scholarly journals Population dynamics of an island population of water voles Arvicola amphibius (Linnaeus, 1758) with one major predator, the eagle owl Bubo bubo (Linnaeus, 1758), in northern Norway

Polar Biology ◽  
2021 ◽  
Author(s):  
Karl Frafjord

AbstractPredator–prey relationships are of great significance to ecosystems, and their effects on the population dynamics of voles and lemmings (Microtinae) in Boreal and Arctic environments have long been of particular interest. A simple ecosystem with one major prey and one major predator could be an ideal setting for a study of their interactions. This is the situation on several small islands on the coast of northern Norway just below the Arctic Circle, with populations of water voles Arvicola amphibius preyed upon by the eagle owl Bubo bubo. The population dynamics of the water vole was studied by trapping and tagging in 2003–2018, eagle owl pellets were collected for analyses, eagle owl breeding attempts were recorded, and some weather variables collected from official recordings. After having been introduced well into the study period, the number of sheep Ovis aries was also recorded. Water voles were the main prey of the eagle owl, with 89% occurrence in pellets, with an overrepresentation of adults and males. Both predation, sheep grazing and extreme weather events influenced the vole population. Predator exclusion, as happened in three summers due to an intensive radio tracking study, especially increased the number of surviving young (in particular from the early cohorts) and the mass of adults. Extreme weather events, such as flooding in summer and deeply frozen ground in winter, most significantly reduced vole populations. Sheep grazing may exacerbate the effects of predation. A similar multitude of factors may affect populations of other rodent species as well.

Eos ◽  
2018 ◽  
Vol 99 ◽  
Author(s):  
Kimberly Cartier

The answer involves the intricacies of stratospheric circulation, which, if better represented in climate models, could help predict extreme weather events in Siberia and elsewhere.


Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2694
Author(s):  
Külli Kangur ◽  
Kai Ginter ◽  
Andu Kangur ◽  
Peeter Kangur ◽  
Tõnu Möls

The population dynamics of fish in northern lakes is strongly influenced by climatic factors. In this study, we investigated whether there is a link between the late 1980s climate regime shift in Europe and the collapse of vendace (Coregonus albula) population at the same time in Lake Peipsi. Until the end of the 1980s, vendace was very abundant in the lake, but then its catches sharply declined. This decline inspired investigations into the extreme weather events preceding the vendace collapse using data on daily water temperatures and ice phenology together with commercial fishery statistics since 1931 and test catch data since 1986. We identified using advanced statistical methods that the hot summer of 1988, which was accompanied by a severe cyanobacterial bloom and extensive fish kill, and the subsequent non-permanent ice cover and early ice-offs in 1989 and 1990 in Lake Peipsi were the main reasons for the disappearance of vendace from catches in 1991. Moreover, a negative correlation appeared between catches of the predatory pikeperch (Sander lucioperca) and vendace. Predation pressure as well as fish habitat degradation caused by lake eutrophication may contribute to the instability of the vendace population too. Our study showed that extreme weather events such as heat waves in summer and non-permanent ice-cover in winter in consecutive years may have long-lasting harmful effects on the population abundance of cool-water fish species such as vendace whose eggs usually develop under an ice cover in north-temperate lakes.


2004 ◽  
Vol 85 (5) ◽  
pp. 697-708 ◽  
Author(s):  
Richard J. Murnane

Extreme weather events produce some of the most deadly and costly natural disasters and are a major concern of the catastrophe reinsurance industry. For example, in 1992 Hurricane Andrew caused over $20 billion (in 2002 U.S. dollars) in insured losses, the largest loss on record due to a natural disaster. In addition, 26 of the top 30 insured losses were produced by extreme weather events, mainly landfalling hurricanes and typhoons and European windstorms. A better understanding of how extreme events vary with climate would benefit the reinsurance industry and society. The Risk Prediction Initiative hosted a workshop on Weather Extremes and Atmospheric Oscillations that examined how extreme meteorological events of interest to the reinsurance industry are influenced by the quasi-biennial oscillation (QBO), the Arctic Oscillation (AO), and the Madden–Julian oscillation (MJO). Workshop participants concluded that the stratosphere is much more relevant to predictions that aid the reinsurance industry than is generally recognized and that there is mutual interest in fostering research on the relationship between the stratospheric circulation and extreme weather events. A preliminary science–business research agenda, based on presentations and discussions during and after the workshop, highlights four areas of mutual interest to scientists and insurers. The research areas focus mainly on understanding how the QBO, AO, and MJO influence the frequency and intensity of extreme events, with particular emphasis on tropical cyclones and European windstorms. An awareness of how the catastrophe reinsurance industry operates provides insights into why specific research areas were chosen. For example, the reinsurance industry operates on the basis of annual contracts, most of which are renewed on 1 January. Thus, although skillful forecasts at any lead are of interest, skillful forecasts of extreme events are of greatest value when made in the final quarter of a calendar year.


Author(s):  
Nikita Afonin ◽  
Elena Kozlovskaya

<p>In some problems of solid Earth geophysics analysis of the huge amount of continuous seismic data is necessary. One of such problems is an investigation of so-called frost quakes or cryoseisms in the Arctic caused by extreme weather events. Weather extremes such as rapid temperature decrease in combination with thin snow cover can result in cracking of water-saturated soil and rock when the water has suddenly frozen and expanded. As cryoseisms can be hazardous for industrial and civil objects located in the near-field zone, their monitoring and analysis of weather conditions during which they occur, is necessary to access hazard caused by extreme weather events. One of the important tasks in studying cryoseisms is the development of efficient data processing routine capable to separate cryoseisms from other seismic events and noise in continuous seismic data. In our study, we present an algorithm for identification of cryoseisms that is based on classical STA/LTA algorithm for seismic event detection and neural network for their classification using selected characteristics of the records.</p><p>To access characteristics of cryoseisms, we used 3-component recordings of a swarm of strong cryoseismic events with similar waveforms that were registered on 06.06.2016 by seismic station OUL in northern Finland. The strongest event from the swarm produced a fracture on the road surface and damaged basements of buildings in the municipality of Oulu. Assuming that all events in the swarm were caused by the same mechanism (freezing of water-saturated soil), we used them as a learning sample for the neural network. Analysis of these events has shown that most of them have many similarities in selected records characteristics (central frequencies, duration etc.) with the strongest event and with each other. Application of this algorithm to the continuous seismic data recorded since the end of November 2015 to the end of February 2016, showed that the number of cryoseisms per day strongly correlates with variations of air temperature.</p>


Author(s):  
T. R. Christensen ◽  
M. Lund ◽  
K. Skov ◽  
J. Abermann ◽  
E. López-Blanco ◽  
...  

Author(s):  
James E Overland ◽  
Thomas J. Ballinger ◽  
Judah Cohen ◽  
Jennifer A. Francis ◽  
Edward Hanna ◽  
...  

2020 ◽  
Vol 163 (2) ◽  
pp. 669-687
Author(s):  
Nathan P. Kettle ◽  
John E. Walsh ◽  
Lindsey Heaney ◽  
Richard L. Thoman ◽  
Kyle Redilla ◽  
...  

AbstractUnderstanding potential risks, vulnerabilities, and impacts to weather extremes and climate change are key information needs for coastal planners and managers in support of climate adaptation. Assessing historical trends and potential socio-economic impacts is especially difficult in the Arctic given limitations on availability of weather observations and historical impacts. This study utilizes a novel interdisciplinary approach that integrates archival analysis, observational data, and climate model downscaling to synthesize information on historical and projected impacts of extreme weather events in Nome, Alaska. Over 300 impacts (1990–2018) are identified based on analyses of the Nome Nugget newspaper articles and Storm Data entries. Historical impacts centered on transportation, community activities, and utilities. Analysis of observed and ERA5 reanalysis data indicates that impacts are frequently associated with high wind, extreme low temperatures, heavy snowfall events, and winter days above freezing. Downscaled output (2020–2100) from two climate models suggests that there will be changes in the frequency and timing of these extreme weather events. For example, extreme cold temperature is projected to decrease through the 2040s and then rarely occurs afterwards, and extreme wind events show little change before the 2070s. Significantly, our findings also reveal that not all weather-related extremes will change monotonically throughout the twenty-first century, such as extreme snowfall events that will increase through the 2030s before declining in the 2040s. The dynamical nature of projected changes in extreme events has implications for climate adaptation planning.


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