Quality and resolution of data features (aerial imagery) (Švajda et al., 2011).
- Recent developments in the growth of CNTs
- Methods to modify the surfaces of CNTs and decorate their surfaces for specific applications
- Applications of CNTs in biocomposites such as in orthopedic bone cement
- Application of CNTs as chemical sensors
- CNTs for fuelcells
- Health related issues when using CNTs
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The world is experiencing a period of climate change, which is very frequently discussed on both local and global levels. The growth in the global mean surface temperature by 0,74 °C ± 0,18 °C, over the last 100 years (1906-2005) is probably related to greenhouse gas emissions and further warming will cause many changes in the global climate system during the 21st century(IPCC, 2007). These changes will affect both the abiotic and biotic conditions of the environment.
High mountains ecosystems represent unique areas for the detection of climate change and the assessment of climate-related impacts (Beniston, 2003). Climate change associated with global warming at higher elevations is more pronounced than at low elevations (Beniston&Rebetez, 1996; Giorgi et al., 1997; Diaz & Bradley, 1997). The effect of elevation on surface warming is especially marked in the winter and spring seasons, since it is mostly associated with a decrease in snowpack and is thus enhanced by the snow–albedo feedback (Giorgi et al., 1997). The main ecological driving force is climate, with temperature and the duration of the snow cover as key factors (Gottfried et al., 1999). Changes in air temperature can extend the length of the average annual growing season (Menzel&Fabian, 1999) and can also cause a shift in phenology (Parmesan &Yohe, 2003; Visser& Both, 2005).
Climate change is an important driving force on natural systems (Parmesan &Yohe, 2003). Many studies show that high mountain ecosystems are vulnerable to climate change (e.g. Theurillat&Guisan, 2001; Dullinger et al., 2003a, 2003b; Dirnböck et al., 2011). Global climate change resulting in warmer climate may cause a variety of risks to mountain habitats (Beniston, 2003). Climate change mainly affects the distribution of plant and animal communities (Beckage et al., 2008) and under the expected climate scenarios in the final perspective results in the loss of rare species of alpine habitats (Dirnböck et al., 2011). In the global meta-analyses from Parmesan &Yohe (2003) and Root et al. (2003) significant range shifts toward the poles or toward higher altitudes for many organisms were documented. Large part of these changes may be attributed to increased global temperatures.
In general terms, we expected that climate related changes in mountain ecosystems will be most pronounced in the "ecoclines" (boundary ecosystem), or Ecotones (Theurillat&Guisan, 2001). Distribution of endemic mountain species is typically severely restricted as a spatial response in mountain areas, however, because of mountain topography (Huntley & Baxter, 2002) and, often, the availability of suitable soils (Theurillat et al., 1998). The upper forest limit is commonly referred to as tree line. Timberline or forest line represents one of the most obvious vegetation boundaries (ecoclines). In reality the transition from the uppermost closed montane forests to the treeless alpine vegetation is commonly not a line, but a steep gradient of increasing stand fragmentation and stuntedness, often called the tree line ecotone or the tree line park land (Körner& Paulsen, 2004).
Scenarios of upward plant species and vegetation shifts are widely discussed in many current research articles. Theurillat&Guisan (2001) released a review discussing this matter concluding that although the alpine vegetation can tolerate an increase of 1-2 °C of average air temperature, in the case of a sharper increase we can expect major changes. Loss of diversity of the alpine communities and fragmentation of plant populations caused by climate warming is expected for comparable high mountains around the world (Grabherr et al, 1995; Sætersdal et al., 1998).
Results from Dirnböck et al. (2003) support the hypothesis, that alpine plant species above the forest line will be affected by heavy fragmentation and habitat loss, but only if the average annual temperature increases by 2 °C or more. Most of these lost alpine plant species habitats are expected to be caused by the expansion of P. mugo in the Alpine zone. The coniferous forest zone has a general tendency to expand to higher elevations (Mihai et al., 2007; Sitko& Troll, 2008). Nicolussi&Patzelt (2006) describe the alpine timberline zone as very sensitive to climate variability. The rise of temperatures during the vegetation period over long periods also induces a rise of the tree line, with higher forest stand density. The tree line is considered to be primarily by temperature controlled, so increases in temperature should result in their upslope expansion (Moen, 2006). Growth and fertility of Pinusmugo is mostly controlled by temperature (Dullinger et al., 2004). Thus the main limiting factor of Pinusmugo growth at high altitude could be the soil temperature (Smith et al., 2003) although Rossi et al. (2007) refers to varying soil temperature thresholds at different sites, indicating that soil temperature may not be the main factor limiting xylogenesis of conifers and provides strong evidence that air temperature is a critical factor limiting xylem cell production and differentiation at high altitudes. However, the air temperature alone may not be the dominant factor determining tree line position, because the direct influence of temperature may be masked by interactions with other factors such as precipitation, cold-induced photoinhibition, disturbance or plant - plant interactions (Harsch, 2009). This evidence is therefore in conclusive. Differences in expert opinions on this matter have lead Smith et al. (2009) to formulate six current hypotheses of the causes of upper tree limit movement: climatic stress, mechanical disturbance, insufficient carbon balance, limitations of the cell growth and tissue formation, limited nutrient supply, and limited regeneration. In the global meta-analyses from Parmesan &Yohe (2003) and Root et al. (2003) significant range shifts toward the poles or toward higher altitudes for many organisms were documented. Large part of these changes may be attributed to increased global temperatures. The expansion of tree line forming species (sub-alpine zone) to higher altitudes is evident in the Pyrenees (Camarero& Gutiérrez, 2004; Peñuelas et al., 2007), in the Alps (Dullinger et al., 2003a, 2003b; Gehrig-Fasel et al., 2007; Vittoz et al., 2008), in the Carpathians (Martazinova et al., 2009; Mihai et al. 2007; Švajda et al., 2011), Sweden (Kullman, 2002), Caucasus (Akatov, 2009) but also in Patagonia (Daniels & Veblen, 2004) and Himalaya (Song et al., 2004; Becker et al., 2007).
Lapin et al. (2005) detected climate changes in the Slovak mountains. The results showed a significant increase in temperature and a decrease in relative humidity in the April to August season after 1990. From 1901–2005, air temperature increased (annual mean) moderately and precipitation decreased (Melo, 2007). This trend of warming is expected to continue in the Slovak mountains. By 2075 the annual average air temperature in Slovakia is expected to increase by 2-4 ° C (with greater warming expected in the winter) and more significant effects of increasing temperatures is expected at the higher altitudes (Mindáš&Škvarenina, 2003). Generally, climate conditions and land use in high mountain areas have been shown to influence the distribution of mountain pine. Since 1965 the ban on grazing in the High Tatras has not yet been raised. This study assesses a potential scenario after the grazing in Tatra Mountains will have been resumed. The potential model of timberline is based on the assumption that climate change as a factor in forest regeneration is primarily responsible for moving the upper limit of the natural forest above the original climatically determined timberline, while the abandonment of farming in the country should be the dominant factor determining forest regeneration below this line.
The Tatra Mountains are situated at the Slovak–Polish border (20°10′E, 49°10′N) and constitute the highest mountain massif within the Carpathian Range of Central Europe. The highest summit reaches 2656 m; the massif is classified as a high-mountain landscape covered by subalpine and alpine zones.
The study area (Figure 1) is situated in the western Tatra Mountains. The geology of the investigated area is based on crystalline bedrock. The western Tatra Mountains contain a significant amount of metamorphics (gneiss and mica schist), in addition to granodiorite(Nemčok et al., 1993). The vegetation of the alpine zone is dominated by alpine meadows (dry tundra with mostly Festucapicturata, Luzulaalpino-pilosa, Calamagrostisvillosa, and Juncustrifidus), with patches of dwarf pine (Pinusmugo) and an increasing percentage of rocks (bare or covered with lichens—commonly Rhizocarpon, Acarosporaoxytona, and Dermatocarponluridum) above the upper tree line of 1800 masl (Vološčuk, 1994).
The average annual air temperature decreases with elevation by 0.6°C per 100 m, being 1.6 and 23.8°C at elevations of 1778 and 2635 m, respectively (Konček&Orlicz, 1974). The amount of precipitation increases with elevation, varying from ~1.0 to ~1.6 m yr-1 between 1330 and 2635 masl but reaching >2.00 m yr-1 21 in some valleys (Chomitz&Šamaj, 1974). Precipitation is generally higher in the northern part than in the southern part of the mountains, as is runoff, which averages 1.42 and 1.57 m yr-1 for the south and north, respectively (Lajczak, 1996). Snow cover usually lasts from October to June at elevations > 2000 masl.
Western Tatra Mountains in Slovakia and detailed view of the 25 sites in the study area (from west to east: Roháče, Baníkov, Baranec, Bystrá, Jamnická, Račkova, Kamenistá, Tichá, Kôprová, and Špania valleys). (Map by JaroslavSolár)
The relationship between climate conditions of the environment (microclimate and vertical climate) and phytocenoses is expressed at different altitudinal zones in the forest. Constant climate conditions definitively influenced the natural distribution of forest species from the sub-Atlantic period (around 2000 years ago), when the current altitudinal zones were formed. Significant changes of forest stratification were caused by the intense human activity since the 13th century. Ecologically, forest altitudinal zones represent vertical classification of vegetation. Horizontal classification is determined by growth condition of forest societies, differentiated especially according to soil conditions, ecological rows, interrows, and hydric files of forest type groups. The climate-driven tree line in the Tatra Mountains is located around 1550 masl and partly includes natural ecotones with individual conifers reaching ages of 350–450 years (Büntgen et al., 2007).
P. mugo is an obligatory prostrate pine with adult canopy height varying between 0.3 and 2.5 m in the study area. The typical dwarf pine altitudinal (subalpine) zone extends from 1500 to between 1850 and 1900 masl. Mountain pine zone developed especially in the western Tatras with glacial-meadow relief, with great antierosion and water retention potential. Closed mountain-pine thickets stretch up to 300 m above the timberline, reaching approximately 1600–1750 masl in the Tatras and encompassing the upper part of the forest alpine tundra ecotone. Mountain pine plays a significant role in the natural environment: it protects the soil and stabilizes the snow cover, thus restricting the release of avalanches, and it provides habitat for many species of flora and fauna (Jodłowski, 2006).
On slovakia in the period 1881-2007 was increase of annual temperature in 1,6°C and annual precipitation decrease in 24 mm (Lapin et al., 2009). The temperature series show an upward trend in all seasons, especially in the spring (Melo et al., 2009). Over the past 20 years, it seems much warmer and especially in the months of January to August (Faško et al., 2008). Warming scenarios based on applied GCM (General Circulation Model) for Slovakia represent the increase in average annual temperature of 2-4 °C until the end of the 21st century (Melo et al., 2009).The climate in the Slovak mountain region is thus becoming warmer. Figure 2 shows a general trend that could partially explain the dynamics of the vegetation zones.
Winter precipitation in the high-mountain positions of Slovakia is abundant and increases with altitude. (Ostrožlík, 2008, 2010). Sensitivity of snow cover will vary depending on the climate and altitude. Also sensitivity causes maritime climates and less continental climate of cold and dry winters, where precipitations play an important role in the variability of snow cover duration. (Brown & Mote, 2009). The Tatra Mountain have significanly more snow cover days on the northern slopes. More over the less windy and forested areas have higher and longer snow cover as well. (Lapin et al., 2007). Snow cover duration on the northern slopes is critical in altitude of about 1800 m and on the southern slopes of about 2300 m. In this altitudinal level in Tatra mountain should be zone, above which would not even occur to the loss of snow cover duration. (Vojtek et al., 2003). It seems that variability and trends in snow cover characteristics are influenced both by air temperature and precipitation variability. This influence depends significantly on the altitude and local topography conditions. Increase of air temperature by about 1,2 °C and change of precipitation totals from -10% to -20% in the November-April season are the main reasons of obtained trends (Lapin et al., 2007).
Trends in annual average temperature (in degrees Celsius) and annual total precipitation (in millimeters) from 1965 to 2002 at the meteorological station of Skalnate´ pleso (1751 masl), Slovak Institute of Hydrometeorology (Švajda et al., 2011).
Changes in landscape can be well observed through remote sensing (RS). RS data (images, aerial photographs, etc.) are further processed and analyzed using Geographic Information Systems (GIS). Progressive development of geo-information technologies offer new approaches to the use of remote sensing in GIS. GIS is very useful for its ability to incorporate the complexity of spatial data in to the various models. Remote-sensing based analysis is particularly useful in mountainous areas where the topography is complex and different environmental gradients require special attention to the spatial patterns (Heywood et al., 1994). Although high mountain environments show a high degree of heterogeneity, we can obtain satisfactory results using the appropriate approaches and high-quality materials. Changes in the natural spatial (morphological, bioenergetical) features can be identified in remote sensing images (Feranec et al., 1997). Using GIS applied approach let us identify the most significant variables and phenomena which affect the natural environment components.
Approach of remote sensing and suitability of this method in detecting of lanscape changes in mountain regions of Slovakia was confirmed in the works: Boltižiar, (2001, 2002, 2003, 2004, 2006, 2007); Čerňanský&Kožuch, (2001); Hreško&Boltižiar, (2001); Kohút, (2006); Olah et al., (2006); Falťan&Saksa, (2007); Olah&Boltižiar, (2009). However, this approach can be difficult in countries with politically sensitive situation, where the products of remote sensing are subject to various degrees of secrecy (Heywood et al., 1994). We have come across some other problems arise in relation to data quality and its precision of position placement, which takes into account the high diversity of the relief. Advantage of access interpolation of aerial imagery lies in the fact, that we can carry out their research in a relatively short time. Especially aerial photographs provides a large amount of quantitative and qualitative information about the landscape structure and are particularly important in high mountain areas, where field research is difficult (Boltižiar, 2009).
This study is based on results published in the original study by Švajda, Solár, Janiga& Buliak „Dwarf Pine (Pinusmugo) and Selected Abiotic Habitat Conditions in the Western Tatra Mountains“ in journal Mountain Research and Development 31/3 in year 2011. The present analysis was carried out using GIS (ArcGIS 9.3), based on aerial photographs from 1965, 1986 and 2003. The aim of the analysis was to verify the temporal trends in the distribution of Pinusmugo and to investigate which environmental variables best explain the changes in the growth and distribution of the mountain pine. The applied modelling approach is based on three major assumptions: (1) The abiotic factors are assumed to be the major driving force of species distribution changes, as well as the post-grazing succession. (2) The models are calibrated using field data, and thus comprise any competitive constraint a species may force upon or experience from its neighbour. (3) The speed of plant migration is consistent with that of climate change so that plant communities are in a permanent equilibrium with their environment (Dirnböck et al., 2003).
Aerial images from 2002 were acquired and georeferenced by Eurosense Ltd. and Geodis Slovakia on the basis of contour lines at a scale 1:10,000 (digital elevation model); we georeferenced aerial photographs from 1986 and 1965 on the basis of orthophotos (Table 1).
Year | Source | Type | Resolution | Width | Height | Format |
2002 | Eurosense Slovakia | Orthophoto | RBG 72 DPI | 2500 | 2000 | .jpg |
1986 | Topographical Institute | Aerial photo | Gray 2400 DPI | 21.829 | 21.924 | .tiff |
1965 | Knazovicky | Aerial photo | Gray 300 DPI | 2164 | 2175 | .jpg |
DPI. dots per inch; RBG. red green blue. |
Quality and resolution of data features (aerial imagery) (Švajda et al., 2011).
Mountain pine fields were extracted from the aerial photos in gray scale and than reclassified into gray scale range representing mountain pine occurrence in the study area. Each photo was examined individually. If mountain pine on the slide was gray, with a value from 75 to 110, all such values in the range were reclassified as 1. The remaining values from 0 to 75 and 110 to 256 were reclassified as 0. We created a grid where each pixel contained either the value 1 or the value 0. Then the grid was automatically vectorized on the basis of the 2 values.
Habitat conditions were spatially simulated using GIS, digital terrain model, meteorological data and existing maps. In addition we analyzed historical records in order to derive information about past land-use changes. The most significant factors explaining the presence of Pinusmugo according to Dirnböck et al., (2003) are the daily temperature, followed by slope, geology, solar radiation in September and duration of snow cover.
To test this hypothesis it was necessary to create an explicit temporal and spatial explicit model of the spread of mountain pine and analyze their sensitivity to predicted climate change trends. Histogram transformation was not carried out due to the misrepresentation of values. The size of the pixels’ (cell) grid was equivalentin all RS images, because each image was adjusted to the same size cell size through the transformation of the grid, as well as during georeferencing. Thus all images and the grids had the same pixels (Figure 3).
Selection of the appropriate areas, which represented 25 localities from the study area, and analysis of imagery were realized in ArcGIS 9.2. Differences in the P. mugo surface cover between the 2 periods were calculated using Statistica 8.
The increments in dwarf pine were reported as means and standard deviations for potential comparison with other studies, but the values showed a highly skewed distribution in most sample groups. Therefore, a nonparametric approach to the analysis of the data was necessary. The significance of difference between groups was tested using the Kruskal–Wallis nonparametric test. When P, 0.05, the data were considered as significantly different. A digital elevation model of the study area was used for the representation of a selected abiotic habitat conditions. A single matrix was analyzed. GIS intersection of study sites with 3 parameters (slope, aspect, and height masl) has divided the studied sites into 325 smaller areas with unique characteristics related to pine increase. Two sites (nos. 5 and 9; Table 2) were excluded due to lack of data from 1986.
The principal component analysis (PCA)–correlation matrix, a multivariate technique was used to extract the potential relationships between the studied variables. Principal components are linear combinations of original variables (slope, height masl, and relative increase of pine during observed periods), each axis being statistically orthogonal to the others. Integration of the variables slope and elevation m asl in different periods enabled us to follow different processes of mountain colonization by mountain pine during the respective periods. Since this statisticale technique produces statistically orthogonal axes, we were able to examine potentially independent biological phenomena. We used 4 variables; consequently, we evaluated 4 principal components. The proportions of the total variance accounted for by each component are shown in Table 3 (see results).
Example of the comparison between aerial photographs: changes between 1965, 1986, and 2002 in 1 analyzed valley (Site 17, Račková valley; Figure 1).
Site number | Average altitude (masl) | Average slope (%) | Covered with Plnus(%) | Difference (+/-) |
1 | 1674 | 34 | 27 | +1/+20 |
2 | 1670 | 46 | 7 | +9/+11 |
3 | 1614 | 44 | 29 | +9/+23 |
4 | 1675 | 36 | 24 | +31/+41 |
5 (excluded) | 1686 | 42 | — | — |
6 | 1614 | 33 | 35 | +8/+-10 |
7 | 1733 | 28 | 28 | +4/+9 |
8 | 1604 | 40 | 53 | +11/+13 |
9 (excluded) | 1807 | 34 | — | — |
10 | 1825 | 22 | 16 | +6/+16 |
11 | 1511 | 47 | 61 | +14/+14 |
12 | 1533 | 43 | 68 | +12A16 |
13 | 1493 | 33 | 47 | +8/+10 |
14 | 1684 | 40 | 19 | +4/+12 |
15 | 1617 | 46 | 21 | +11/+13 |
16 | 1561 | 43 | 62 | +3/+10 |
17 | 1640 | 45 | 48 | +18/+28 |
18 | 1777 | 35 | 34 | +6/+11 |
19 | 1595 | 55 | 45 | +10/+12 |
20 | 1627 | 30 | 42 | +6/+23 |
21 | 1449 | 33 | 49 | +21/+20 |
22 | 1632 | 45 | 42 | +11/+31 |
23 | 1599 | 36 | 68 | +11/+20 |
24 | 1428 | 31 | 59 | +20/+16 |
25 | 1533 | 38 | 65 | -2/+2 |
Overview of evaluated sites with different P. mugo cover in the period.
Mountain pine cover in the western Tatra Mountains in the period 1965–2002 permanently increased at all observed sites. The total surface area covered by mountain pine increased from 8,173,812 m2 in 1965 to 10,141,505 m2 in 1986 and 11,394,461 m2 in 2002. The percentage of total surface area covered thus increased from 41.8% in 1965 to 51.8% in 1986 and 58.2% in 2002. Only in one case (No. 25) surface area covered by dwarf pine decreased. In two cases (No 21 and 24) the area decreased in the first, and increased in the second period (Table 2, Figure 4). This was probably due to the influence by human activities or avalanches.
The results also indicate that the mean increase of mountain pine surface cover was in all periods about 0.4 percent per year (0.42% first period, 0.40% second period) from the total surface area but results in relation to selected abiotic conditions still showed some differences.
Comparison of area covered with P. mugo (23 sites) in 1965, 1986, and 2002, in square meters (Švajda et al., 2011).
From 1965 to 1986, mountain pine showed a rapid expansion in surface cover at the lower elevations (Table 3 – PC1, Figure 5A). This could be observed as thickening of mountain pine cover at lower elevations, indicating that the mountain pine is able to recolonize sites of previous occurrence.
Variable | PCI | PC2 | PC3 | PC4 |
Slope | 0.51 | -0.64 | -0.50 | 0.26 |
Elevation | -0.71 | 0.11 | -0.63 | -0.27 |
Pine increment (1965-1986) | 0.73 | 0.14 | -0.10 | -0.64 |
Pine increment (1986-2002) | -0.38 | -0.78 | 0.30 | -0.37 |
Variability (%) | 36.5 | 26.8 | 18.9 | 17.8 |
PC. principal component. |
Component vectors (loadings) and percent variance associated with the components indicating the pattern of natural reforestation with dwarf pine in the Tatra Mountains (n 5 325; snaps from aerial photographs) (Švajda et al., 2011).
In the period from 1986 to 2002, pine grew rapidly on steeper slopes (Table 3 – PC2), mainly at elevations from 1500 to 1700 masl (Figure 5B). During the first analyzed period, mountain pine was able to concentrate to such a level at elevations between 1300–1400 m a.s.l. that in the following period it completely covered this zone and further increments were minimal.
The third factor (Table 3 - PC3) is less important for the explanation of the historical pine increments: it shows a positive relation between slope and elevation. PC4 (Table 3) is a unipolar vector indicating that mountain pine grew more rapidly in the earlier period (1965–1986) than later (1986–2002).
In the earlier period mountain pine grew intensely at all locations (Figure 5C) whereas in the period 1986–2002 it mainly preferred northwest and northeast aspects on steeper slopes, probably the most suitable locations for plant development in the Tatra Mountains. These sites might have more favorable conditions for the growth of the mountain pine due to changes in climate in terms of higher surface temperature of environment.
Increments in dwarf pine cover in the western Tatra Mountains in the periods 1965–1986 and 1986–2002, according to (A and B) elevation and (C and D) aspect. In the earlier period, the groups did not differ according to aspect. (C) Kruskal–Wallis nonparametric test at p = 0.05. (D) In the period 1986–2002, the following aspects differed significantly: N:NE, N:SW, NE:W, NE:S, NE:SE, SE:SW, S:SW, and SW:W (Švajda et al., 2011).
In both periods, the increments in the areas covered by mountain pine were very low at the elevation of 1900 m, reflecting its natural upper line of occurrence. In the earlier monitored period the increments at 1900 m were 0%, whereas between 1986 and 2002 they were approximately 3% (Figures 5A, 4B). The trend is probably associated with climate warming in the region. Changed enviromental conditions caused by climate change promote the expansion of mountain pine and favour it in competition against alpine meadow communities.
The interaction of individual components of the environment is an ongoing process. The result of this interaction as seen in our results, show an apparent shift of mountain pine to higher altitudes. The expansion of mountain pine was confirmed by remote sensing. The precision of our results was limited by the fact that we performed a very fast automatic extraction of mountain pine fields. However, this analysis was repeated several times in order to avoid any errors during the extraction of mountain pine fields. We also recorded other factors which might affect the results of the distribution and growth of dwarf pine. This mainly relates to landslides, avlanches, snow cover and shadows from clouds or hills on the air photographs. Generally, a place where we have identified these problems, we excluded from the assessment in all of times periods. Due to aim of this study was not to highlight the processes that operate in the opposite direction to the expansion of mountain pine, so we did not dealt with this problem more. We had some problems in the lower parts of fields where the scrub of mountain pine interleaved with spruce forest. Analysis of this border and its response to climate change would be also interesting. We can assume, that spruce forest has pushed the lower limit of mountain pine to the higher altitudes (Mihai et al., 2007). But this process is slower than the expansion of mountain pine due to problems with the successful survival of spruce seedlings and seed production (Dullinger et al., 2005). Evidence of spruce forest move to higher altitudes was shown by Mihai et al. (2007) in the Southern Carpathians of Romania. In comparison to spruce stands the mountain pine cover in our study represented comparatively homogeneous areas easy to extract in our aerial images.
Similarly to other high mountains also the Carpathians show a trend of climate change and possibly the shift of vegetation types with altitude. Expected changes in tree line boundaries are evident in Carpathians but also other mountain ranges around the world, which could present a threat to the habitats of many rare species in the future. Over the last 50 years, summer temperatures in the Tatra Mountains summer temperatures have increased by 0.7 ° C at higher elevations, and 1.4 ° C at lower elevations. Winter temperatures have increased by 1.4 ° C at higher elevations, and 1.9 ° C at lower elevations (Melo, 2005). The temperature limit of the mountain pine zone is determined by the bio-temperature threshold in the range of 3.0 to 2.0 ° C (°C Max - Min ° C) (Miňdáš&Škvarenina, 2003). Considering the rate of current temperature changes (2-3 °C for 100 years) we can expect more turmoil changes to the growth within a single generation of woody plants. According to Miňdáš et al. (1996) a model scenario expects a complete extinction of conditions for alpine communities and their replacement by bioclimatic conditions for sub-alpine forest. The occurrence of mountain pine is subject to extreme habitat conditions, including soil. Mountain pine is a strongly heliophilic shrub. The most important factor of habitat which has a decisive role in the expansion of mountain pine is the light intensity. The spreading of mountain pine is conditioned mainly by altitude, slope, moisture conditions, but also the horizontal and vertical slope curvature. This can be seen in the fact that the mountain pine is spreading up along the ridges.
Minďáš et al. (2004) predict the following changes in an area of mountain pine zone timberline: (1) an increase in the abundance of tree species, (2) dominant representation of spruce, (3) a decrease of dwarf pine, and (4) an increase of general production and biomass of about 200–300%. These changes could also be contributed by the changes in phenological phases, which reflect the changing climate condtions. The onset of individual phenological stages and their proceeding is mainly influenced by air temperature, as well as temperature and humidity of soil and other meteorological variables (Škvareninová, 2009). Development of climate can to some extent affect phenological trends (Bauer 2006; Škvareninová, 2008) and identyfying these relationships can help us use trees as bio-climatic indicators of climate change (Škvareninová, 2009).At high altitudes the vegetation is under constant environmental stress and thus abiotic conditions become more important for the community development than biotic relationships (Pauli et al., 1996).
The main results of our case study confirm the results of previous research on mountain vegetation zones in the Slovak Tatras. Boltižiar (2007) analyzed spatiotemporal landscape structure change in the alpine environment of the Tatra Mountains. The landscape structure in 1949 in the study area was dominated by grassland, which resulted mainly from human activity. Statistical analysis of thematic maps from 2003 suggests extension of mountain pine cover, advance of forest, and reduction of grassland areas. Martazinova et al. (2009) conducted research on grasslands above the upper forest limit in the Ukrainian Carpathians. Grass cover significantly decreased in the sites whit conifer presence. Spruce stands mainly on the northern slopes moved to higher altitudes, while the beech stands in the same area on the southern slopes did not show any significant movement. Apparently the greatest changes were recorded at those sites where upper forest limit was marked at higher elevations. In their study of alpine, subalpine, and forest landscapes in the Iezer Mountains (southern Carpathians), Mihai et al. (2007) described how mountain pine–subalpine associations developed and gradually covered subalpine meadows and barren land (between 1986 and 2002, colonization averaged 0.14 km2/y). This might be important in the context of the surface of the subalpine and alpine zones in the mountains. However, mountain pine area has lost some lower stands because of spruce forests, which increased in elevation. This is largely a feature of southern aspect slopes (sunny), where the natural timberline is under some local conditions higher. It is also related to shorter duration of snow cover on the southern slopes (Lapin et al., 2007). Peneuelas et al., (2007) also observed a shift and change in the distribution of species on the tree line in the Montseny Mountains (Span). As observed from historic photographs for the last 60 years, beech stands significantly increased in abundance, which is reflected in the shift of this species to higher altitudes by about 30-50 meters.
However, there are interesting comparisons with studies from other European mountains. The results of the study conducted by Dirnböck et al. (2003) support earlier hypotheses that alpine plant species on mountain ranges with restricted habitat availability above the tree line will experience severe fragmentation and habitat loss, but only if the mean annual temperature increases by 2 °C or more. Even in temperate alpine regions, it is important to consider precipitation, in addition to temperature, when climate impacts are to be assessed. Another example from the Alps in Austria (Dullinger et al., 2004), after running a model for 1000 years, predicted that the area covered by pines will increase from 10% to between 24% and 59% of the studied landscape. The shape of the dispersal curve and spatial patterns of competitively controlled recruitment suppression affect range size dynamics at least as severely as does variation in assumed future mean annual temperature (between 0 and 2°C above the current mean). Moreover, invasibility and shape of the dispersal curve interacted with each other due to the spatial patterns of vegetation cover in the region. Dullinger et al. (2003a) indicated that a shift of tree and shrub species caused by landuse and expected climate change can be expected in the European Alps. Abandonment of pasture will allow invasive expansion of Pinusmugo scrubs to new areas. In the peripheral areas this process will be dependent on the competitive struggle for light with abandoned grasslands after the grazing has ceased. Gehrig-Fasel et al. (2007) compared upward shifts to the potential regional tree line by calculating the difference in elevation of the respective pixels. The altitude of the potential regional tree line was considered as a reference. Upward shifts above the potential regional tree line were considered to be influenced primarily by climate change, while upward shifts below the potential regional tree line were interpreted as primarily influenced by land abandonment. Generally, dwarf pine forest lost a total surface area under pressure from lower vegetation communities and even secondary pastures (Mihai et al., 2007).
In addition to climate change, human land use may drive changes in tree line. Land use in subalpine and alpine areas (grazing and extraction) affects the distribution of flora just as much as climate. Since the 13th–14th century, anthropogenic land cover change has involved clearing mountain-pine thickets to obtain new pastures for sheep and cattle grazing, for extensive charcoal and oil production, and for copper and iron-ore mining, sometimes leading to degradation. Jodłowski (2007) described how establishing national parks in the Tatras—Babia Góra and Giant Mountains enabled secondary succession, which has led to colonization of previously abandoned habitats. However, these processes have been hampered by harsh edaphic and climatic conditions as well as by avalanches and debris flows. Extensive planting of mountain pine in former Czechoslovakia significantly facilitated the regeneration of mountain pine thickets. After the absolute restriction of grazing in some national parks, we observed progressive long-term trends in secondary succession and patterns of plant establishment driven by climate.
Closed mountain pine thickets stretch up to 300 m above timberline, reaching approximately 1600–1750 masl in the Tatras and encompassing the upper part of the forest-alpine tundra ecotone. Habitats in the peripheral or isolated mountain belts at or above the tree line are generally rich in diversity of endemic species. In these habitats, tree line expansion disproportionally reduces habitats of high-altitude species. Such legacies of climate history, which may aggravate extinction risks under future climate change, have to be expected for many temperate mountain ranges (Dirnböck et al., 2011). Minimizing greenhouse gas emissions effectively in order to reduce climate warming, and thus the expansion of tree line species to higher altitudes.Furthermore, slowing down forest expansion by land use. The maintenance of large summer farms may contribute to preventing the expected loss of nonforest habitats for alpine plant species and might provide additional refuges for those endemic species which can survive in managed habitats (Dirnböck et al., 2003).
Our study shows an apparent shift and densification of Pinusmugo scrubs at higher altitudes. This shift was shown to correlate with climate change. Longer growing seasons, milder winters, shorter duration of snow cover create favourable conditions for the growth of mountain pine. This shift has not only had a devastating effect on alpine plant communities due to habitat loss, but also due to greater fragmentation, which ultimately will strongly affect the population of different animal species dependent on these habitats.
More research on vegetation dynamics in Slovakia’s mountain areas is needed in light of the significance of vegetation in the context of global change. The results of our study can be used not only as a baseline for future research to test possible climate change influences (resulting upward shifts compared to a potential surface size and trends in approach of dwarf pine extension) but also to compare trends in other mountainous areas. Further understanding of dispersal, persistence, and survival strategies of mountain pine in the western Carpathians is also required. We will continue to monitor dispersal of P. mugo in Slovakia and extend our studies to the central Tatras. This workwill help to describe and evaluate the total tree surface area as a basis for the State Nature Conservancy’s management of mountain national parks and protected areas in Slovakia.
This research was partly supported by the European Economic Area and Norwegian financial mechanism grant SK-00061. We thank Dr L. Kňazovický for aerial photos from 1965. Let us also thank to authors Dr. J. Švajda, Prof. M. Janiga and M. Buliak who created the previous study entitled "Dwarf Pine (Pinusmugo) and Selected Habitat Abiotic Conditions in the Western Tatra Mountains" published in journal Mountain Research and Development 31/3 in the year 2011.
UTI affects approximately 150 million people worldwide, which is most common infection with female predominance [1]. Around 15–25% hospitalized patients receiving indwelling urinary catheter develops CAUTI with prolonged catheterization and in among 40% nosocomial UTI, 80% is due to CAUTI [2]. CAUTI causes about 20% of episodes of health-care acquired bacteraemia in intensive care facilities and over 50% in long term care facilities [3]. The microbiology of biofilm on an indwelling catheter is dynamic with continuing turnover of organisms in the biofilm. Patients continue to acquire new organisms at a rate of about 3–7%/day. In long term catheterization that is by the end of 30 days CAUTI develops in 100% patients usually with 2 or more symptoms or clinical sign of haematuria, fever, suprapubic or loin pain, visible biofilm in character or catheter tube and acute confusion all state [4]. In CAUTI the incidence of infection is Escherichia coli in 24%, Candida in 24%, Enterococcus in 14% Pseudomonas in 10%, Klebsiella in 10% and remaining part with other organisms [5]. Bacteraemia occurs in 2–4% of CAUTI patients where case fatality is three times higher than nonbacteremic patients [6]. Adhesions in bacteria initiate attachment by recognizing host cell receptors on surfaces of host cell or catheter. Adhesins initiate adherence by overcoming the electrostatic repulsion observed between bacterial cell membranes and surfaces to allow intimate interactions to occur [7]. A biofilm is an aggregate of micro-organisms in which cells adhere to each other on a surface embedded within a self-produced matrix of extracellular polymeric substance [8]. In biofilm micro-organisms growing in colonies within an extra-cellular mucopolysaccharide substance which they produce. Tamm-Horsfall protein and magnesium and calcium ions are incorporated into this material. Immediately after catheter insertion, biofilm starts to form and organisms adhere to a conditioning film of host proteins along the catheter surface. Both the inner and outer surfaces of catheter are involved. In CAUTI biofilms are initially formed by one organism but in prolonged Catheterization multiple bacteria’s are present. In biofilm main mass is formed by extra cellular polymeric substance (EPS) within which organisms live. So there are three layers in biofilm, where deeper layer is abiotic, than environmental zone and on surface biotic zone [9]. Growth of bacteria in biofilms on the inner surface of catheters promotes encrustation and may protect bacteria from antimicrobial agents and the consequence is more drug resistance of biofilm organisms. When antibiotic treatment ends the biofilm can again shed bacteria, resulting recurrent acute infection. The patients may present as asymptomatic bacteriuria or symptomatic. In symptomatic bacteriuria patient present with fever, suprapubic or costovertebral angle tenderness, and systemic symptoms such as altered mentation, hypotension, or evidence of a systemic inflammatory response syndrome. In asymptomatic CAUTI diagnosis is made with presence of 105 cfu/mL of one bacterial species in a single catheter urine specimen [10]. In symptomatic CAUTI bacteriological criteria is present with clinical symptoms.
It is recommended that urine specimens be obtained through the catheter port using aseptic technique or, if a port is not present, puncturing the catheter tubing with a needle and syringe in patients with short term catheterization [11]. In long term indwelling catheterization, the ideal method of obtaining urine for culture is to replace the catheter and collect the specimen from the freshly placed catheter. In a symptomatic patient, this should be done immediately prior to initiating antimicrobial therapy. Culture specimens from the urine beg should not be obtained [10, 12]. Urine sample can be collected from suprapubic puncture also. Biofilm can be cultured from the catheter, for this swab is taken from inner side of catheter.
Catheter Associated Asymptomatic Bacteriuria (CA-ASB) is diagnosed when one or more organisms are present at quantitative counts ≥105 cfu/mL from an appropriately collected urine specimen in a patient with no symptoms [13]. Lower quantitative counts may be isolated from urine specimens prior to ≥105 cfu/mL being present, but these lower counts likely reflect the presence of organisms in biofilm forming along the catheter, rather than bladder bacteriuria [14]. Thus, it is recommended that the catheter be removed and a new catheter inserted, with specimen collection from the freshly placed catheter, before antimicrobial therapy is initiated for symptomatic infection [13]. In biofilm culture, most biofilm contains mixed bacterial communities meaning polymicrobial colonization.
Patients who remain catheterized without having antimicrobial therapy and who have colony counts ≥10 2 cfu/mL (or even lower colony counts), the level of bacteriuria or candiduria uniformly increases to >105 cfu/mL within 24–48 h [14]. Given that colony counts in bladder urine as low as 102 cfu/mL are associated with symptomatic UTI in non-catheterized patients [15], untreated catheterized patients and those who have colony counts ≥102 cfu/mL or even lower, the level of bacteriuria or candiduria uniformly increases to >105 cfu/mL within 24–48 h [10, 16]. Colony counts as low as 102 cfu/mL in bladder urine may be associated with symptomatic UTI in non-catheterized patients. Whereas low colony counts in catheter urine specimens are likely to be contaminated by periurethral flora, and the colony counts will increase rapidly if untreated. Low colony counts in catheter urine specimens are also reflective of significant bacteriuria in patients with intermittent catheterization [14].
Pyuria is usually present in CA-UTI, as well as in CA-ASB. The sensitivity of pyuria for detecting infections due to enterococci or yeasts appears to be lower than that for gram-negative bacilli. Dipstick testing for nitrites and leukocyte esterase was also shown to be unhelpful in establishing a diagnosis in catheterized patients hospitalized in the ICU [17].
It is the most common cause of CAUTI in 24–60% patients [5, 18]. In CAUTI the source of this organism is usually patients own colonic flora. E. coli is large and diverse group of bacteria found in environment, foods and intestine of human and animal. Among many species of E. coli only a few causes disease in human being. It is beneficial in that it prevents the growth and proliferation of other harmful species of bacteria. Even it plays an important role in current biological engineering.
E. coli was discovered in 1885 by Theodor Escherich, German bacteriologist, is gram negative rod, lactose fermenter, composed of one circular chromosome which is common facultative anaerobes in colon and farces of human. Distribution is diverse and most of them are harmless belonging to genus Escherichia. Harmful species causes infection of urinary tract, gastrointestinal tract, respiratory system and rarely bacteraemia and septicemia. Phylogenetic analysis of E. coli showed majority of the strains responsible for UTI belongs to the phylogenetic group B2 and D, while in smaller percentage belong to A and B1 [19].
It has three antigens O-cell was antigen, H- flagella antigen and k- Capsular antigen. It has pili—a capsule, fimbriae, endotoxins and exotoxins also. Uropathogenic E. coli use P fimbriae (pyelonephritis-associated pili) to bind urinary tract endothelial cells. Vast majority of catheter-colonizing cells (up to 88%) express type 1 fimbriae and around 73% in E. coli causing CAUTI [20]. In UPEC fimbrial genes are ygiL, yadN, yfcV, and c2395 [21]. Pathogenesis of CAUTI initiated with UPEC colonization in periurethral and vaginal areas. Then it ascends to bladder lumen and grows as planktonic cells in urine. Sequentially adherence to bladder epithelium, then biofilm formation and invasion with replication and kidney colonization and finally bacteremia [22] (Figure 1).
Gram stain picture and morphology of E. coli. Adapted from CCBC faculty web. BIOL 230 Lab Manual: gram stain of E. coli and infection landscapes: Escherichia coli. http://faculty.ccbcmd.edu/courses/bio141/labmanua/lab16/gramstain/gnrod.html.
Diagnosis of E. coli infection is simple, by isolation and laboratory identification of bacterium from urine or biofilm. Laboratory diagnosis by culture of specimen—urine or catheter biofilm in blood agar, MacConkey’s agar or eosin-methylene blue agar (which reveal lactose fermentation). Immunomagnetic separation and specific ELISA, latex agglutination tests, colony immunoblot assays, and other immunological-based detection methods are other ways for diagnosis of E. coli.
Proteus species, member of the Enterobacteriaceae family of gram-negative bacilli are distinguishable from most other genera by their ability to swarm across an agar surface [23, 24]. Proteus species are most widely distributed in environment and as other enterobacteriaceae, this bacteria is part of intestinal flora of human being [25, 26]. Proteus also found in multiple environmental habitats, including long-term care facilities and hospitals. In hospital setting, it is not unusual for proteus species to colonize both the skin and mucosa of hospitalized patient and causing opportunistic nosocomial infections. It is one of the common causes of UTI in hospitalized patients undergoing urinary catheterization [26, 27].
UTIs are the most common manifestation of Proteus infection. Proteus infection accounts for 1–2% of UTIs in healthy women and 5% of hospital acquired UTIs. Catheters associated UTI have a prevalence of 20–45%. Proteus mirabilis causes 90% of proteus infection and proteus vulgaris and proteus penneri also isolated from long-term care facilities and hospital and from patients with underlying disease or specialized care. Most common age group is 20–50 years. More common in female group and the ratio between male female begins to decline after 50 years. UTI in men younger than 50 are usually caused by urologic abnormalities. Patients with recurrent infections, those with structural abnormalities of the urinary tract, those who have had urethral instrumentation or catheterization have an increase frequency of infection caused by proteus species [28].
Proteus mirabilis produces an acidic capsular polysaccharide which was shown from glycose analysis, carboxyl reduction, methylation, periodate oxidation and the application high resolution nuclear magnetic resonance techniques. Proteus species possess an extracytoplasmic outer membrane, a common feature shared with other gram-negative bacteria. Infection depends upon the interacting organism and the host defense mechanism. Various component of the membrane interplay with the host to determine virulence. Virulence factors associated with adhesion, motility, biofilm formation, immunoavoidance, nutrient acquisition and as well as factors that cause damage to the host [29, 30] (Figure 2).
Gram stain picture and morphology of Proteus. Adapted from CCBC faculty web. BIOL 230 Lab Manual: gram stain of Proteus mirabilis and Proteus vulgaris bacteria (SEM) | Macro & Micro: Up Close and Personal | Pinterest | Microbiology, Bacteria shapes and Fungi. https://www.pinterest.com › pin.
Certain virulence factors such as adhesin, motility and biofilm formation have been identified in Proteus species that has a positive correlation with risk of infection. After attachment of Proteus with urothelial cells, interleukin 6 and interleukin 8 secreted from the urothelial cells causes apoptosis and mucosal endothelial cell desquamation. Urease production of proteus also augments the risk of UTI. Urease production, together with the presence of bacterial motility and fimbriae or pili, as well as adhesins anchored directly within bacterial cell membrane may favor the upper urinary tract infection. Once firmly attached on the uroepithelium or catheter surface, bacteria begin to phenotypically change, producing exopolysaccharides that entrap and protect bacteria. These attached bacteria replicate and form microcolonies that eventually mature into biofilms [31, 32]. Once established, biofilms inherently protect uropathogens from antibiotic and the host immune response [33, 34]. Proteus mirabilis as with other uropathogens is capable of adapting to the urinary tract environment and acquiring nutrients. And this is accomplished by the production of degradative enzymes such urease and proteases, toxins such as Haemolysin Hpm A and iron nutrient acquisition proteins.
The infection with Proteus can be diagnosed by taking a urine sample for microscopy and culture which is sufficient in most of the cases except in few cases where advanced diagnostic tools are used. If the urine is alkaline, it is suggestive of infection with Proteus sp. The diagnosis of Proteus is made on swarming motility on media, unable to metabolized lactose and has a distinct fishy door. Ultrasound or CT scan to identify renal stone (Struvite stone) or to visualized kidneys or surrounding structures. It will allow to exclude other possible problems, mimicking symptoms of urinary tract infection [35, 36].
Pseudomonas is a gram-negative bacteria belonging to the family Pseudomonadaceae and containing 191 validly described species [37]. Because of their widespread occurrence in water and plant seeds, the pseudomonas was observed in early history of microbiology. Pseudomonas is flagellated, motile, aerobic organism with Catalase and oxidase-positive. Pseudomonas may be the most common nuclear or of ice crystals in clouds, thereby being of utmost importance to the formation of snow and rain around the world [38]. All species of Pseudomonas are strict aerobes, and a significant number of organisms can produce exopolysaccharides associated with biofilm formation [39]. Pseudomonas is an opportunistic human pathogen that is especially adept at forming surface associated biofilms. Pseudomonas causes catheter associated urinary tract infection(CAUTIs) through biofilm formation on the surface of indwelling catheters, and biofilm mediated infection including ventilator associated pneumonia, infections related to mechanical heart valves, stents, grafts, sutures, and contract lens associated corneal infection [40].
Pseudomonas is third ranking causes nosocomial UTI about 12%, where E. coli remain on the top [41]. CAUTI is directly associated with duration of catheterization. Within 2–4 days of catheterization 15–25% patients develop bacteriuria [42].
Pseudomonas aeruginosa is a gram-negative, rod shaped, asporogenous and monoflagellated, noncapsular bacterium but many strains have a mucoid slime layer. Pseudomonas has an incredible nutritional versatility. Pseudomonas can catabolize a wide range of organic molecule including organic compounds such as benzoate. This, then make Pseudomonas a very ubiquitous microorganism and Pseudomonas is the most abundant organism on earth [43] (Figure 3).
Gram stain picture and morphology of Pseudomonas aeroginosa. Adapted from Science News. A new antibiotic uses sneaky tactics to kill drug-resistant Pseudomonas aeruginosa illustration and Pseudomonas Aeruginosa Stock Photos & Pseudomonas Aeruginosa Stock Images—Alams. https://www.alamy.com › stock-photo.
Pseudomonas is widely distributed in nature and is commonly present in moist environment of hospitals. It is pathogenic only when introduce into areas devoid of normal defense such as disruption of mucous membrane and skin, usage of intravenous or urinary catheters and neutropenia due to cancer or in cancer therapy. Its pathogenic activity depends on its antigenic structure, enzymes and toxins [44]. Among the enzymes Catalase, Pyocyanin, Proteases, elastase, haemolysin, Phospholipase C, exoenzyme S and T and endotoxin and endotoxin A play role in disease process and as well as immunosuppression. Pseudomonas can infect almost any organ or external site. Pseudomonas in invasive and toxigenic. It attached to and colonized the mucous membrane of skin. Pseudomonas can invade locally to produce systemic disease and septicemia. Pseudomonal UTs are usually hospital acquired and are associated with catheterization, instrumentation and surgery. These infections can involve the urinary tract through an ascending infection or through bacteriuria spread. These UTIs may be a source of bacteraemia or septicemia [45].
Identification of bacterium with microscopy is simple method of identification of pseudomonas. Culture and antibiotic sensitivity pattern can be done in most laboratory media commonly on blood agar or eosin-methylthionine blue agar. Pseudomonas has inability to ferment lactose and has a positive oxidase reaction. Fluorescence under UV light is helpful in early identification of colonies. Fluorescence is also used to suggest the presence of pseudomonas in wounds [46].
Urinary catheters are standard medical devices utilized in both hospital and nursing home settings are associated with a high frequency of catheter-associated urinary tract infections (CAUTI). The contribution of Klebsiella spp. in CAUTI is near about 7.7% [47].
Klebsiella pneumoniae is a gram-negative pathogenic bacterium, is part of the Enterobacteriaceae family. It has got polysaccharide capsule attached to the bacterial outer membrane, and it ferments lactose. Klebsiella species are found ubiquitously in nature, including in plants, animals, and humans. They are the causative agent of several types of infections in humans. It has a large accessory genome of plasmids and chromosomal gene loci. This accessory genome divides K. pneumoniae strains into opportunistic, hyper virulent, and multidrug-resistant groups [48] (Figure 4).
Gram stain picture and morphology of Klebsiella pneumonie. Adapted from studyblue.com. Microbio Lab Practical I—Microbiology 101 with Johnson at University of Vermont—StudyBlue. Study 368 Microbio Lab Practical I flashcards from Tess H. on StudyBlue and Klebsiella Pneumoniae Stock Photos and Pictures. Getty Images https://www.gettyimages.com › photos.
The source of Klebsiella causing CAUTI can be endogenous typically via meatal, rectal, or vaginal colonization or exogenous, such as via equipment or contaminated hands of healthcare personnel. They typically migrate along the outer surface of the indwelling urethral catheter, until they enter the urethra.
Migration of the Klebsiella along the inner surface of the indwelling urethral catheter occurs much less frequently, compared with along the outer surface Internal (intraluminal) bacterial ascension occurs by Klebsiella tend to be introduced when opening the otherwise closed urinary drainage system, ascend from the urine collection bag into the bladder via reflux, biofilm formation occurs.
A critical step in progression to CAUTI by Klebsiella is to adhere to host surfaces, which is frequently achieved using pili (fimbriae) [49]. Pili are filamentous structures extending from the surface of Klebsiella. They can be as long as 10 μm and between 1 and 11 nm in diameter. Among the two types of pili—type 1 (fim) pili and type 3 (mrk) pili, type 1 aids virulence by their ability to adhere with mucosal surfaces and type 3 pili strongly associated with biofilm production [50]. Both fim and mrk pili are considered part of the core genome [51]. It is thought that both types of pili play a role in colonization of urinary catheters, leading to CAUTI [52]. In addition to fim and mrk pili, a number of additional usher-type pili have been identified in Klebsiella with an average of ~8 pili clusters per strain. Based on varying gene frequencies, some of these appear to be part of the accessory genome. Immediately after catheterization Klebsiella starts biofilm production on the inner as well as outer surface of the catheter and on urothelium. Biofilm augments migration of Klebsiella into urethra and urinary bladder. Biofilm formation on the catheter surface by Klebsiella pneumoniae causes severe problem. Type 1 and type 3 fimbriae expressed by K. pneumoniae enhance biofilm formation on urinary catheters in a catheterized bladder model that mirrors the physicochemical conditions present in catheterized patients. These two fimbrial types does not is expressed when cells are grown planktonically. Interestingly, during biofilm formation on catheters, both fimbrial types are expressed, suggesting that they are both important in promoting biofilm formation on catheters [53]. The biofilm life cycle illustrated in three steps: initial attachment events with inert surfaces type 1 and type 3 fimbriae encoded by the mrk ABCDF gene cluster within K. pneumoniae promotes biofilm formation [54, 55]. Detachment events by clumps of Klebsiella or by a ‘swarming’ phenomenon within the interior of bacterial clusters, resulting in so-called ‘seeding dispersal’.
Modifiable risk factor are prolonged catheterization, lack of adherence to aseptic catheter care, insertion of the indwelling urethral catheter in a location other than an operating room, presence of a urethral stent, feecal incontinence. Non-modifiable risk factor—renal disease (i.e., serum creatinine >2 mg/dL), diabetes mellitus, older age (i.e., age > 50 years old), female sex, malnutrition and severe underlying illness [53]. For infection several virulence factors such as surface factors (fimbriae, adhesins, and P and type 1 pili) and extracellular factors toxins, siderophores, enzymes, and polysaccharide coatings are necessary for initial adhesion with colonization of host mucosal surfaces for tissue invasion overcoming the host defense mechanisms, and causing chronic infections [55].
Diagnosis of klebsiella infection is by isolation and laboratory identification of bacterium from urine or biofilm. Laboratory diagnosis can be done by culture of specimen—urine or catheter biofilm in blood agar, MacConkey’s agar. Specific ELISA, latex agglutination tests, PCR and other immunological-based detection methods are sophisticated alternatives for diagnosis of klebsiella. Determination of a gene on capsule of Klebsiella is rapid and simple method for the determination of the K types of most K. pneumoniae clinical isolates [56].
Enterobacter species, particularly Enterobacter cloacae and Enterobacter aerogenes, are important nosocomial pathogens responsible for about 1.9–9% CAUTI, rarely causes bacteremia [57, 58]. Enterobacter cloacae exhibited the highest biofilm production (87.5%) among isolated pathogens [53].
Enterobacter bacteria are motile, rod-shaped cells, facultative anaerobic, non-spore-forming, some of which are encapsulated belonging to the family Enterobacteriaceae. They are important opportunistic and multi-resistant bacterial pathogens. As facultative anaerobes, some Enterobacter bacteria ferment both glucose and lactose as a carbon source, presence of ornithine decarboxylase (ODC) activity and the lack of urease activity. In biofilms they secrete various cytotoxins (enterotoxins, hemolysins, pore-forming toxins. Though it is microflora in the intestine of humans, it is pathogens in plants and insects. Amp C β-lactamase production by E. cloacae is responsible for cephalosporin resistance. They possess peritrichous, amphitrichous, lophotrichous, polar flagella. E. aerogenes flagellar genes and its assembly system have been acquired in bloc from the Serratia genus [59] (Figure 5).
Gram stain picture and morphology of Enterobacter species. Adapted from Gram Stain Kit | Microorganism Stain | abcam.comAdwww.abcam.com/ and Science Prof Online. Gram-negative Bacteria Images: photos of Escherichia coli, Salmonella & Enterobacter and Enterobacter aerogenes | Gram-negative microorganism—HPV Decontamination | Hydrogen Peroxide Vapour—Bioquellhealthcare.bioquell.com › microbiology.
The most important test to document Enterobacter infections is culture. Direct gram staining of the specimen is also useful. In the laboratory, growth of Enterobacter isolates is occurs in 24 h or less; Enterobacter species grow rapidly on selective (i.e., MacConkey) and nonselective (i.e., sheep blood) agars.
Enterococci are gram-positive facultative anaerobic cocci, two species are common commensal organisms in the intestines of humans: Enterococcus faecalis (90–95%) and Enterococcus faecium (5–10%) [60]. Though normally a gut commensal, these organisms are commonly responsible for nosocomial infection of urinary tract, biliary tract and blood, particularly in intensive care units (ICU) [61]. E. coli is usually the most frequent species isolated from bacteremic catheter associated urinary tract infections (CAUTI). However, Enterococcus spp. (28.4%) and Candida spp. (19.7%) were also reported to be most common [62]. In another study, E. coli was found the commonest (36%) followed by Enterococcus spp. (25%), Klebsiella species (20%) and Pseudomonas spp. (5%) [63].
The most important cause of bacteriuria is the formation of biofilm along the catheter surface [64]. Enterococcus is gram positive bacteria often found in pairs or short chains. Broadly, Enterococcus is in two groups—faecalis and non-faecalis (E. gallinarum and E. casseliflavus). Enterococcus faecalis formerly classified as part of the group D Streptococcus is a gram-positive, commensal bacterium inhabiting the gastrointestinal tracts of humans and other mammals, survive harsh environmental conditions including drying, high temperatures, and exposure to some antiseptics [65]. E. faecalis has the important characteristics of complex set of biochemical reactions, including fermentation of carbohydrates, hydrolysis of arginine, tolerance to tellurite, and motility and pigmentation. Presence of the catheter itself is essential for E. faecalis persistence in the bladder, E. faecalis depends on the catheter implant for persistence via an unknown mechanism that more than likely involves its ability to produce biofilms on the silicone tubing and immune-suppression [66].
E. faecalis produce a heteropolymeric extracellular hair-like fimbrial structure called the endocarditis- and biofilm-associated pilus-Ebp, having three components the organelle (EbpC), a minor subunit that forms the base of the structure (EbpB) and a tip-located adhesin (EbpA) [67]. EbpA is responsible for adhesion in urothelial and catheter surface for biofilm production (Figure 6).
Morphology of Enterococcus. Adapted from Science Photo Library/Alamy Stock Photo Image ID: F6YBC3.
Urine sample and biofilm microscopy can identify this gram positive organism. Culture yields the growth of E. faecalis in appropriate media. Advanced diagnostic methods like immunological-based detection methods and PCR are rarely needed for diagnosis.
One of the common causes of catheter associated urinary tract infection is fungal infection. Bacterial infections are accounted for 70.9% of catheter associated urinary infection. E. coli is the most commonly isolated organism (41.6%) whereas fungal infections are accounted for 16.6% and mixed fungal and bacterial infections accounted for 12.5% [68]. The National nosocomial infections surveillance (NNIS) data indicated that C. albicans caused 21% of catheter-associated urinary tract infections, in contrast to 13% of non-catheter-associated infections [69]. In one study 24% of the cases showing fungal yeast growth. Candida spp. was the commonest. Non-albicans Candida (86%) isolated more commonly than Candida albicans (14%) [70]. Candida are commensals, and to be pathogenic, interruption of normal host defenses is crucial which is facilitated in conditions like immunocompromised states as AIDS, diabetes mellitus, prolonged broad spectrum antibiotic use, indwelling devices, intravenous drug use and hyperalimentation fluids [71]. Diabetes mellitus has been reported as the most common risk factor for fungal infection [72, 73]. The duration of catheterization is also an important risk factor as the duration increases the incidence of fungal infection is increased [74].
Candida albicans is an oval, budding yeast, which is a member of the normal flora of mucocutaneous membrane. Twenty species of Candida yeasts can cause in human infection but most common is Candida albicans. Sometimes it can gain predominance and can produce disease. Other candida species that can cause disease occasionally are Candida parapsilosis, Candida tropicalis and Candida krusei [75]. Although Candida albicans are common isolates in CAUTI, Candida tropicalis is increasingly reported in CAUTI [76]. The majority of Candida albicans infections are associated with biofilm formation on host or abiotic surfaces such as indwelling medical devices, which carry high morbidity and mortality [63, 77]. Several factors and activities contribute to the pathogenesis of this fungus which mediate adhesion to and invasion into host cells, which are in sequences are the secretion of hydrolases, the yeast-to-hypha transition, contact sensing and thigmotropism, biofilm formation, phenotypic switching and a range of fitness attributes [78] (Figure 7).
Morphology of Candida albicans. Adapted from biomedik8888, Aug 24, 2011. http://www.BioMedik.com.au3.
Urine and materials removed from catheter are needed. Microscopic examinations of gram-stained specimen showed pseudohyphae and budding cells. Culture on Sabouraud’s agar at room temperature and at 37°C showed typical colonies and budding pseudomycelia [79].
It is facultative anaerobic bacilli gram-negative rod of Enterobacteriaceae family considered opportunistic human pathogen but not a component of human facial flora. It is capable of producing a pigment called prodigiosin, which ranges in color from dark red to pale pink. It is ubiquitously spent in nature and has preference for damp conditions. Though previously known as nonpathogenic, but since 1970s it is associated with multi drug resistant infection due to presence of R factor—a plasmid. A study in Japan showed 6.8% incidence of UTI with this organism [80]. It also causes bacteraemia rarely. Diagnosis is confirmed by culture of the urine specimen or catheter biofilm. Automated bacterial identification systems and Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) is the other modality for diagnosis of serratia as well as other enterobacteriaceae [81].
This non-fermentative gram-negative rod discovered as plant growth-promoting bacterium and potential biocontrol agent against plant pathogens. Infection with this uncommon organism in CAUTI occurs in combination with commonest bacteria E. coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. D. tsuruhatensis and E. coli coexist and tend to co-aggregate over time and also cooperate synergistically [82]. D. tsuruhatensis metabolized citric acid more rapidly leaving more uric acid available in the medium to be used by E. coli for dynamic growth of both organisms. Identification of this organism is not confirmatory with culture, so molecular methods are more reliable [83].
Achromobacter denitrificans is gram negative bacterium formerly known as Alcaligenes denitrificans. Infection with this organism predominantly observed in elderly patients with predisposing factors as urological abnormalities, malignancies and immune-suppression. Rarely it causes bacteraemia. This bacterium has high level of antibiotic resistance [84].
In polymicrobial biofilm, Achromobacter xylosoxidans cohabits with common organisms E. coli, Pseudomonas aeruginosa and Klebsiella pneumoniae. Diagnosis is by bacterial culture and molecular methods.
Staphylococci (methicillin-sensitive Staphylococcus aureus [MSSA] and methicillin-resistant S. aureus [MRSA], Staphylococcus saprophyticus. These are the common gram positive bacteria usually responsible for skin and soft tissue infections but rarely cause CAUTI and bacteraemia [85].
The incidence of Staphylococcal UTI as well as CAUTI is increasing and the organisms carry wide variety of multidrug-resistant genes on plasmids, which augment spread of resistance among other species [86].
Diagnosis is easy, gram stain of the sample, culture is sufficient. Advanced techniques rarely needed (Figure 8).
Morphology of Staphylococcus aureus. Adapted from abcam.comAdwww.abcam.com/ pharmacist-driven intervention improves care of patients with S aureus Bacteremia/Staph aureus. Nebraska Medicine https://asap.nebraskamed.com.
CAUTI is one of the most nosocomial Infection worldwide resulting from rational as well as sometimes irrational use of indwelling urinary catheter. Cause of CAUTI is formation of pathogenic biofilm commonly due to UPEC, Proteus, Klebsiella, Pseudomonas, Enterobacter rarely Candida and other uncommon opportunistic organisms. CAUTI has got high impact on morbidity and mortality as biofilm producing organisms are more antibiotic resistant. Antibiotic resistance is a global problem. Early detection of CAUTI is simple by examination of urine and catheter biofilm with microscopy as well as culture with antibiogram. It is easy and cost effective with early diagnosis and treatment for good clinical outcome. Advanced and sophisticated methods like Immunomagnetic separation, specific ELISA, colony immunoblot assays and PCR for diagnosis of CAUTI is seldom necessary.
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