scholarly journals Sampling bias and model choice in continuous phylogeography: Getting lost on a random walk

2021 ◽  
Vol 17 (1) ◽  
pp. e1008561
Author(s):  
Antanas Kalkauskas ◽  
Umberto Perron ◽  
Yuxuan Sun ◽  
Nick Goldman ◽  
Guy Baele ◽  
...  
Keyword(s):  
Brownian Motion ◽  
Alternative Model ◽  
Sampling Bias ◽  
Model Choice ◽  
Viable Option ◽  
Continuous Space ◽  
Location Data ◽  
Getting Lost ◽  
Sampling Biases ◽  
Living Organisms

Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography—with location data provided in the form of latitude and longitude coordinates—describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak’s spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV’s robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.

scholarly journals Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

2020 ◽  
Author(s):  
Antanas Kalkauskas ◽  
Umberto Perron ◽  
Yuxuan Sun ◽  
Nick Goldman ◽  
Guy Baele ◽  
...  
Keyword(s):  
Brownian Motion ◽  
Alternative Model ◽  
Sampling Bias ◽  
Model Choice ◽  
Viable Option ◽  
Continuous Space ◽  
Location Data ◽  
Getting Lost ◽  
Sampling Biases ◽  
Living Organisms

Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography — with location data provided in the form of latitude and longitude coordinates — describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreak’s spatial distribution. We therefore consider an alternative model, the spatial Λ-Fleming-Viot process (ΛFV), which has recently gained popularity in population genetics. Despite the ΛFV’s robustness to sampling biases, we find that the different assumptions of the ΛFV and BMP models result in different applicabilities, with the ΛFV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.

scholarly journals Albedo over rough snow and ice surfaces

2014 ◽  
Vol 8 (3) ◽  
pp. 1069-1086 ◽  
Author(s):  
S. Lhermitte ◽  
J. Abermann ◽  
C. Kinnard
Keyword(s):  
Complex Terrain ◽  
Surface Albedo ◽  
Sampling Bias ◽  
Mountain Glaciers ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Sampling Biases ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Errors ◽  
Effect Of Surface

Abstract. Both satellite and ground-based broadband albedo measurements over rough and complex terrain show several limitations concerning feasibility and representativeness. To assess these limitations and understand the effect of surface roughness on albedo, firstly, an intrasurface radiative transfer (ISRT) model is combined with albedo measurements over different penitente surfaces on Glaciar Tapado in the semi-arid Andes of northern Chile. Results of the ISRT model show effective albedo reductions over the penitentes up to 0.4 when comparing the rough surface albedo relative to the albedo of the flat surface. The magnitude of these reductions primarily depends on the opening angles of the penitentes, but the shape of the penitentes and spatial variability of the material albedo also play a major role. Secondly, the ISRT model is used to reveal the effect of using albedo measurements at a specific location (i.e., apparent albedo) to infer the true albedo of a penitente field (i.e., effective albedo). This effect is especially strong for narrow penitentes, resulting in sampling biases of up to ±0.05. The sampling biases are more pronounced when the sensor is low above the surface, but remain relatively constant throughout the day. Consequently, it is important to use a large number of samples at various places and/or to locate the sensor sufficiently high in order to avoid this sampling bias of surface albedo over rough surfaces. Thirdly, the temporal evolution of broadband albedo over a penitente-covered surface is analyzed to place the experiments and their uncertainty into a longer temporal context. Time series of albedo measurements at an automated weather station over two ablation seasons reveal that albedo decreases early in the ablation season. These decreases stabilize from February onwards with variations being caused by fresh snowfall events. The 2009/2010 and 2011/2012 seasons differ notably, where the latter shows lower albedo values caused by larger penitentes. Finally, a comparison of the ground-based albedo observations with Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer)-derived albedo showed that both satellite albedo products capture the albedo evolution with root mean square errors of 0.08 and 0.15, respectively, but also illustrate their shortcomings related to temporal resolution and spatial heterogeneity over small mountain glaciers.

scholarly journals Amphibian embryos as an alternative model to study the pharmaceutical toxicity of cyclophosphamide and ibuprofen

2019 ◽  
Vol 92 (2) ◽  
Author(s):  
Blerta Turani ◽  
Valbona Aliko ◽  
Caterina Faggio
Keyword(s):  
Alternative Model ◽  
Biological Effects ◽  
Aquatic Environments ◽  
Chemotherapeutic Drugs ◽  
Indicator Organisms ◽  
Cell Frequency ◽  
Cellular Physiology ◽  
Blood Smears ◽  
Frog Tadpoles ◽  
Living Organisms

Pharmaceuticals are becoming potentially ubiquitous pollutants because of their extensive use by man. One of the most frequent groups of pharmaceuticals that have been identified as particularly concerning is that of nonsteroidal anti-inflammatory and chemotherapeutic drugs. In Albania, studies to determine the risk of pharmaceuticals in conjunction with their occurrence in water bodies and their adverse effects on living organisms, including humans, are scarce. The purpose of this study was to elucidate the possible toxic effects of ibuprofen (IBU) and cyclophosphamide (CP) on cellular physiology of frog tadpoles. For this purpose, individuals of Pelophylax shqipericus belonging to stage 21 Gosner were exposed to sub-lethal concentration (5 μg/L) of IBU and CP for 48 hours, and erythrocyte abnormalities and micronucleated cell frequency were evaluated as endpoints. Blood smears from tadpoles exposed to CP for 48 hours showed a pronounced decrease in the number of red blood cells and an increase in the percentage of micronucleated erythrocytes through chromatin fragmentation, while abnormalities like cellular and nuclear vacuolization, collapse and rupture of the cell membrane were caused by IBU toxicity. Understanding the biological effects of these drugs on frog tadpoles can help in using these animals as reliable bio-indicator organisms in monitoring aquatic environments health.

scholarly journals Sampling bias adjustment for sparsely sampled satellite measurements applied to ACE-FTS carbonyl sulfide observations

2019 ◽  
Vol 12 (4) ◽  
pp. 2129-2138
Author(s):  
Corinna Kloss ◽  
Marc von Hobe ◽  
Michael Höpfner ◽  
Kaley A. Walker ◽  
Martin Riese ◽  
...  
Keyword(s):  
Regression Model ◽  
Carbonyl Sulfide ◽  
Sampling Bias ◽  
Adjustment Factor ◽  
Satellite Measurements ◽  
Bias Adjustment ◽  
Adjustment Procedure ◽  
Mixing Ratios ◽  
Data Product ◽  
Sampling Biases

Abstract. When computing climatological averages of atmospheric trace-gas mixing ratios obtained from satellite-based measurements, sampling biases arise if data coverage is not uniform in space and time. Homogeneous spatiotemporal coverage is essentially impossible to achieve. Solar occultation measurements, by virtue of satellite orbit and the requirement of direct observation of the sun through the atmosphere, result in particularly sparse spatial coverage. In this proof-of-concept study, a method is presented to adjust for such sampling biases when calculating climatological means. The method is demonstrated using carbonyl sulfide (OCS) measurements at 16 km altitude from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer). At this altitude, OCS mixing ratios show a steep gradient between the poles and Equator. ACE-FTS measurements, which are provided as vertically resolved profiles, and integrated stratospheric OCS columns are used in this study. The bias adjustment procedure requires no additional information other than the satellite data product itself. In particular, the method does not rely on atmospheric models with potentially unreliable transport or chemistry parameterizations, and the results can be used uncompromised to test and validate such models. It is expected to be generally applicable when constructing climatologies of long-lived tracers from sparsely and heterogeneously sampled satellite measurements. In the first step of the adjustment procedure, a regression model is used to fit a 2-D surface to all available ACE-FTS OCS measurements as a function of day-of-year and latitude. The regression model fit is used to calculate an adjustment factor that is then used to adjust each measurement individually. The mean of the adjusted measurement points of a chosen latitude range and season is then used as the bias-free climatological value. When applying the adjustment factor to seasonal averages in 30∘ zones, the maximum spatiotemporal sampling bias adjustment was 11 % for OCS mixing ratios at 16 km and 5 % for the stratospheric OCS column. The adjustments were validated against the much denser and more homogeneous OCS data product from the limb-sounding MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument, and both the direction and magnitude of the adjustments were in agreement with the adjustment of the ACE-FTS data.

scholarly journals Sampling bias in reptile occurrence data for the Kruger National Park

Koedoe ◽  
2020 ◽  
Vol 62 (1) ◽  
Author(s):  
Jody M. Barends ◽  
Darren W. Pietersen ◽  
Guinevere Zambatis ◽  
Donovan R.C. Tye ◽  
Bryan Maritz
Keyword(s):  
National Park ◽  
Conservation Management ◽  
Species Distributions ◽  
Sampling Bias ◽  
Kruger National Park ◽  
Data Set ◽  
Occurrence Data ◽  
Reptile Species ◽  
Sampling Biases ◽  
Occurrence Records

o effectively conserve and manage species, it is important to (1) understand how they are spatially distributed across the globe at both broad and fine spatial resolutions and (2) elucidate the determinants of these distributions. However, information pertaining to the distributions of many species remains poor as occurrence data are often scarce or collected with varying motivations, making the resulting patterns susceptible to sampling bias. Exacerbating an already limited quantity of occurrence data with an assortment of biases hinders their effectiveness for research, thus making it important to identify and understand the biases present within species occurrence data sets. We quantitatively assessed occurrence records of 126 reptile species occurring in the Kruger National Park (KNP), South Africa, to quantify the severity of sampling bias within this data set. We collated a data set of 7118 occurrence records from museum, literature and citizen science sources and analysed these at a biologically relevant spatial resolution of 1 km × 1 km. As a result of logistical challenges associated with sampling in KNP, approximately 92% of KNP is data deficient for reptile occurrences at the 1 km × 1 km resolution. Additionally, the spatial coverage of available occurrences varied at species and family levels, and the majority of occurrence records were strongly associated with publicly accessible human infrastructure. Furthermore, we found that sampled areas within KNP were not necessarily ecologically representative of KNP as a whole, suggesting that areas of unique environmental space remain to be sampled. Our findings highlight the need for substantially greater sampling effort for reptiles across KNP and emphasise the need to carefully consider the sampling biases within existing data should these be used for conservation management decision-making. Modelling species distributions could potentially serve as a short-term solution, but a concomitant increase in surveys across the park is needed.Conservation implications: The sampling biases present within KNP reptile occurrence data inhibit the inference of fine-scale species distributions within and across the park, which limits the usage of these data towards meaningfully informing conservation management decisions as applicable to reptile species in KNP.

scholarly journals Evaluation of ACCMIP ozone simulations and ozonesonde sampling biases using a satellite-based multi-constituent chemical reanalysis

2017 ◽  
Vol 17 (13) ◽  
pp. 8285-8312 ◽  
Author(s):  
Kazuyuki Miyazaki ◽  
Kevin Bowman
Keyword(s):  
Atmospheric Chemistry ◽  
Radiative Forcing ◽  
Lower Troposphere ◽  
Sampling Bias ◽  
The Arctic ◽  
Model Bias ◽  
Middle Troposphere ◽  
Ensemble Mean ◽  
Global Coverage ◽  
Sampling Biases

Abstract. The Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) ensemble ozone simulations for the present day from the 2000 decade simulation results are evaluated by a state-of-the-art multi-constituent atmospheric chemical reanalysis that ingests multiple satellite data including the Tropospheric Emission Spectrometer (TES), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Measurement of Pollution in the Troposphere (MOPITT) for 2005–2009. Validation of the chemical reanalysis against global ozonesondes shows good agreement throughout the free troposphere and lower stratosphere for both seasonal and year-to-year variations, with an annual mean bias of less than 0.9 ppb in the middle and upper troposphere at the tropics and mid-latitudes. The reanalysis provides comprehensive spatiotemporal evaluation of chemistry-model performance that compliments direct ozonesonde comparisons, which are shown to suffer from significant sampling bias. The reanalysis reveals that the ACCMIP ensemble mean overestimates ozone in the northern extratropics by 6–11 ppb while underestimating by up to 18 ppb in the southern tropics over the Atlantic in the lower troposphere. Most models underestimate the spatial variability of the annual mean lower tropospheric concentrations in the extratropics of both hemispheres by up to 70 %. The ensemble mean also overestimates the seasonal amplitude by 25–70 % in the northern extratropics and overestimates the inter-hemispheric gradient by about 30 % in the lower and middle troposphere. A part of the discrepancies can be attributed to the 5-year reanalysis data for the decadal model simulations. However, these differences are less evident with the current sonde network. To estimate ozonesonde sampling biases, we computed model bias separately for global coverage and the ozonesonde network. The ozonesonde sampling bias in the evaluated model bias for the seasonal mean concentration relative to global coverage is 40–50 % over the western Pacific and east Indian Ocean and reaches 110 % over the equatorial Americas and up to 80 % for the global tropics. In contrast, the ozonesonde sampling bias is typically smaller than 30 % for the Arctic regions in the lower and middle troposphere. These systematic biases have implications for ozone radiative forcing and the response of chemistry to climate that can be further quantified as the satellite observational record extends to multiple decades.

scholarly journals DEVELOPMENT OF IOT ENABLED GLOBAL TRACKING SYSTEM AND MOBILE APPLICATION FOR PEOPLE WITH ALZHEIMER’S DISEASE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 287-290
Author(s):  
K. İleri ◽  
A. Duru ◽  
İ. R. Karaş
Keyword(s):  
Mobile Application ◽  
Tracking System ◽  
Memory Loss ◽  
High Accuracy ◽  
High Demand ◽  
Gps Technology ◽  
Location Data ◽  
Getting Lost ◽  
Tracking Device ◽  
Tracking Devices

Abstract. Alzheimer’s is a degenerative disease meaning that it gets worse with time. Memory loss, speaking problems, wandering, and getting lost are some of the signs of the disease. The risk of wandering results in high demand for extensive monitoring solutions for the patients suffering from the disease. Tracking solutions are crucial, especially for family members and caregivers, so researchers develop new wearable tracking devices to overcome missing patients. GPS technology can provide location data with high accuracy, but it is not sufficient to use only by itself. Thus, a more extensive solution should be provided. In this paper, a mobile wearable tracking device that can provide data to the mobile application through internet has been developed for patient tracking purposes.

Group Mentoring as an Alternative Model for Women

2009 ◽  
Vol 18 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Glenna G. Bower
Keyword(s):  
Alternative Model ◽  
Professional Organization ◽  
Primary Reason ◽  
Sport Organizations ◽  
Leadership Positions ◽  
Viable Option ◽  
Group Mentoring ◽  
Advancement Opportunities ◽  
Mentoring Relationship ◽  
Women In Sport

Women continue to be underrepresented in leadership positions within sport. As the number of women entering sport increases, a growing number of professionals recognize the inherent benefits of the mentoring relationship across a range of professional settings including sport (Bower, Hums, & Keedy, 2006; Grappendorf, Burton, & Lilienthal, 2007). Unfortunately, mentors are not always a viable option for women wanting to advance within leadership positions in sport. A primary reason for limited opportunities is the shortage of female in leadership positions within sport organizations creating a dearth of potential female mentors (Weaver & Chelladurai, 2002). Therefore, this paper explored the dynamics of the mentoring relationship between one professional organization (NAGWS) and potential career outcomes for women in sport. Specifically, how does NAGWS use group mentoring initiatives for girls and women in sport which may lead to potential advancement opportunities?’

scholarly journals Characterizing Sampling and Quality Screening Biases in Infrared and Microwave Limb Sounding

2017 ◽  
Author(s):  
Luis F. Millán ◽  
Nathaniel J. Livesey ◽  
Michelle L. Santee ◽  
Thomas von Clarmann
Keyword(s):  
Data Quality ◽  
Trace Gases ◽  
Michelson Interferometer ◽  
Quality Criteria ◽  
Sampling Bias ◽  
Vertical Range ◽  
Intrinsic Quality ◽  
Sampling Biases ◽  
The Tropics

Abstract. This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected. The results show that upper tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 % to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 % to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although the vertical range of reliable measurements is slightly reduced. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality.

Phylogeographic Estimation and Simulation of Global Diffusive Dispersal

2020 ◽  
Author(s):  
Stilianos Louca
Keyword(s):  
Brownian Motion ◽  
Geographic Location ◽  
R Package ◽  
Estimation Methods ◽  
Influenza B ◽  
Independent Contrasts ◽  
Time Resolved ◽  
Location Data ◽  
Brownian Motion Model ◽  
Random Walk Simulation

Abstract The analysis of time-resolved phylogenies (timetrees) and geographic location data allows estimation of dispersal rates, for example, for invasive species and infectious diseases. Many estimation methods are based on the Brownian Motion model for diffusive dispersal on a 2D plane; however, the accuracy of these methods deteriorates substantially when dispersal occurs at global scales because spherical Brownian motion (SBM) differs from planar Brownian motion. No statistical method exists for estimating SBM diffusion coefficients from a given timetree and tip coordinates, and no method exists for simulating SBM along a given timetree. Here, I present new methods for simulating SBM along a given timetree, and for estimating SBM diffusivity from a given timetree and tip coordinates using a modification of Felsenstein’s independent contrasts and maximum likelihood. My simulation and fitting methods can accommodate arbitrary time-dependent diffusivities and scale efficiently to trees with millions of tips, thus enabling new analyses even in cases where planar BM would be a sufficient approximation. I demonstrate these methods using a timetree of marine and terrestrial Cyanobacterial genomes, as well as timetrees of two globally circulating Influenza B clades. My methods are implemented in the R package “castor.” [Independent contrasts; phylogenetic; random walk; simulation; spherical Brownian motion.]

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