Lichens have important ecological functions in black pine forests, such as nitrogen fixation and nutrient cycling. Understanding lichen diversity could provide a better understanding of black pine ecosystems. The aim of this study was to identify the factors affecting the composition of lichen communities and their specific diversity in Mediterranean black pine forests. Research was conducted in 48 sampling plots. For the analysis, presence–absence and frequency data of lichen species were used. For stand level analysis, four community composition tables were created. We used bioclimate, topography, stand, and parent rock as variables. A total of 33 epiphytic lichen species were identified in the black pine forests from 282 sampled trees. Indicator lichen species were determined according to geographic region and stand age classes. Hypocenomyce scalaris was found to be an indicator species for old forests. Frequency data were more useful for revealing lichen species composition than presence–absence data. Of the topographic variables, elevation was the most prominent and had the highest explanation ratio for the composition of lichen species with a coefficient of correlation (R2) value of 0.49. Significantly positive (p < 0.001) relationships were found between epiphytic lichen richness and tree crown height, tree height, and bark pH. Our results revealed that to retain the trees in the stands rich in lichen species diversity is recommended in the managed forests.
The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.
Species distribution modeling was used to determine factors among the large predictor candidate data set that affect the distribution of Muscari latifolium, an endemic bulbous plant species of Turkey, to quantify the relative importance of each factor and make a potential spatial distribution map of M. latifolium. Models were built using the Boosted Regression Trees method based on 35 presence and 70 absence records obtained through field sampling in the Gönen Dam watershed area of the Kazdağı Mountains in West Anatolia. Large candidate variables of monthly and seasonal climate, fine‐scale land surface, and geologic and biotic variables were simplified using a BRT simplifying procedure. Analyses performed on these resources, direct and indirect variables showed that there were 14 main factors that influence the species’ distribution. Five of the 14 most important variables influencing the distribution of the species are bedrock type, Quercus cerris density, precipitation during the wettest month, Pinus nigra density, and northness. These variables account for approximately 60% of the relative importance for determining the distribution of the species. Prediction performance was assessed by 10 random subsample data sets and gave a maximum the area under a receiver operating characteristic curve (AUC) value of 0.93 and an average AUC value of 0.8. This study provides a significant contribution to the knowledge of the habitat requirements and ecological characteristics of this species. The distribution of this species is explained by a combination of biotic and abiotic factors. Hence, using biotic interaction and fine‐scale land surface variables in species distribution models improved the accuracy and precision of the model. The knowledge of the relationships between distribution patterns and environmental factors and biotic interaction of M. latifolium can help develop a management and conservation strategy for this species.
Abstract:The River Buyukmelen is located in the province of Duzce in northwest Turkey and its water basin is approximately 470 km 2 . The Aksu, Kucukmelen and Ugursuyu streams flow into the River Buyukmelen. It flows into the Black Sea with an output of 44 m 3 s 1 . The geological succession in the basin comprises limestone and dolomitic limestone of the Yılanlı formation, sandstone, clayey limestone and marls of the Akveren formation, clastics and volcano-clastics of the Caycuma formation, and cover units comprised of river alluvium, lacutrine sediments and beach sands. The River Buyukmelen is expected to be a water source that can supply the drinking water needs of Istanbul until 2040; therefore, it is imperative that its water quality be preserved.The samples of rock, soil, stream water, suspended, bed and stream sediments and beach sand were collected from the Buyukmelen river basin. They were examined using mineralogical and geochemical methods. The amount of suspended sediment in the River Buyukmelen in December 2002 was 120 mg l 1 . The suspended and bed sediments in the muddy stream waters are formed of quartz, calcite, plagioclase, clay (kaolinite, illite and smectite), muscovite and amphibole minerals. As, Co, Cd, Cr, Pb, Ni, Zn and U have all accumulated in the Buyukmelen riverbed sediments. The muddy feature of the waters is related to the petrographic features of the rocks in the basin and their mineralogical compositions, as most of the sandstones and volcanic rocks (basalt, tuffite and agglomerate) are decomposed to a clay-rich composition at the surface. Thus, the suspended sediment in stream waters increases by physical weathering of the rocks and water-rock interaction. Owing to the growing population and industrialization, water demand is increasing. The plan is to bring water from the River Buyukmelen to Istanbul's drinking-water reservoirs. According to the Water Pollution Regulations, the River Buyukmelen belongs to quality class 1 based on Hg, Cd, Pb, As, Cu, Cr, Zn, Mn, Se, Ba, Na C , Cl , and SO 2 4 ; and to quality class 3 based on Fe concentration. The concentration of Fe in the River Buyukmelen exceeds the limit values permitted by the World Health Organization and the Turkish Standard. Because water from the River Buyukmelen will be used as drinking water, it will have an adverse effect on water quality and humans if not treated in advance. In addition, the inclusion of Mn and Zn in the Elmali drinking-water reservoir of Istanbul and Fe in the River Buyukmelen water indicates natural inorganic contamination. Mn, Zn and Fe contents in the waters are related to geological origin. Moreover, the River Buyukmelen flow is very muddy in the rainy seasons and it is inevitable that this will pose problems during the purification process.
Intellectual capital is among the new, advanced management notions developed to overcome the inadequacy of previous administrations, to adapt to new situations and forge ahead of the competition. Intellectual capital means the information, experience and skills that offer advantage in competition and reveal the values existing within the structure of an enterprise. These values also exist in the relationship between the enterprise and the environment and with the employees. Although some research studies on intellectual capital (IC) have been conducted, to date no research has been carried out on the effects of IC on qualitative and quantitative organizational performance. For this reason, IC and its effects on firm performance (both qualitative and quantitative) were evaluated in this study. Following the evaluation of the intellectual capital and its sub-elements, the differentiation of the sub-elements is made. Then the reliability and validity of these sub-factors are calculated. The intellectual capital model has been tested by the structural equality model (SEM). According to research results, IC explains 92 per cent of a firm's performance. The effect of IC on qualitative performance is 0,84, while on quantitative performance it is 0,72. RC impresses qualitative performance with coefficient 0,94, quantitative performance with coefficient 0,60; HC impresses qualitative performance with coefficient 0,92, quantitative performance with coefficient 0,54 less; SC impresses qualitative performance with coefficient 0,90, quantitative performance with coefficient 0,53. According to the results of the research, IC affects both the qualitative and the quantitative performance of firms by supplying extensive knowledge to the managers.
© iForest -Biogeosciences and Forestry IntroductionInformation about extant forest communities is of considerable importance in forest management planning. The stand is the smallest production and silvicultural unit, and the most crucial part of this information. The location of planning units, their size and silvicultural features are used to select management objectives and determine the working cycle, management regulations and silvicultural goals. Thus, the precision of decisions to be implemented and the planning to be realized are both closely related to the accuracy of the stand maps. Moreover, map accuracy depends on the type of forest inventory, the sample size, and the techniques used to evaluate the inventory results.Stand maps have previously been produced subjectively (Feng et al. 2006) in conjunction with current forest management practices. In conventional management planning, stands are defined through the planning process. The type of of the management objectives is a component of this process (Gunnarsson et al. 1998). However, the use of computers has changed this conventional approach, and spatially and temporally dynamic description and treatment units can readily be produced (Holmgren & Thuresson 1997). Thus, dynamic forest planning can be achieved with the use of geopositioned field plot data and computers (Wallerman et al. 2002).The sampling methods used in forest inventories vary according to different countries' forestry objectives and forest structures. Systematic sampling methods are recommended for large and homogeneous forest areas and have been implemented in all production and conservation areas of Turkey since 1964. However, if this sampling method is implemented with an insufficient number of sample plots and/or in heterogeneous forests, the results will be highly questionable because they may fail to reflect the true forest composition. Although Sherman (1996), Aurbi & Debouzie (2000), Flores et al. (2003) andD'Orazio (2003) used remote sensing data to improve the results of such inventories, the desired outcome was never achieved in practice.Spatial interpolation methods, which have been classified by Li & Heap (2008) as nongeostatistical, geostatistical and combined, came into use at the end of the 1960s and have been investigated for use in forest management. As described in Akhavan et al. (2010), Guibal (1973 was the first study to use kriging in the forest inventory process. Jost (1993) also used geostatistical methods to compare conventional inventory results based on systematic sampling with the results of the kriging method. Geostatistically based methods that utilize textural information are frequently used to analyze remote-sensing (RS) images (Zawadzki et al. 2005). The quality and quantity of these methods have increased through advances in the computer sciences and in the science of geographical information systems. These methods allow the use of a variety of ecological and technical parameters in the assessment of inventory results. Spatial interpolation and multi-...
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