scholarly journals A Trial of a Solar-Powered, Cooperative Sensor/Actuator, Opto-Acoustical, Virtual Road-Fence to Mitigate Roadkill in Tasmania, Australia

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 752 ◽  
Author(s):  
Englefield ◽  
Candy ◽  
Starling ◽  
McGreevy
Keyword(s):  
Temporal Trends ◽  
Additive Model ◽  
Generalised Additive Model ◽  
Point Estimates ◽  
Statistical Detection ◽  
Solar Powered ◽  
Spatial And Temporal Trends ◽  
Adequate Power ◽  
Modest Effect ◽  
Do So

Adjustment for spatial and temporal trends using a Generalised Additive Model with Poisson error also failed to detect a significant VF effect. A simulation study used to estimate the power to detect a statistically significant reduction in roadkill rate gave, for median estimates of reduction of 21%, 48%, and 57%, estimates of power of 0.24, 0.78, and 0.91, respectively. Therefore, this study failed to confirm previously reported estimates of reduction in roadkill rates claimed for this VF of 50%–90%, despite having adequate power to do so. However, point estimates obtained for these three species of reductions ranging from 13% to 32% leave open the question of there being a real but modest effect that was below statistical detection limits.

scholarly journals Quantifying Wire Snares as a Threat to Leopards in Karnataka, India

2021 ◽  
Vol 14 ◽  
pp. 194008292110232
Author(s):  
Sanjay Gubbi ◽  
Aparna Kolekar ◽  
Vijaya Kumara
Keyword(s):  
Wildlife Conservation ◽  
Anthropogenic Impacts ◽  
Geographical Area ◽  
Temporal Trends ◽  
Additive Model ◽  
Panthera Pardus ◽  
Target Species ◽  
Generalised Additive Model ◽  
Study Results ◽  
Spatial And Temporal Trends

Though large felids are flagship species for wildlife conservation they are threatened due to various anthropogenic impacts. Mapping spatial patterns and quantification of threats to large felines can help conservation planning and resource allocation. The Leopard Panthera pardus, is categorized as Vulnerable by the IUCN as it faces a variety of threats. However, quantified data on the threats faced by leopards is scant. Hunting of wildlife using wire snares is one of the severest threats in India and elsewhere. Snaring, one of the simplest and most effective hunting techniques impacts other non-target species like the leopard. In this study, we document the spatial and temporal trends of snaring of leopards from India. Through content analysis of newspapers and news portals for the period January 2009-December 2020, we documented 113 incidents of leopards caught in snares of which 59.3% (5.5 leopards/year) resulted in mortality of leopards. Most snares (97.5%) were set to catch wild prey. Of the 84 incidents for which exact location details were available, the proportion of leopards caught in snares (54.7%, n = 46) and resulted in mortality (50%) in human-dense areas was significantly higher depicting an elevated threat from snares in these landscapes. Results from Generalised Additive Model indicated that snaring incidences increased with human population density. Percentage of protexted area to the geographical area within a district had little impact on the number of snaring incidents. The study results could help threat monitoring and conservation programs for leopards, especially outside the protexted area system.

scholarly journals Quantifying the Impact of the Covid-19 Lockdown Measures on Nitrogen Dioxide Levels throughout Europe

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 131
Author(s):  
Sverre Solberg ◽  
Sam-Erik Walker ◽  
Philipp Schneider ◽  
Cristina Guerreiro
Keyword(s):  
Nitrogen Dioxide ◽  
Temporal Trends ◽  
Measurement Data ◽  
Additive Model ◽  
Generalised Additive Model ◽  
The Difference ◽  
The One ◽  
The Impact ◽  
Good Agreement ◽  
Model Approach

In this paper, the effect of the lockdown measures on nitrogen dioxide (NO2) in Europe is analysed by a statistical model approach based on a generalised additive model (GAM). The GAM is designed to find relationships between various meteorological parameters and temporal metrics (day of week, season, etc.) on the one hand and the level of pollutants on the other. The model is first trained on measurement data from almost 2000 monitoring stations during 2015–2019 and then applied to the same stations in 2020, providing predictions of expected concentrations in the absence of a lockdown. The difference between the modelled levels and the actual measurements from 2020 is used to calculate the impact of the lockdown measures adjusted for confounding effects, such as meteorology and temporal trends. The study is focused on April 2020, the month with the strongest reductions in NO2, as well as on the gradual recovery until the end of July. Significant differences between the countries are identified, with the largest NO2 reductions in Spain, France, Italy, Great Britain and Portugal and the smallest in eastern countries (Poland and Hungary). The model is found to perform best for urban and suburban sites. A comparison between the found relative changes in urban surface NO2 data during the lockdown and the corresponding changes in tropospheric vertical NO2 column density as observed by the TROPOMI instrument on Sentinel-5P revealed good agreement despite substantial differences in the observing method.

scholarly journals Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

2014 ◽  
Vol 14 (10) ◽  
pp. 15257-15281 ◽  
Author(s):  
F. Salimi ◽  
Z. Ristovski ◽  
M. Mazaheri ◽  
R. Laiman ◽  
L. R. Crilley ◽  
...  
Keyword(s):  
Size Distribution ◽  
Source Apportionment ◽  
Particle Number ◽  
Temporal Trends ◽  
Additive Model ◽  
Quality Parameters ◽  
Generalised Additive Model ◽  
Clustering Techniques ◽  
Number Size Distribution ◽  
Particle Number Size Distribution

Abstract. Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

scholarly journals Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

2014 ◽  
Vol 14 (21) ◽  
pp. 11883-11892 ◽  
Author(s):  
F. Salimi ◽  
Z. Ristovski ◽  
M. Mazaheri ◽  
R. Laiman ◽  
L. R. Crilley ◽  
...  
Keyword(s):  
Size Distribution ◽  
Source Apportionment ◽  
Particle Number ◽  
Temporal Trends ◽  
Additive Model ◽  
Size Spectrum ◽  
Generalised Additive Model ◽  
Clustering Techniques ◽  
Number Size Distribution ◽  
Particle Number Size Distribution

Abstract. Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods that have been recently employed to analyse PNSD data; however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectrum to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Spatial and temporal trends and effects of contaminants in the Canadian Arctic marine ecosystem: a review

1999 ◽  
Vol 230 (1-3) ◽  
pp. 83-144 ◽  
Author(s):  
D Muir ◽  
B Braune ◽  
B DeMarch ◽  
R Norstrom ◽  
R Wagemann ◽  
...  
Keyword(s):  
Marine Ecosystem ◽  
Temporal Trends ◽  
Canadian Arctic ◽  
Spatial And Temporal Trends

scholarly journals Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

2021 ◽  
Vol 18 (16) ◽  
pp. 8259
Author(s):  
Sarah L. Jackson ◽  
Sahar Derakhshan ◽  
Leah Blackwood ◽  
Logan Lee ◽  
Qian Huang ◽  
...  
Keyword(s):  
United States ◽  
Great Plains ◽  
Explanatory Power ◽  
Temporal Trends ◽  
The United States ◽  
First Year ◽  
Significant Case ◽  
Urban Rural ◽  
Spatial And Temporal Trends ◽  
Mitigation Policies

This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020–January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban–rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban–rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

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