scholarly journals Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1377
Author(s):  
Jeong-Soo Park ◽  
Hyohyemi Lee ◽  
Donghui Choi ◽  
Youngha Kim
Keyword(s):  
Environmental Factors ◽  
South Korea ◽  
Least Squares ◽  
Alien Species ◽  
Ordinary Least Squares ◽  
Alien Plants ◽  
Economic Losses ◽  
Least Squares Regression ◽  
Alien Plant ◽  
Plant Distributions

Invasive alien plants can severely threaten biodiversity and cause economic losses in the agricultural industry; therefore, identifying the critical environmental factors related to the distribution of alien plants plays a crucial role in ecosystem management. In this study, we applied partial least squares regression (PLSR) and geographically weighted regression (GWR) to estimate the important environmental factors affecting the spread of two invasive and expansive plants, Lactuca scariola L. and Aster pilosus Willd., across South Korea. GWR provides more accurate predictions than ordinary least squares regression, and the local coefficients of GWR allow for the determination of the spatial relationships between alien plant distributions and environmental variables. Based on the model’s results, the distributions of these alien species were significantly associated with anthropogenic effects, such as human population density, residential area, and road density. Furthermore, the two alien species can establish themselves in habitats where native plants cannot thrive, owing to their broad tolerance to temperature and drought conditions. This study suggests that urban development and expansion can facilitate the invasion of these species in metropolitan cities.

scholarly journals Major environmental factors and traits of invasive alien plants determine their spatial distribution: a case study in Korea

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Minwoo Oh ◽  
Yoonjeong Heo ◽  
Eun Ju Lee ◽  
Hyohyemi Lee
Keyword(s):  
Environmental Factors ◽  
Alien Species ◽  
Plant Species ◽  
Functional Traits ◽  
Invasive Plants ◽  
Alien Plants ◽  
Summer Temperature ◽  
Alien Plant Species ◽  
Alien Plant ◽  
Cold Spots

Abstract Background As trade increases, the influx of various alien species and their spread to new regions are prevalent, making them a general problem globally. Anthropogenic activities and climate change have led to alien species becoming distributed beyond their native range. As a result, alien species can be easily found anywhere, with the density of individuals varying across locations. The prevalent distribution of alien species adversely affects invaded ecosystems; thus, strategic management plans must be established to control them effectively. To this end, this study evaluated hotspots and cold-spots in the degree of distribution of invasive alien plant species, and major environmental factors related to hot spots were identified. We analyzed 10,287 distribution points of 126 species of alien plant species collected through a national survey of alien species using the hierarchical model of species communities (HMSC) framework. Results The explanatory and fourfold cross-validation predictive power of the model were 0.91 and 0.75 as area under the curve (AUC) values, respectively. Hotspots of invasive plants were found in the Seoul metropolitan area, Daegu metropolitan city, Chungcheongbuk-do Province, southwest shore, and Jeju Island. Hotspots were generally found where the highest maximum summer temperature, winter precipitation, and road density were observed. In contrast, seasonality in temperature, annual temperature range, precipitation during summer, and distance to rivers and the sea were negatively correlated to hotspots. The model showed that functional traits accounted for 55% of the variance explained by environmental factors. Species with a higher specific leaf area were found where temperature seasonality was low. Taller species were associated with a larger annual temperature range. Heavier seed mass was associated with a maximum summer temperature > 29 °C. Conclusions This study showed that hotspots contained 2.1 times more alien plants on average than cold-spots. Hotspots of invasive plants tended to appear under less stressful climate conditions, such as low fluctuations in temperature and precipitation. In addition, disturbance by anthropogenic factors and water flow positively affected hotspots. These results were consistent with previous reports on the ruderal and competitive strategies of invasive plants, not the stress-tolerant strategy. Our results supported that the functional traits of alien plants are closely related to the ecological strategies of plants by shaping the response of species to various environmental filters. Therefore, to control alien plants effectively, the occurrence of disturbed sites where alien plants can grow in large quantities should be minimized, and the waterfront of rivers must be managed.

scholarly journals Tolerance Intervals in a Heteroscedastic Linear Regression Context with Applications to Aerospace Equipment Surveillance

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Janet Myhre ◽  
Daniel R. Jeske ◽  
Michael Rennie ◽  
Yingtao Bi
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Performance Metrics ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Automated System ◽  
Least Squares Regression ◽  
Data Set ◽  
Tolerance Intervals ◽  
Power Of The Test

A heteroscedastic linear regression model is developed from plausible assumptions that describe the time evolution of performance metrics for equipment. The inherited motivation for the related weighted least squares analysis of the model is an essential and attractive selling point to engineers with interest in equipment surveillance methodologies. A simple test for the significance of the heteroscedasticity suggested by a data set is derived and a simulation study is used to evaluate the power of the test and compare it with several other applicable tests that were designed under different contexts. Tolerance intervals within the context of the model are derived, thus generalizing well-known tolerance intervals for ordinary least squares regression. Use of the model and its associated analyses is illustrated with an aerospace application where hundreds of electronic components are continuously monitored by an automated system that flags components that are suspected of unusual degradation patterns.

scholarly journals Using Quantile Regression to Estimate Intervention Effects Beyond the Mean

2019 ◽  
Vol 79 (5) ◽  
pp. 883-910 ◽  
Author(s):  
Spyros Konstantopoulos ◽  
Wei Li ◽  
Shazia Miller ◽  
Arie van der Ploeg
Keyword(s):  
Quantile Regression ◽  
Least Squares ◽  
Large Scale ◽  
Social Science Research ◽  
Empirical Work ◽  
Science Research ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Intervention Effects ◽  
Education Data

This study discusses quantile regression methodology and its usefulness in education and social science research. First, quantile regression is defined and its advantages vis-à-vis vis ordinary least squares regression are illustrated. Second, specific comparisons are made between ordinary least squares and quantile regression methods. Third, the applicability of quantile regression to empirical work to estimate intervention effects is demonstrated using education data from a large-scale experiment. The estimation of quantile treatment effects at various quantiles in the presence of dropouts is also discussed. Quantile regression is especially suitable in examining predictor effects at various locations of the outcome distribution (e.g., lower and upper tails).

scholarly journals Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

2019 ◽  
Vol 11 (14) ◽  
pp. 1730 ◽  
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Spectral Band ◽  
Ordinary Least Squares ◽  
Landsat 8 ◽  
Eastern Siberia ◽  
Landscape Changes ◽  
Least Squares Regression ◽  
Spectral Bandpass ◽  
Sentinel 2

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.

Informing Prescribing Allocations at District Level in England

1997 ◽  
Vol 2 (3) ◽  
pp. 154-159 ◽  
Author(s):  
Nigel Rice ◽  
Roy Carr-Hill ◽  
David Roberts ◽  
David Lloyd
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Least Squares ◽  
General Practitioners ◽  
Supply And Demand ◽  
Ordinary Least Squares ◽  
Reciprocal Relationship ◽  
District Level ◽  
Least Squares Regression ◽  
Adult Males

Objectives: To derive a predictive model based on the morbidity, demographic and socio-economic characteristics of district populations to explain variations in prescribing costs in England. Method: Inter-relations between morbidity, demographic, socio-economic, general practice supply characteristics and net ingredient cost per age, sex and temporary resident originated prescribing unit (ASTRO-PU) were explored statistically for 90 districts in England using 1994 cost data. The possibility of mutual inter-relationship between ‘supply’ and ‘demand’ was examined; then the associations between a range of factors and prescribing costs were estimated using ordinary least squares regression and the predictive power of the possible models was systematically examined. Results: Whilst there was a relatively weak relationship between the supply factors that were measured, there did not appear to be any reciprocal relationship. Three parsimonious models estimated using ordinary least squares multiple regression techniques based on combinations of permanent sickness, low birth weight and the proportion of general practitioners registered for postgraduate certificate of education were identified. The models explained up to 61% of variation between districts in prescribing costs. Conclusions: ‘Need’ and ‘supply’ characteristics are independently associated with variations in prescribing costs at district level. The negative association between the proportion of general practitioners eligible for postgraduate education allowance and prescribing costs may reflect ‘better’ prescribing but could not be introduced into a resource allocation formula without introducing perverse incentives. The combination of permanent sickness and low birth weight complement each other by providing a proxy measure of morbidity mostly applicable to adult males (permanent sickness) and mothers (low birth weight being a measure of maternal health). These variables should be considered further for use in the process of allocating resources for prescribing to districts.

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