scholarly journals Environmental drivers of autumn migration departure decisions in midcontinental mallards

2022 ◽  
Vol 10 (1) ◽  
Author(s):  
Florian G. Weller ◽  
William S. Beatty ◽  
Elisabeth B. Webb ◽  
Dylan C. Kesler ◽  
David G. Krementz ◽  
...  

Abstract Background The timing of autumn migration in ducks is influenced by a range of environmental conditions that may elicit individual experiences and responses from individual birds, yet most studies have investigated relationships at the population level. We used data from individual satellite-tracked mallards (Anas platyrhynchos) to model the timing and environmental drivers of autumn migration movements at a continental scale. Methods We combined two sets of location records (2004–2007 and 2010–2011) from satellite-tracked mallards during autumn migration in the Mississippi Flyway, and identified records that indicated the start of long-range (≥ 30 km) southward movements during the migration period. We modeled selection of departure date by individual mallards using a discrete choice model accounting for heterogeneity in individual preferences. We developed candidate models to predict the departure date, conditional on daily mean environmental covariates (i.e. temperature, snow and ice cover, wind conditions, precipitation, cloud cover, and pressure) at a 32 × 32 km resolution. We ranked model performance with the Bayesian Information Criterion. Results Departure was best predicted (60% accuracy) by a “winter conditions” model containing temperature, and depth and duration of snow cover. Models conditional on wind speed, precipitation, pressure variation, and cloud cover received lower support. Number of days of snow cover, recently experienced snow cover (snow days) and current snow cover had the strongest positive effect on departure likelihood, followed by number of experienced days of freezing temperature (frost days) and current low temperature. Distributions of dominant drivers and of correct vs incorrect prediction along the movement tracks indicate that these responses applied throughout the latitudinal range of migration. Among recorded departures, most were driven by snow days (65%) followed by current temperature (30%). Conclusions Our results indicate that among the tested environmental parameters, the dominant environmental driver of departure decision in autumn-migrating mallards was the onset of snow conditions, and secondarily the onset of temperatures close to, or below, the freezing point. Mallards are likely to relocate southwards quickly when faced with snowy conditions, and could use declining temperatures as a more graduated early cue for departure. Our findings provide further insights into the functional response of mallards to weather factors during the migration period that ultimately determine seasonal distributions.

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 187
Author(s):  
Olympia E. Anastasiou ◽  
Anika Hüsing ◽  
Johannes Korth ◽  
Fotis Theodoropoulos ◽  
Christian Taube ◽  
...  

Background: Seasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR. Methods: We performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors. Results: Coronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates. Conclusions: Coronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.


2021 ◽  
Vol 66 (5) ◽  
pp. 949-967
Author(s):  
Andrew L. Labaj ◽  
Adam Jeziorski ◽  
Joshua Kurek ◽  
Joseph R. Bennett ◽  
Brian F. Cumming ◽  
...  

2014 ◽  
Vol 18 (11) ◽  
pp. 4579-4600 ◽  
Author(s):  
P. Da Ronco ◽  
C. De Michele

Abstract. Snow cover maps provide information of great practical interest for hydrologic purposes: when combined with point values of snow water equivalent (SWE), they enable estimation of the regional snow resource. In this context, Earth observation satellites are an interesting tool for evaluating large scale snow distribution and extension. MODIS (MODerate resolution Imaging Spectroradiometer on board Terra and Aqua satellites) daily Snow Covered Area product has been widely tested and proved to be appropriate for hydrologic applications. However, within a daily map the presence of cloud cover can hide the ground, thus obstructing snow detection. Here, we consider MODIS binary products for daily snow mapping over the Po River basin. Ten years (2003–2012) of MOD10A1 and MYD10A1 snow maps have been analysed and processed with the support of a 500 m resolution Digital Elevation Model (DEM). We first investigate the issue of cloud obstruction, highlighting its dependence on altitude and season. Snow maps seem to suffer the influence of overcast conditions mainly in mountain and during the melting period. Thus, cloud cover highly influences those areas where snow detection is regarded with more interest. In spring, the average percentages of area lying beneath clouds are in the order of 70%, for altitudes over 1000 m a.s.l. Then, starting from previous studies, we propose a cloud removal procedure and we apply it to a wide area, characterized by high geomorphological heterogeneity such as the Po River basin. In conceiving the new procedure, our first target was to preserve the daily temporal resolution of the product. Regional snow and land lines were estimated for detecting snow cover dependence on elevation. In cases when there was not enough information on the same day within the cloud-free areas, we used temporal filters with the aim of reproducing the micro-cycles which characterize the transition altitudes, where snow does not stand continually over the entire winter. In the validation stage, the proposed procedure was compared against others, showing improvements in the performance for our case study. The accuracy is assessed by applying the procedure to clear-sky maps masked with additional cloud cover. The average value is higher than 95% considering 40 days chosen over all seasons. The procedure also has advantages in terms of input data and computational effort requirements.


1974 ◽  
Vol 28 (2) ◽  
pp. 128-134 ◽  
Author(s):  
Douglas L. Golding

To evaluate the usefulness of ERTS imagery for obtaining information on snow cover for small mountain watersheds, two specific objectives were set: (1) to determine if snowpack ablation due to chinooks can be detected on ERTS imagery, and (2) to determine if melting snow can be distinguished from snow that has not yet begun to melt. The length of ERTS return period and the frequency of cloud cover over the mountains in winter combined to make the ERTS system almost useless in studying transient phenomena of short-return period such as the chinook. Melting snow could be distinguished from snow that had not reached melting temperature. The latter appeared light toned on both visible and near-infrared imagery because of its high reflectivity in these portions of the spectrum. Melting snow, however, appeared dark on near-infrared imagery because much of the incident infrared radiation is absorbed by the thin film of water on the surface of the melting snow.


2019 ◽  
Vol 23 (5) ◽  
pp. 2401-2416 ◽  
Author(s):  
Xinghua Li ◽  
Yinghong Jing ◽  
Huanfeng Shen ◽  
Liangpei Zhang

Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.


2010 ◽  
Vol 381 (3-4) ◽  
pp. 203-212 ◽  
Author(s):  
J. Parajka ◽  
M. Pepe ◽  
A. Rampini ◽  
S. Rossi ◽  
G. Blöschl
Keyword(s):  

2008 ◽  
Vol 8 (1) ◽  
pp. 1-8 ◽  
Author(s):  
U. Strasser

Abstract. In January/February 2006, heavy snowfalls in Bavaria (Germany) lead to a series of infrastructural damage of catastrophic nature. Since on many collapsed roofs the total snow load was not exceptional, serious engineering deficiencies in roof construction and a sudden rise in the total snow load were considered to be the trigger of the events. An analysis of the then meteorological conditions reveals, that the early winter of 2005/2006 was characterised by an exceptional continuous snow cover, temperatures remained around the freezing point and no significant snowmelt was evident. The frequent freezing/thawing cycles were followed by a general compaction of the snow load. This resulted in a re-distribution and a new concentration of the snow load on specific locations on roofs. With respect to climate change, the question arises as to whether the risks relating to snow loads will increase. The future probability of a continuous snow cover occurrence with frequent freezing/thawing cycles will probably decline due to predicted higher temperatures. However, where temperatures remain low, an increase in winter precipitation will result in increased snow loads. Furthermore, the variability of extremes is predicted to increase. If heavy snowfall events are more frequent, the risk of a trigger event will likely increase. Finally, an attempt will be made here in this paper to outline a concept for an operational warning system for the Bavarian region. This system envisages to predict the development and risk of critical snow loads for a 3-day time period, utilising a combination of climate and snow modelling data and using this together with a snow pillow device (located on roofs) and the results of which.


2011 ◽  
Vol 68 (4) ◽  
pp. 904-917 ◽  
Author(s):  
Stefan Sobolowski ◽  
Gavin Gong ◽  
Mingfang Ting

Abstract Continental-scale snow cover represents a broad thermal forcing on monthly-to-intraseasonal time scales, with the potential to modify local and remote atmospheric circulation. A previous GCM study reported robust transient-eddy responses to prescribed anomalous North American (NA) snow cover. The same set of experiments also indicated a robust upper-level stationary wave response during spring, but the nature of this response was not investigated until now. Here, the authors diagnose a deep, snow-induced, tropospheric cooling over NA and hypothesize that this may represent a pathway linking snow to the stationary wave response. A nonlinear stationary wave model is shown to reproduce the GCM stationary wave response to snow more accurately than a linear model, and results confirm that diabatic cooling is the primary driver of the stationary wave response. In particular, the total nonlinear effects due to cooling, and its interactions with transient eddies and orography, are shown to be essential for faithful reproduction of the GCM response. The nonlinear model results confirm that direct effects due to transients and orography are modest. However, with interactions between forcings included, the total effects due to these terms make important contributions to the total response. Analysis of observed NA snow cover and stationary waves is qualitatively similar to the patterns generated by the GCM and linear/nonlinear stationary wave models, indicating that the snow-induced signal is not simply a modeling artifact. The diagnosis and description of a snow–stationary wave relationship adds to the understanding of stationary waves and their forcing mechanisms, and this relationship suggests that large-scale changes in the land surface state may exert considerable influence on the atmosphere over hemispheric scales and thereby contribute to climate variability.


1971 ◽  
Vol 34 (3) ◽  
pp. 140-141 ◽  
Author(s):  
B. J. Demott

Changes in composition of milks from individual cows did not always respond to differences in atmospheric conditions to the same extent as did herd milk. Milk from individual cows tended to have a lower chloride concentration because of conditions related to wet bulb temperature than did herd milk. Caution should be used in translating results from individual cow's milk to herd milk.


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