1. Introduction
The physiological stress caused by temperature anomalies and drought as a result of global climate change makes forests more vulnerable to dieback worldwide [1,2]. This threat is increasing when combined with biotic stresses, such as forest pests, and, as a result, significant damage is caused to forests, both ecologically and economically [3,4]. The Czech Republic suffers from one of the most aggressive bark-beetle species, Ips typographus (L.) of Eurasia. This species damaged 3.1–5.4% of the Norway spruce (Picea abies (L.) H. Karst.) growing annually across the country in 2017–2019, with economic losses of EUR 260 million [5]. Due to the broad application of sanitation felling and the traditional management strategy against bark beetles [6], the forests in the outbreak areas remain considerably fragmented. These patched forest stands create many forest margins, with a specific microclimate characterized by higher temperatures, increased solar radiation, wind exposure, higher evaporative demand, greater vapor-pressure deficit, and lower soil-water potential [7,8,9,10,11,12]. The altered microclimate on the forest edges is also expected to affect the physiological responses of the trees, e.g., sap flow [13,14].
Fresh forest edges are favorable sites for the aggregation of bark beetles [15], with a higher predisposition to infestation [16]. In general, significant predisposition factors are the distance from previous infestations [16], the presence of wind-thrown trees [17], exposure [18], the age structure of stands [19], the stand density, and spruce representation [20].
However, the forest-stand predisposition to spruce-bark-beetle infestation is a complex process that shows no monocausal relationship. The high density of spruce stands, triggered by economic goals, causes the lower availability of sunlight, precipitation, or nutrients. As a result, it has a negative effect on trees and causes intensive stress [21,22,23].
Tree-transpiration deficits are addressed for bark-beetle host susceptibility, and acute transpiration deficits reduce trees’ defenses against bark-beetle infestations [24]. The reasons for this are higher terpene emissions from the trees and the restricted cooling effect of transpiration [25]. Moreover, lower sap-flow rates are positively correlated with insufficient tree-resin flow, resulting in a higher probability of successful bark-beetle attacks [26]. Furthermore, host factors are more important than site factors for bark-beetle infestation [27]. Trees with higher transpiration rates can defend themselves against bark-beetle attacks more successfully than trees with lower transpiration rates [24]. Hence, tree-sap flow can be considered as an indicator of the strength of the defense mechanism against bark-beetle infestation [15].
Prior studies found that bark beetles mostly target forest-edge trees [16,28], and that it is relatively easy for bark beetles to detect and colonize sun-stressed edge trees [16,29]. Nevertheless, although forest margins are preferable niches for swarming bark beetles, Norway spruce trees were not attacked by I. typographus during a short period [15]. Therefore, the effect of forest-edge clearing on the transpiration of single trees on the susceptibility to bark-beetle I. typographus infestation is still a matter of concern regarding the acute position change in stands.
Two hypotheses were tested to understand the effect of forest-edge clearing on the sap flow of individual trees: (I) The forest edge trees would decrease their sap flow due to more direct sunlight exposure; (II) the decreased competition of the forest-edge trees for resources after cutting would lead to an increase in sap flow. The results of this study provide further insight into the sap-flow response of trees on the forest edge to edge formation. In addition, they provide a scientific base for researchers and decision makers for better and more effective management strategies for bark-beetle infestations.
2. Materials and Methods
2.1. Study Site and Stand Characteristics
The study was carried out in a forest on the property of the Czech University of Life Sciences Forest Enterprise, near Stříbrná Skalice town (09′48″—41°10′55″ N, 28°57′27″—28°59′27″ E, Figure 1). Four plots were created to compare the sap flow of the sample trees from the forest edge after clearcutting and forest interior without any effect. Two plots (named A and B) were chosen as treatment plots, while the other two plots (C and D) remained untreated. We chose the parts of the forest stand in a similar density, without gaps. The specific trees were selected regarding their health status, lacking defoliation, wounds on the stem, or any other damage markers (Table 1). The experimental plots were situated at an altitude of 430 m above sea level. The mineral bedrock was granodiorite (so-called Říčanská žula) covered by Luvisol, with a transition to Pseudogley in some parts. Soils had good nutrition levels and sufficient buffering capacity. The production conditions for forest trees can be evaluated as good or very good. Both experimental sites are situated on a plane or slight slope in an oak–beech zone. The prevailing edaphic category on both sites is the nutrient medium transitional category without significant soil-water influence. The dominant tree species is a 95-year-old spruce, with an admixture of pine, larch, and oak. The average annual temperature is 7–7.5 °C, the average annual sum of precipitation ranges from 600 to 650 mm, and the vegetation-season duration is 150–160 days [30].
2.2. Forest-Edge Cutting
The forest-edge cutting was conducted on the 21st of April 2020 on the treatment plots, and an approximately 50–meter-long open stripe was created on each edge from the south. Changes in the canopy density were recorded using airborne laser scanning (LiDAR) acquired during the first decade of May 2019 and 2020. Scanning was executed in both cases using Leica ALS70 on small Cessna-type aircraft and, simultaneously, with NIR photography. The flight height and overlapping trajectories were set to ensure a minimum of 12 points per m2. Canopy density was evaluated in a 10-meter buffer zone around each measured tree. The point cloud 2019 was set up as a baseline, and the change in the canopy density was calculated as a percentage of newly emerged gaps in 2020. Control plots remained untreated. The distance among the subplots changed from 60 to 120 m (Figure 1). Six trees from the treatment plots and nine trees from the control plots monitored one vegetation period before and after the treatment. The original balanced design of measured trees (5 trees for each plot) was not possible to use due to sensor failure. Hence, only one tree from plot A was included in the study.
2.3. Meteorological Observations
Data for air temperature (°C), global radiation (W·m−2), air relative humidity (%), wind speed (m s−1), and precipitation (mm) were collected with an automatic weather station during the study period. The weather station (Minikin group, EMS Brno, Brno, Czech Republic) was located at a height of 2 m in a bare, open space close to the plots (<200 m in a straight-line distance) (Figure 1.) Data were recorded in 10-min intervals, and daily values were calculated as averages of all measurements.
2.4. Soil-Water Potential and Bark-Surface Temperatures
Soil-water potential (SWP) (Ψ, kPa) (Teros 21, Meter Group, München, Germany) was recorded at topsoil (10 cm in depth) with 5 soil-water-potential sensors in each plot collecting the data in 10-min intervals. Mean daily SWP was calculated by averaging overall measurements from five sensors for each plot. Bark-surface temperatures were measured for every monitored tree from the north and the south in 10-min intervals via infrared thermometers (Apogee Instruments, Logan, UT, USA) placed 3 m above the ground and 30 cm away from the tree bark. Mean values were recorded by averaging the measurements for 1 h and 24 h.
2.5. Sap-Flow Measurements
Six sample trees from treatment plots and nine sample trees from control plots were chosen by taking into account their similarities, such as height, crown area, and diameter at breast height. Sap-flow sensors (EMS 81, EMS Brno, Brno, Czech Republic) were installed at a height of 2 m in 2018 on the sample trees in all plots. Since forest-edge cutting was conducted on 21st of April 2020 in treatment plots, only the data recorded from May to November were evaluated for the calibration and treatment years. Sap-flow sensors were based on the trunk-heat-balance method [31,32], consisting of four electrodes; three electrodes positioned at the top were heated via electricity and the fourth electrode, placed 10 cm below, was used as reference. The temperature difference between the heated and reference electrodes was set to 1 K. Method calculates the heat balance of a defined heated space according to the equation below [33]:
(1)
where Q is the sap-flow rate (kg s−1 cm−1), P is the power of heat input (W), ΔT is the temperature difference in the measuring points (K), cw is the specific heat of water (J kg−1 K−1), and d is the circumferential distance covered by the electrodes (cm). Z/cw represents the heat loss determined under conditions in which there is no sap flow. The THB method yields sap flow as kilograms; therefore, the Q of the measured area was multiplied by the length of the circumference of the xylem at the height of installation to calculate total tree-water use [33].All the data collected from the field sensors (meteorological data, SWP, bark surface temperature and sap flow data) were stored in a single data logger (EMS Brno GreyBox N2N 3P, Brno, Czech Republic), connected to a cloud system via GSM, providing the data online and in a secure manner.
2.6. Statistical Analyses
Sample trees from the Cp (a total of 9 sample trees from plots C and D) were used as a baseline for evaluation of the forest edge effect in Tp. For this purpose, a general (mean) sap-flow file was created according to the data of calibration period (2019). A regression equation was set between each sample tree on the Tp and the created general file. This regression equation was used to calculate predicted values after the treatment period (2020, after the forest-edge clearance). Because of the non-normal distribution and small sample size, Wilcoxon signed paired samples ranks test was applied for statistical comparison before and after the treatment (measured vs. predicted). The same test was used to examine whether there was any statistically significant difference between bark-surface temperatures of Cp and Tp groups and for each sample tree in Tp from the north and the south aspects before and after the treatment. The correlation between bark-surface temperature and sap flow was tested by Spearman correlation analysis. Statistical analyses were performed with a 0.05 confidence interval using Statistical Package for the Social Sciences 21.0 (SPSS IBM Corp.; Armonk, NY, USA). A linear regression model was used to evaluate sap flow and stand canopy density. Model selection and validation were conducted based on the rules in [34]. The main procedure for model selection was comparison of Akaike’s information criterion of the regression formulas, and for model validation, graphic representation of the model residua against model fit. This part of the data analysis was performed in R 4.0.2 environment [35].
3. Results
3.1. Environmental Variables
The temporal patterns of some of the environmental parameters during the study period are given in Figure 2, and the mean values of the same parameters are shown in Table 2. The annual precipitation was 631 mm in 2019 and 711 mm in 2020. The rainfall with the highest intensity was recorded in June 2020, with 16 mm of precipitation within 20 min. The daily mean air temperature was 9.6 °C and the maximum air temperature reached 35.5 °C in June 2019. In 2020, the daily mean air temperature was recorded as 9.2 °C, and the maximum value reached 33 °C in August. The global radiation was similar in both years, and the mean daily global radiation was 308 W·m−2 during the study period. The mean daily relative humidity was 83.8% and the wind speed was 0.6 m s−1.
3.2. Soil-Water Potential and Bark-Surface Temperatures
The soil-water-potential measurements in the experimental plots showed that no significant drought period was observed in the topsoil in 2019. On the other hand, moderate drought (−800 to −1400 kPa) was recorded for plots A and C, and severe drought was recorded (<−1500 kPa) for plots B and D in September 2020 due to the lack of precipitation in this month (only 2 mm of precipitation were recorded in September 2020, Figure 2). As a result of this, the soil-water potentials of the study plots were lower in 2020 than in 2019 (Table 2). However, this extreme period lasted only about a week.
The average values of the bark-surface temperatures in the calibration period did not differ (North Tp: 15.4 ± 6.3 °C, Cp: 15.5 ± 6.3 °C, South Tp: 16.2 ± 6.3 °C; Cp: 16.2 ± 6.3 °C). On the contrary, during the treatment period, the difference in the average bark-surface temperatures between the Tp and Cp groups showed a statistically significant difference (p < 0.05; North Tp:16.0 ± 5.3 °C; Cp: 14.8 ± 5.4 °C, South Tp: 16.0 °C ± 6.3 °C; Cp: 15.1 ± 5.9 °C). The bark-surface temperatures in this study were taken as the indicators of the temperature changes in the canopies of individual trees caused by the forest-edge clearing, since there was a significant correlation between the sap flow and bark temperatures (Table 3).
3.3. Sap Flow
The diurnal pattern of the sample trees was similar in 2019 in both the treatment and the control plots (Figure 3). In Cp, the average daily sap flow was between 11 and 44 kg, and the maximum daily sap flow was between 24 and 92 kg during the calibration period. The average daily maximum sap flow ranged from 10 to 36 kg, and the maximum daily sap flow ranged from 24 to 77 kg in Tp in 2019. The sap-flow rates varied according to the diameter class of the sample trees in the stands. The diurnal pattern did not change for the control plots in 2020, and the average daily sap flow ranged between 8 and 34 kg, while the daily maximum reached 77 kg. After clear-cutting on treatment plots, the sap flows of four sample trees on the first and second rows from the new edge increased, and the sap flows of two trees on the third row slightly decreased (Table 4). All the differences between the measured and predicted values were statistically significant on the Wilcoxon signed-ranks test (p < 0.05). There was a positive correlation between the increase in the sap flow of the trees and the decrease rate of the surrounding canopy (p < 0.05; Figure 4).
4. Discussion
The physiological responses of trees are related to the existing environmental conditions, and ecophysiological models, tree-formation studies, or evaluations of climate-vegetation dynamics are often explained based on these relationships [36]. Forest-edge cutting changes the equilibrium between the canopy and the surrounding microclimate [37,38]. It is generally thought that the momentum flux across the edge and increased exposure to wind enhances the latent heat flux between the canopy and the atmosphere [39]. Therefore, it is expected to monitor the effect of changing microclimatic gradients due to the clear cutting on sap flow, one of the primary physiological responses of trees linked to atmospheric conditions.
Studies have been conducted to understand the edge effect on the sap flow of trees. Hernandez-Santana et al. [40] indicated that the sap-flow rates of Acer saccharinum L. trees (dominant species) growing on the south-east edge (prevailing winds) were 39% greater than south-east interior trees and 30% and 69% greater than north-west edge and interior trees, respectively. Giambelluca et al. [41] compared several tropical trees on the edge and interior of a stand and found that there was a decreasing transpiration trend with distance from the edge at a statistically significant rate of −0.0135 mm per m. In addition, the transpiration rates of well-exposed trees were higher than those of poorly exposed trees. Herbst et al. [13] reported that due to the enhanced sap flow (by 33–82%, depending on the size class) of trees located at the edge, there was a substantial influence on stand transpiration in ash (Fraxinus excelsior L.), whereas in oak (Quercus robur L.), the sap flow was similar among edge and inner trees. They addressed the species-specific physiological response to the new micrometeorological conditions as the reason for this difference. Bogss et al. [42] indicated that a 27% cut in an upland forest riparian buffer zone resulted in a 43% increase in sap flow.
Our results showed that forest-edge cutting increased the transpiration of three sample trees by approximately 16% (B2, B3, B6) and of one sample tree by 8% (A8) in the first vegetation period. The hypothesis that the sap flow would increase after the forest-edge formation due to the enhancement of light, mineral nutrients, and soil water, which were lacking due to the competition, was supported (Table 4). These results are consistent with those of Giambelluca et al. [41] and Herbst et al. [13], who reported that the average sap-flow increase was 16% in a tropical-forest fragment and within the range for three of the four species for the relationship between edge and inner trees.
Two sample trees on the third row decreased in sap flow after clear cutting (B9 and B10); however, the rates were lower, such as 3% and 8% (Table 4). The different responses of these two trees to the forest-edge cutting can be explained based on the decrease in the bark-surface temperatures compared to the calibration period, since bark-surface temperature significantly correlated with the sap flow (Table 3). While the bark-surface temperatures of the trees A8, B2, B3, and B6 increased compared to the calibration-period average, the average bark-surface temperature of the B9 did not differ, and that of the B10 decreased. The reason for the increase in the bark temperatures of the trees in the first two rows is the enhanced solar radiation due to the forest-edge cutting. Another reason these two trees reacted differently from the others is that the amount of canopy around them did not change as much (87–96% compared to the year 2019) as in the first two rows (69–83% compared to the year 2019) as a decrease in the surrounding canopy density caused a greater sap-flow response (Figure 4.) According to Jarvis [43], the average stomatal conductance of a tree crown can either increase due to the higher irradiance or decrease due to the drier air and lower leaf-water potential. This is why the differences recorded for the trees from the forest edge and forest interior were assigned to the forest-edge formation. Soil moisture, the important factor influencing the sap flow, ranged between no and mild stress. The acute change in wind exposure and irradiance caused an increase in the sap flows of the sample trees in the first and second rows. On the other hand, the inner trees with a buffer zone in front of them could have been sheltered and not affected by the changes as strongly as those on the front line. In other words, the effect of clear cutting was best observed in the sample trees, which were the closest to the clearance. In addition, a 50–meter-long forest edge might not have been enough to observe the same response from the inner trees.
New forest edges and forest fragmentations occurred because of rapid disturbances, such as storm damages, windbreaks and timber harvesting, which are connected to the host selection of Ips typographus, one of the most destructive forest pests for the Norway spruce [44,45,46]. Previous research found that trees at forest edges are favored by bark beetles, as the stress of sun exposure facilitates the detection and colonization of these trees [16,28,29]. On the other hand, a recent study revealed that Norway spruce individuals were not attacked by Ips typographus after edge creation during a short period, while the sap flow of the edge trees was slightly higher than that of the shaded trees [15].
Bark-beetle attacks are linked to sap flow. Trees with higher transpiration rates are expected to be more resistant than those with reduced transpiration rates due to the emission of terpenes, depending on the temperature [25]. In addition, lower transpiration rates cause more insufficient tree-resin flow, resulting in a higher proportion of successful bark-beetle attacks [26]. Low transpiration rates due to drought can lead to tree death, as transpiration is part of constitutive and induced defenses [47]. The xylem and phloem parts are interconnected according to the pressure-flow theory [48], which may influence the transport of carbohydrates and, consequently, the defense compounds [49]. This investigation showed that the sudden formation of new forest edges increased the transpiration of the sample trees on the first and second rows if there was enough water potential in the soil horizons. The bark-surface temperatures increased on these trees; however, this situation did not cause any damage and was compensated by increased sap flow. Therefore, depending on the linkage between the sap-flow rate and bark-beetle attacks, it could be said that forest-edge formation reduces the likelihood of sudden bark-beetle attacks on trees if trees are healthy, and there is enough water in the soil to not limit the responses of trees to new micro-meteorological conditions.
5. Conclusions
We found that the effect of a 50–meter-long forest edge increased the sap flow of the Norway spruce individuals near the margin by up to 16% in the first vegetation period. By contrast, the sap flow of the sample trees farther from the forest edge decreased. The bark-surface temperatures between the treatment and control groups showed statistically significant differences after forest-edge creation, with higher values in the first row of the forest margin. The results of the study could be used for forest-management studies, since forest fragmentation is discussed for attenuating drought stress on forests. The results are useful for watershed-management practices, due to the changing water balance of forested watersheds, and to forest-protection efforts, because of the linkage between plants’ physiological traits and forest pests.
Long-term follow-up studies with increased sample numbers under different scenarios, such as forest-edge cutting of varying lengths, and alternative aspects are needed to better understand the effect of forest-edge formation on the sap flow responses of trees to new microclimate conditions.
Conceptualization, I.T. and R.M.; methodology, M.S.Ö., I.T. and R.M.; software, M.S.Ö., R.M. and P.S.; validation, I.T., R.M., and M.S.Ö.; formal analysis, M.S.Ö., I.T. and R.M.; investigation, M.S.Ö., I.T. and R.M.; resources, I.T. and R.M.; data curation, M.S.Ö., R.M., P.S. and I.T.; writing—original draft preparation, M.S.Ö. and I.T.; writing—review and editing, M.S.Ö., I.T. and R.M.; visualization, M.S.Ö. and R.M.; supervision, I.T. and R.M.; funding acquisition, I.T. and R.M. All authors have read and agreed to the published version of the manuscript.
Data are available on a request.
The authors are grateful to Jiří Kučera from Environmental Measuring Systems for his help and contribution to the data evaluation and to Daniel Tyšer for improving the visual form of the figures.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Location of the study site and sub-plots. The clearcutting of the forest edges can be seen in the right photograph.
Enlarge this image.Figure 2. Temporal pattern in meteorological parameters during the study period at the study site. (A): global radiation (W·m−2), (B): air temperature (°C), (C): relative humidity (%), (D): precipitation and cumulative precipitation (mm), (E): wind speed (m s−1), (F): soil-water potential (kPa) of the study plots (x: plot A, ○: plot B, □: plot C, ∆: plot D).
Enlarge this image.Figure 3. The diurnal mean sap-flow pattern of the study plots. G: sap flow (kg day−1). ∆ with red color: the measured sap flow of Cp. X with blue color: the measured sap flow of Tp. □ with green color: the predicted values of Tp.
Enlarge this image.Figure 4. Relationship between stand-canopy density and sap-flow difference on the forest edge with the 95% confidence interval. X-axis reduction in canopy density between 2019 and 2020 on 10-meter buffer around the tree, Y-axis change in sap flow measured df = 4; R2 = 0.69; p < 0.05).
Stand characteristics of the experimental plots.
| Treatment | Plot/Tree ID | DBH | Height | Bark Thickness |
|---|---|---|---|---|
| [cm] | [m] | [cm] | ||
| forest edge | A8 | 33 | 28 | 0.5 |
| forest edge | B2 | 41 | 31 | 0.9 |
| forest edge | B3 | 37 | 26 | 0.7 |
| forest edge | B6 | 35 | 31 | 0.6 |
| forest edge | B9 | 38 | 32 | 0.8 |
| forest edge | B10 | 47 | 32 | 0.8 |
| Forest-edge average | 38.5 | 30 | 0.72 | |
| Control | ||||
| forest interior | C2 | 37 | 29 | 0.7 |
| forest interior | C5 | 36 | 26 | 0.8 |
| forest interior | C7 | 38 | 26 | 0.9 |
| forest interior | C8 | 35 | 26 | 0.6 |
| forest interior | C10 | 31 | 25 | 0.6 |
| forest interior | D3 | 34 | 26 | 0.7 |
| forest interior | D4 | 36 | 27 | 0.8 |
| forest interior | D6 | 40 | 25 | 0.6 |
| forest interior | D8 | 43 | 29 | 0.9 |
| Forest-interior average | 36.6 | 27 | 0.73 | |
Mean values (mean ± standard deviation) of some of the climate variables were measured at the study site. * Standard deviation. ** Global radiation in the column represents the mean daytime radiation. *** Daily precipitation data were obtained from annual precipitation by dividing them by 365.
| Variable | 2019 | 2020 | 2019–2020 |
|---|---|---|---|
| Mean ± SD * | Mean ± SD * | Range | |
| Global Radiation (W·m−2) ** | 307 ± 82.7 | 309 ± 83 | 1.5–870 |
| Air temperature (°C) | 9.6 ± 8.2 | 9.2 ± 7.7 | −9.8–35.5 |
| Relative Humidity (%) | 83.2 ± 18.9 | 84.4 ± 19.1 | 11.0–100 |
| Daily precipitation (mm) *** | 1.7 ± 4.4 | 1.9 ± 4.5 | 0–39 |
| Wind Speed (m s−1) | 0.6 ± 0.4 | 0.5 ± 0.4 | 0–5.5 |
| Soil water potential (kPa) (Plot A) | −37 ± 44.7 | −82 ± 207 | −8.5–−1138.5 |
| Soil water potential (kPa) (Plot B) | −39.2 ± 37.4 | −97 ± 234 | −10.1–−1795.9 |
| Soil water potential (kPa) (Plot C) | −18.6 ± 15.6 | −55.6 ±148 | −8.6–−957.2 |
| Soil water potential (kPa) (Plot D) | −71.0 ± 110 | −123 ± 297 | −8.8–−1941 |
Bark-surface temperatures of the sample trees before and after forest-edge cutting and the correlation between bark-surface temperatures and tree-sap flow. Superscript letters in the same line indicate significant differences (p < 0.05) between the same aspects for 2019 and 2020.
| Tree Identity | Bark-Surface Temperature (°C) in Calibration Period (Mean ± SD) | Bark-Surface Temperature (°C) in Treatment Period (Mean ± SD) | Correlation Coefficient between Bark-Surface Temperature and Tree-Sap Flow (p ˂ 0.05) | |||
|---|---|---|---|---|---|---|
| North | South | North | South | North | South | |
| A8 | 15.5 ± 6.3 x | 15.9 ± 6.4 a | 15.5 ± 5.8 x | 16.2 ± 6.6 a | 0.63 | 0.65 |
| B2 | 15.5 ± 6.2 x | 15.8 ± 6.4 a | 15.7 ± 5.8 x | 17.0 ± 7.3 a | 0.62 | 0.67 |
| B3 | 15.5 ± 6.2 x | 15.7 ± 6.4 a | 16.2 ± 5.7 x | 16.3 ± 6.2 a | 0.60 | 0.70 |
| B6 | 15.6 ± 6.3 x | 15.9 ± 6.5 a | 16.1 ± 5.4 x | 16.1 ± 6.2 a | 0.61 | 0.71 |
| B9 | 15.5 ± 6.4 x | 15.6 ± 6.4 a | 15.5 ± 5.7 x | 15.6 ± 6.0 a | 0.64 | 0.67 |
| B10 | 15.4 ± 6.3 x | 15.8 ± 6.4 a | 15.4 ± 5.0 x | 14.6 ± 5.7 a | 0.57 | 0.61 |
The effect of forest-edge clearance on sap flow of sample trees. “+” represents an increase, and “−“represents a decrease in sap flow. (*) denotes statistical significance at p < 0.05 level.
| Plot Code/Tree Number | Measured Average Daily Sap Flow (kg) | Predicted Average Daily Sap Flow |
The Response of Sample Tree to Forest Edge Cutting | Position after the Clear Cutting |
|---|---|---|---|---|
| A8 | 15.6 ± 10.6 | 14.0 ± 9.3 | +8% (*) | Second Row |
| B2 | 10.4 ± 7.4 | 9.0 ± 5.9 | +16% (*) | Second Row |
| B3 | 20.7 ± 12.0 | 17.5 ± 11.7 | +16% (*) | First Row |
| B6 | 15.2 ± 9.7 | 13.0 ± 8.7 | +16% (*) | Second Row |
| B9 | 20.0 ± 14.8 | 20.7 ± 13.8 | −3% (*) | Third Row |
| B10 | 28.1 ± 21.1 | 30.7 ± 20.4 | −8% (*) | Third Row |
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; Modlinger, Roman 2
1 Faculty of Forestry, Isparta University of Applied Sciences, 32260 Isparta, Turkey
2 Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, 16500 Prague, Czech Republic