depth and [Mg] were identified as the variables with the greatest contributions to the first ordination axis. Maximum depth contributed most strongly to axis 2. Constrained redundancy analyses were used to test the ability of individual environmental variables to explain the variance in the cyst data; no one variable was shown to have an overriding effect on cyst distributions. Five groups of cysts were identified using the ordination diagrams and the correlations between each cyst and each environmental variable. Partial least squares regression was used to construct inference models that quantified the relationship between the cyst distributions and four environmental variables (pH, [Mg], total phosphorus, and Secchi depth). For each variable, the best model included only those cysts which were significantly correlated with that variable. The inference model for pH yielded the strongest relationship (r2 = 0.51) and best predictive ability (root mean square error of prediction = 0.32). All the inference models showed a strong trend in the residuals, such that inferences at the low end of the observed gradient tended to be overestimates and inferences at the high end tended to be underestimates. Thus, paleolimnological inferences of past environmental conditions using these models will tend to underestimate the degree of change. Key words: British Columbia, phytoplankton, Chrysophyceae, stomatocysts, paleolimnology, eutrophication.

Sedimentary Cladoceran remains and their relationship to nutrients and other limnological variables in 53 lakes from British Columbia, Canada

2003 ◽  
Vol 60 (10) ◽  
pp. 1177-1189 ◽  
Author(s):  
Darren G Bos ◽  
Brian F Cumming
Keyword(s):  
British Columbia ◽  
Sediment Cores ◽  
Species Abundance ◽  
Environmental Variable ◽  
Weighted Averaging ◽  
Eutrophic Lakes ◽  
Inference Models ◽  
Cladoceran Remains ◽  
Explanatory Variables ◽  
Averaging Techniques

To develop models to predict past lake-water nutrient levels, the sedimentary remains of Cladocera were sampled from 53 lakes in central British Columbia, Canada. At the same time, the lakes were sampled for a suite of chemical variables. In addition, a host of physical and spatial explanatory variables were collected from each site. Canonical correspondence analysis showed that total phosphorus (TP), which ranged from 5 to 146 µg·L–1, was the measured environmental variable that best described the differences in species composition among the lakes. Additionally, lake depth and surface water temperature were also important in explaining the distribution of cladoceran taxa. Chydorus brevilabris, Daphnia ambigua, Daphnia cf. pulex, and Graptoleberis testudinaria had a preference for eutrophic lakes, whereas Acroperus harpae, Alonella nana, Alonella excisa, Chydorus piger, Daphnia cf. dentifera, and Eubosmina spp. were found in the less productive lakes. Predictive models to estimate TP from species abundance data were developed using weighted averaging techniques. This research has produced strong and significant inference models, which can now be used to reconstruct past changes in lake trophic status from remains of Cladocera in sediment cores.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

2016 ◽  
Author(s):  
Abhishek K Kala ◽  
Chetan Tiwari ◽  
Armin R Mikler ◽  
Samuel F Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

Background. The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2=0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). Conclusions. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3070 ◽  
Author(s):  
Abhishek K. Kala ◽  
Chetan Tiwari ◽  
Armin R. Mikler ◽  
Samuel F. Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

BackgroundThe primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.MethodsWe examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.ResultsLSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjustedR2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjustedR2 = 0.71).ConclusionsThe spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

2016 ◽  
Author(s):  
Abhishek K Kala ◽  
Chetan Tiwari ◽  
Armin R Mikler ◽  
Samuel F Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

Background. The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2=0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). Conclusions. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

scholarly journals Islands in the desert: environmental distribution modelling of endemic flora reveals the extent of Pleistocene tropical relict vegetation in southern Arabia

2019 ◽  
Vol 124 (3) ◽  
pp. 411-422 ◽  
Author(s):  
James S Borrell ◽  
Ghudaina Al Issaey ◽  
Darach A Lupton ◽  
Thomas Starnes ◽  
Abdulrahman Al Hinai ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Narrow Range ◽  
Biodiversity Hotspot ◽  
Environmental Data ◽  
Distribution Data ◽  
Endemic Plants ◽  
Environmental Niche ◽  
Environmental Distribution ◽  
Southern Arabia ◽  
Endemic Flora

AbstractBackground and AimsSouthern Arabia is a global biodiversity hotspot with a high proportion of endemic desert-adapted plants. Here we examine evidence for a Pleistocene climate refugium in the southern Central Desert of Oman, and its role in driving biogeographical patterns of endemism.MethodsDistribution data for seven narrow-range endemic plants were collected systematically across 195 quadrats, together with incidental and historic records. Important environmental variables relevant to arid coastal areas, including night-time fog and cloud cover, were developed for the study area. Environmental niche models using presence/absence data were built and tuned for each species, and spatial overlap was examined.Key ResultsA region of the Jiddat Al Arkad reported independent high model suitability for all species. Examination of environmental data across southern Oman indicates that the Jiddat Al Arkad displays a regionally unique climate with higher intra-annual stability, due in part to the influence of the southern monsoon. Despite this, the relative importance of environmental variables was highly differentiated among species, suggesting that characteristic variables such as coastal fog are not major cross-species predictors at this scale.ConclusionsThe co-occurrence of a high number of endemic study species within a narrow monsoon-influenced region is indicative of a refugium with low climate change velocity. Combined with climate analysis, our findings provide strong evidence for a southern Arabian Pleistocene refugium in Oman’s Central Desert. We suggest that this refugium has acted as an isolated temperate and mesic island in the desert, resulting in the evolution of these narrow-range endemic flora. Based on the composition of species, this system may represent the northernmost remnant of a continuous belt of mesic vegetation formerly ranging from Africa to Asia, with close links to the flora of East Africa. This has significant implications for future conservation of endemic plants in an arid biodiversity hotspot.

scholarly journals Improved Predictive Ability of KPLS Regression with Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 506
Author(s):  
Jorge Daniel Mello-Román ◽  
Adolfo Hernández ◽  
Julio César Mello-Román
Keyword(s):  
Kernel Function ◽  
Linear Method ◽  
Predictive Ability ◽  
Feature Space ◽  
Memetic Algorithms ◽  
Least Squares Regression ◽  
Optimal Parameters ◽  
Number Of Components ◽  
Kernel Partial Least Squares ◽  
Dependent Variables

Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

scholarly journals Reduction in mobility and COVID-19 transmission

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Pierre Nouvellet ◽  
Sangeeta Bhatia ◽  
Anne Cori ◽  
Kylie E. C. Ainslie ◽  
Marc Baguelin ◽  
...  
Keyword(s):  
Predictive Ability ◽  
Control Measures ◽  
Social Distancing ◽  
Beneficial Effects ◽  
Mobility Data ◽  
Strict Control ◽  
Before And After ◽  
Proxy Measure ◽  
Lower Transmission ◽  
The Relationship

AbstractIn response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27–77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49–91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12–48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.

The impact of executives’ perceptions of environmental threats and organizational slack on innovation strategies

2016 ◽  
Vol 7 (2) ◽  
pp. 216-230 ◽  
Author(s):  
Chengyuan Wang ◽  
Biao Luo ◽  
Yong Liu ◽  
Zhengyun Wei
Keyword(s):  
Cognitive Theory ◽  
Environmental Changes ◽  
Ordinary Least Squares ◽  
Organizational Slack ◽  
Least Squares Regression ◽  
Content Type ◽  
Environmental Threats ◽  
Innovation Strategies ◽  
The Impact ◽  
The Relationship

Purpose The paper aims to study the relationship between executives’ perceptions of environmental threats and innovation strategies and investigate the moderating effect of contextual factor (i.e. organizational slack) on such relations. It proposes a dualistic relationship between executives’ perceptions of environmental threats and innovation strategies, in which different perceptions of environmental threats will lead to corresponding innovation strategies, and dyadic organizational slack can promote such processes. Design/methodology/approach The paper is based on a survey with 163 valid questionnaires, which were all completed by executives. Hierarchical ordinary least-squares regression analysis is used to test the hypotheses proposed in this paper. Findings The paper provides empirical insights about that executives tend to choose exploratory innovation when they perceive environmental changes as likely loss threats, yet adopt exploitative innovation when perceiving control-reducing threats. Furthermore, unabsorbed slack (e.g. financial redundancy) positively moderates both relationships, while absorbed slack (e.g. operational redundancy) merely positively influences the relationship between the perception of control-reducing threats and exploitative innovation. Originality/value The paper bridges the gap between organizational innovation and cognitive theory by proposing a dualistic relationship between executives’ perceptions of environmental threats and innovation strategies. The paper further enriches innovation studies by jointly considering both subjective and objective influence factors of innovation and argues that organizational slack can moderate such dualistic relationship.

Diagnostic grammar and assessment: translating criteria into questions

1989 ◽  
Vol 19 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Lee N. Robins
Keyword(s):  
Diagnostic Criteria ◽  
Catchment Area ◽  
Predictive Ability ◽  
Diagnostic Interview ◽  
Good Prediction ◽  
Interview Schedule ◽  
Validity Data ◽  
Validity Assessment ◽  
Standardized Interview ◽  
The Relationship

SynopsisThere has been concern about whether standardized psychiatric interviews make valid diagnoses. Agreements between the Diagnostic Interview Schedule (DIS), as an example of a standardized interview, with independent assessments by a clinician are reasonably high in most studies, but the clinical assessment is itself of uncertain validity. Using predictive ability is an alternative way of judging validity. Data are presented to show that the DIS is almost as good at prediction as a clinician's assessment, but here too there are problems. Because prediction is probabilistic (i.e. the same disorder can have multiple outcomes, and different disorders can share outcomes), it is not possible to say how good prediction has to be to demonstrate perfect validity.Across varied methods of validity assessment, some disorders are regularly found more validly diagnosed than others, suggesting that part of the source of invalidity lies in the diagnostic grammar of the systems whose criteria standardized interviews evaluate. Sources of invalidity inherent in the content and structure of a variety of diagnoses in DSM-III and its heir, DSM-III-R, are reviewed and illustrated, in part with results from the Epidemiological Catchment Area study.The relationship between diagnostic criteria and standardized interviews is symbiotic. While attempts to adhere closely to existing diagnostic criteria contribute to the diagnostic accuracy of standardized interviews, the exercise of translating official diagnostic criteria into standardized questions highlights problems in the system's diagnostic grammar, enabling standardized interviews to contribute to improvements in diagnostic nosology.

scholarly journals A revision of daily egg production estimation methods, with application to Atlanto-Iberian sardine. 2. Spatially and environmentally explicit estimates of egg production

2011 ◽  
Vol 68 (3) ◽  
pp. 528-536 ◽  
Author(s):  
Miguel Bernal ◽  
Yorgos Stratoudakis ◽  
Simon Wood ◽  
Leire Ibaibarriaga ◽  
Luis Valdés ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Environmental Variables ◽  
Egg Production ◽  
Stock Assessment ◽  
Production Model ◽  
Estimation Methods ◽  
Statistical Framework ◽  
Spatio Temporal ◽  
The Relationship ◽  
Made In

Abstract Bernal, M., Stratoudakis, Y., Wood, S., Ibaibarriaga, L., Uriarte, A., Valdés, L., and Borchers, D. 2011. A revision of daily egg production estimation methods, with application to Atlanto-Iberian sardine. 2. Spatially and environmentally explicit estimates of egg production. – ICES Journal of Marine Science, 68: . A spatially and environmentally explicit egg production model is developed to accommodate a number of assumptions about the relationship between egg production and mortality and associated environmental variables. The general model was tested under different assumptions for Atlanto-Iberian sardine. It provides a flexible estimator of egg production, in which a range of assumptions and hypotheses can be tested in a structured manner within a well-defined statistical framework. Application of the model to Atlanto-Iberian sardine increased the precision of the egg production time-series, and allowed improvements to be made in understanding the spatio-temporal variability in egg production, as well as implications for ecology and stock assessment.

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