Relationship Between Environmental Temperature and Yields of Subarctic and Temperate Zone Fish Species

1983 ◽  
Vol 40 (10) ◽  
pp. 1829-1837 ◽  
Author(s):  
David A. Schlesinger ◽  
Henry A. Regier
Keyword(s):  
Optimal Growth ◽  
Temperate Zone ◽  
Data Sets ◽  
Coregonus Clupeaformis ◽  
Influence Of Temperature On ◽  
Northern United States ◽  
Using Data ◽  
Curvilinear Relationships ◽  
Fish Yields

Fishes inhabiting subarctic and temperate zone lakes exhibit distinct optimal growth temperatures and temperature preferenda. However, within regional data sets, attempts to correlate fish yields with temperature variables have generally been unsuccessful. In our study, curvilinear relationships between "long-term mean annual air temperature" (TEMP) and sustained yields of three species were fitted using data from 23 intensively fished lakes in Canada and the northern United States. Optimum TEMP values for sustained yield were approximately −1.0, 1.5, and 2 °C, respectively, for lake whitefish (Coregonus clupeaformis), northern pike (Esox lucius), and walleye (Stizostedion vitreum vitreum). These differences suggest that the influence of temperature on sustained fish yields from subarctic and temperate zone lakes may, in the past, have been underestimated.

Vegetation-Climate-Water coupling in a changing environment

2020 ◽  
Author(s):  
Zhiyong Liu
Keyword(s):  
Arid Regions ◽  
Positive Influence ◽  
Effect Of Temperature ◽  
Long Term Effects ◽  
Tight Coupling ◽  
Negative Effects ◽  
Positive Effects ◽  
Influence Of Temperature On ◽  
Using Data

<div> <p>The directionality of the response of gross primary productivity (GPP) to climate has been shown to vary across the globe. This effect has been hypothesized to be the result of the interaction between multiple bioclimatic factors, including environmental energy (i.e. temperature and radiation) and water availability. This is due to the tight coupling between water and carbon cycling in plants and the fact that temperature often drives plant water demand. Using GPP data extracted from 188 sites of FLUXNET2015 and observation-driven terrestrial biosphere models (TBMs), we disentangled the confounding effects of temperature, precipitation and carbon dioxide on GPP, and examined their long-term effects on productivity across the globe. Based on the FLUXNET2015 data, we observed a decline in the positive effect of temperature on GPP, while the positive effects of precipitation and CO<sub>2</sub> were becoming stronger during 2000–2014. Using data derived from TBMs between 1980 and 2010 we found similar effects globally. The modeled data allowed us to investigate these effects more thoroughly over space and time. In arid regions, the modeled response to precipitation increased since 1950, approximately 30 years earlier than in humid regions. We further observed the negative effects of summer temperature on GPP in arid regions, suggesting greater aridity stress on productivity under global warming. Our results imply that aridity stress, triggered by rising temperatures, has reduced the positive influence of temperature on GPP, while increased precipitation and elevated CO<sub>2</sub> may alleviate negative aridity impacts.</p> </div>

Using Data-Collection Activities to Enrich Science Courses

2010 ◽  
Vol 72 (6) ◽  
pp. 369-372 ◽  
Author(s):  
Richard Grumbine
Keyword(s):  
Data Collection ◽  
Environmental Science ◽  
Data Sets ◽  
Science Students ◽  
Introductory Biology ◽  
Science Courses ◽  
Level I ◽  
Using Data ◽  
Term Data

I describe the use of long-term data-collection projects for introductory biology or environmental science students at both the high school and the college nonmajors level. I provide specific examples of projects and information on guiding students as they learn to gather, organize, and describe data sets.

scholarly journals Non-stationarity in daily and sub-daily intense rainfall – Part 1: Sydney, Australia

2011 ◽  
Vol 11 (8) ◽  
pp. 2263-2271 ◽  
Author(s):  
D. Jakob ◽  
D. J. Karoly ◽  
A. Seed
Keyword(s):  
Statistical Significance ◽  
Daily Rainfall ◽  
Data Sets ◽  
Intense Rainfall ◽  
Rainfall Extremes ◽  
Using Data ◽  
The One ◽  
Quantile Estimates ◽  
Frequency Magnitude

Abstract. This study was driven by a need to clarify how variations in climate might affect intense rainfall and the potential for flooding. Sub-daily durations are of particular interest for urban applications. Worldwide, few such observation-based studies exist, which is mainly due to limitations in data. While there are still large discrepancies between precipitation data sets from observations and models, both show that there is a tendency for moist regions to become wetter and for dry regions to become drier. However, changes in extreme conditions may show the opposite sign to those in average conditions. Where changes in observed intense precipitation have been studied, this has typically been for daily durations or longer. The purpose of this two-part study is to examine daily and sub-daily rainfall extremes for evidence of non-stationarity. Here the problem was addressed by supplementing one long record (Part 1) by a set of shorter records for a 30-yr concurrent period (Part 2). Variations in frequency and magnitude of rainfall extremes across durations from 6 min to 72 h were assessed using data from sites in the south-east of Australia. For the analyses presented in this paper, a peaks-over-threshold approach was chosen since it allows investigating changes in frequency as well as magnitude. Non-parametric approaches were used to assess changes in frequency, magnitude, and quantile estimates as well as the statistical significance of changes for one station (Sydney Observatory Hill) for the period 1921 to 2005. Deviations from the long-term average vary with season, duration, and threshold. The effects of climate variations are most readily detected for the highest thresholds. Deviations from the long-term average tend to be larger for frequencies than for magnitudes, and changes in frequency and magnitude may have opposite signs. Investigations presented in this paper show that variations in frequency and magnitude of events at daily durations are a poor indicator of changes at sub-daily durations. Studies like the one presented here should be undertaken for other regions to allow the identification of regions with significant increase/decrease in intense rainfall, whether there are common features with regards to duration and season exhibiting most significant changes (which in turn could lead to establishing a theoretical framework), and assist in validation of projections of rainfall extremes.

Asian Americans and Pacific Islanders: Employment Issues in the United States

2011 ◽  
Vol 9 (1-2) ◽  
pp. 58-69
Author(s):  
Marlene Kim
Keyword(s):  
United States ◽  
Asian Americans ◽  
The United States ◽  
Current Data ◽  
Pacific Islanders ◽  
Native Hawaiians ◽  
Data Sets ◽  
High Poverty ◽  
Unemployment Rates

Asian Americans and Pacific Islanders (AAPIs) in the United States face problems of discrimination, the glass ceiling, and very high long-term unemployment rates. As a diverse population, although some Asian Americans are more successful than average, others, like those from Southeast Asia and Native Hawaiians and Pacific Islanders (NHPIs), work in low-paying jobs and suffer from high poverty rates, high unemployment rates, and low earnings. Collecting more detailed and additional data from employers, oversampling AAPIs in current data sets, making administrative data available to researchers, providing more resources for research on AAPIs, and enforcing nondiscrimination laws and affirmative action mandates would assist this population.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

Epidemiological Evidence that Maternal Influenza Contributes to the Aetiology of Schizophrenia

1993 ◽  
Vol 163 (4) ◽  
pp. 522-534 ◽  
Author(s):  
W. Adams ◽  
R. E. Kendell ◽  
E. H. Hare ◽  
P. Munk-Jørgensen
Keyword(s):  
Data Sets ◽  
Time Lags ◽  
Epidemiological Evidence ◽  
Intrauterine Development ◽  
Increased Risk ◽  
Schizophrenic Patients ◽  
The Relationship ◽  
Monthly Incidence ◽  
Maternal Influenza

The epidemiological evidence that the offspring of women exposed to influenza in pregnancy are at increased risk of schizophrenia is conflicting. In an attempt to clarify the issue we explored the relationship between the monthly incidence of influenza (and measles) in the general population and the distribution of birth dates of three large series of schizophrenic patients - 16 960 Scottish patients born in 1932–60; 22 021 English patients born in 1921–60; and 18 723 Danish patients born in 1911–65. Exposure to the 1957 epidemic of A2 influenza in midpregnancy was associated with an increased incidence of schizophrenia, at least in females, in all three data sets. We also confirmed the previous report of a statistically significant long-term relationship between patients' birth dates and outbreaks of influenza in the English series, with time lags of - 2 and - 3 months (the sixth and seventh months of pregnancy). Despite several other negative studies by ourselves and others we conclude that these relationships are probably both genuine and causal; and that maternal influenza during the middle third of intrauterine development, or something closely associated with it, is implicated in the aetiology of some cases of schizophrenia.

Clinical impact of myositis-specific autoantibodies on long-term prognosis of juvenile idiopathic inflammatory myopathies: multicentre study

Rheumatology ◽  
2021 ◽  
Author(s):  
Yuichi Yamasaki ◽  
Norimoto Kobayashi ◽  
Shinji Akioka ◽  
Kazuko Yamazaki ◽  
Shunichiro Takezaki ◽  
...  
Keyword(s):  
Matrix Protein ◽  
Multiple Drug ◽  
Idiopathic Inflammatory Myopathies ◽  
Inflammatory Myopathies ◽  
Gamma Subunits ◽  
Specific Autoantibody ◽  
Long Term Prognosis ◽  
Using Data ◽  
Drug Free

Abstract Objectives This study aimed to investigate the clinical characteristics, treatment and prognosis of juvenile idiopathic inflammatory myopathies (JIIM) in Japan for each myositis-specific autoantibody (MSA) profile. Methods A multicentre, retrospective study was conducted using data of patients with JIIM at nine paediatric rheumatology centres in Japan. Patients with MSA profiles, determined by immunoprecipitation using stored serum from the active stage, were included. Results MSA were detected in 85 of 96 cases eligible for the analyses. Over 90% of the patients in this study had one of the following three MSA types: anti-melanoma differentiation-associated protein 5 (MDA5) (n = 31), anti-transcriptional intermediary factor 1 alpha and/or gamma subunits (TIF1γ) (n = 25) and anti-nuclear matrix protein 2 (NXP2) (n = 25) antibodies. Gottron papules and periungual capillary abnormalities were the most common signs of every MSA group in the initial phase. The presence of interstitial lung disease (ILD) was the highest risk factor for patients with anti-MDA5 antibodies. Most patients were administered multiple drug therapies: glucocorticoids and MTX were administered to patients with anti-TIF1γ or anti-NXP2 antibodies. Half of the patients with anti-MDA5 antibodies received more than three medications including i.v. CYC, especially patients with ILD. Patients with anti-MDA5 antibodies were more likely to achieve drug-free remission (29 vs 21%) and less likely to relapse (26 vs 44%) than others. Conclusion Anti-MDA5 antibodies are the most common MSA type in Japan, and patients with this antibody are characterized by ILD at onset, multiple medications including i.v. CYC, drug-free remission, and a lower frequency of relapse. New therapeutic strategies are required for other MSA types.

Increase associated risk of gynaecological cancer due to long-term exposure to high concentration of atmospheric SO2 industrial pollutant

2021 ◽  
pp. 1420326X2110036
Author(s):  
Qian Xu ◽  
Chan Lu ◽  
Rachael Gakii Murithi ◽  
Lanqin Cao
Keyword(s):  
Time Windows ◽  
Air Pollutant ◽  
Gynaecological Cancer ◽  
High Concentration ◽  
The Past ◽  
Short And Long Term ◽  
Using Data ◽  
Air Quality Monitoring Stations ◽  
Control Study

A cohort case–control study was conducted in XiangYa Hospital, Changsha, China, which involved 305 patients and 399 healthy women, from June 2010 to December 2018, to evaluate the association between Chinese women’s short- and long-term exposure to industrial air pollutant, SO2 and gynaecological cancer (GC). We obtained personal and family information from the XiangYa Hospital electronic computer medical records. Using data obtained from the air quality monitoring stations in Changsha, we estimated each woman’s exposure to the industrial air pollutant, sulphur dioxide (SO2), for different time windows, including the past 1, 5, 10 and 15 years before diagnosis of the disease. A multiple logistic regression model was used to assess the association between GC and SO2 exposure. GC was significantly associated with long-term SO2 exposure, with adjusted odds ratio (95% confidence interval) = 1.56 (1.10–2.21) and 1.81 (1.07–3.06) for a per interquartile range increase in the past 10 and 15 years, respectively. Sensitivity analysis showed that different groups reacted in different ways to long-term SO2 exposure. We concluded that long-term exposure to high concentration of industrial pollutant, SO2 is associated with the development of GC. This finding has implications for the prevention and reduction of GC.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

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