scholarly journals Phenological segregation suggests speciation by time in the planktonic diatom Pseudo-nitzschia allochrona sp. nov.

2021 ◽  
Author(s):  
Isabella Percopo ◽  
Maria Valeria Ruggiero ◽  
Diana Sarno ◽  
Lorenzo Longobardi ◽  
Rachele Rossi ◽  
...  
Keyword(s):  
Time Series ◽  
New Species ◽  
Evolutionary Dynamics ◽  
Marine Microorganisms ◽  
Spring Temperature ◽  
Gulf Of Naples ◽  
Physical Barriers ◽  
Molecular Information ◽  
Shed Light

The emergence of new species is poorly understood in marine plankton, where the lack of physical barriers and homogeneous environmental conditions limit spatial and ecological segregation. Here we combine molecular and ecological information from a long term time series and propose Pseudo-nitzschia allochrona, a new cryptic diatom, as a possible case of speciation by temporal segregation. The new species differs in several genetic markers (18S, LSU and ITS rDNA fragments and rbcL) and is reproductively isolated from its closest relative, which is morphologically identical. Data from a long term plankton time series show P.allochrona invariably occurring in summer-autumn in the Gulf of Naples, where its sister species are instead found in winter-spring. Temperature and nutrients are the main factors associated with the occurrence of P. allochrona, which could have evolved in sympatry by switching its phenology and occupying a new ecological niche. This case of possible speciation by time shows the relevance of combining ecological time series with molecular information to shed light on the eco-evolutionary dynamics of marine microorganisms

Long-term trends of bloater (Coregonus hoyi) recruitment in Lake Michigan: evidence for the effect of sex ratio

2006 ◽  
Vol 63 (4) ◽  
pp. 832-844 ◽  
Author(s):  
David B Bunnell ◽  
Charles P Madenjian ◽  
Thomas E Croley II
Keyword(s):  
Time Series ◽  
Sex Ratio ◽  
Egg Production ◽  
Lake Michigan ◽  
Linear Models ◽  
Spring Temperature ◽  
Intrinsic Factors ◽  
Underlying Mechanisms ◽  
Coregonus Hoyi

Long-term population trends are generally explained by factors extrinsic (e.g., climate, predation) rather than intrinsic (e.g., genetics, maternal effects) to the population. We sought to understand the long-term population dynamics of an important native Lake Michigan prey fish, the bloater Coregonus hoyi. Over a 38-year time series, three 10- to 15-year phases occurred (poor, excellent, and then poor recruitment) without high interannual variability within a particular phase. We used dynamic linear models to determine whether extrinsic (winter and spring temperature, alewife predator densities) or intrinsic factors (population egg production, adult condition, adult sex ratio) explained variation in recruitment. Models that included population egg production, sex ratio, winter and spring temperature, and adult bloater condition explained the most variation. Of these variables, sex ratio, which ranged from 47% to 97% female across the time series, consistently had the greatest effect: recruitment declined with female predominance. Including biomass of adult alewife predators in the models did not explain additional variation. Overall our results indicated that bloater recruitment is linked to its sex ratio, but understanding the underlying mechanisms will require additional efforts.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Cocirculation of Two Strains of Pepino Mosaic Virus in Tomato Crops and Its Effect on Population Genetic Variability

2020 ◽  
Vol 110 (1) ◽  
pp. 49-57 ◽  
Author(s):  
C. Alcaide ◽  
M. P. Rabadán ◽  
M. Juárez ◽  
P. Gómez
Keyword(s):  
Genetic Variability ◽  
Mosaic Virus ◽  
Evolutionary Dynamics ◽  
Geographical Area ◽  
Viral Fitness ◽  
Mixed Infections ◽  
Supporting Evidence ◽  
Pepino Mosaic Virus ◽  
Nucleotide Variability

Mixed viral infections are common in plants, and the evolutionary dynamics of viral populations may differ depending on whether the infection is caused by single or multiple viral strains. However, comparative studies of single and mixed infections using viral populations in comparable agricultural and geographical locations are lacking. Here, we monitored the occurrence of pepino mosaic virus (PepMV) in tomato crops in two major tomato-producing areas in Murcia (southeastern Spain), supporting evidence showing that PepMV disease-affected plants had single infections of the Chilean 2 (CH2) strain in one area and the other area exhibited long-term (13 years) coexistence of the CH2 and European (EU) strains. We hypothesized that circulating strains of PepMV might be modulating the differentiation between them and shaping the evolutionary dynamics of PepMV populations. Our phylogenetic analysis of 106 CH2 isolates randomly selected from both areas showed a remarkable divergence between the CH2 isolates, with increased nucleotide variability in the geographical area where both strains cocirculate. Furthermore, the potential virus–virus interaction was studied further by constructing six full-length infectious CH2 clones from both areas, and assessing their viral fitness in the presence and absence of an EU-type isolate. All CH2 clones showed decreased fitness in mixed infections and although complete genome sequencing indicated a nucleotide divergence of those CH2 clones by area, the magnitude of the fitness response was irrespective of the CH2 origin. Overall, these results suggest that although agroecological cropping practices may be particularly important for explaining the evolutionary dynamics of PepMV in tomato crops, the cocirculation of both strains may have implications on the genetic variability of PepMV populations.

EXPRESS: How Does Bonus Payment Affect the Demand for Auto Loans and Their Delinquency?

2021 ◽  
pp. 002224372110092
Author(s):  
Zhenling Jiang ◽  
Dennis J. Zhang ◽  
Tat Chan
Keyword(s):  
Consumer Products ◽  
Data Set ◽  
Financial Decisions ◽  
Consumer Panel ◽  
Loan Demand ◽  
Bonus Payments ◽  
Base Salary ◽  
Long Term Impact ◽  
Shed Light

This paper studies how receiving a bonus changes the consumers’ demand for auto loans and the risk of future delinquency. Unlike traditional consumer products, auto loans have a long-term impact on consumers’ financial state because of the monthly payment obligation. Using a large consumer panel data set of credit and employment information, the authors find that receiving a bonus increases auto loan demand by 21 percent. These loans, however, are associated with higher risk, as the delinquency rate increases by 18.5 −31.4 percent depending on different measures. In contrast, an increase in consumers’ base salary will increase the demand for auto loans but not the delinquency. By comparing consumers with bonuses with those without bonuses, the authors find that bonus payments lead to both demand expansion and demand shifting on auto loans. The empirical findings help shed light on how consumers make financial decisions and have important implications for financial institutions on when demand for auto loans and the associated risk arise.

scholarly journals Getting the Most From Surveys: How Method Selection and Method Modification Impact Butterfly Survey Data

2021 ◽  
Author(s):  
Katherine C Kral-O’Brien ◽  
Adrienne K Antonsen ◽  
Torre J Hovick ◽  
Ryan F Limb ◽  
Jason P Harmon
Keyword(s):  
Species Richness ◽  
New Species ◽  
Count Data ◽  
Distance Sampling ◽  
Line Transect ◽  
Similar Species ◽  
Method Selection ◽  
M 10 ◽  
Sampling Procedures

Abstract Many methods are used to survey butterfly populations, with line transect and area surveys being prominent. Observers are typically limited to search within 5 or 10 m from the line, while observers are unrestricted in larger specified search regions in area surveys. Although methods differ slightly, the selection is often based on producing defendable data for conservation, maximizing data quality, and minimizing effort. To guide method selection, we compared butterfly surveys using 1) line versus area methods and 2) varying width transects (5 m, 10 m, or unrestricted) using count data from surveys in North Dakota from 2015 to 2018. Between line and area surveys, we detected more individuals with area surveys, even when accounting for effort. However, both methods accumulated new species at similar rates. When comparing transect methodology, we detected nearly 60% more individuals and nine more species when transect width increased from 5 m to unrestricted, despite similar effort across methodology. Overall, we found line surveys slightly less efficient at detecting individuals, but they collected similar species richness to area surveys when accounting for effort. Additionally, line surveys allow the use of unrestricted-width transects with distance sampling procedures, which were more effective at detecting species and individuals while providing a means to correct count data over the same transect length. Methods that reduce effort and accurately depict communities are especially important for conservation when long-term datasets are unavailable.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

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