The effects of experimental forestry treatments on site ... · The effects of experimental forestry treatments on site conditions: short response study from an oak-hornbeam forest

The effects of experimental forestry treatments on site

conditions: short response study from an oak-hornbeam

forest

Bence Kovács Corresp., 1, 2, 3 , Flóra Tinya 1 , Erika Guba 1 , Csaba Németh 3 , Vivien Sass 4 , András Bidló 4 , PéterÓdor 1, 3

1 Institute of Ecology and Botany, MTA Centre for Ecological Research, Vácrátót, Hungary2 Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Budapest, Hungary3 GINOP Sustainable Ecosystems Research Group, MTA Centre for Ecological Research, Tihany, Hungary4 Institute of Environmental and Earth Sciences, University of Sopron, Sopron, Hungary

Corresponding Author: Bence Kovács

Email address: [email protected]

Background

Forest management alters the forest site, however, information is still limited about how different

silvicultural treatments modify these conditions. In the past decades, besides rotation forestry, new

silvicultural systems were introduced, fulfilling the requirements of multipurpose forestry. In this study

we investigated the short-term effects of different forestry treatments on microclimate, litter and soil

conditions in a European oak-dominated forest.

Methods

A forest ecological experiment was established in a homogenous, managed, 80 years old, Quercus

petraea and Carpinus betulus dominated forest, in 2014. Five treatments of three different forestry

systems were installed following a complete block design in six replicates: clear-cutting with a circular

retention tree group as typical elements of the clear-cutting system, preparation cutting (partial harvest)

belonging to the shelterwood system, gap-cutting as a common tool of continuous cover forestry in

Europe and uncut control. Microclimate, litter and soil variables were measured systematically since

2014. Here we present the results of the analyses of the first growing season following the interventions

(2015).

Results

We found that there is strong treatment effect in the case of microclimate and litter varibles, but for soil

characteristics the impacts will presumably appear in longer term. The increment of total and diffuse

light was the greatest in clear-cutting, in gap-cutting the illuminance was intermediate, while light-levels

were lower and less variant in preparation cutting and retention tree group. Air and soil temperature as

well as vapor pressure deficit increased the most in clear-cutting; both means and variances were the

highest in this treamtment. Retention tree group could not buffer the means of the temperature

variables, but a small group of tree individuals was able to ameliorate the extremes of the microclimate.

Significant increase of soil moisture was measured as a consequence of gap-cutting and less

pronouncedly in clear-cutting. Similarly, litter pH and moisture were the highest in these treatment types.

Significant increment in soil pH was detected in retention tree group. Through the analysis of

microclimate variables during the growing season, we could demonstrate the buffering effect of forest

PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.26643v1 | CC BY 4.0 Open Access | rec: 8 Mar 2018, publ: 8 Mar 2018

canopy: differences between treatments were the greatest in summer for all microclimate variables.

Discussion

We can conclude that in oak–hornbeam forest, only less intensive and spatially heterogeneous

silvicultural treatments could preserve the stable, cooler and humid below-canopy microclimate,

therefore, group selection using gaps and irregular shelterwood systems are favourable. Our findings can

support the mitigation of the negative impacts of climate change in managed forest. Moreover, besides

basic research we can formulate implications for foresters and conservationists to preserve biodiversity

in temperate forests.


1 The effects of experimental forestry treatments on site conditions: short response study

2 from an oak-hornbeam forest

3

4 Kovács, B.1,2,3, Tinya, F.1, Guba, E.1, Németh, Cs.3, Sass, V.4, Bidló, A.4, Ódor, P.1,3

5

61 MTA Centre for Ecological Research, Institute of Ecology and Botany, Alkotmány út 2-4, H-

7 2163 Vácrátót, Hungary

82 Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University,

9 Pázmány Péter sétány 1/C, H-1117 Budapest, Hungary

103 MTA Centre for Ecological Research, GINOP Sustainable Ecosystems Research Group,

11 Klebelsberg Kuno utca 3, H-8237 Tihany, Hungary

124 University of Sopron, Institute of Environmental and Earth Sciences, Bajcsy-Zsilinszky utca 4,

13 H-9400 Sopron, Hungary


14 ABSTRACT

15 Background

16 Forest management alters the forest site, however, information is still limited about how different

17 silvicultural treatments modify these conditions. In the past decades, besides rotation forestry, new

18 silvicultural systems were introduced, fulfilling the requirements of multipurpose forestry. In this study

19 we investigated the short-term effects of different forestry treatments on microclimate, litter and soil

20 conditions in a European oak-dominated forest.

21 Methods

22 A forest ecological experiment was established in a homogenous, managed, 80 years old, Quercus

23 petraea and Carpinus betulus dominated forest, in 2014. Five treatments of three different forestry

24 systems were installed following a complete block design in six replicates: clear-cutting with a circular

25 retention tree group as typical elements of the clear-cutting system, preparation cutting (partial harvest)

26 belonging to the shelterwood system, gap-cutting as a common tool of continuous cover forestry in

27 Europe and uncut control. Microclimate, litter and soil variables were measured systematically since

28 2014. Here we present the results of the analyses of the first growing season following the interventions

29 (2015).

30 Results

31 We found that there is strong treatment effect in the case of microclimate and litter varibles, but for soil

32 characteristics the impacts will presumably appear in longer term. The increment of total and diffuse light

33 was the greatest in clear-cutting, in gap-cutting the illuminance was intermediate, while light-levels were

34 lower and less variant in preparation cutting and retention tree group. Air and soil temperature as well as

35 vapor pressure deficit increased the most in clear-cutting; both means and variances were the highest in

36 this treamtment. Retention tree group could not buffer the means of the temperature variables, but a small

37 group of tree individuals was able to ameliorate the extremes of the microclimate. Significant increase of

38 soil moisture was measured as a consequence of gap-cutting and less pronouncedly in clear-cutting.

39 Similarly, litter pH and moisture were the highest in these treatment types. Significant increment in soil

40 pH was detected in retention tree group. Through the analysis of microclimate variables during the

41 growing season, we could demonstrate the buffering effect of forest canopy: differences between

42 treatments were the greatest in summer for all microclimate variables.

43 Discussion

44 We can conclude that in oak–hornbeam forest, only less intensive and spatially heterogeneous

45 silvicultural treatments could preserve the stable, cooler and humid below-canopy microclimate,

46 therefore, group selection using gaps and irregular shelterwood systems are favourable. Our findings can

47 support the mitigation of the negative impacts of climate change in managed forest. Moreover, besides


48 basic research we can formulate implications for foresters and conservationists to preserve biodiversity in

49 temperate forests.


50 INTRODUCTION

51

52 Forest management induces substantial alterations in environmental conditions that fundamentally

53 influence ecosystem structure and functions (Edwards et al., 2014; Zhao and Jackson, 2014).

54 Furthermore, these impacts also affect the long-term survival, regeneration and diversity of forest-

55 dwelling organism groups (e.g. Paillet et al., 2010; Chaudhary et al., 2016).

56 Silvicultural treatments cause changes in biodiversity both directly and indirectly (Keenan &

57 Kimmins, 1993; Rosenvald & Lõhmus, 2008). Direct effects are pronounced by the elimination of

58 food resources and indispensable microhabitats, such as (host) tree individuals that are substrate

59 of epiphytes (Lõhmus & Lõhmus, 2010), woody debris for deadwood-dependent communities

60 (Seibold et al., 2015) or standing dead trees as commodity for cavity nesters (Ibarra et al., 2017).

61 However, most species of the forest biota are influenced by forest management through indirect

62 pathways: through the alteration of forest site conditions – microclimate, litter attributes, soil

63 characteristics – and biogeochemical cycles (Zheng et al., 2000; Sayer, 2005; Thiffault et al., 2011;

64 Kishchuk et al., 2014; Frey et al., 2016). Microclimate is also a major driver of ecosystem

65 processes such as decomposition, respiration and nutrient dynamics (Thibodeau et al., 2000;

66 Stoffel et al., 2010; Knapp et al., 2014).

67 Studying the effects of different management types on forest site conditions – and especially on

68 microclimate – at local scale could provide evidences that support the adaptation strategies

69 mitigating the negative impacts of climate change (Suggitt et al., 2011; Latimer & Zuckerberg,

70 2017). Fine-scale measurements and models are necessary to calculate probable species

71 distributions and creating predictions for the ecological processes (De Frenne et al., 2013; Lenoir,

72 Hattab & Pierre, 2017; Greiser et al., 2018). Therefore, for conservation purposes, it is important

73 to investigate how forestry treatments alter the forest site conditions, through numerous, highly

74 interrelated effects.

75 Forest stands create unique, buffered below-canopy microclimates (Geiger, Aron & Todhunter,

76 1995; Chen et al., 1999), compared to open-fields (von Arx, Dobbertin & Rebetez, 2012) or to

77 plantations (Hardwick et al., 2015). It is mainly determined by the tree species composition, tree

78 species richness and the stand structure (Lin et al., 2017; Gebauer, Horna & Leuschner, 2012; von

79 Arx, Dobbertin & Rebetez, 2012; Ehbrecht et al., 2017): foliage of the different vegetation layers

80 absorbs a great proportion of incoming energy and reduces the loss of longwave radiation from


81 the surface. Soil and litter characteristics are also influential (von Arx et al., 2013; Kovács, Tinya

82 & Ódor, 2017). As a result of these effects forest microclimate can be characterized by lower

83 diurnal and seasonal magnitudes in case of several microclimatic variables (Geiger, Aron &

84 Todhunter, 1995; von Arx et al., 2013). Similarly, litter layer is important for many decomposer

85 groups as habitat and food resource, for the nutrient cycling, smoothing the fluctuations of the

86 physical and chemical properties of the topsoil and below-canopy ambient air (Ogée & Brunet,

87 2002; Sayer, 2005).

88 The alterations of the microclimate, litter and soil properties and thus changes in forest

89 communities are highly dependent on the spatial extent, the spatiotemporal pattern, frequency or

90 severity of the applied forest management approaches (Bicknell et al., 2014; Dieler et al., 2017;

91 Schall et al., 2018). Changes in the main structural elements of forests (e.g. canopy closure,

92 horizontal and vertical foliage distribution) result in considerable alterations in the processes of

93 the soil-vegetation-atmosphere system. This effect is unambiguous at clear-cuttings (Keenan &

94 Kimmins, 1993; Aussenac, 2000; Marshall, 2000), but it is also observable at partial cuttings like

95 thinning, group selection or retention tree harvesting (Carlson & Groot, 1997; Heithecker &

96 Halpern, 2006; Ryu et al., 2009; Bigelow & North, 2012; Kishchuk et al., 2014; Coulombe, Sirois

97 & Paré, 2017).

98 As Shall et al. (2018) stated, forest management in the temperate zone is shifting globally from

99 even-aged rotation management systems towards continuous cover silvicultural approaches that

100 support structural heterogeneity. Bernes et al.(2015) pointed out that there is a knowledge gap

101 concerning Central European deciduous forests in the aspect of possible multipurpose forest

102 management alternatives. Forestry experiments are necessary for the understanding of the complex

103 relationships between different management practices and forest site, regeneration and biodiversity

104 characteristics. There are several studies that examine the effects of multiple treatment levels

105 within a particular silvicultural system on microclimate: different artificial gap sizes and shapes

106 (Carlson & Groot, 1997; Gálhidy et al., 2006), various combinations of retention levels and spatial

107 configurations (Heithecker & Halpern, 2006), thinning intensities and patterns (Weng et al., 2007;

108 Rambo & North, 2009) and fuel-reduction oriented treatments (Ma et al., 2010). Similarly, several

109 research presented results of changes in soil characteristics and biochemical processes by the

110 investigation of lower intensity harvest types (Thibodeau et al., 2000; Ryu et al., 2009; Jerabkova

111 et al., 2011; Coulombe, Sirois & Paré, 2017)). Most of the studies dealing with the interactions of


112 harvesting and forest site conditions concentrate on one selected forestry system, whereas it is rare

113 to apply two or more silvicultural strategies within one experimental design (but see e.g. Kishchuk

114 et al., 2014; Knapp et al., 2014). The “Pilis Experiment” was implemented to compare the effects

115 of different treatment types – belonging to three opposing silvicultural systems eligible in Europe

116 (clear-cutting, shelterwood system and continuous-cover forestry with gap-cutting; Matthews,

117 1991) – on site conditions, regeneration and biodiversity. Our open-field, multi-taxa experiment

118 was established in 2014 (http://piliskiserlet.okologia.mta.hu/en). As a target habitat type, sessile

119 oak-hornbeam forests were chosen, which represent a widespread deciduous woodland habitat

120 type in the Pannon Ecoregion (Bölöni et al., 2008) and generally in Central Europe (Brus et al.,

121 2012). Furthermore, in line with that, this is one of the focal indigenous forest types for timber

122 harvesting in this region, due to the high-quality timber of sessile oak (Annighöfer et al., 2015).

123 Here, we focus on the effects of the performed experimental treatments on forest site conditions

124 during the first post-harvest period. Our objectives are to quantify differences induced by the

125 applied management treatments (clear-cutting, gap-cutting, preparation cutting and retention tree

126 group) (1) on the mean and magnitude of microclimate, soil and litter variables during the growing

127 season, (2) on the temporal pattern of site condition variables through a growing season, and (3)

128 on the diurnal patterns of selected microclimate variables.


http://piliskiserlet.okologia.mta.hu/en

129 MATERIALS AND METHODS

130

131 Study area

132 The study was conducted in the Pilis Mountains, north-eastern ridge of the Transdanubian Range,

133 Hungary (47°40’N, 18°54’E; Fig. 1A). Plots are situated on a horst ridge (370–470 m a.s.l) on

134 moderate (7.0–10.6°), north-facing slopes. Average annual mean temperature is 9.0-9.5°C (16.0–

135 17.0°C in the growing season) with a mean annual precipitation of 650 mm (Dövényi, 2010).

136 The bedrock consists of Dachsteinian limestone and Lattorfian sandstone with loess (Dövényi,

137 2010). According to the soil profiles established on the study site, depth of the soil is changing

138 along a slight topographic gradient (Supplemental Information 1): deep (250 cm) at the lower parts

139 and shallow (70 cm) nearer the ridge. In the lower parts, the soil type is brown forest soil with clay

140 illuviation (luvisol), while in the upper parts it is rendzic leptosol (Krasilnikov et al., 2009). Soils

141 are slightly acidic (pH of the 0-20 cm layer is 4.6±0.2). The physical and chemical characteristics

142 of the upper 50 cm of soil are similar in the whole area independently from soil depth

143 (Supplemental Information 1). The variety of soil types did not cause discernible variability in the

144 woody vegetation (Table 1).

145 The study site was established in an approximately 40 ha sized homogeneous block in a managed,

146 two-layered sessile oak-hornbeam forest stand (Natura 2000 code: 91G0; Council Directive

147 92/43/EEC). The study site is legally protected, the experiment was approved by the Pest Megyei

148 Kormányhivatal (Pest County Administration; permission number: KTF:30362-3/2014). The

149 stand is even-aged (80 years old) and has a relatively uniform structure (Table 1) and species

150 composition due to the applied shelterwood silvicultural system. Upper canopy layer (average

151 height: 21 m, mean DBH: 27.6 cm) is dominated by sessile oak (Quercus petraea Matt. (Liebl.)),

152 while the second most abundant tree species, hornbeam (Carpinus betulus L.) forms a subcanopy

153 layer with an average height of 11 m and mean DBH of 11.6 cm (Table 1 and Fig. S2.2). Other

154 woody species are rare, individuals of Fraxinus ornus L., Fagus sylvatica L., Quercus cerris L.

155 and Cerasus avium L. were recorded in the tree layer as admixing tree species. Before the

156 experimental treatments, mean basal area (BA) of the upper layer was 29.4(±4.3) m2ha-1 and

157 8.8(±2.6) m2ha-1 in the case of the secondary canopy layer, respectively (Table 1). Canopy closure

158 was rather homogenous, it varied between 81 and 94%. Shrub layer was scarce and mainly

159 consisted of the regeneration of hornbeam and Fraxinus ornus L. with lower cover of shrub species


160 e.g. Crataegus monogyna Jacq., Cornus mas L., Ligustrum vulgare L. and Euonymus verrucosus

161 Scop.. The understory layer was formed by general and mesic forest species, dominant species are

162 Carex pilosa Scop., Melica uniflora Retz., Cardamine bulbifera L., Galium odoratum (L.) Scop.

163 and Galium schultesii Vest. Before the treatments (in 2014), cover of herb layer was approximately

164 40% (unpublished data).

165

166 Study design

167 Five treatment types (Fig. S2.1) were implemented in a randomized complete block design in six

168 replicates (hereafter blocks, Fig. 1B and 1C):

169 1. Control (C): The original stand characteristics remained unaltered.

170 2. Clear-cutting (CC): Approximately 0.5 ha sized circular clear-cuts were formed surrounded

171 by closed-canopy stand. The area of the treatment was designated as the area surrounded by the

172 trunks of the peripheral dominant forest trees: the applied diameter was 80 m. Within CC, every

173 tree individuals (DBH ≥ 5 cm and/or height ≥ 2 m) were cut, that caused drastic changes in tree

174 structure (BA change: -39.6 m2ha-1) and canopy closure (change: -85.4%).

175 3. Gap-cutting (G): Circular artificial gaps were established in the closed stand by the

176 elimination of all tree individuals within a diameter of 20 m (~0.03 ha). Gap size was defined

177 as expanded gaps (Runkle, 1981), i.e. by measuring the base of surrounding canopy trees. The

178 chosen 1:1 gap diameter/intact canopy height ratio is widely used in Central Europe for

179 transition system applying gap-cuttings and it also fits well for the records of gap area in oak

180 forests (Wijdeven & van Hees, 2001; Dey, 2002).

181 4. Preparation cutting (P): Uniform partial cutting was applied within a circle with a diameter

182 of 80 m; 30% of the initial total basal area of the upper canopy layer was cut and the felled trees

183 were distributed evenly (BA change: -8.4 m2ha-1). Furthermore, the complete subcanopy- and

184 shrub layer were also removed.

185 5. Retention tree group (R): All tree and shrub individuals were retained within a 0.03 ha sized

186 circular plot (diameter=20 m) in the clear-felled area, that resulted a small patch of remained

187 stand with approximately 8-12 trees of the former upper layer.

188 Clear-felled areas in the hilly region of Hungary are less than 5 ha according to the operative law

189 (Act No. LXVI of 2017). The created clear-cuttings are substantially smaller than it is typical in

190 Hungary or in the temperate deciduous forests in Europe (3-10 ha, Standovár, 2006). Therefore,


191 changes in the site conditions resulted by this treatment may be less pronounced and drastic.

192 Neither the plot of clear-cuttings, nor the retention tree groups were placed in the center of the

193 clear-cuts (Fig. S2.2). These plots were shifted to the 1:3 intersections along the east-west

194 diameter, because we intended to minimize the bias caused by the shading of the remained trees

195 of retention tree groups in the clear-cutting plots.

196 All treatments were carried out in the winter of 2014-2015. In the center of the treatments, a

197 6 × 6 m fenced area (hereafter plot) was established to exclude the effects of the large-bodied game

198 species.

199

200 Data collection

201 The “Pilis Experiment” follows BACI (Before-After-Control-Impact, Stewart-Oaten, Murdoch &

202 Parker, 1986) design, therefore, all measurements are systematically repeated as well as in the pre-

203 treatment year (2014) and the following post-treatment year (2015). Microclimate variables (total

204 light, air temperature, relative humidity, soil temperature and soil moisture) were recorded in every

205 month of the growing season, litter and soil variables (litter mass, litter pH, litter moisture content,

206 soil pH, hygroscopicity, nutrient content) were measured in two sampling periods per year.

207 Systematic microclimate measurements were taken place in the center of each plot. Temporally

208 synchronized data collections were carried out using 4-channeled Onset ‘HOBO H021-002’ data

209 loggers (Onset Computer Corporation, Bourne, US-MA) mounted on wooden poles (Fig. S2.3). In

210 every month through the growing season (March-October), 72-hour logging periods were applied

211 with 10-min logging intervals. Photosynthetically active radiation (PAR, λ=400-700 nm;

212 μEm−2s−1) was measured at 150 cm above ground level using Onset ‘S-LIA-M003’ quantum

213 sensors. Air temperature (Tair; °C) and relative humidity (RH; %) data were collected at 130 cm

214 above ground level with Onset ‘S-THB-M002’ combined T/RH sensors housed in standard

215 radiation shields to avoid direct sunlight. Soil temperature (Tsoil; °C) was measured with ‘S-TMB-

216 M002’ 12-Bit temperature sensors by Onset placed 2 cm below ground. Soil water content

217 (volumetric water content, SWC; m3/m3) data were collected by Onset ‘S-SMD-M005’ soil

218 moisture sensors buried 20 cm below ground level to measure the average soil moisture in 10-20

219 cm soil depth. Air temperature and relative humidity data were used to calculate vapor pressure

220 deficit (VPD; kPa) values at every logged occasion following the protocol of Allen et al. (1998):

221 VPD=(0.6108){exp[17.27·T/(237.3+T)]}·(1-RH/100). The reason of using VPD as a background


222 variable is that it can give a direct indication of the atmospheric moisture conditions independently

223 of the actual temperature. Therefore, it is a good standalone indicator of the atmospheric factors

224 influencing evaporation: VPD describes the actual drying capacity of the air, i.e. the higher the

225 VPD is, the more intensive is the evaporation (Anderson, 1936). Additionally, relative diffuse light

226 (DIFN; %) was measured by LAI-2000 Plant Canopy Analyzer (LI-COR Inc., Lincoln, US-NE)

227 in the center of each plot at 130 cm above ground level. Measurements were carried out in August

228 at dusk to avoid direct light getting into the sensor. Repeated measurements are not needed with

229 this device (Tinya et al., 2009). A 270° view restrictor masked the portion of the sky containing

230 the sun and the operator (LI-COR Inc., 1992). Reference above-canopy measuring was performed

231 on an adjacent open field.

232 At each plot, four litter (30 × 30 cm area) and topsoil (0-20 cm depth) samples were systematically

233 collected within the adjoining 3-meter sphere of the plots. The samples were taken twice a year:

234 in April and in October. All samples were returned to the laboratory and following the necessary

235 preparation steps, litter mass, litter pH, litter moisture content, Kuron’s hygroscopicity (hy), soil

236 organic matter content and nutrient content were measured. Litter mass was measured after air-

237 drying for 48 h. Litter moisture content (%) was calculated as the mass loss of the freshly collected

238 litter samples (i.e. the difference of the fresh and dried litter).

239 Litter and soil pH was potentiometrically measured using supernatant suspension of air-dried and

240 sieved (<2 mm) samples and 25 ml of distilled water, the applied mass was 5 g for litter and 10 g

241 for soil samples, respectively (MSZ-08-0205:1978). Kuron’s method was applied for gauging hy

242 of air-dry soils (Verstraeten & Livens, 1971): with 50% (v/v) H2SO4 solution and 35.2% RH

243 according to MSZ-08-0205:1978. Chemical compounds were evaluated on composite samples of

244 the 1:1 mixture of the four, sieved (<0.5 mm) subsamples per plot. Total soil carbon and nitrogen

245 content were determined by dry combustion method using Elementar vario MAX CNS-analyzer

246 (Elementar Analysesysteme, Langenselbold, Germany) applying the ISO standards (ISO

247 10694:1995; ISO 13878:1998): soil samples were weighed up to 80-100 g, and a tungsten oxide

248 catalyst was added. The applied combustion temperature was 1140°C. Plant available phosphorus

249 and potassium were determined by ammonium lactate (AL) solution method (0.1M NH4-

250 lactate + 0.4 M HOAc, adjusted to pH 3.75) developed by Egnér et al. (1960 cf. Carter &

251 Gregorich, 2008) according to the operative Hungarian standards (MSZ 20135:1999). PAL was

252 measured colorimetrically, KAL was quantified by flame photometry.


253

254 Data analysis

255 Microclimate data were initially screened and obvious errors caused by technical failures

256 (indicated by e.g. unrealistic data or large spikes in variables), were replaced by missing values.

257 The manually corrected data were imported into the database built in SpatiaLite 4.3.0a (Furieri,

258 2015).

259 Firstly, observations were split into 24-hour datasets. Based on these diurnal data, on the one hand

260 descriptive statistics, as daily mean and daily interquartile range (IQR), were calculated for each

261 plot, on the other hand using the raw data, differences from the values collected at the control were

262 calculated for every recording (control values were subtracted from the treatment values of the

263 block) and then mean and IQR were computed. To measure the direct effects of the silvicultural

264 treatments on site condition variables, these relative data were used to avoid the effects of the

265 actual synoptic situation (in case of the microclimate variables) and to minimize spatio-temporal

266 heterogeneity of soil and litter variables. For the analyses, one randomly chosen 24-hr

267 microclimate dataset was used in every month. Daily IQR of SWC was excluded from the analysis,

268 because soil moisture is a rather stationary variable. For RH and VPD by virtue of numerous

269 missing data, the subset of October was excluded.

270 The temporal patterns of the measured variables were investigated using two different temporal

271 resolutions: according to the distinct methods for soil chemical variables and litter parameters

272 seasons were compared (spring vs. autumn), while in the case of microclimate variables we used

273 months as factor levels.

274 As a result of the relatively short measurement campaigns (3 days per month), the classical BACI

275 design was not suitable for data analysis, due to the possible different weather conditions.

276 Therefore, only the analyses based on the dataset of year 2015 are presented here, while results of

277 the pre-treatment datasets (2014) can be found in Supplemental Information 3.

278 To explore the effect of treatments and time on the measured site condition variables, linear mixed

279 models were used (Faraway, 2006). Data were transformed where it was necessary to achieve the

280 normality of model residuals. The effects of different treatment levels across (1) the whole growing

281 season and (2) over the applied temporal resolution (month or season) were tested by the same

282 modeling framework: treatment, time and their interaction were used as fixed factors, while block

283 as a random factor. Models’ goodness-of-fit values were measured by likelihood-ratio test-based


284 coefficient of determination (R2LR; Bartoń, 2016). Differences between treatment levels were

285 evaluated by all-pairwise comparisons of Tukey procedure (alpha=0.05): multiple comparisons

286 were applied for the differences among treatments through the growing season by general linear

287 hypotheses (Hothorn, Bretz & Westfall, 2008), while comparisons across treatments within the

288 time levels were performed among least-squares means (Lenth, 2016). The significance of

289 differences between control and the other treatment levels was tested by random effect models

290 without intercept (Zuur, 2009). The diurnal pattern was only analyzed qualitatively without any

291 statistical test applying standard LOWESS analyses with 95% confidence intervals. Datasets (raw

292 data) were pooled into two groups: the peak of the growing season (i.e. June, July, August) and

293 the transitional period (March, April, September, October) with lower canopy closure. Smoothing

294 procedures were applied on three or four 24-hr datasets with six replications of each treatment

295 levels.

296 Data analyses were performed with R version 3.4.1. (R Core Team, 2017). Mixed models were

297 conducted by R package ‘nlme’ (Pinheiro et al., 2017), multiple comparisons were appraised by

298 ‘multcomp’ (Hothorn, Bretz & Westfall, 2008) and ‘lsmeans’ (Lenth, 2016) packages,

299 determination coefficients of the mixed models were calculated by ‘rsquaredLR’ function of

300 ‘MuMIn’ package (Bartoń, 2016). For graphing means and SDs the modified script of ‘errorbars’

301 function was used (Reiczigel, Harnos & Solymosi, 2014).


302 RESULTS

303

304 The effects of experimental treatments on site condition variables

305 According to the performed linear mixed effect models, we found that experimental treatments

306 affect microclimate and litter variables more, while soil chemical characteristics did not differ

307 significantly among treatment types – except for topsoil acidity (Table 2, Fig. 2).

308 Both mean and variability of total as well as mean of relative diffuse light were substantially higher

309 in all treatments than in the controls (Fig. 2a, Fig. 2b and Fig. 2c, respectively). The largest values

310 were detected in clear-cutting and the increment was also considerable in gap-cutting. Preparation

311 cutting and retention tree group had similar light conditions, but diffuse light was lower in retention

312 tree groups. In retention tree groups and clear-cutting the mean of dTair were significantly higher

313 than in the other two treatments (Fig. 2d). Air temperature was buffered the most in the preparation

314 cutting, but the means of dTair were not different between gap-cutting and preparation cutting. The

315 interquartile range of air temperature also departed significantly from the control in all treatments

316 (Fig. 2e). In the clear-cutting, both the mean and the standard deviation of the IQR were the

317 highest. The lowest range was measured in the gap-cutting, while in the other treatments, IQR was

318 intermediate. dRHmean was the lowest in clear-cutting and retention tree group (Fig. 2f). In

319 preparation cutting and gap-cutting, humidity remained similar to control; furthermore, these

320 treatments did not differ from each other. It is noticeable that the range of dRH was the lowest in

321 gap-cutting, and highest in clear-cutting, however in all treatment, IQRs of dRH were departed

322 from control (Fig. 2g). The pattern of dVPD (both mean and IQR, Fig. 2h and Fig. 2i, respectively)

323 across treatment levels was similar to dTair (because of the high contribution of temperature to this

324 variable), and all treatments differed also significantly from control. Soil temperature, means just

325 as IQR differed significantly in every treatment from control (Fig. 2j, Fig. 2k, respectively). Clear-

326 cutting created soil thermal conditions that divaricated the most from the closed stand: both mean

327 and IQR of the dTsoil were the highest there. In retention tree group mean of dTsoil did not differ

328 significantly from clear-cutting, but IQR were significantly lower. The coolest soil environment

329 with the less diurnal variability was created by gap-cutting. Soil moisture differed significantly in

330 clear-cutting and even more in gap-cutting from the controls. The highest SWC was measured in

331 gap-cutting, the difference was smaller in the clear-cutting, while in preparation cutting and

332 retention tree groups a slight decrease was detected, and the latter was the driest treatment (Fig. 2l).


333 Litter variables showed almost as strong relationship with treatment levels as microclimate

334 variables. In the first year there was no significant response in litter mass, however, it showed an

335 increasing trend from clear-cutting to retention tree group (Fig. 2m). In clear-cutting and gap-

336 cutting litter pH departed significantly from that in control, and litter were more neutral in these

337 two treatments than in preparation cutting and retention tree group (Fig. 2n). Litter moisture

338 followed the sequence of dSWC: forest floor was the driest in retention tree group, but it was not

339 significantly different from that in control; and litter moisture was significantly higher in the other

340 three treatments – the highest values were measured in gap-cutting (Fig. 2o). Topsoil was less

341 acidic in preparation cutting than in the other treatments and it was the only treatment level where

342 soil pH differed from control (Fig. 2p).

343

344 Temporal differences among treatments through the growing season

345 Besides the study of the treatment effects, the temporal differences during a growing season were

346 also analyzed (Table 2). Except for light and soil moisture variables, the effect of time was similar

347 or stronger than that of treatments. For litter mass and potassium content, only the time effect was

348 significant.

349 The largest differences in microclimate variables were detectable in summer (Fig. 3). dPAR was

350 the highest in clear-cutting almost in every month, but the differences were highest in full-leaved

351 months – from May to August (Fig. 3a). dVPD values were highly divergent among treatment

352 levels during summer, the drying capacity of air was significantly higher in clear-cutting and in

353 the retention tree group, while dVPD did not depart substantially from control in the two other

354 treatment types (Fig. 3b). In the case of soil temperature (Fig. 3c), some important results could

355 be asserted: thermal input is the largest in clear-cutting with the highest variance during summer;

356 the differences between treatment levels accelerated as the canopy closure increased, but gap-

357 cutting and preparation cutting remained similar to the control during the whole growing season;

358 retention tree groups could partly buffer the heating through the shading of the remained trees; and

359 as the first frosts appeared, dTsoil differed greatly from the other treatments (i.e. soil was the coldest

360 in clear-cutting in October). The soil moisture increment in gap-cutting was detectable through the

361 whole measurement period, and it enhanced during summer. dSWC was also relatively high in

362 clear-cutting, but the difference was less pronounced than in gap-cutting (Fig. 3d). A moderate soil

363 desiccation was present in retention tree group from June to September.


364 Litter mass decreased from spring to autumn in the treatments except for retention tree groups

365 (Fig. 4a). Litter pH increased in gap-cutting and clear-cutting in autumn, while in preparation

366 cutting and retention tree group it stayed close to the values measured in the control (Fig. 4b).

367 Litter moisture content increased marginally in preparation cutting and rather particularly in clear-

368 cutting as well as in gap-cutting as compared to the degree of humidity measured in spring

369 (Fig. 4c). We found that time effect was significant for pH and KAL-concentration (Table 2). Soil

370 pH was lower in spring than in autumn, in both periods, it was higher in retention tree group than

371 in other treatments (Fig. 4).

372

373 Diurnal pattern of microclimate variables among the treatments

374 When we analyzed the 24 hour datasets, a clear diurnal pattern could be detected for the different

375 microclimate variables with large variability between the treatments in summer. Contrarily, when

376 the pooled early spring and autumn subsets were analyzed, differences were much smaller and the

377 pattern was not that obvious (Fig. 5). In the case of light, we found that in summer, a large

378 difference came off among the treatments, the amount of PAR in clear-cutting could exceed 2000

379 μEm−2s−1, while in the second brightest plots, in gap-cutting, the maximum values were under

380 1930 μEm−2s−1. There was a detectable lag of the maximum values also: in clear-cutting at 12:00-

381 12:20 UTC, in gap-cutting at around 12:30-12:40 UTC and in preparation cutting at 13:10-13:20

382 UTC. In retention tree groups the values showed interesting pattern – presumably according to

383 their spatial configurations, i.e. the surrounding clear-cutting –, there is a certain excessive amount

384 of light from east, therefore the maximums occurred between 9:00 and 10:30 UTC (in the morning

385 there is an observable period when the light is higher in retention tree groups than in the other

386 treatments), and that was followed by a decrease in radiation because of the shading effect of the

387 canopy patch. The differing pattern of irradiance among the treatments was also detectable in the

388 case of VPD and soil temperature: e.g. in the morning, retention tree group was the warmest and

389 driest treatment. In clear-cutting, soil temperature could reach 38.8 °C in summer, but even in

390 retention tree group, Tsoil was maximized in 31.3 °C. In the gap-cutting, the moist soil (SWC was

391 the highest; Fig. 2) resulted a distinct peak in VPD, which was followed by a quick decrease, and

392 soil temperature was lower despite the higher total light than in preparation cutting. Clear-cutting

393 could cool down the most: in the summer, between 2:00 and 7:30 AM and especially in transitional

394 period, it was the coldest treatment. In the transition period (in the right panels of Fig. 5), the


395 amplitudes of the diurnal cycles are substantially smaller. Furthermore, the applied treatments do

396 not differ as much as during the peak of the growing season.


397 DISCUSSION

398

399 Rapid changes in microclimate and litter variables, but not in soil properties

400 In general, microclimate variables and litter attributes showed strong short-term response among

401 the different silvicultural treatments, but we could not measure significant differences for most of

402 the investigated soil chemical variables.

403 Two different, but highly interrelated processes can be highlighted in the context of microclimate

404 alteration by forest management: radiation balance and evapotranspiration. Forest canopy plays an

405 important role in both mechanisms. The maintenance of the buffered microclimate in the below-

406 canopy space of closed forest stands is based on its shielding effect: through the (partial) shading

407 and the absorption of the foliage there is significantly less net radiation to heat the forest floor;

408 moreover, the canopy insulates the understory environment by reducing the longwave radiative

409 loss (Geiger, Aron & Todhunter, 1995; Rambo & North, 2009; von Arx et al., 2013). Furthermore,

410 as it was demonstrated by Bristow and Campbell (1984) there is a strong correlation between solar

411 irradiance and transferred heat-related variables of the ambient air such as air temperature, relative

412 humidity and vapor pressure deficit. Therefore, in general, in the harvested sites, we measured

413 higher and temporally more variable irradiance, temperatures, vapor pressure deficit and lower,

414 but also more unbalanced air humidity. Changes in soil moisture following the different treatments

415 are based (1) on the lower rate of interception and evaporation through the canopies or trunks and

416 consequently the increased throughfall; and (2) on lower transpiration rates due to tree removal

417 (Keenan & Kimmins, 1993; Wood, Hannah & Sadler, 2007; Chang, 2013; Muscolo et al., 2014).

418 The major effect of these alterations is that soil moisture typically increases in sites where felling

419 was applied on a larger continuous extent (i.e. gap-cutting and clear-cutting).

420

421 Light variables

422 As the applied management practices were planned based both on the total basal area and spatial

423 arrangement of the retained standing trees, we found that (1) total and diffuse light departed from

424 control in every treatment due to harvest-induced canopy modifications, and (2) the light variables

425 had the strongest response to the treatments. The amount and the range of light was the largest in

426 clear-cutting, and decreased in the following order: gap-cutting, retention tree group, preparation

427 cutting and control. Our findings are congruent with previous researches showing that the


428 increment in irradiance and its variability are larger as the size of canopy openings increase (or

429 even more as sky view factor enhances, Oke, 1987) that is highly correlated with the leaf area

430 index (Geiger, Aron & Todhunter, 1995; Carlson & Groot, 1997; Aussenac, 2000; Hardwick et

431 al., 2015). Therefore, solar radiation is higher and temporally more variable in clear-cutting than

432 in forest edges (Matlack, 1993), stands harvested by various types of green tree retention schemes

433 (Heithecker & Halpern, 2006) or the management practices related to the uneven-aged systems

434 (Zheng et al., 2000). This altered light regime with extreme means and maxima created by clear-

435 cutting is substantially different from any forest environment. Keenan and Kimmins (1993)

436 mentioned that it could be harmful to assimilating organs: in clear-felled areas the extreme

437 radiation (10- to 20-fold increase compared to closed forest stand) – and as a consequence, the

438 strikingly increased leaf surface temperatures – could suppress the efficiency of photosynthesis

439 and even destruct plant tissues. Canopy gaps also create a more illuminated environment (Gray,

440 Spies & Easter, 2002; Ritter, Dalsgaard & Einhorn, 2005; Abd Latif & Blackburn, 2010), though

441 the irradiance was significantly lower than it was detected in clear-cutting regarding the smaller

442 sky view factor (Carlson & Groot, 1997) and consequently, the shading of the surrounding trees

443 (Gálhidy et al., 2006). According to the light level and its variance, preparation cutting and

444 retention tree group did not show clear distinction, these treatments provide similarly brighter

445 environment than the uncut sites (Heithecker & Halpern, 2006; Brose, 2011; Grayson et al., 2012).

446 The spatial arrangement of the trees had influence on the direct-diffuse light proportions: in the

447 preparation cutting, the uniform distribution of trees could strongly inhibit the direct irradiation,

448 but less notably the diffuse light; therefore, the amount of the diffuse insolation is similar to that

449 in the gap-cutting, but the amount of total light is significantly lower (Abd Latif & Blackburn,

450 2010; Musselman, Pomeroy & Link, 2015). Retention tree group – in the first year – was very

451 open to the adjacent clear-cutting and to the lateral irradiance due to the lack of lower branches

452 and scarce shrub layer. However, we can expect that illumination in retention tree groups will

453 decrease and return to the level characteristic in control as the natural regeneration (shrubs, sprouts

454 and juvenile trees) grows and as the epicormics shoots emerge.

455

456 Air variables

457 Forest management (especially clear-cutting) has a long-lasting effect on air temperature and

458 relative humidity (Dovčiak & Brown, 2014; Baker et al., 2014); silvicultural treatments could


459 generate alterations in these variables that persist over 25 years. Contrary to previous studies

460 reporting that temperature and humidity in stands harvested by moderately intensive management

461 practices had only slight modifications on microclimate compared to uncut plots (e.g. group

462 selection or patch-cuts – Brooks & Kyker-Snowman, 2008; weak thinning – Weng et al., 2007;

463 gaps – Muscolo et al., 2014), we found that almost every treatment types resulted significant

464 departures from control in these variables. Our treatments resulted similar trends in Tair, RH and

465 VPD changes as other studies (e.g. Chen, Franklin & Spies, 1995; Gray, Spies & Easter, 2002;

466 Heithecker & Halpern, 2006; Ma et al., 2010).

467 Since the highest levels of incoming solar radiation were measurable in clear-cutting, this

468 treatment type can be characterized by the highest air temperature and vapor pressure deficit along

469 with the lowest relative humidity values (Keenan & Kimmins, 1993; Chen et al., 1999; Heithecker

470 & Halpern, 2006). Our findings are in agreement with the results of Carlson and Groot (1997) and

471 von Arx and colleagues (2012) as means of Tair in clear-cuttings were less than 1°C warmer than

472 in control plots. Differences in Tair or RH between clearings and closed stands were greater in

473 many studies that could be addressed to the larger clearing size. In our experiment, due to the size

474 of 0.5 ha of the clear-cutting, the shading effect of the forest edge as well as the cooling effect by

475 mixing air from the nearby stand could be more pronounced (Davies-Colley, Payne & van Elswijk,

476 2000; Baker, Jordan & Baker, 2016; Arroyo-Rodríguez et al., 2017).

477 Since the highest levels of incoming solar radiation were measurable in clear-cutting, this

478 treatment type can be characterized by the highest air temperature and vapor pressure deficit as

479 well as the lowest relative humidity values (Keenan & Kimmins, 1993; Chen et al., 1999;

480 Heithecker & Halpern, 2006). Our findings are in agreement with the results of Carlson and Groot

481 (1997) and von Arx et al. (2012) as mean of Tair in clear-cutting were less than 1°C warmer than

482 that of control plots. Differences in Tair or RH between clearings and closed stands were greater in

483 many studies that could be addressed to the larger clearing size. In our experiment, due to the

484 relatively small size (0.5 ha) of the clear-cutting, the shading effect of the forest edge as well as

485 the cooling effect by mixing air from the nearby stand could be more pronounced (Davies-Colley,

486 Payne & van Elswijk, 2000; Baker, Jordan & Baker, 2016; Arroyo-Rodríguez et al., 2017).

487 Regarding to the gap-cutting, despite the high PAR values and the generally observed strong

488 correlation between direct radiation and air temperature in gaps (Gray, Spies & Easter, 2002), in

489 our case the mean Tair and VPD in gaps were significantly lower than those in clear-cutting or


490 retention tree group. As an explanation the higher soil moisture in gap-cutting should be

491 underlined: as a consequence, energy absorption by the water particles in the air and evaporative

492 cooling is more pronounced in gaps (Robson et al., 2008; von Arx et al., 2013). In the applied gap-

493 cutting, the mean increment in temperature (~0.1°C) was comparable with the studies of Carlson

494 and Groot (1997) or Abd Latif and Blackburn (2010). Air humidity could remain unaltered in gaps

495 due to the lowered ratio of air mixing and the shading by adjacent closed stands (Geiger, Aron &

496 Todhunter, 1995; Abd Latif & Blackburn, 2010) and the moist topsoil as a source of water vapour

497 (Ogée & Brunet, 2002).

498 The thermal conditions of the preparation cutting (where 70% of the original BA was retained)

499 were similar to gap-cutting. The mean of VPD and RH in preparation cutting remained similar to

500 the control, but the ranges of these variables departed due to the higher input of solar energy (Weng

501 et al., 2007).

502 We expected that a retained patch of overstory trees in the clear-felled area could substantially

503 buffer the thermal effects of clear-cutting – as it was measured in case of light variables. However,

504 it was found that retention tree group could not compensate the thermal loading and the drying

505 capacity of the warmer air coming from the clearing: the mean of Tair and VPD were not

506 significantly different from those of the clear-cutting. This phenomenon could be addressed to the

507 edge effect (in the case of Tair, VPD and RH) that overlaps with the applied patch size (Matlack,

508 1993; Ewers and Banks-Leite, 2013; Baker et al, 2016; Arroyo-Rodriguez et al. 2017). In contrast,

509 retention tree group could successfully reduce the variability of the extreme values by the

510 insulation of the remained patch of canopies despite the effective lateral mixing (Heithecker &

511 Halpern, 2007; Ewers & Banks-Leite, 2013).

512

513 Soil temperature

514 Increased incoming solar radiation caused significant increment in soil temperature (both in the

515 case of means and IQRs), in all treatments. Higher departures from control levels were measured

516 than in the case of air temperature. As it was emphasized by von Arx et al. (2013), despite the

517 shading by the canopy affects both Tsoil and Tair temperature, the moderating effect is typically less

518 pronounced regarding air temperature. It is mainly because air is a mobile medium, thus turbulent

519 mixing of the air reduces the differences more (Morecroft, Taylor & Oliver, 1998; Hari, Heliövaara

520 & Kulmala, 2013). According to the strong correlation between Tair and Tsoil (Kang et al., 2000;


521 Ritter, Dalsgaard & Einhorn, 2005), the decreasing order of these variables across the treatments

522 was similar. Implicitly, because of the enhanced solar heating in daytime and the longwave

523 radiation loss in nighttime, clear-cutting was led to the greatest increase in Tsoil regarding both

524 mean and variability (Carlson & Groot, 1997; Davies-Colley, Payne & van Elswijk, 2000; von

525 Arx, Dobbertin & Rebetez, 2012). Retention tree group could moderate the extremes of Tsoil better

526 than Tair due to shading of the patch of standing trees. It is similar to the results of Williams-Linera,

527 Dominguez-Gastelu & Garcia-Zurita (1998) about isolated trees, but in our case a more

528 pronounced smoothing effects was recorded on the variability of Tsoil. Moreover, the range of Tsoil

529 in retention tree group was comparable with that measured in preparation cutting and gaps. For

530 gaps and preparation cutting, we recorded smaller increase in Tsoil than it was reported by previous

531 studies (Gray, Spies & Easter, 2002; Ritter, Dalsgaard & Einhorn, 2005; Abd Latif & Blackburn,

532 Thibodeau et al., 2000).

533

534 Soil moisture

535 Forest stands have high evapotranspiration rates; therefore, as a general rule, any opening in the

536 canopy cover results in a reduction in the amount of water consumed (Aussenac, 2000). The impact

537 of the elimination of tree individuals is particularly significant on soil moisture content (Keenan

538 & Kimmins, 1993; Gray, Spies & Easter, 2002). In clear-cutting, the soil moisture was

539 significantly higher than in the control, because of the drastic decrease of transpiring surface but

540 it was lower than in gap-cuttings due to the enhanced irradiation that increased the evaporation of

541 the surface and the great wind exposure (Geiger, Aron & Todhunter, 1995). The greatest increment

542 was detectable in gap-cutting as it was expected according to previous studies (Gray, Spies &

543 Easter, 2002; Ritter, Dalsgaard & Einhorn, 2005; Gálhidy et al., 2006; Abd Latif & Blackburn,

544 2010). This change of the water balance is usually rapid: initially soil moisture increases after the

545 applied treatments, but drops to the pre-harvest levels within a short time period following the

546 reestablishment of vegetation. This time-span is approximately four-five years in thinned stands

547 and clear-cuttings (Aussenac & Granier, 1988; Adams, Flint & Fredriksen, 1991). A similar, but

548 even faster return to the pre-harvest level of SWC could be predicted for? gaps as well

549 (Lewandowski et al., 2015), due to the development of the natural regeneration, the increased root

550 extraction and improved interception by the enhanced lateral growth of the surrounding trees

551 (Gray, Spies & Easter, 2002; Ritter, Dalsgaard & Einhorn, 2005). Here, we found that in retention


552 tree group, despite the higher VPD, the enhanced heat load and the transpiration of remnant trees,

553 soil water content was only slightly lower than in the uncut plots. SWC in the preparation cutting

554 remained also similar to the intact stands, although every third tree individual was cut. We can

555 suppose that this moderately increased amount of throughfall and somewhat lowered

556 evapotranspiration rate could not increase the water table as notably as it was suggested by

557 previous thinning studies (Aussenac & Granier, 1988; Chase et al., 2016), but this result is

558 comparable with the findings of Weng et al. (2007).

559

560 Litter variables

561 As the microclimate variables, litter characteristics showed a rapid response to the management

562 types. Albeit the amount of litter did not depart significantly from control and showed non-

563 significant treatment effect, there are observable differences between the treatment levels:

564 retention tree group could be characterized by litter accumulation due to the continuity of the local

565 tree litter input and lower soil moisture content that could slow the decomposition rates. This

566 phenomenon can also be explained by the different quality of the litter and the understory

567 vegetation. In clear-cutting and gap-cutting the cover of the herbaceous species considerably

568 increased (Tinya pers. comm.), that resulted in higher proportion of herb leaves in the litter mass.

569 Its decomposition provides more neutral litter conditions than the leaves of trees, especially leaves

570 of oak species (Finzi, Canham & Van Breemen, 1998). The higher litter moisture in clear-cutting

571 and gap-cutting can be explained by the higher SWC and the high understory cover that can

572 enhance humid conditions by insulating the surface (Keenan & Kimmins, 1993).

573

574 Soil chemical variables

575 Changes in nutrient availability and -cycling following forestry treatments are complex and in

576 numerous cases, trajectories are governed by multiple factors – thus studies show inconsistent results

577 (Nykvist & Rosén, 1985; Thiffault et al., 2011; Binkley & Fisher, 2013). In general, the regional

578 climate, the soil type and tree species composition can be emphasized as important determinants

579 (Keenan & Kimmins, 1993; Nave et al., 2010); and based on long-term data biomass removal per se

580 appears to have been little or no effect on site fertility, the effects are mostly transitory (Binkley &

581 Fisher, 2013). Nevertheless, nutrient loss following clear-cutting is typically reported (Lindo &

582 Visser, 2003) – especially in the case of available N (Jerabkova et al., 2011) –, while gaps are known


583 as environments with high rate of soil organic matter decomposition and mineralization causing

584 increased levels of nutrients (Muscolo et al., 2014). It is also suggested that nutrient losses can be

585 reduced by applying harvesting practices that only cause smaller scale disturbances such as gap-

586 cutting (Ritter, Starr & Vesterdal, 2005) or partial-cut harvesting (Lindo & Visser, 2003).

587 Based on the models we can conclude that only pH of the upper mineral soil showed immediate

588 treatment effect, while the other soil chemical variables remained similar to those of uncut sites.

589 These findings are roughly concurring with studies that besides the harvest effects investigate the

590 temporal changes in soil properties (Thiffault et al., 2011; Kishchuk et al., 2014). However, in their

591 meta-analysis Jerabkova et al. (2011) found that in deciduous forests as an impact on nitrate

592 concentration and N flux following clear-cutting a prompt and short-lived increment is typical.

593 The treatment effect in soil pH could be addressed to the changes in soil moisture: only retention

594 tree group differed both from control and the other treatments, where the lowest SWC and litter

595 moisture were measured. The relationship between the soil moisture and acidity has various

596 correlation strength (it is highly dependent e.g. from the soil type), but in general pH and SWC show

597 reverse pattern (Allen et al., 1989; Ji et al., 2014). In retention patches of deciduous forest stands,

598 Lando and Visser (2003) found also decreased level of soil acidity. As Sayer (2005) pointed out,

599 natural accumulation of litter could be associated with an increase in pH.

600

601 Distinct temporal patterns over the first growing season

602 By investigating the temporal pattern of the microclimate variables throughout the growing season

603 we found that (1) the differences between treatment levels increase as the shading capacity –

604 provided by the tree canopy – enhances; and (2) the evapotranspiration rates increase as the

605 vegetation becomes fully-leaved. This was unambiguous in the case of total light: during the

606 emergence and senescence phase the light conditions were homogeneous among the applied

607 treatments – except for the clear-cutting due to the lack of shading trunks and branches –, whereas

608 during the vegetation peak a very pronounced treatment effect could be detected. The seasonal

609 changes of soil temperature followed the pattern of the incoming radiation, except in gaps. In clear-

610 cutting, Tsoil was much lower in the end of the growing season than in the other treatments that

611 suggests the more pronounced frost-exposure of the regeneration (Aussenac, 2000; von Arx,

612 Dobbertin & Rebetez, 2012). In general, the buffering capacity of the forests concerning numerous

613 microclimate variables (e.g. the diurnal variability of Tair, RH, VPD or Tsoil) is related to the leaf area


614 index and also to the soil water potential (Ashcroft & Gollan, 2013; von Arx et al., 2013; Hardwick

615 et al., 2015). Gray et al. (2002) found similar annual pattern in the case of SWC means in gaps vs.

616 uncut control.

617 For the litter and soil variables, in the most cases, time had stronger effect than the treatment levels.

618 From spring to autumn – due to the reduced litter inputs and the enhanced decomposition rates –

619 litter mass decreased in most treatments. The only exception was the retention tree group, where the

620 dry and warm soil could deplete the performance of the decomposer organisms (Lindo & Visser,

621 2003). The role of microbial communities, fungi and soil fauna could be corroborated by the results

622 of Boros (unpublished data) who found that both the abundance and species richness of Enchytraeid

623 worms – an important decomposer group in the temperate zone (Schaefer, 1990) – decreased

624 significantly in retention tree groups. Litter pH and litter moisture content followed the similar

625 pattern as SWC, both variables showed a considerable increase in gap-cutting and clear-cutting from

626 spring to autumn. We can speculate that this increment can be the result of the enhanced leaching of

627 tannic acids, or the changed contribution of the different species to litter.

628 Soil pH increased in autumn in all treatments compared to that in control. That could be partly

629 explained by the weakened buffering capacity of litter layer on soil pH followed the decomposition

630 of leaf litter through the growing season (Sayer, 2005), although seasonal fluctuations in the range

631 of 0.5 unit is part of the natural dynamics.

632

633 Diurnal patterns across treatments differed more during the vegetation peak

634 According to the diurnal changes, clear differences were found between treatments that were more

635 pronounced during the fully-leaved period than in the transitional period. Moreover, the amplitudes

636 of the microclimate variables were also greater during the leaved period. The daily courses presented

637 here were mainly determined by the solar azimuth. During the transitional period (spring and

638 autumn) the maxima of the insolation were not influenced by canopy, thus it was synchronous in all

639 treatments. However, in summer, control was evenly shaded whole day, while light increment

640 peaked close to noon in the cclear-cutting. In the gap and preparation cutting some lags were

641 observable, because of the shading by the surrounding trees and the patchy environment,

642 respectively. In the retention tree group, a large proportion of total daily illuminance arrived in

643 forenoon – or before the sun passes through the meridian. This result confirms our hypothesis that

644 the main source of the total (and diffuse) light in the small forest remnants is the lateral irradiance.


645 As it was suggested by previous studies (Carlson & Groot, 1997; Morecroft, Taylor & Oliver, 1998;

646 Holst, Mayer & Schindler, 2004), the turning points of the heat-driven variables – as VPD or soil

647 temperature presented here – are behind time to the pattern of PAR due to the latent heat loss.

648 However, von Arx, Dobbertin & Rebetez (2012) found that there is no significant time lag between

649 the daytime peaks of below-canopy and open-field temperature and relative humidity: according to

650 their interpretation, this quasi-synchronous pattern is a consequence to the main drivers of these two

651 variables: the solar radiation and vertical air exchange. By inspecting the VPD and Tsoil courses of

652 retention tree group we can detect the effect of enhanced insolation in the forenoon. According to

653 the temperature, we can speculate that the increase demonstrated via overall means is a consequence

654 of the elongated thermal load. It is also noticeable that VPD in gap-cutting increases synchronously

655 with that in the clear-cutting, but presumably because of enhanced evaporation of the moist soil

656 surface and the transpiration of herbs, its value sinks to the level of the uncut control (Ritter,

657 Dalsgaard & Einhorn, 2005; von Arx et al., 2013). As Aschroft and Gollan (2013) demonstrated

658 moister conditions reduce the diurnal variability of soil and air temperature and VPD more that could

659 be added as a further explanation for the more stable microclimate in gap-cutting.


660 CONCLUSIONS AND MANAGEMENT IMPLICATIONS

661

662 Because of the relatively short time frame, the interpretation of our results has its limitations. The

663 ongoing measurements and the experimental setup give the opportunity to study the long-term

664 processes. We expect that (1) differences in microclimate variables between treatments are going

665 to weaken as the vegetation grows and variables will have different trajectories over time; (2) the

666 response of litter properties will be more pronounced and (3) soil variables will show treatment

667 effects in at least the cutting treatments. It is still a question how the different silvicultural

668 treatments create spatial pattern of abiotic variables. Therefore, our plan is to extend the

669 investigations to study the forest site conditions at fine-scale.

670 Using the results measured during the first growing season, we could demonstrate that the applied

671 management practices considerably changed the below-canopy microclimate in a short time

672 following the harvests. Clear-cutting had the most drastic impact on microclimate variables due to

673 the absence of tree canopies on large areas. According to the extreme light increment, the mean

674 air and soil temperature, vapor pressure deficit and their variability increased the most in this

675 treatment type. Organisms in clear-cutting are more exposed to thermal extremes and early frost

676 damages as well. Limited but positive moderating effect could be addressed to the application as

677 such small retention tree group even if the mean air and soil temperature and VPD are similar to

678 the clear-cutting. Gap-cutting provide more available light and consumable soil moisture that could

679 be favorable for herbs. Artificial gaps at this size can ensure the buffered environment. Preparation

680 cutting preserved forest conditions the most, although in Central Europe it is only a transitional

681 state before the terminal cutting.

682 We can conclude that in oak-hornbeam stands, for the achievement of the conservational aims and

683 to guarantee the more complete rate of ecosystem functionality, it is recommendable to apply

684 small-scale or spatially dispersed forestry treatments to preserve the original characteristics of the

685 forest environment as much as possible. Gap-cutting and – similarly to our preparation cutting –

686 irregular shelterwood or precommercial thinning can be suitable to achieve this aim. If the use of

687 the large, even-aged forestry practices is unavoidable, the application of different retention tree

688 group schemes seems to be particularly important (e.g. in clear-felled or slash and burn areas) to

689 provide the “lifeboat” environment for the forest-dwelling organism groups during the


690 regeneration (Heithecker & Halpern, 2007; Rosenvald & Lõhmus, 2008; Gustafsson, Kouki &

691 Sverdrup-Thygeson, 2010).


692 ACKNOWLEDGEMENTS

693 We are grateful for the cooperation and the joint efforts of the Pilisi Parkerdő Ltd., especially for

694 Péter Csépányi, Viktor Farkas, Gábor Szenthe and László Simon. We thank Tibor Standovár for

695 the LAI-2000 instruments. BK is deeply thankful for the help of Beáta Biri-Kovács, Blanka Biri

696 and András Guba.


697 REFERENCES

698 Abd Latif Z., Blackburn GA. 2010. The effects of gap size on some microclimate variables during

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1024 responses to different thinning intensities in a Japanese cedar plantation of northern Taiwan.

1025 Forest Ecology and Management 241:91–100. DOI: 10.1016/j.foreco.2006.12.027.

1026 Wijdeven S., van Hees A. 2001. Natural gaps in beech forests in The Netherlands, North-West

1027 Germany, Belgium and France. In: Natural canopy gap characteristics in European beech

1028 forests. Nat-Man Working Report 2. Forest & Landscape Denmark, 29.


1029 Williams-Linera G., Dominguez-Gastelu V., Garcia-Zurita ME. 1998. Microenvironment and

1030 Floristics of Different Edges in a Fragmented Tropical Rainforest. Conservation Biology

1031 12:1091–1102. DOI: 10.1046/j.1523-1739.1998.97262.x.

1032 Wood PJ., Hannah DM., Sadler JP. (eds.) 2007. Hydroecology and ecohydrology: past, present

1033 and future. Chichester, England ; Hoboken, NJ: Wiley.

1034 Zheng D., Chen J., Song B., Xu M., Sneed P., Jensen R. 2000. Effects of silvicultural treatments

1035 on summer forest microclimate in southeastern Missouri Ozarks. Climate Research 15:45–59.

1036 Zuur AF. (ed.) 2009. Mixed effects models and extensions in ecology with R. New York, NY:

1037 Springer.


Figure 1

The study site of the “Pilis Experiment” in northern Hungary.

A) Site location (47°40’N, 18°54’E) in the Pilis Mountains. B) Experimental design showing the

five treatments replicated within six blocks. C) Aerial photograph revealing a block with the

applied forestry treatments: clear-cutting (red) with a retention tree group (blue);

preparation cutting (orange), gap-cutting (purple) and control (green). Drone photo by Viktor

Tóth.


Table 1(on next page)

Characteristics of forest structure around the plots before and after treatments.

Structural attributes (mean ± standard deviation) presented here are diameter at breast

height (DBH, cm), canopy height (m), basal area (m2ha-1) and canopy closure (%). Canopy

closure was measured by spherical densitometer Model-A (Lemmon, 1956). Letter ‘U’ refers

to upper layer and ‘S’ to sub-canopy layer. ‘C’ – control; ‘CC’ – clear-cutting; ‘G’ – gap-cutting;

‘P’ – preparation cutting and ‘R’ – retention tree group. Mean and SD were calculated based

on the six replicates for each treatment type. In the case of clear-cutting and preparation

cutting 0.5 ha sized sampling area (diameter=80 m) were used for forest structure

measurements, while for estimations in control, gap cutting and retention tree groups 0.03

ha area (diameter=20 m) were applied.


Pre-treatment (2014) Post-treatment (2015)DBH Height

Basal area Basal areaTreat

-ment

U S U S U S

Canopy

closure U S

Canopy

closure

C 28.0(±5.8) 11.9 (±3.8) 20.9(±1.5) 10.8(±3.5) 29.32(±0.12) 8.83(±0.10) 89.8(±2.6) 29.32(±0.12) 8.83(±0.10) 93.5(±3.9)

CC 28.0 (±5.7) 11.8(±4.2) 21.6(±1.6) 10.4(±3.8) 29.58(±6.47) 9.98(±4.66) 87.9(±3.6) 0.00 0.00 2.5(±2.1)

G 27.3(±5.3) 12.5(±2.8) 20.5(±1.1) 11.2(±2.9) 29.53(±9.03) 9.33(±4.51) 88.4(±4.4) 0.00 0.00 44.8(±10.4)

P 27.2 (±5.3) 10.9(±4.1) 21.2(±1.4) 10.0(±3.5) 28.07(±2.10) 8.03(±1.33) 89.4(±4.4) 19.67(±1.48) 0.00 70.2(±6. 9)

R 27.3 (±5.8) 11.1(±3.4) 20.4(±1.9) 11.8(±3.9) 30.47(±3.73) 8.17(±2.35) 88.7(±3.2) 30.47(±3.73) 8.17(±2.35) 81.9(±9.2)

1


Table 2(on next page)

The results of linear mixed models performed for site condition variables.

PAR: photosynthetically active radiation (μEm−2s−1); DIFN: relative diffuse light (%);Tair: air

temperature (°C); RH: relative humidity (%); VPD: vapor pressure deficit (kPa); Tsoil: soil

temperature (°C); SWC: soil moisture (m3/m3); Litter mass: total mass of collected litter on

the surface (gm-2); Litter pH: litter pH in water; Litter moisture content: gravimetric moisture

content of litter samples (%); Soil pH: soil pH in water; hy: Kuron’s hygrscopicity (%); [SOC]:

total soil carbon content (%); [N]: total nitrogen content (%); [PAL]: concentration of AL-

soluble phosphorus (mg/100 g soil); [KAL]: concentration of AL-soluble potassium (mg/100 g

soil). ‘d’ refers to the difference from values measured in ‘Control’ plots. For modeling, 24-

hour-means were used except in the case of PAR, where daytime (6:00-18:00 UTC) means

were calculated. Treatment types: ‘CC’ – clear-cutting; ‘G’ – gap-cutting; ‘P’ – preparation

cutting and ‘R’ – retention tree group. Superscripts refer to significant differences among

treatments (pairwise Tukey comparisons, alpha=0.05), treatment codes marked with bold

indicates significant departures from control (alpha=0.05).


Model Treatment Time Treatment:TimeDependent

variable Chi2

1p R2

LRF p F p F p

dPAR mean 454.711 <0.0001 0.922 225.579 <0.0001 133.928 <0.0001 8.941 <0.0001

dPAR IQR 343.698 <0.0001 0.852 114.259 <0.0001 57.575 <0.0001 6.292 <0.0001

dDIFN 29.086 <0.0001 0.766 21.699 <0.0001 -- -- -- --

dTair mean 273.305 <0.0001 0.781 21.888 <0.0001 54.082 <0.0001 4.903 <0.0001

dTair IQR 265.160 <0.0001 0.771 44.487 <0.0001 47.139 <0.0001 2.016 0.0086

dRH mean 46.096 <0.0001 0.434 5.177 0.0021 2.939 0.0105 0.609 0.8866

dRH IQR 125.451 <0.0001 0.569 14.054 <0.0001 16.694 <0.0001 1.275 0.2173

dVPD mean 122.668 <0.0001 0.595 13.2782 <0.0001 13.9286 <0.0001 1.8528 0.0267

dVPD IQR 259.555 <0.0001 0.823 37.279 <0.0001 63.435 <0.0001 5.491 <0.0001

dTsoil mean 261.975 <0.0001 0.768 9.107 <0.0001 44.611 <0.0001 7.368 <0.0001

dTsoil IQR 201.537 <0.0001 0.674 24.397 <0.0001 24.166 <0.0001 3.248 <0.0001

dSWC mean 109.965 <0.0001 0.534 29.145 <0.0001 2.3129 0.0292 1.089 0.3666

dLitter mass 21.338 0.0033 0.424 2.164 0.1097 10.812 0.0057 1.955 0.1387

dLitter pH 35.390 <0.0001 0.524 8.888 0.0002 8.685 0.0057 3.646 0.0218

dLitter

moisture

47.003 <0.0001 0.624 9.318 0.0001 16.478 0.0003 7.355 0.0009

dSoil pH 23.863 0.0012 0.544 3.633 0.0221 15.754 0.0003 0.041 0.9889

dhy 10.428 0.1656 0.219 2.824 0.0528 0.115 0.7369 0.426 0.7358

d[SOC] 5.008 0.6590 0.352 1.202 0.3242 0.159 0.6930 0.223 0.8799

d[N] 3.415 0.8442 0.357 0.912 0.4451 0.008 0.9316 0.074 0.9738

d[PAL] 10.308 0.1718 0.388 1.936 0.1418 1.034 0.3163 0.965 0.4200

d[KAL] 12.735 0.0788 0.299 1.641 0.1821 6.956 0.0124 0.173 0.9143


1


Figure 2(on next page)

Changes in means and interquartile ranges (IQR) of relative values of site condition

variables among forestry treatments in Pilis Mountains, Hungary in 2015.

Treatment types are coded as ‘CC’ – clear-cutting; ‘G’ – gap-cutting; ‘P’ – preparation cutting and ‘R’ –

retention tree group. PAR: photosynthetically active radiation (μEm−2s−1); DIFN: relative diffuse light (%);

SWC: soil moisture (m3/m3); Tair: air temperature (°C); RH: relative humidity (%); VPD: vapor pressure deficit

(kPa); Tsoil: soil temperature (°C); Litter mass: total mass of collected litter on the surface (g/m2); Litter pH:

litter pH in water; Litter moisture content: gravimetric moisture content of litter samples (%); Soil pH: soil pH

in water. Letter ‘d’ in the variable abbreviations refers to the differences from the mean values measured in

the ‘Control’ plots. Full circles show the mean; vertical lines denote the standard deviation of the samples.

Letters designate the significant differences among treatments (pairwise comparisons based on the linear

mixed models; Tukey-test, alpha=0.05), while asterisks denote significant differences from values measured

at control plots (random intercept models, alpha=0.05). The horizontal blue line shows the level of control.


0.00

0.10

0.200.25

0.15

0.05dVPD

IQR (

kPa)

a*

b*bc*

c*

Treatments

0200

400

600

800

1000

dPAR

IQR (μE

m−

2s−

1)

a*

b*

c*c*

20

40

60

80

0

dD

IFN

(%

)

a*

b*bc* c*

0.0

0.2

0.4

0.6

dTair

mean (

°C)

a*

b*b*

a*

dTair

IQR (

°C)

a*

b*bc*

c*

-2-1

01

2

dRH

mean (

%)

a*b b

ab

-0.05

0.00

0.05

0.10

dSW

Cm

ean (

m3/m

3)

a*

b*

acc

0.00

0.05

0.10

dVPD

mean (

kPa)

a*

bb

a*

02

46

8

dTsoil

IQR (

°C)

a*

b* b*

b*

CC G P R CC G P R CC G P R

-10

12

34

dTsoil

mean (

°C)

a*

b* b*

ab*

d L

itte

r m

ass (

g/m

2)

-400

-200

0200

400

aa

a a

d L

itte

r pH

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

a* a*

b b

d L

itte

r m

ois

ture

(%

)

-50

510

15

20

25

a*

a*

a*

b

d S

oil p

H

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

a a a

b*

CC G P R

a)

i) l)

g)e)

j)

p)o)n)m)

f)

b) c) d)

h)

k)

200

400

600

800

dPAR

mean (μE

m−

2s−

1)

a*

b*

c* c*

0

dRH

IQR (

%)

a*

b* bc*

ac*

01

23

4

0.0

0.5

1.0

1.5

2.0



Temporal variability of selected microclimate variables among experimental treatments

and months in Pilis Mountains, Hungary in 2015.

Treatment types are coded as ‘CC’ – clear-cutting; ‘G’ – gap-cutting; ‘P’ – preparation cutting

and ‘R’ – retention tree group. PAR: photosynthetically active radiation (μEm−2s−1); VPD:

vapor pressure deficit (kPa); Tsoil: soil temperature (°C) and SWC: soil moisture (m3/ m3).

Letter ‘d’ in the variable abbreviations refers to the differences from the mean values

measured in the ‘Control’ plots. Full circles show the mean; vertical lines denote the standard

deviation of the samples. Letters designate the significant differences among treatments and

months (pairwise comparisons based on the linear mixed models: Tukey-test, alpha=0.05).

The horizontal blue line shows the level of control.


-0.0

50

.00

0.0

50

.10

0.1

50

.20

CC G P R CC G P R CC G P R CC G P R CC G P R CC G P R CC G P R CC G P R

March April May June July August September October

Treatment

(mean±

SD

; kPa)

dVPD

NA

a a a a a a a a a a a a a b b ab a b bc ac a b bc ac a a a a a a a

a a bcab c bc a



Treatment

(mean±

SD

; °C)

dT

soil

0-6

-4-2

24

6 a a a a a a a a a ab bc c a b b b a b bc ac a b ab a a a a a a b b b

a ab cc c bc ab d



(mean±

SD

; m

3/m

3)

dSW

C

-0.1

0.0

0.1

0.2

Treatment

a a a a a a a a a a a a a b a a a b ac c ab a ab b ab a b b a a a a

abc ab cabc bc ab abc a

02

00

40

06

00

80

01

00

0



Treatment

(mean±

SD

; μE

m−

2s−

1)

dPAR

a b b b a b bc c a b c d a b c c a b c c a bc b c a b b b ac b ab c

a ab cc c c ab ba) b)

c) d)



Seasonal changes in selected soil and litter variables in Pilis Mountains, Hungary in

2015.

Treatment types are coded as ‘CC’ – clear-cutting; ‘G’ – gap-cutting; ‘P’ – preparation cutting

and ‘R’ – retention tree group. Letter ‘d’ in the variable abbreviations refers to the differences

from the mean values measured in the ‘Control’ plots. Full circles show the mean; vertical

lines denote the standard deviation of the samples. Letters designate the significant

differences among treatments and time (pairwise comparisons based on the linear mixed

models: Tukey-test, alpha=0.05). The horizontal blue line shows the level of control.


Spring Autumn

a a a a a a a a

Treatment

CC G P R CC G P R

(mean±

SD

)d S

oil p

H

a b

-0.2

0.0

0.2

0.4

0.6

Spring Autumn Spring Autumn

a a a a a ab ab b

Treatment

CC G P R

Treatment

CC G P R CC G P R

(mean±

SD

)d L

itte

r pH

(mean±

SD

; g/m

2)

d L

itte

r m

ass

a a a a a a b b

a b a b

CC G P R

-500

0500

1000

0.0

0.5

1.0

Spring Autumn

a a a a

Treatment

(mean±

SD

; %

)d L

itte

r m

ois

ture

conte

nt

a a a b

a b

CC G P R CC G P R

-10

010

20

a) b)

c) d)



Diurnal pattern of selected microclimate variables.

Diurnal magnitudes of PAR (photosynthetically active radiation), VPD (vapor pressure deficit)

and Tsoil (soil temperature) among treatments in the peak of the growing season (i.e. June,

July, August; left) and during the transition period (March, April, September, October; right),

respectively. Lines represent means calculated by LOWESS function (based on the 6

replications for each variable per month), bands are 95% confidence intervals. Colors are

coding the treatments: control – green; clear-cutting – red; gap-cutting – purple; preparation

cutting – orange and retention tree group – blue. Note that scales of y-axes vary among the

graphs.


PA

R (

µE

m-2

s-1

)V

PD

(kP

a)

1600

1400

1200

1000

800

600

400

200

0

1.75

1.60

1.45

1.30

1.15

1.00

0.85

0.70

0.55

0.40

0.25

Time (hh:mm)

Tsoil

(°C

)

27

26

25

24

23

22

21

20

19

18

17

16

31

30

29

28

15

PA

R (

µE

m-2

s-1

)V

PD

(kP

a)

Tsoil

(°C

)

550

500

450

400

350

300

250

200

150

100

50

0

0.40

0.35

0.30

0.25

0.20

0.15

0.10

0.0512

11

10

9

8

7

6

5

4

Time (hh:mm)

00:00 06:00 12:00 18:00 24:00

00:00 06:00 12:00 18:00 24:00

Summer Transition period


Documents

The effects of experimental forestry treatments on site ... · The effects of experimental forestry treatments on site conditions: short response study from an oak-hornbeam forest