Helicopter emergency medical services are important in many health care systems. Norway has a nationwide physician manned air ambulance service servicing a country with large geographical variations in population density and incident frequencies. The aim of the study was to compare optimal air ambulance base locations using both population and incident data.
We used municipality population and incident data for Norway from 2015. The 428 municipalities had a median (5-95 percentile) of 4675 (940-36,264) inhabitants and 10 (2-38) incidents. Optimal helicopter base locations were estimated using the Maximal Covering Location Problem (MCLP) optimization model, exploring the number and location of bases needed to cover various fractions of the population for time thresholds 30 and 45 min, in green field scenarios and conditioned on the existing base structure.
The existing bases covered 96.90% of the population and 91.86% of the incidents for time threshold 45 min. Correlation between municipality population and incident frequencies was -0.0027, and optimal base locations varied markedly between the two data types, particularly when lowering the target time. The optimal solution using population density data put focus on the greater Oslo area, where one third of Norwegians live, while using incident data put focus on low population high incident areas, such as northern Norway and winter sport resorts.
Using population density data as a proxy for incident frequency is not recommended, as the two data types lead to different optimal base locations. Lowering the target time increases the sensitivity to choice of data.
A factor of the association between gene geographical data and populations of the mapped region was analyzed. This allowed for correction of the equations for the major statistical parameters of gene geographical maps (mean, variance, etc.) and gene geographical methods of estimating the spatial nonstationarity of data within a mapped region. The proposed approach is based on the use of the population density in a mapped region as a factor reflecting the anisotropy of the geographical space. The population density of the North Eurasian indigenous populations was mapped, and the application of the resulting map was illustrated with an example.
The paper presents a prediction of changes in the potential areas of tertian malaria till 2025. It shows that possible climatic changes whose main features are an increase in average annual temperatures do not imply a uniform expansion of areas for parasitic infections. The regional and seasonal trends of temperature changes in Russia (both established for the 20th century and predicted for the early 21st century) will both expand the area in its one part and reduce in its another part. Overall, the changes caused by climatic factors in the potential area of human malaria in Russia in the first quarter of the 21st century will not lead to a drastically aggravated malaria epidemiological situation.
We continued to study the diversity of responses of the grass moth Loxostege sticticalis L. to variations of density. We estimated the dynamics of the internal state of individuals and considered the influence of the population prehistory on ecological characteristics of the phytophage. In experiments on studying the structure of flower forms of the larval stage, we showed the dependence of the parameters of the internal state of individuals on prehistory, specifically on the conditions of life and type of individuals of the preceding generation. At the same time, comparison of the experimental results of 1991 and 1994 revealed a drift of the parameters of the reaction to variations in the grass moth population density by the structure of the larval flower forms and actual fertility of the imago. On the whole, the data obtained suggests that the studied species is characterized by a complex system of endogenous mechanisms underlying the regulation of numbers. The dynamics of environmental parameters is mediated by a cascade of endogenous rearrangements, as a result of which transition from depression to mass reproduction is realized through succession of the types of individuals in the population, when instead of a single phase, the flock phase starts to dominate.
Conservation of grizzly bears (Ursus arctos) is often controversial and the disagreement often is focused on the estimates of density used to calculate allowable kill. Many recent estimates of grizzly bear density are now available but field-based estimates will never be available for more than a small portion of hunted populations. Current methods of predicting density in areas of management interest are subjective and untested. Objective methods have been proposed, but these statistical models are so dependent on results from individual study areas that the models do not generalize well. We built regression models to relate grizzly bear density to ultimate measures of ecosystem productivity and mortality for interior and coastal ecosystems in North America. We used 90 measures of grizzly bear density in interior ecosystems, of which 14 were currently known to be unoccupied by grizzly bears. In coastal areas, we used 17 measures of density including 2 unoccupied areas. Our best model for coastal areas included a negative relationship with tree cover and positive relationships with the proportion of salmon in the diet and topographic ruggedness, which was correlated with precipitation. Our best interior model included 3 variables that indexed terrestrial productivity, 1 describing vegetation cover, 2 indices of human use of the landscape and, an index of topographic ruggedness. We used our models to predict current population sizes across Canada and present these as alternatives to current population estimates. Our models predict fewer grizzly bears in British Columbia but more bears in Canada than in the latest status review. These predictions can be used to assess population status, set limits for total human-caused mortality, and for conservation planning, but because our predictions are static, they cannot be used to assess population trend.
Cites: Science. 2002 Mar 22;295(5563):2273-611910114
Cites: Oecologia. 2004 Feb;138(3):465-7414673639
Cites: Oecologia. 2005 Sep;145(2):276-8116001227
Cites: Ecol Appl. 2006 Dec;16(6):2333-4317205908
Cites: Ecol Appl. 2007 Jul;17(5):1424-4017708219
Cites: Ecol Appl. 2008 Jun;18(4):1014-2718536259
Cites: PLoS One. 2010;5(5):e1041620463959
Cites: Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2245-5420566501
Cites: Philos Trans R Soc Lond B Biol Sci. 2010 Jul 27;365(1550):2255-6520566502
Habitat models provide information about which habitat management should target to avoid species extinctions or range contractions. The willow ptarmigan inhabits alpine- and arctic tundra habitats in the northern hemisphere and is listed as near threatened (NT) in the Norwegian red list due to declining population size. Habitat alteration is one of several factors affecting willow ptarmigan populations, but there is a lack of studies quantifying and describing habitat selection in willow ptarmigan. We used data from an extensive line transect survey program from 2014 to 2017 to develop resource selection functions (RSF) for willow ptarmigan in Norway. The selection coefficients for the RSF were estimated using a mixed-effects logistic regression model fitted with random intercepts for each area. We predicted relative probability of selection across Norway and quantile-binned the predictions in 10 RSF bins ranging from low-(1) to high-(10) relative probability of selection.
Random cross-validation suggest that our models were highly predictive, but validation based spatial blocking revealed that the predictability was better in southern parts of Norway compared to the northernmost region. Willow ptarmigan selected for herb-rich meadows and avoided lichen rich heathlands. There was generally stronger selection for vegetation types with dense field layer and for rich bogs and avoidance of vegetation types with sparse field layer cover and for lowland forest. Further, willow ptarmigan selected for areas around the timberline and for intermediate slopes. Mapping of the RSF showed that 60% of Norway is in the lowest ranked RSF bin and only 2% in the highest ranked RSF bin.
Willow ptarmigan selected for vegetation types with dense field layer and bogs at intermediate slopes around the timberline. Selection coincides with previous habitat selection studies on willow ptarmigan. This is the first attempt to assess and quantify habitat selection for willow ptarmigan at a large scale using data from line transect distance sampling surveys. Spatial variation in predictability suggests that habitat selection in late summer might vary from north to south. The resource selection map can be a useful tool when planning harvest quotas and habitat interventions in alpine areas.