text:
+ 1 Introduction
+ 2 Material and methods
Fig. 1. The geographical distribution of moose d..
Table 1. Description of the most relevant..
Table 2. The occurrence of different stan..
+ 3 Results
3.1 The occurrence of moose damage
Fig. 2. The proportion of moose damage in young ..
3.2 The symptoms caused by moose
3.3 Moose damage and the silvicultural quality of the stands
3.4 The occurrence of moose damage in relation to NFI variables, descriptive analysis
Table 3. The proportion of moose damage ..
Fig. 4. The relationships between the proportion..
Table 4. The occurrence of moose damage ..
Fig. 5. The proportion of moose damage in raw da..
3.5 The occurrence of moose damage in relation to NFI variables, analysis based on the GLMM-model
Table 5. Classification table (observed ..
Table 6. Results of the generalized mixe..
+ 4 Discussion
+ Acknowledgements
Silva Fennica vol. 50 no. 2 article id 1410 | 2016 | Research article
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Seppo Nevalainen , Juho Matala, Kari T. Korhonen, Antti Ihalainen, Ari Nikula
Moose damage in National Forest Inventories (1986–2008) in Finland
Nevalainen S., Matala J., Korhonen K. T., Ihalainen A., Nikula A. (2016). Moose damage in National Forest Inventories (1986–2008) in Finland. Silva Fennica vol. 50 no. 2 article id 1410. https://doi.org/10.14214/sf.1410
Almost 100 000 stands were studied
The proportion of damage doubled during the study period
Tree species mixture had a clear effect on the damage frequency
The damage was more common in mineral soils than in peatlands, in artificially than in naturally regenerated stands and in stands that needed thinning or clearing or in which soil preparation was used.
The occurrence of moose damage was studied using data from three National Forest Inventories (NFIs) accomplished between 1986 and 2008 in Finland. The combined data included a total of 97 390 young stands. The proportion of moose damage increased from 3.6% to 8.6% between the 8th NFI (1986–1994) and the 10th NFI (2004–2008). The majority (75%) of the damage occurred in Scots pine-dominated stands. The proportion of damage was higher in aspen-dominated stands than in stands dominated by any other tree species. The tree species mixture also had a clear effect on the occurrence of damage. Pure Scots pine stands had less damage than mixed Scots pine stands, and moose damage decreased linearly with the increasing proportion of Scots pine. Stands on mineral soil had more frequent moose damage than stands on peatlands. The fertility class of the site had no straightforward effect on the damage frequency. Artificially regenerated stands had more damage than naturally regenerated stands. Accomplished soil preparation measures and the need for thinning or clearing operations increased moose damage. High proportions of moose damage in young stands were found around the country. In the 10th NFI, the largest concentration of damage was found in southwestern Finland. Our study shows the temporal and spatial changes in the occurrence of moose damage and pinpoints some important silvicultural factors affecting the relative risk of young stands over a large geographical area.
Populus tremula; Pinus sylvestris; regeneration; soil preparation; thinning; Betula spp.; Keywords Alces alces; seedling stands; tree species mixture
Nevalainen, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 68, FI-80101 Joensuu, Finland E-mail seppo.nevalainen@luke.fi
Matala, Natural Resources Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 68, FI-80101 Joensuu, Finland E-mail juho.matala@luke.fi
Korhonen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 68, FI-80101 Joensuu, Finland E-mail kari.t.korhonen@luke.fi
Ihalainen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail antti.ihalainen@luke.fi
Nikula, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 16, FI-96301 Rovaniemi, Finland E-mail ari.nikula@luke.fi
Received 22 June 2015 Accepted 25 January 2016 Published 5 February 2016
Available at https://doi.org/10.14214/sf.1410 | Download PDF
Browsing by moose (Alces alces L.) can influence the entire ecosystem through changes of plant species diversity, vegetation structure, biomass production and nutrient cycling (McInnes et al. 1992; Niemelä et al. 2001). Moose cause damage to trees by browsing apical shoots, lateral twigs and foliage, breaking the main stem and peeling the bark. Severe damage may lead to a reduction of the technical quality of timber, growth losses and impaired development of the stand (e.g., Heikkilä and Härkönen 1993; Heikkilä and Löyttyniemi 1992; Härkönen et al. 2009; Bergqvist et al. 2001, 2003, 2014). The biggest economic losses to forestry by moose are due to winter time browsing in Scots pine (Pinus sylvestris L.) sapling stands in Fennoscandia (Helle et al. 1987; Lavsund 1987). In addition to being severe pests in forests, moose is a valuable game animal (e.g., Mattsson 1990).
Moose populations in Fennoscandian countries increased in the 1970s and 1980s due to controlled hunting, a low level of predators and the commencement of so-called plantation forestry since the 1950s (Markgren 1974; Lavsund 1987; Cederlund and Bergström 1996; Lavsund et al. 2003). Forestry has aimed at an economically optimal age-class distribution of the forests with a high proportion of young forest, which has also favoured the moose (Markgren 1974; Lavsund et al. 2003).
The following factors have been linked to moose damage: structural properties of the stand (e.g., Edenius 1993; Ball and Dahlgren 2002; Milligan and Koricheva 2013), tree carbon/nutrient balance (Bryant et al. 1983), site type (Heikkilä and Härkönen 1993; Jalkanen 2001) and soil properties (Löyttyniemi 1985; Löyttyniemi and Hiltunen 1978; Haukioja et al. 1983; Heikkilä 1990; Heikkilä and Mikkonen 1992; Heikkilä and Härkönen 1993, 1996; Jalkanen 2001; Vehviläinen and Koricheva 2006). The structural characteristics of landscapes adjacent to regeneration areas have also been found to affect moose damage (Heikkilä 1990; Heikkilä and Härkönen 1993; Månsson 2009). Some of these factors, like site productivity, have been found to increase browsing by moose and, thus, the risk of moose damage. However, studies of some other factors, like tree species composition (Heikkilä and Härkönen 1996; Ball and Dahlgren 2002) or the density of stands (Heikkilä and Mikkonen 1992) have produced contradictory results, regarding whether these factors increase or decrease the amount of browsing and, consequently, moose damage.
Most of the studies cited above have been local studies conducted in areas where moose damage is common. Representative large scale surveys on the damage frequency are rare.
Large scale studies of moose browsing (Hörnberg 2001; Bergqvist 2003) have suggested that the consumption of different food items by moose is proportional to the occurrence of forage. Thus, because the occurrence of forage species varies among regions, one could expect also differences in the effects of different factors that affect moose damage.
The National Forest Inventories (NFIs) produce information on forest resources for the whole area of Finland, which covers over 20 million hectares of forest land. The forest resource data collected in the inventories is based on diverse on-site surveys conducted on stands selected on the basis of a systematic statistical sample. The main purpose of NFI is to produce national and regional data on the volume, growth and quality of the forest resources, land use structure and forest ownership. In recent years, aspects like forest health, biodiversity of forests and forest carbon stocks have become more and more important.
The first data on moose damage was collected in Finland in the 3rd NFI (1951–1953) (Löyttyniemi 1982), and data on moose damage was also recorded in the 7th NFI (1977–1984). However, detailed stand and sample tree level observations of damaging agents were first included in the 8th NFI (1986–1994). In the 9th NFI (1996–2003), these aspects were further developed. In the 10th NFI (2004–2008), the inventory cycle was intensified to 5 years from the 8 to 10 years of the previous NFIs, and a portion of the field plots were measured throughout the country every year (= rolling NFI), while in the 8th and 9th NFI one region of the country was inventoried each year.
The aim of this study was to describe, based on a representative NFI sample plot networks, the occurrence and spatial and temporal changes of the moose damage in forest stands in Finland from 1986 to 2008. Furthermore, the aim was to describe the effect of silvicultural practices and stand characteristics on the relative damage risk.
2 Material and methods
In this study, we used the field measurements of the 8th (1986–1994), 9th (1996–2003) and 10th (2004–2008) National Forest Inventories. NFIs used plots that were arranged in detached L- or square-shaped clusters (also called tracts). Sampling design varied slightly between the NFI cycles and in different parts of the country. The distance between clusters increased from south to north, and was 6 × 6 km in southern and 10 × 10 km in northernmost Finland in the 10th NFI, for instance (Korhonen et al. 2013). The clusters were comprised of 12–14 relascope (Bitterlich) plots, with a 250 or 300 m distance between plots. Each year, the field work was normally carried out by 8–14 teams, each consisting of a team leader and one or two assistants. The 8th and the 9th NFIs were regional inventories, and the annual spatial coverages are shown in Fig. 1. In the 10th NFI, plots were measured each year throughout the country, except that the area of the three northernmost Forest Centers was not measured in 2004 and no measurements were carried out in the northernmost Lapland in the 10th NFI.
Fig. 1. The geographical distribution of moose damage in young stands (development classes 2–4) in the three National Forest inventories (NFIs). The approximate borders of the inventory areas as well inventory years of the 8th and 9th NFI are shown in a) and b). The borders of the former Forest Centers are shown in c). The map was produced from tractwise means of moose damaged plots by kriging (spherical model)in ArcGis. The output cell size was 10 km.
For the forest stand where a plot was located (hereafter referred to as a plot or a stand), more than 100 variables were measured or assessed to describe the site, growing stock, damage and accomplished or proposed stand management measures. A 'stand' is defined in NFI as a geographic unit that is homogeneous with respect to land use, ownership, site properties, and the structure of the growing stock (development class and tree species composition as well as with respect to the recently accomplished and needed silvicultural and cutting operations) (see Tomppo et al. 2011).
According to the instructions for field work, only the nearest 0.25 hectares (or 0.5 hectares in North Finland) around the plot was considered when assessing the stand level parameters. Only stand-wise data was used in this study. For the definitions and classifications of the stand variables used in this study, see Table 1. Only those stand variables which were common in all the three inventories were included in the analysis. Some variables were reclassified for the analyses, e.g., the least fertile site types (6–8) were combined, owing to the small size of classes 7 and 8, resulting in six classes. The tree species codes were reduced into four or seven classes, instead of the original species list. The birch species, e.g., Betula pendula Roth. and Betula pubescens Ehrh., were combined in the analysis, because of the small number of Betula pubescens-dominated stands.
Table 1. Description of the most relevant stand variables and description of the variables used for assessing forest damage at the stand level in the 8th–10th National Forest Inventories (NFI) of Finland 1986–2004. The numbers (8,9,0) after the damage codes refer to the NFIs (10th NFI expressed as 0). For a more detailed description, see Tomppo et al. 2011. View in new window/tab.
For this study, we compiled a database of the field observations made in the 8th–10th NFIs. We used only data from the stands, in which the centerpoint of the plot was located. To estimate the damage area, all data on forest land were used first. For further analyses we included only stands in development classes 2–4 (young seedling stands, advanced seedling stands and young thinning stands) in forestland (for definitions of the classes, see Table 1). These stands are hereafter referred to as 'young stands'. The combined data included a total of 97 390 stands in development classes 2–4 (35 143, 27 837 and 34 410 from the 8th, 9th and 10th NFI respectively) and 120 variables.
Forest damage was assessed in stands situated on forest land, i.e. where the potential mean annual increment of growing stock is at least 1 m3 ha–1. In this study, we used only stand-wise damage assessments, made by the team leader. The damage was described using three different variables: the symptom, the cause and the degree of damage (i.e., the severity of the damage from the silvicultural point of view). At the stand level, this is the summed importance of all damage causes. In principle, a similar coding system was used in all three inventories, although the codes for symptoms and causal agents varied somewhat in different inventories and changed a little even between years. In the 8th and 9th NFI, only one (the most severe damage agent in the stand) was reported. Since the 10th NFI, the two most severe damage agents were described for each stand. In this study we used only one (the most severe) damage record per stand, also for the 10th NFI. For the purposes of this study, we used only damage cause code B2: moose, deer or reindeer. Since the 9th NFI, the time and duration of the damage event have also been recorded. Also, the symptom classification in the 8th NFI was somewhat different, and it was impossible to make a coherent classification that suited all three inventories. The damage variables are described in Table 1.
The presence/absence of moose damage in a stand was used as the response variable.
When analysing the occurrence of damage, sample plot observations were treated ignoring the slightly varying sampling intensity between regions and years. In the analysis of moose damage in relation to NFI-variables, a descriptive data analysis was done first. Calculations were mostly performed with IBM SPSS Statistics, version 20 (IBM Corp. 2011). Statistical analyses were done within the R environment (R core team 2015) using the base package. In individual testing of the predictors, Fisher's exact test was used In 2 × 2 tables instead of Chi-square tests. One-way analysis of variance was performed using the Welch correction. The Dunnett-Tukey-Kramer test in the DTK package of R (Matthew 2013) was used for multiple comparisons after Welch's anova.
GIS analyses were performed with ArcGIS, ver.10.0 (ESRI Inc. 2010). We also estimated the total area of moose damage using sample based estimator (Table 2), i.e. the sampling intensity in each region was taken into account. Areas were estimated by summing the number of plots in each category, dividing the sum by number of plots on land in each region and multiplying by the land area of each region. The standard errors of area estimates were estimated using the standard error estimate of a ratio estimator (Cochran 1977).
Table 2. The occurrence of different standwise damage degree classes of moose damage in forest land in Finland in three National Forest Inventories, split by the main tree species and the development class of the stand. The numbers present the estimated area in km2. View in new window/tab.
In addition to the individual testing of the predictors in the descriptive analyses, the contributions of explanatory variables for the relative moose damage risk was analyzed using a generalized linear mixed-effects model (GLMM). A model with a binomial response (logit-link function) was fitted with glmer-procedure, in the lme4-package of R (Bates et al. 2015). The presence/absence of moose damage in a stand was used as the binary response variable.
Potential explanatory variables were seeked through recursive partitioning and Random Forest methods in R (package rpart (Therneau et al. 2015) and package randomForest (Liaw and Wiener 2002)).
The multilevel hierarchy of the data (forest center area, municipality, cluster and sample plot levels) was taken into account by including random effects at different levels in the variance component model, and by allowing the intercept to vary randomly across the levels (e.g. Goldstein 2003). Overdispersion was tested with the dispersion_glmer-procedure in the blmeco-package (Korner-Nievergelt et al. 2015).
The form of the model was:
where log(p/(1−p)) describes the outcome binary variable (logit link); X´ijkl are the fxed predictor variables with corresponding coefficients vector β; u represent the random effects at different levels of hierarchy. The subscripts i, j, k and l refer to the forest center area, municipality, cluster and sample plot levels, respectively.
The fit of the model was estimated using the predict-function in lme4-package, which gives the predicted probabilities. The mean of the predicted values was used as a cutoff value to convert the predictions to 0or 1. A predicted-vs-observed table was then formed to compute the specificity-sensitivity values and other measures of the prediction success. In addition, the area under the curve (AUC) was computed with the pROC-package in R (Robin et al. 2011). Package lsmeans (Lenth 2015) was used in pairwise comparisons between classes of predictor variables.
The occurrence of moose damage was more than two times higher in the 10th NFI (2004–2008) as compared to the 8th NFI (1986–1994) in the whole country. The area of all forest land with moose damage was 443 000 hectares (8th NFI), 653 000 hectares (9th NFI) and 990 000 hectares (10th NFI). These areas represent 2.2%, 3.2% and 4.9% of the forest land area, respectively (Table 2). Between the 9th and 10th NFIs the area of moose damage had increased by 343 000 hectares. The standard error of the area estimate in the 10th NFI was 26 600 hectares. Thus, the increase in damage area was statistically significant.
The proportion of moose damage increased in all development classes, but especially in development class 3 (advanced seedling stand) of Scots pine- or broadleaved-dominated stands. For definitions, see Table 1.
The degree of damage was slight (did not decrease the silvicultural quality of the stand) in 43.8% of the cases in the combined data of the three inventories (Table 2). The share of at least moderate damage (i.e., damage that has decreased the quality of the stand by more than one class) was 10%. The proportion of stands classified as complete damaged was 1.1%. The proportion of moderate, severe, and total moose damage out of the all moose damage has increased by more than 10%-units, from 49.9% to 61.9% between the 8th and 10th inventory.
Although moose damage was slight in about half of the cases, damage by moose was generally more severe than damage by other agents. For the other causes of damage, the mean proportion of at least severe damage was 6% and the proportion of complete damage was 0.5%.
Moose damage was most common in development classes 2–4 (in 'young stands'), and the analyses later on concentrate on these classes. As a grand mean, moose damage was recorded in 5835 (6.0%) of the studied 97 390 stands belonging to development classes 2–4 in the 8th–10th NFI's. In the development classes 2–4 the proportion of moose damage was 3.9% in the 8th NFI, 5.5% in the 9th NFI and 8.6% in the 10th NFI. As the majority of the stands in this study were dominated by Scots pine (see Table 2), the largest number of the damage cases (4380 or 75.1%) was found in Scots pine-dominated stands and 780 (13.4%) in birch-dominated stands.
The increase in the proportion of moose damage (development classes 2–4) was evident around the country, in almost all Forest Centers, except in Forest Centers 5 (Pirkanmaa), 9 (Pohjois-Savo) and 0(the Åland Archipelago). Moose damage was especially common and had increased the most in Forest Center 6 (Etelä-Savo) (Fig. 2).
Fig. 2. The proportion of moose damage in young stands (development classes 2–4) in the area of Forest Centers in Finland. The borders of the Forest Centers are shown in Fig. 1.
As mentioned earlier, the 8th and 9th NFI were regional inventories, while the 10th NFI principally covered the whole country each year. The area of moose damage in the 10th NFI was 1.4–2.8 fold, as compared to the damage in the very same areas in the 8th NFI, and 1–2.1 fold as compared to the same areas in the 9th NFI. The geographical distribution of moose damage in the three NFIs is shown in Fig. 1. The most prominent change was the increase of damage in southeastern Finland in the 10th NFI, although areas with high moose damage densities were found throughout the country, e.g., in North-Bothnia and in the southwestern corner and, compared to the 9th NFI, in Lapland (Fig. 1).
Damage age was recorded in the 9th and 10th NFI. Only 10% of the observed moose damage in development classes 2–4 was recorded to be less than two years old. Almost 71% of the damage cases were recorded to be more than five years old. In 80% of the cases, the damage was reported to be continuing, e.g., clear signs of fresh and repeated effects of moose in the stand were observed. No significant difference in these proportions was found between the 9th and the 10th NFI (data not shown).
In this chapter we focused on moose damage in the 9th and 10th. Moose damage was recorded in 4469 of the 62 980 stands studied in the abovementioned two inventories. The majority of the symptoms caused by moose were injuries in the tree crowns. 41% of these were classified as 'change of leader shoot, multiple leader shoots or other crown malformations' and 30% as 'broken or dead tops'. For damage symptom classification, please see Table 1. Symptoms 'fallen or broken stems', 'stem injuries' and 'technical malformations in the stem' were the next three most common types of damage (the proportion of each 6.6%–6.8% of the cases with moose damage). The occurrence of the symptoms varied according to the dominant species; for instance, more stem damage was found in deciduous trees (75% for aspen (Populus tremula L. ) and 40% for alders (Alnus sp., mostly grey alder, Alnus incana (L.) Moench) as compared with coniferous trees (4.9% for Scots pine and 7.6% for Norway spruce, Picea abies L. Karst.).
The silvicultural quality (see Table 1) was worse than 'good' in 66.1% of the young stands (in 64 381 stands) in development classes 2–4. Damage due to any cause reduced the quality of these stands in 17.2% of the cases (in 39 241 stands). Moose damage was present in 8.3% of the stands (3279 stands) in which the silvicultural quality had decreased. The importance of moose damage for the stand quality increased as the stand quality decreased. The proportion of moose damage was 3.5% in the stands classified as 'good', 6.5% in the stands classified as 'satisfactory' and 9.3% in the stands classified as at least 'adequate'. The proportion of moose damage in other quality classes than 'good' has increased also between the NFIs, from 3.0% in 8th NFI to 4.3% in 9th NFI, and to 9.0% in the 10th NFI.
In stands dominated by Scots pine, Norway spruce or birches moose damage was most common when stands were about 10 years old. However, in stands dominated by other tree species, especially aspen and alder species, damage was common also in older stands (Fig. 3). In those stands, symptoms like 'stem damage' were common (see above), which explains the divergence.
Fig. 3. The proportion of moose damage in young stands (development classes 2–4) related to stand age in stands dominated by different tree species. The line in produced with Loess-smoothing algorithm in IBM SPSS Statistics.
The proportion of stands with moose damage was different according to the main (dominating) tree species. In development classes 2–4, moose damage was most common in stands dominated by aspen (27%), yet the number of these stands was quite small (374 stands). A high proportion of stands with moose damage was observed in stands having admixtures of aspen – birches, aspen – Norway spruce and Scots pine – aspen, for instance (Table 3). The proportion of moose damage was 6.5% in Scots pine-dominated stands. The damage frequency was very low in the 607 stands dominated by alder species. According to Welch's one-way anova, the means (proportions) of moose damage were statistically significantly different across categories of the main tree species (F = 112.418, df = 6, 649, p < 0.001). In pairwise comparisons, the proportion of damage was statistically significantly higher in aspen-dominated stands than in all the other categories, for instance. But the difference in the proportion of moose damage between, e.g., Scots pine- and birch-dominated stands was not statistically significant (adjusted significance = 0.2523). Pure Norway spruce stands had less damage (0.6%) than mixed stands dominated by spruce (4.2%) (Chi-Square = 171.939, p > 0.001). In birch-dominated stands, the difference between pure and mixed stands was not statistically significant (Chi-Square = 3.782, p = 0.052).
Table 3. The proportion of moose damage in young stands (development classes 2–4) by main tree species and mixed species in the stand. Data: combined data of NFI8–NFI10 (1986–2008). N = number of plots, s.d. = standard deviation.
Main tree species Mixed species Proportion of moose damage N s.d.
Scots pine None .0497 34795 .217
Norway spruce .1054 10709 .307
Birches .0696 19926 .254
Aspen .1287 171 .336
Alders .0545 110 .228
Rowan .0769 13 .277
Other .0677 1581 .251
Total .0651 67305 .247
Norway spruce None .0059 5726 .077
Scots pine .0838 3698 .277
Birches .0238 7059 .152
Other .0198 605 .140
Birches None .0893 3393 .285
Norway spruce .0640 2967 .245
Aspen None .1429 28 .356
Scots pine .2963 54 .461
Norway spruce .2551 98 .438
Birches .3357 143 .474
Alders .1538 13 .376
Rowan .0000 2 .000
Other .1944 36 .401
Total .2727 374 .446
Alders None .0082 122 .091
Norway spruce .0000 107 .000
Aspen .0000 17 .000
Rowan None .0000 16 .000
Scots pine .3333 3 .577
Norway spruce .0000 3 .000
Birches .2174 23 .422
Aspen .3333 3 .577
Alders .1667 6 .408
Total .1406 64 .350
Other None .0323 62 .178
All stands None .0469 44142 .212
In Scots pine-dominated stands, the proportion of moose damage decreased linearly when the proportion of Scots pine increased (R2 linear = 0.956). In these stands, the damage also increased steeply linearly with the increase of the proportion of birches R2 linear = 0.898) or Norway spruce (R2 linear = 0.518) (Fig. 4). A high proportion of moose damage was observed in admixtures of aspen – birches, aspen – Norway spruce and Scots pine – aspen, for instance (Table 3).
Fig. 4. The relationships between the proportion of moose damage and the proportion of tree species in young Scots pine dominated stands (development classes 2–4) in the combined data of the 8th–10th National Forest Inventories. The black dots and the solid line show the proportion of Scots pine. The open squares and the dashed line show the proportion of birch species, the open triangles and the dotted line show the proportion of Norway spruce (in Scots pine dominated stands).
Moose damage increased along with subsequent NFIs regardless of the dominant tree species (see Table 2). In pure pine, spruce and birch stands, the proportion of moose-damaged stands was about two times higher in the 10th than in the 8th NFI. However, for example, in the birch – spruce mixture stands the proportion of damage was 4 fold, and in the birch mixtures 3.5 fold in the 10th NFI as compared to the 8th NFI.
Less damage occurred in peatland stands (3.7%) than in stands on mineral soil (7.0%) (Table 4). The difference was most pronounced in stands dominated by aspen, rowan (Sorbus aucuparia L.) and other species and in birch-dominated stands. Moose damage also increased along with the time since drainage (F = 18.7557, df = 3, 5715, p < 0.001). In pairwise comparisons, the difference between undrained and recently drained peatland stands was not significant, but recently drained peatland stands had less damage (1.6%) than transforming or transformed drained peatland stands (4.1% and 3.6% respectively).
Table 4. The occurrence of moose damage in relation to main site type (mineral soil/peatland, proposed thinning operation, accomplished soil preparation or artificial regeneration, according to the GLMM-model and raw data. Data: Stands in development classes 2–4. NFI8–NFI10, all tree species. The moose damage in the model columns were averaged over the levels of class variables, and the continuous variables were fixed in their mean values (for the variables, see Table 6). In the data columns, the p-value is based on the Fishers's exact test. The odds ratios are also shown. The values in the data columns were computed from the actual plot numbers in a 2 × 2 table.
Variable Value Model Data
Probalility of damage p-value Proportion of damage p-value Odds ratio
Peatland plot No 5.15 <.0001 7.00 <.0001 0.505
Yes 4.09 3.67
Thinning or clearing proposed No 3.77 <.0001 4.04 <.0001 3.386
Yes 5.58 12.47
Soil preparation accomblished No 4.17 <.0001 4.69 <.0001 3.273
Artificial regeration No 4.09 <.0001 4.26 <.0001 2.094
Yes 5.16 8,52
In peatland stands, the means (proportions) of moose damage was almost the same (3.2%–3.9%) across site fertility classes (all tree species). In mineral soil plots the damage proportions were different (F = 42.6797, df = 5, 5049, p < 0.001), and the damage was most common in the least fertile, barren and poorer sites (all tree species). However, also mesic heath forest sites had much damage (Fig. 5). The results were almost the same, when the main tree species is in taken into account.
Fig. 5. The proportion of moose damage in raw data (column "Data") and the predicted probability of damage by the GLMM model (column "Model"), by site types of the stand in mineral soils. Data: development classes 2–4 in the combined data of the 8th–10th National Forest Inventories. The moose damage in the model columns were averaged over the levels of class variables, and the continuous variables were fixed in their mean values (for the variables, see Table 6). The bars marked with the same letter (bar groups treated separately) do not differ in pairwise comparisons (95% confidence level).
Artificially regenerated stands had twice as much damage as naturally regenerated stands (Table 4). The difference was statistically significant in all stands, and in stands dominated by Scots pine, Norway spruce or birches, according to Fisher's exact test. The difference was especially high in the stands dominated by birch species.
The proportion of moose-damaged stands was significantly higher in the stands in which soil preparation measures were accomplished, as compared to non-treated stands, according to Fisher's exact test (Table 4). However, in stands dominated by tree species in the 'other' category (i.e. other than Scots pine, Norway spruce and birches) no significant differences were found. The difference in relative risks (expressed as column percentages) and in the odds ratio between treated and non-treated stands was especially high in stands dominated by birch species. In our data, 86% of the soil preparation operations comprised of harrowing, scarification, ploughing or mounding the site.
The proportion of moose damage was also higher in the stands in which clearing or thinning was proposed by the NFI team leader, as compared to the stands in which it was not proposed (Table 4).
According to the GLMM, the residual variance was highest at the cluster level (1.171, standard deviation 1.082, n = 10882), next at the municipality (0,341, s.d. 0.584, n = 467) and Forest Center area level (0,149, s.d. 0.386, n = 14). Thus, the variation at the cluster level was 70% of the total variation. Because overdispersion was nonexistent (test value 0.566), the lowest (i.e., sample plot) level random effect was not included in the final model (Eq. 1).
The prediction by the GLMM model was very close to the observed proportion of moose damage in the whole data (5.9% model vs 6.0% data, see Tables 3 and 5). Despite the differences in the observed and predicted probabilities in categories of single predictors, the p-values computed from the data directly or with the GLMM-model showed similarly very significant differences between classes of the explanatory variables (Table 4).
Table 5. Classification table (observed vs. predicted number of plots with or without moose damage). Model: generalized mixed linear (GLMM) model, estimated for the presence/absense of moose damage with the glmer function in lme4-package in R. Various measures for the success of the classification are also given. Data: NFI8–NFI10 (1986–2008), development classes 2–4.
Observed No 70467 21087
Yes 543 5291
Sensitivity 0.907
Specificity 0.770
Correct classification rate 0.778
False positive 0.230
False negative 0.093
Positive predictive value 0.200
Negative predictive value 0.992
Area under the curve (AUC) 0.920
Although the overall classification success was almost 78% and the AUC value was also quite good (0.920), it should be noted, that e.g. the proportion of false positive predictions was high (23%), and the positive predictive value rather poor (0.2) (Table 5).
The results of the fixed part of the model (Table 6) showed the effect of each explanatory variable to the relative risk, after taking the effect of other factors and the hierarchical structure of the data into account. The results supported the results of the descriptive analysis: The probability of moose damage increased when the site fertility decreased. The effect was not straightforward, however (Fig. 5). The probability of damage was higher in mineral soil than in peatland plots. The probability increased the most, when aspen was the main or the mixed tree species in a stand. For instance, the relative risk of moose damage in aspen-dominated stands was 17 times higher (e 2.848) than in pine-dominated stands (Table 6). Proposed thinning, artificial regeneration and soil preparation also increased the risk. The probability of moose damages decreased with increasing age and density (here the basal area) of the stand. The estimates of the fixed part of the model were significant (i.e. |>2|), except for alder, rowan and other species as main or mixed tree species of the stand.
Table 6. Results of the generalized mixed linear model (GLMM), estimated for the presence/absense of moose damage with the glmer function in lme4-package in R. The fixed effects of the explanatory variables are shown. Data: NFI8– NFI10 (1986–2008), development classes 2–4, total N = 93390.
Variable Estimate Std. Error z-value p-value
(Intercept) –3.213e+00 1.979e–01 –15.020 < 0.001
Stand mean age (years) –1.832e–02 1.950e–03 –9.394 < 0.001
Site type 2 1) 5.017e–01 1.417e–01 3.539 < 0.001
Site type 6 1) 1.108e+00 2.051e–01 5.401 < 0.001
Elevation of the plot (m) 3.927e–04 2.954e–05 13.297 < 0.001
Stand basal area (m2/ha) –9.158e–02 3.711e–03 –24.680 < 0.001
Peatland (0/1) –2.411e–01 6.045e–02 –3.988 < 0.001
Thinning proposed (0/1) 4.099e–01 3.620e–02 11.322 < 0.001
Artificial regeneration (0/1) 2.427e–01 3.970e–02 6.114 < 0.001
Soil preparation accompished (0/1) 2.053e–01 4.066e–02 5.048 < 0.001
Main tree species Norway spruce 2) –1.224e+00 6.059e–02 –20.203 < 0.001
Main tree species birches 2) 1.597e–01 6.364e–02 2.510 0.012
Main tree species aspen 2) 2.848e+00 1.530e–01 18.613 < 0.001
Main tree species alder 2) –8.108e–01 3.808e–01 –2.129 0.033
Main tree species rowan 2) 3.891e–01 4.163e–01 0.935 0.349
Main tree species other 2) –8.537e–01 3.290e–01 –2.594 0.009
Mixed tree species Scots pine 3) 3.881e–01 7.951e–02 4.881 < 0.001
Mixed tree species Norway spruce 3) 4.332e–01 4.615e–02 9.387 < 0.001
Mixed tree species birches 3) 2.648e–01 3.981e–02 6.651 < 0.001
Mixed tree species aspen 3) 1.724e+00 1.533e–01 11.244 < 0.001
Mixed tree species alder 3) –9.798e–02 2.624e–01 –0.373 0.708
Mixed tree species rowan 3) –4.107e–01 5.885e–01 –0.698 0.485
Mixed tree species other 3) 1.345e–01 1.034e–01 1.301 0.193
1) Compared with the most fertile site type 1
2) Compared with s stands with Scots pine as the main tree species
3) Compared with s stands without any mixed species
4 Discussion
This study qualifies as a comprehensive review of moose damage in Finland during the last decades. The general trend during our study period was the continuous increase in the amount and severity of damage. Tomppo and Joensuu (2003) found that the area of moose damage in young stands in southern Finland almost doubled between the 8th and 9th NFI. Also, Nevalainen and Korhonen (2007) reported the increase of moose damage during the first years of the 10th NFI.
The obvious reason for the increased moose damage seems to be the increase in moose population (Nygrén and Pesonen 1993; Lavsund et al. 2003). Hörnberg (2001) found a significant correlation between reported population changes and damage in young pine stands reported in NFI in Sweden. However, the moose population in Finland has varied considerably during the study period from the 1980s to the mid 2000 (Finnish Statistical Yearbook of Forestry 2014). The moose population started to decrease from the winter population of about 110 000 2–3 years before the launch of the 8th inventory and continued to decrease during the whole period of the 8th inventory. In 1994 the moose winter population was at the lowest level during the study period. The moose population started to grow again in the mid-1990s, i.e. a couple of years before the 9th inventory was launched, and reached the highest level ever recorded in Finland during the study period, about 140 000 moose in 2001 and 2002. During the 10th inventory (2004–2008), the moose population was decreasing again but the average population was at about the same level as in the 9th inventory, about 110 000 moose. Thus, at least at the level of the whole country, the connection between the moose population and the amount of damage is not straightforward.
There are several possible explanations for this. More than two thirds of the observed moose damage was more than five years old, and in most cases the damage was reported to be continuing (see Table 1). Thus, the high damage level during NFI10 (2004–2008) probably reflects the high population level during the previous years. Also, considerable uncertainties may exist in moose population estimates. A similar-sized population may cause different amounts of damage depending on the level of spatial clustering of the population. In winter, when most of the damage occurs, moose spend their winter in an area of about 2500 to 5000 ha. A more aggregated population causes more browsing pressure on local food items. On the other hand, the changes in the structure of forests may have an effect. The area of, e.g., Scots pine seedling stands decreased between the 8th and 10th NFI from 3.3 (Tomppo et al. 2001) to 2.4 million hectares (Korhonen et al. 2013).
NFIs as such are statistically representative samples of Finland's forests. The weaknesses in the NFI data can be related to sampling error and detection error; in other words: i) spatial representativeness of field plots, ii) reliability of the field survey, including observers' (in this case the team leader's) abilities to identify the causes and iii) in some cases, the epidemic nature of the damage in question. As to the reliability of the field surveys, at least recent moose damage is easily recognizable in the field. It should be remembered, however, that a large proportion of all damage remains unidentified in the field survey (15% of all damage in our data). Therefore, it is obvious that the proportion of moose damage is underestimated in NFI. In older stands and older damage, the cause may remain unknown more often. Despite the large sample size, the NFI samples may be spatially too sparse, especially in northern Finland to detect aggregated local damage. Theoretically, one sample plot represented an area of less than 300 hectares in southernmost Finland, and more than 1000 hectares in northern Lapland. Furthermore, the NFIs before 2004 were regional inventories, i.e. the field work was carried out in different areas in each year, which makes the annual comparisons with, e.g., density of moose population more difficult. Despite the differences in the occurrence of moose damage, e.g., in the areas of Forest Centers (Fig. 2), the summed proportion of moose damage area was the same in South and North Finland (5.0% and 4.8% of the forest land area respectively) in the 10th NFI (Korhonen et al. 2013). 'South Finland' comprises, in this case, Forest Centers 0–10 and 'North Finland' comprises centers 11–13 (see Fig. 1).
Our study was based on standwise damage assessments, and this poses some limitations to the interpretation of the results. For instance, the damaged tree species is not necessarily the dominant species of the stand. Furthermore, the moose damage may have changed the dominant species of the stand. If, e.g., pine seedlings have been seriously damaged, the stand may be registered as a birch-dominated stand or vice versa. This might partially explain our results about the increased damage in Scots pine stands with an increasing proportion of mixed tree species. However, the main conclusions are still valid, in our opinion. Our results are in line with some other studies that found a similar effect (Heikkilä 1990; Heikkilä and Härkönen 1993, 1996; Ball and Dahlgren 2002). Starting from the 11th NFI, damage is recorded for each species in the stand, which will help with the interpretation of NFI results in the future.
Many studies have indicated that more damage may occur on more fertile sites (Heikkilä 1990; Heikkilä and Härkönen 1993; Jalkanen 2001). The results of this study do not show a clear relationship between site fertility class and the risk of moose damage. Instead, the GLM-model showed a decrease in the damage probability with increasing site fertility. These discrepancies may be partially explained by the size and type of the data, by tree species considered and by different ways of recording the damage. In the stand-wise assessments of our study, only clearly recognizable damage was recorded. Also, the very large sample over the whole country can mean a large variation in the bedrock composition and, thus, the fertility of the soil within the very same site-fertility classes in different parts of the country. Moreover, the tree species composition changes according to site fertility class: on the most fertile sites, the proportion of less palatable tree species (Norway spruce and alders) is the highest, while the proportion of Scots pine-dominated stands increases with the decreasing site fertility class. The results concerning the main tree species are, as such, in good agreement with earlier palatability studies (Hörnberg 2001; Månsson et al. 2007).
Moose damage occurred less in pure pine stands. Admixture of broadleaved species, especially aspen, increased the proportion of damaged stands in this study. This is also in line with several earlier studies. Browsing of pine was common in the patches with high densities of aspen and rowan, the preferred and highly utilized deciduous tree species (Heikkilä and Härkönen 1996). High amounts of bush, especially aspen, increased damage to pine (Heikkilä 1990). Jalkanen et al. (2005) also found more moose damage in pine stands with more sprouts. Jalkanen (2001) found that moose preferred aspen stands and the occurrence of aspen was closely connected to the occurrence of moose damage in pine sapling stands. Pine stem breakage was higher when birch occurred as overgrowth above pine and with high birch densities (Heikkilä and Härkönen 1993). The number of planted pines and deciduous trees overtopping the pines were the most important predictors of an increase in browsing probability (Nikula et al. 2008). Also Härkönen et al. (2008) and Bergqvist et al. (2014) have found that the number of damaged pines increases with higher numbers of overtopping broadleaved trees and taller broadleaved trees.
Cleaning of birches has reduced damage to pine trees in several studies (Bergqvist et al. 2001; McLaren et al. 2000; Karlsson et al. 2002; Härkönen et al. 2008). Cleaned stands may be less attractive, but, on the other hand, cleaning may increase the browsing pressure and damage severity to conifer species (Heikkilä and Mikkonen 1992; Lyly and Saksa 1992; McLaren et al. 2000).
Accomplished soil preparation measures, as well the need for thinning or cleaning of the stand increased the proportion of damaged stands. Also, Nikula et al. (2008) found that heavy soil scarification, such as ploughing or mounding, increased the browsing probability. Soil preparation was done much more frequently (in 18–20% of the cases) in richer sites compared to barren and less fertile sites (2.4%) in mineral soils in this study, but this could not alter the prevalence of moose damage on the less fertile sites. However, the combined effect of site type and soil preparation measures could also explain the divergence between the results using raw data or the GLMM model (Table 4).
Artificially regenerated stands had more damage than naturally regenerated stands in our study. This result is supported by some studies, e.g., by Jalkanen et al. 2005 in northern Finland, although the results concerning this point are very scarce.
The effects of some relevant variables (e.g. tree size) were not included in the presented results. Partly this was because they were not common variables in all three inventories, or because, for example, the mean height of growing stock gave no further information when the stand age was included in the GLMM-model.
In field surveys, as compared to experimental studies, many factors affect simultaneously and are often intercorrelated. Although the effect of a factor can be tested, it is in no way an indicator of a cause-effect relationship, and one must be cautious when drawing conclusions. The p-values in these single-variable tests computed directly from the data (in the descriptive analysis in Table 4) were too optimistic, because the hierarchical structure of the data and the effect of other confounding variables was not taken into account. Despite of this, the p-values (Table 4) according the to raw data and the GLMM-model were similar. In GLMMs, the effect of a factor can be tested after taking the effect of other factors into account. In addition, the random effects in GLMM take into account the dependency of sample plots caused by different sampling levels. The GLMM produced a reasonable fit. The high proportion of false positive predictions reflect also the structure of the data (low number positive cases, i.e, diseased stands). The prediction success is dependent on the threshold (cutoff value), too. Here we used the mean of the predicted values as the cutoff value. As no cutoff is optimal according to all possible performance criteria, the cutoff choice involves a trade-off among different measures. Partly this problem was overcome by using receiver operating characteristic (ROC curves), i.e., by plotting the true positive rate against the false positive rate at various threshold settings. However, the predictions for the categories of a single explanatory variable may differ from the observed proportion, because of the unbalanced structure of the inventory data (very few cases with damage) or due to the poor explanatory power of the variable in question, for instance (Table 4). The model did work well as an explanatory model, i.e., it showed the magnitude and the direction of the effect of a variable (when the random factors and the impact of other variables were taken into account), but it was not suitable for accurate predictions.
In forest and wildlife management, browsing by moose in young stands should be taken into account. It is most important to identify the conditions in which damage, overconsumption and possible long-term effects on forest ecosystems take place (Heikkilä and Härkönen 1996).
Our study was able to show the temporal and spatial increase in the occurrence of moose damage and to pinpoint some important silvicultural factors that affect the relative risk of young stands to damage. However, an analysis between moose population density and damage is still needed. Most of the variation in GLMM results was at the cluster, i.e., at the landscape level. The properties of the landscape, i.e. areas surrounding the stands, may significantly affect the suitability of the area for moose. These include tree species distribution, tree age distribution, size of adjacent areas, proportion of agricultural land, proportion of peatland, topography, density of roads, amount of human influence, and snow depth, for instance. Many of these factors could be further studied utilizing the data from the multi-source inventory of Finland (e.g., Tomppo 1996).
The study would not have been possible without the devoted work of the NFI field teams. We are also grateful to Dr. Ville Hallikainen, Mr. Jaakko Heinonen, Dr. Jari Miina and Dr. Seija Sirkiä for statistical advice, and Scribendi Inc, Canada, for checking the language.
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Name and ISO code : Wolof (WOL) (WOF for Gambian Wolof), alternatively called Olof by speakers, and Walaf, Oulof or Volof in some ancient publications.
Speakers : probably over 5 million speakers (Wolof ethnic group) in Senegal, approximately 200 000 in Gambia and the same in Mauritania. Wolof is also used as a vehicular language in Senegal, and including the Wolof diaspora all over the world, the total amount of speakers might reach 10 million.
Classification : Wolof is a Niger-Congo language belonging to the Northern branch of the Atlantic group (The existence of the Atlantic group is currently under discussion).
Dialectology : language variation in Wolof is relatively weak. Local varieties have their particularities (mainly phonological and sometimes morphological), but it seems more appropriate to distinguish between varieties rather than dialects, except for the Lebu (lébou) variant spoken in the Cap-Vert peninsula of Senegal and the Petite Côte, and to a certain extent for the Wolof of Gambia. In Senegal, the Dakar variant has been gradually established as the standard contemporary Wolof, without formal recognition, because of the influence of the capital, of the radio and TV broadcastings. This urban Wolof is characterized by 'interlarded codeswitching' with French (for words, phrases or sentences), often criticized by purists.
Status : Wolof is the main vernacular language of Senegal, originally spoken in the ancient kingdoms of Baol, Walo, Kaajor, Jolof, and Saalum. It is nowadays the mother-tongue of more than 40% of the inhabitants of Senegal but has also been used, for a long time, as a vehicular language (estimated to be used by 83% of the population in 1983) all over the country (except in the Eastern part), in all domains, by all ages, in particular in urban centers. Language use is vigorous and expanding, even in Casamance (southern Senegal) and the Gambia where Mandinka used to prevail as a vehicular language. Most of the speakers are at least bi-lingual (with French in Senegal, English in the Gambia), and often multilingual for those who do not belong to the Wolof ethnic group. French is the official language of Senegal but Wolof was one of the first six languages of Senegal recognized as national languages, mentioned as such in the 2001 Constitution. It received an official orthography (using Latin script) in 1971 with several corrections afterwards. However, this official orthography is seldom used by the population since Wolof is not taught in the education system. Its main function is oral (including radio and TV programs) but since the 70's, newspapers, websites and also a written literature (mostly novels) in Wolof have developed. A non-standardized notation using Arabic script, called Wolofal, still survives mostly in religious (islamic) poetry.
Main typological features : Wolof is a non tonal language with accusative alignment, no case distinction between subjects and objects (except for clitics), an SVO basic word order (excepted in the case of clitic object pronouns or complement focusing), and a simplified noun class system (class prefixes have fused with the root and the belonging of a word to a class is visible only in the noun modifier's agreement), with 8 classes for singular and 2 for plural. The verbal system is characterized by an inflectional morphology whereby modal specifications, and, more remarkably, grammaticalized focus and polarity distinctions have fused with personal and aspectual specifications. The unmarked paradigms have a present perfective (completive) value, from which imperfective (incompletive) and pasts are derived with suffixes or by an imperfective copula. Wolof has auxiliaries with various values, and a large set of derivational affixes (mainly suffixes), including suffixes coding valency changes (e.g. applicatives, antipassive). This language has no adjectives but two classes of adverbial ideophones. A remarkable triplet of spatial deictic suffixes (proximal, distal and not localized) pervades the whole language system and is used for definite articles, demonstratives, a verbal inflection (the Presentative), as well as for subordinating conjunctions (temporal and conditional). Wolof has a simple consonant inventory including prenazalized phonemes and geminates (whose phonological status remains controversial), 8 short vowels (i, ɛ, e, ә, a, ɔ, o, u spelled i, e, é, ë, a, o, ó) and 7 long vowels (transcribed ii, ee, ée, aa, oo, óo) : the central vowel /ә/ does not have a long counterpart; /a:/ has an unusual open and abbreviated realization (transcribed à), before a group of consonants.


text:
Greek Church Escalates Battle Over ID Cards
The showdown began with government plans to strip the religious affiliation entry from state identity cards
THESSALONIKI, Greece, June 14 (AP)--The firebrand leader of the Greek Orthodox Church told followers Wednesday to unite against the government's ``sword of power'' in an identity card dispute that has set Greece's most powerful institutions on a collision course.
Speaking to a rally of more than 100,000 people, Archbishop Christodoulos sharply escalated a fight that strikes at some bedrock issues: the essence of the Greek identity and whether political leaders can truly govern without the consent of the Orthodox hierarchy.
``Your efforts are futile,'' cried Christodoulos in an obvious attack against the long-governing Socialists. ``The people do not follow you ... You think you will accomplish you plans with the sword of power. You are mistaken.''
``For our faith, we will be lions,'' he told a cheering crowd.
Rally organizers had predicted a turnout of 500,000, but the crowd appeared to fall well short of their goal.
The showdown began with government plans to strip the religious affiliation entry from state ID cards.
The government says privacy laws require the change, which is strongly supported by rights groups and religious minorities such as Jews and Muslims who claim the IDs make them targets for discrimination.
The church and many followers see the religious entry as an important symbol of the historical role of the Orthodox church, which sees itself as the true guardian of Greek identity.
But larger--and more volatile--issues lurk just below.
For church leaders, there is the fear of possible moves toward a formal separation of church and state in Greece. About 97 percent of the native-born population are baptized into the Greek Orthodox Church.
The government is faced with another major hurdle in the nation's difficult transition from its traditional order to a more Western model based on muting ethnic and religious differences.
Evoking the names of Greek independence heroes, Christodoulos portrayed the church as the genuine voice of the nation and warned the government of the potential risks of challenging its wishes.
``Many before you have tried and failed...All of the Greek people will resist,'' said Christodoulos, who is listed in polls as one of Greece's most popular and influential figures.
``We will remain what we are,'' he proclaimed, ``Greeks and Christians first, then Europeans.''
Such words shake the pillars of Premier Costas Simitis' government, which is preoccupied with trying to bring Greece into the mainstream of the European Union and shake its unruly image.
Government spokesman Dimitris Reppas complained the church was adopting a ``political character ... not in line with its role.''
Parliament speaker Apostolos Kaklamis called the rally ``a mob, not the people.'' "I fear the situation in the church could spawn great dangers for the future of this country,'' he said.
The peaceful, seaside rally took on the atmosphere of a pilgrimage as black-clad monks, zealots with crosses and others traveled from across northern Greece--by foot, train, in car convoys and aboard hundreds of chartered buses. Some came from as far away as Australia.
Peddlers sold Greek flags and banners adorned with the two-headed eagle symbol of the Byzantine Empire. Baseball hats carried the message: ``I am Greek Orthodox.'' Some children held balloons of Pokemon cartoon characters.
Church leaders are furious with Simitis for rejecting any dialogue on a possible compromise.
``The church had to take to the streets,'' said a theology student, Irineiou Verikakis.
Conservative lawmakers have encouraged the church. But general elections are not scheduled for nearly four years and the Socialists have a comfortable majority in parliament.
The crusade also has rocked the church itself. Some top clerics have refused to join the protests, saying the ID issue is purely a political issue.
In another blow, the international spiritual head of the Orthodox faith, Istanbul-based Patriarch Bartholomew I, issued a letter to Simitis saying the ID reforms are the ``jurisdiction of the state,'' according to the government-run Athens News Agency.
Police were on full alert around Thessaloniki. Church leaders have urged its followers to be peaceful, but the rally could also draw ultra-nationalists and other fringe groups.
Religious feelings are particularly strong in northern Greece. The Muslim Ottomans still held land there less than 100 years ago and the affinity is deep for the Greek-dominated Byzantine Empire that ended with the fall of nearby Constantinople, now Istanbul, in the 15th century.
Another rally is planned for Athens on June 21.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: asymmetric encryption for text field I updated my question to reflect the sql encryption rather than asp.net.
I created my key in sql
USE DatabaseName
GO
CREATE ASYMMETRIC KEY MyKey
WITH ALGORITHM = RSA_2048
ENCRYPTION BY PASSWORD = 'Password1'

Then I Modified the keys private key password
ALTER ASYMMETRIC KEY MKey
WITH PRIVATE KEY
(ENCRYPTION BY PASSWORD = 'ADifferentPassword',
DECRYPTION BY PASSWORD = 'Password1')

I would like to know why the ENCRYPTION BY PASSWORD is the password used to decrypt the data instead of the DECRYPTION BY PASSWORD?
and then too ENCRYPT I dont really need a password I just need to reference MyKey is this correct?
INSERT dbo.BookSellerBankRouting
(BookSellerID, BankRoutingNBR)
VALUES (22,
EncryptByAsymKey(AsymKey_ID('MyKey'),
'1234567'))

I thought you would have to specify the encryption password to encrypt and the decryption password to decrypt.
But when i use the encryption password in the statement below it decrypts the data and the decrypt password fails
SELECT CAST(DecryptByAsymKey
( AsymKey_ID('MyKey'),
BankRoutingNBR,
N'ADifferentPassword') as varchar(100)) BankRoutingNBR
FROM dbo.BookSellerBankRouting
WHERE BookSellerID = 22

A: It sounds like you are way overcomplicating things.  SQL Server has built in support for encryption and user roles.  You're better off trusting that the professionals who built that knew what they were doing than trying to implement your own scheme.  Start reading about SQL Server encryption here: http://technet.microsoft.com/en-us/library/bb510663.aspx

redpajama_set_name: RedPajamaStackExchange


FedEx Office Print & Ship Center in the Gaylord National Resort and Convention Center offers you virtually everything you need to meet your convention exhibiting needs — from packing and shipping to signage, copying and last-minute office supplies. Do you need presentations, flyers, signs or other marketing materials for your meeting or event? Submit your orders online and we'll have them waiting for you at the hotel or convention center when you arrive.


In our line of work, not that we EVER need reminding but it was amazing to see that romance is not a thing of the past when we had the pleasure of photographing Tracey & Terry's Vow Renewals in October this year. Tracey & Terry had been married 20 years and had their vows re-newed in front of their wonderful family and friends on the weekend of their 20th Wedding Anniversary!
From our first communications with Tracey & Terry they said that they were going "all out' for their vow re-newals and having the wedding that they didn't have the first time around - well they certainly achieved their goal! Their special day was nothing short of beautiful from start to finish with all the small details that make a fantastic wedding totally covered!
Jason started the day with meeting Tracey at her home for some bridal preparation shots. Tracey had a lovely home near the church which helped with getting some really pretty detail shots of her shoes, dress & other accessories. Jason got some lovely, natural shots of the girls getting ready in the morning too, which is really important in helping the couple remember the whole day, not just the more formal parts.
Tracey chose a stunning gown for her re-newals. It was an ivory, floor length dress with a beautiful sweetheart neckline and a sheer, floral overlay skirt. The fitted bodice was accentuated by a pretty diamonte belt and floral appliqué. Tracey had some fantastic Irregular Choice Ivory shoes and cute little lace socks! Tracey's Bridesmaids wore classic Navy chiffon dresses with beaded details and Navy faux fur shrugs to keep the October chill off! All the females in the wedding party carried Ivory Rose bouquets with blue accents.
Sam met Terry and his Dad who was also his Best Man at the Church in Crawley for some shots before the ceremony and to get some of the detail shots of the church. Terry went for a suit which reflected his love of the Mod era for his wedding attire, complete with slim fit three button pin stripe suit, Tassle Loafers & we never did get to see if he had the obligatory braces underneath his suit! Either way he looked the business!
Soon it was time for Tracey to walk up the aisle to meet her already Husband (we don't get to write the often) and the love of her life! It was a beautiful service and I'm sure everything that the couple hoped for. The already happy couple, but even more so after renewing their vows walked back up the Church to lots of smiles and cheers from their friends and family. We made the most of the pretty church front to get some of the more formal group shots that Tracey & Terry wanted and then we all headed off to Highley Manor for the celebrations!
Tracey & Terry had done an amazing job of choosing the decor for their Reception Venue. Highley Manor is a lovely old manor house with lots of original features. The couple had gone along the theme of vintage with a Tea twist. The wedding breakfast room was decked out in Navy and White with lots of flowers in vases and pretty tea cup candles. Each guest had a Fortnum & Mason "wedding breakfast" blend of tea bag as their favour along with pretty ivory paper cages of sweets. It was very personal and well thought out. There was a Jenga game to personalise with messages for the couple instead of a traditional guest book, flip flops for all the ladies to retire their aching feet for dancing and a photo booth provided by Cheesy Smiles.com for some evening entertainment. The cake was nothing short of amazing with a tea cup on top cascading flowers down the tiers and supplied by Willow & Bloom Cakes.
Tracey & Terry mingled with their guests for a little while, enjoying a glass of champagne & then we took them outside to brave the October weather for some private shots. You wouldn't have guessed it was a touch chilly by the smiles on their faces in the photos, particularly when they looked at each other! The grounds around Highley Manor are lovely so we made the most of this getting some great shots of Tracey & Terry to hopefully treasure for many years to come.
The day progressed without a hitch into the wedding breakfast, speeches and then lots of dancing in the evening to a live band.
We had the best day photographing such a lovely couples special day and wish Tracey & Terry many, many more years of happiness together.


text:
See all Disinfo Cases - Detail
The "conveyor of death" will stop, when the authorities of…
The "conveyor of death" will stop, when the authorities of Ukraine are changed radically.
Disproof
Repeating already debunked disinformation. The war in Eastern Ukraine is a consequence of Russian military aggression (bit.ly/1KpsngN), . The Ukrainian army in Eastern Ukraine is defending the country's sovereignty, territorial integrity and citizens (bit.ly/1RMKnDr), .
publication/media
Reported in: Issue 24
Language/target audience: Russian
Keywords: War in Ukraine
Outlet: Dmitriy Kiselyev's "Vesti nedeli" news show, 40:10
Obama overthrew Yanukovych's government in Ukraine
Obama overthrew Yanukovych's government in Ukraine, which was followed by ethnic cleansing. // The USA supported the wars Georgia led against Abkhazia and South Ossetia.
Recurring pro-Kremlin disinformation narrative about Euromaidan.
The demonstrations which began in Kiev in November 2013 were born out of Ukrainians' own desire for a closer relationship with the European Union, and their frustration when former President Yanukovych halted progress toward that goal as a result of Russian pressure (bit.ly/1Ri9ldy), .
Italian Foreign Minister Paolo Gentiloni called for the lifting of…
Italian Foreign Minister Paolo Gentiloni called for the lifting of EU sanctions against Russia following talks with Russian Deputy Prime Minister Arkady Dvorkovich in Rome.
Gentiloni did not call for the lifting of EU restrictive measures. Gentiloni said: "The sanctions will be discussed at the European Council at the end of June. We all know that the decision will be taken based on an assessment of the implementation of the Minsk agreements regarding the Ukrainian crisis. We will evaluate the situation in the coming months" (bit.ly/1rscgM8), . Following the Foreign Affairs Council on 14 March 2016, the HRVP recalled that the implementation of the Minsk agreement is a key condition for any substantial change in the EU's stance towards Russia (bit.ly/22cDUNo), .
The EU is preparing to lift anti-Russian sanctions
The EU is preparing to lift anti-Russian sanctions.
On March 10 the European Council extended until 15 September 2016 EU restrictive measures against 146 people and 37 companies, in view of the continuing undermining or threatening of the territorial integrity, sovereignty and independence of Ukraine (bit.ly/1SUhHZO), . Following the Foreign Affairs Council on 14 March 2016, the HRVP recalled that the implementation of the Minsk agreements is a key condition for any substantial change in the EU's stance towards Russia (bit.ly/22cDUNo), .


Youth can drive the transition planning process by setting their own goals for what they hope to achieve.
1. Check out a webinar from the Federation for Children with Special Needs on Creating a Post-Secondary Vision.
2. Check out Youthood.org to start preparing for what you want to do in the future.
3. Use MY PLAN a tool to help youth think about their strengths, preferences and interests to make a plan to achieve their future vision.


Rumble - A cute video has emerged of a two-year-old girl begging her mother for tomatoes.
The footage, shot on June 10 in Harvest, AL, USA, shows Mellie asking her mother for orange tomatoes.
"She has been obsessed with tomatoes, just like me, ever since she first tried them," her mother wrote online.
"We had to put a fence around our garden to keep her from picking them," she added.
(VIDEO) Annoying Toddler and Cute Foal!
Cute toddler "washes" her hair with sweet tea!


We can't thank our Sponsors, Celebrities, Donors, and Supporters enough for making this one of the most successful Mike Alstott Celebrity Outdoor Weekends to date. We can't wait to see everyone next year. Stay tuned for the date.


Weekly classes in Drama preparing students at Millthorpe, All Saints, Knavesmire and Scarcroft to sit LAMDA exams. In the exam students perform either two monologues or duologues or a combination and answer questions on the pieces. Depending on the grade, the exams pieces can be from two to four minutes in length.
This service provider has not updated their information since 13/11/2017. Our target is that all service information should be no older than six months. You may wish to contact the provider to confirm the information before attending.