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- Springer Journals
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- Copyright © The Author(s) 2021
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- 2197-5620
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- 10.1186/s40663-021-00317-9
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Abstract
Background: A challenge in current forestry is adaptation of managed forests to climate change, which is likely to alter the main processes of forest dynamics, i.e. natural regeneration. Scots pine will probably lose some parts of its distribution area in Europe. However, two native oaks, pedunculate and sessile may maintain or expand the area of their occurrence in central Europe. The utilization of spontaneous (not initialized by foresters) oak regeneration in Scots pine stands for the creation of next generation stands is one of the adaptation methods to climate change. Many factors influencing pedunculate oak regeneration are well known, but there is a lack of knowledge on the relation between soil enzyme activity and the establishment and development of the species. The aim of the study was to identify the relationships among stand characteristics, herb species composition, soil enzyme activity and the establishment or recruitment of oak regeneration in Scots pine-dominated stands. Results: The one of the most influential factors shaping the oak seedling count was dehydrogenase activity in the humus horizon. We found that plots without litter and fern cover had higher seedling density. The raspberry ground cover and birch crown projection area had a positive influence on oak seedling number. The factor indicating good conditions for high density of oak saplings was phosphatase activity in the organic horizon. The same enzyme activity but in humus horizon described conditions in which more numerous recruits were observed. Conclusions: The activity of soil enzymes can be used as the predictor of the establishment and advancement of oak regeneration but also could be seen as a new dimension of oak regeneration. The general density of spontaneous oak regeneration was not sufficient for the creation of new generation forest stands dominated by oak, but it is possible to use them as admixtures in new generation stands. Keywords: Forest stand conversion, Spontaneous regeneration, Regeneration niche, Dehydrogenase, Phosphatase Introduction differently to climate change (Huang et al. 2017). Scots Adaptation of managed forests to climate change is a pine (Pinus sylvestris L.), which was promoted in previ- challenge in current forestry (Lindner et al. 2014). Cli- ous centuries and covers large areas in the lowlands of mate change is likely to alter the main processes of for- northwestern and central Europe (Goris et al. 2007), will est dynamics, i.e., tree growth, mortality, and likely experience decreases in its distribution in Europe regeneration (Seidl et al. 2016). Tree species will respond (Sáenz-Romero et al. 2017; Dyderski et al. 2018). How- ever, other tree species may expand their ranges. Eco- * Correspondence: d.dobrowolska@ibles.waw.pl nomically important tree species that may maintain or Forest Research Institute, Sękocin Stary, Braci Leśnej 3, 05-090 Raszyn, expand the area of their occurrence in central Europe Poland include two native oaks, pedunculate (Quercus robur L.) Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Dorota et al. Forest Ecosystems (2021) 8:43 Page 2 of 17 and sessile (Q. petraea (Matt.) Liebl.) (Hanewinkel et al. well as in parks, gardens, and yards with plenty of ma- 2013; Takolander et al. 2019). Both grow across the ture trees (Vander Wall 2001). These birds hide acorns European lowlands and occur in many forest types (Ea- in hoards for winter time. The regeneration of oak estab- ton et al. 2016). Currently, Scots pine vitality has de- lishes under a canopy of old pine and gradually grows creased in Poland (Report of Forest Status 2018) and in up as more light becomes available in the gaps (Kint other countries. It has been observed in Europe that cli- et al. 2004, 2006). The regeneration of pedunculate oak mate warming has made Scots pine stands more vulner- has been intensively studied in Europe (Worrell and able to attack by bark beetles (Jaime et al. 2019)or Nixon 1991; Paluch and Bartkowicz 2004; Harmer et al. mistletoe (van Halder et al. 2019). The changes in spe- 2005; Ligot et al. 2013; Annighöfer et al. 2015); however, cies distribution ranges could generate large-scale eco- knowledge of spontaneous oak regeneration in Scots nomic problems that are difficult to solve with pine-dominated stands is still limited. Moreover, most of predominant even-aged management because the bigger the investigations have concentrated on the natural pro- compositional adjustment could be made only at the cesses in aging pine stands (Kint et al. 2004). The results end of production cycle and the cost of acceleration of of these studies suggested that mixtures with oaks in- such activities could be prohibitive (Schelhaas et al. crease Scots pine growth and that those stands can be 2015). One of the possible methods to increase the speed more productive (higher volume) than pure stands (Bie- of response for problems connected to climate change is lak et al. 2014; Steckel et al. 2019). the utilization of oak spontaneous regeneration pro- Many factors influencing pedunculate oak regener- cesses observed in current Scots pine stands for the cre- ation are well known (Kelly 2002; Annighöfer et al. ation of next generation stands or at least to enhance 2015; Didenko and Polyakov 2018), but the knowledge the species enrichment of current pine-dominated forest on the impact of the soil enzyme activity on the estab- ecosystems of artificial origin (Galiano et al. 2010; lishment and development of the species is insufficient. Rigling et al. 2013). Enzyme activities are critical to ecosystem functioning, Spontaneous regeneration of both oaks, silver birch affecting nutrient transformation, carbon sequestration, (Betula pendula Roth), downy birch (Betula pubescens and biogeochemical cycling of carbon, nitrogen, phos- Ehrh.) or common beech (Fagus sylvatica L.) has been phorus and sulphur. Phosphatases catalyse the hydrolysis detected in many aging Scots pine forests (Zerbe 2002; of ester bonds between phosphate and carbon com- Dobrowolska 2006; Kint et al. 2006; Gniot 2007; Goris pounds in organic substrates (Schneider et al. 2001). et al. 2007). However, spontaneous natural regeneration Urease activity may reflect the decomposition rate of ni- established under the Scots pine layer is not often used, trogen compounds in soil. The presence of asparginase even though it can play a great role in the restoration in the substrate is responsible for the decomposition of processes of forest stands and enhances their adaptabil- organic nitrogen compounds, that supply nitrogen to ity to changing environmental conditions (Diaci et al. plants. Dehydrogenases are responsible for oxidative re- 2008; Vizoso-Arribe et al. 2014). The success of spon- actions in soil (Wolińska and Stępniewska 2012). En- taneous oak regeneration depends heavily on animal ac- zyme activity correlates with soil physicochemical tivity. Numerous mammals and bird species contribute conditions, and nutrient availability (Tarafdar and Jungk to oak dispersal by in European forests. Eurasian jays 1987). Enzyme activities have been used as sensitive in- (Garrulus glandarius) and wood mouse (Apodemus syl- dicators of soil quality changes under the influence of vaticus) are the most important dispersers and hoarders management (Acosta-Martinez et al. 2014) and as an in- of acorns in Europe. Most other species that forage on dicator of heavy metal contamination (Bojarczuk and acorns are mainly seed predators or only occasional dis- Kieliszewska-Rokicka 2010; Yang et al. 2019). persers (Den Ouden et al. 2005). In the case of Scots Analysis of soil enzyme activity provides information pine dominated forest stands where older, seed produ- on soil microbial status (Yang et al. 2019). One of the cing oaks are sparse or absent (Mosandl and Kleinert most important components of these organism are fungi 1998; Frost and Rydin 2000; Kurek and Dobrowolska that form mycorrhizae. Burke et al. (2011) showed that 2016) jays seems to be a more important factor allowing ectomycorrhizal fungal communities were positively cor- colonisation of those stands by oaks than mice. Dispersal related with most soil enzymes, including enzymes in- distances of acorns by jays are in the order of several volved in C, N, and P cycling. Some observations linked hundred metres (Bossema 1979; Kollmann and Schill the success of young tree regeneration to the presence 1996) and much greater than dispersal distances by of mycorrhiza, which could also be responsible for soil mice, which according to Den Ouden et al. (2005) enzyme activity. Pedunculate oak seedling growth, bud- ranged between 2.7 to 9.2 m with exceptional situations burst, survival, biomass and foliar nitrogen and phos- achieving 70 m. Eurasian jays prefer thick, deciduous for- phorus content depend on root colonization by ests but are also found in coniferous or mixed forests as ectomycorrhizal fungi (Newton and Pigott 1991; Dorota et al. Forest Ecosystems (2021) 8:43 Page 3 of 17 Egerton-Warburton and Allen 2001). Seedling growth is stands older than 100 years represent 41% of the area of correlated with the number of mycorrhizal types (New- the forest district. Clear-cutting (areas less than 4 ha) ton 1991; García de Jalón et al. 2020). Moreover, oak re- and planting are the usual methods used for Scots pine generation surrounded by phylogenetically distant forest stand regeneration. However, spontaneous oak re- neighbours show increased abundance and enzymatic generation (originating from animal acorn dispersal) is activity of ectomycorrhizal fungi in the litter (Yguel et al. frequently observed 1–2 years after clear-cutting, even if 2014). The dependence between soil enzyme activity and there were no oak trees in or nearby the former stand various ecosystem characteristics described above sug- (foresters observations from the Nowe Ramuki Forest gests that they may interact directly or indirectly with District). oak regeneration establishment and advancement in our The investigation was conducted in Scots pine stands, study area. and the total area of our study covered 90 ha. The age of The aim of our study was to expand our knowledge on the Scots pine stands ranged from 26 to 140 years. All the spontaneous regeneration of pedunculate oak in investigated stands were regenerated by planting on two Scots pine-dominated stands of different ages. In par- site types: poorer (mixed coniferous forest site type, ticular, we addressed the following questions: where the plant community Querco roboris-Pinetum is commonly observed) and richer (mixed deciduous forest – Which stand characteristics influence the site type where patches of Tilio-Carpinetum calamagros- establishment and recruitment of oak spontaneous tietosum are abundant). These site types refer to the fol- regeneration in Scots pine stands? lowing soils: Dystric Albic Brunic Arenosols and Dystric – Does the presence of an herb layer and its species Brunic Arenosols (WRB 2015). composition affect the density of oak regeneration? – Does soil enzyme activity influence the Data collection establishment and advancement of new oak To investigate the influence of stand characteristics on generation? oak regeneration, we collected data from 13 pine- – Which factors influence the density of oak recruits dominated stands of different ages (Additional file 1: belonging to different quality classes? Table A2). There are numerous pine stands in the study region that grow under very similar conditions to our Methods studied stands, but have no or very sparse oak regener- Study area ation. The most likely factors responsible for this differ- The study was conducted in northeastern Poland in the ence are limitations in oak dispersal related to the Masurian Lake District. Although it is the lowland part availability of seed sources and the activity of seed dis- of Poland, the landscape is very diverse with many hills persing animals. Investigation of these factors would re- and lakes due to the influence of repeated glacial events, quire a different experimental design and should be especially by the Baltic glaciations. In this region, the conducted at a larger spatial scale, and of course would continental climate clashes with the Atlantic climate; require identification of potential seed sources. Our thus, typical temperate zone forests grow in the Masur- study focuses on other questions, namely the role of ian Lake District. The average temperature in July is local stand conditions and soil enzyme activity in oak re- 18 °C, the average temperature in January is − 4 °C, the generation establishment. We chose a pine stand with annual precipitation ranges from 500 to 634 mm, and abundant oak regeneration under the assumption that the growing season lasts 190–200 days (Lorenc 2005). seed rain density there was not as limiting a factor as The study was carried out in 2013–2015 in managed other factors considered in our experiment. This as- forests located in the Nowe Ramuki Forest District (138 sumption might immediately raise the question of m a.s.l.; 20°30′ E, 53°47′ N). The Nowe Ramuki Forest whether seed rain density limitations were comparable District is part of the great complex of the Napiwodzko- in all these stands and whether other factors not in- Ramucka Forest. The forest cover of the study area is cluded in our experiment acted in a comparable manner high (67.9%), much higher than the average for the Ma- in the selected stands. It is likely that these large-scale surian Lake District (Plan of Forest Survey in Nowe factors act in a more or less unequal manner in each se- Ramuki Forest District). The main tree species is Scots lected stand, which could have some influence on oak pine, which occupies 91% of the forest area. Other im- regeneration density. To address this issue, mixed effects portant trees are pedunculate oak (4%) and silver birch models were used in the statistical analysis to separate (3%). The big share of Arenosols soils may suggest that the variability of the dependent variable attributable to in the primeval forest in this region the share of broad- the factors studied (fixed effect, e.g. local stand basal leaved trees (Quercus, Betula, Carpinus) was substantial. area) from the variability caused by factors not directly Most forests are old (average stand age is 83 years). Pine Dorota et al. Forest Ecosystems (2021) 8:43 Page 4 of 17 included in the model that are specific to the particular released after a 48 h incubation at 37 °C. The concentra- forest stand (random effect). tion of NH was measured at 410 nm by the colorimet- Starting from a random point, we established sample ric method (Alef and Nannipieri 1995). The asparginase plots in a rectangular grid fitted to the area of each activity was determined with the use of the colorimetric stand. The total number of sample plots in all stands technique and expressed as NH mg in 10 g of soil per was 240 (on average 18.5 per stand). The measurements 48 h. The acid phosphatase activity was determined by were carried out on three concentric circular plots of the calorimetric method and expressed in mg of p-nitro different radii. Seedlings of oak and other tree species phenol (pNP) per 10 g of soil (Tabatabai and Bremner (h ≤ 0.5 m) were counted only in the smallest area of 10 1969; Olszowska 2018). m (radius 1.78 m). On the second circle (area of 100 m , radius 5.64 m) saplings (h > 0.5 and diameter at Statistical analyses breast height (DBH) ≤ 2 cm) and recruits (DBH: 2–7 cm) Generalized linear mixed models with a log-link function were counted. Trees (specimens with DBH > 7 cm) were were developed for five separate response variables (oak measured in the largest circle (area of 250 m , radius seedling counts, oak sapling counts, oak recruit counts 8.92 m). We measured the DBH of all trees, the height first grade oak recruits counts and summarised medium of trees, and the radii of the tree crown in four direc- and lower grade recruits counts) to assess factors influ- tions using tape then, the crown projection area was cal- encing oak numbers belonging to particular regeneration culated using formula for ellipse area. In the case of stages observed on the sampling plot. Although sample regeneration, we measured the height of all seedlings plots were placed on systematic grids, plots placed in and saplings and the DBH of saplings taller than 1.3 m. one forest stand could be more related to each other The quality of the recruit stem was categorized into the (dependent) than to other plots. Additionally, some of following classes: 1 – straight, 2 – curved (one or more the independent variables (e.g., describing soil enzymatic curves), and 3 – deformed (bushy shape). In further ana- activity or the age of Scots pine trees on sample plots) lysis we divided recruits into two quality classes: first were estimated at the stand level. To disentangle poten- quality (straight) and combined low quality (curved and tial interactions between many factors and address auto- deformed). The cover (%) of litter, moss, and herbs was correlation, generalized linear mixed effect models described for each circular plot (in the area of 250 m ). (GLMMs) were used (Zuur et al. 2009; Bolker et al. Soil enzyme activity data were collected in 10 sample 2009). The influence of the common location of a group plots (the same plots established for stand measure- of sampling plots from one forest stand was modelled as ments) in each stand studied. After removing the litter a random effect, and other potential factors were mod- layer (Ol layer), soil samples were taken and collected elled as fixed effects. In the mixed models, the intercept from the top 15 cm of soil (this is the depth where the for the random effect was allowed to vary between forest main microbial activity occurs) using a 200 cm probe. stands, but the slope was fixed. The soil samples were divided into two soil horizons: or- Count data are discrete and nonnegative; thus, the ganic (Oh holorganic layer) and humus (Ah organomin- Poisson distribution is often used for modelling such eral horizon). The soils were then sieved (< 2 mm), data. An important limitation of the Poisson distribution removing all debris, stones, roots and plant remnants. is the assumption that the mean and variance of the ob- Samples taken from one stand were pooled and further served data are equal. The situation that the observed processed to obtain an estimate representing the entire variance is greater than the mean is quite common in stand. ecological data sets and is referred as over-dispersion Samples taken from one stand were pulled together (Zuur et al. 2009). To account for the possibility of such and processed further to receive one estimate represent- a situation in oak regeneration modelling, a negative bi- ing the whole stand. Four enzymatic activities were ana- nomial distribution of the Poison distribution (GLMM lysed (dehydrogenase, urease, phosphatase and Poisson) was also used. The negative binomial distribu- asparginase). Dehydrogenase activity was determined by tion can be parameterized differently regarding the de- the reduction of 2,3,5-triphenyltetrazolium chloride pendence of the variance on the mean (Crotteau et al. (TTC) to triphenyl formazan (TPF) using Lenhard’s 2014). In the present study, two variants of dependence method according to the Casida procedure (Alef and were implemented: one when the variance increases Nannipieri 1995). Briefly, 1 g of soil was incubated with linearly with the mean (GLMM nbinom1) and the sec- 1 mL of 3% TTC for 24 h at 37 °C. TPF was extracted ond when the variance increases quadratically with the with ethyl alcohol and measured spectrophotometrically. mean (GLMM nbinom2). Urease activity was determined according to Tabatabai Some histograms from Fig. 1 suggest that the propor- and Bremner (1972) using a water-urea solution as a tion of zeroes is so large that it could not be possible to substrate. This activity was determined by the NH readily fit these data to standard (Poisson or binomial) 4 Dorota et al. Forest Ecosystems (2021) 8:43 Page 5 of 17 seedlings saplings recruits number o observed specimens = 488 number o observed specimens = 1921 number o observed specimens = 388 average density = 2043 pcs./ha average density = 801 pcs./ha average density = 162 pcs./ha zeroes proportion = 39.58 % zeroes proportion = 12.50 % zeroes proportion = 67.92 % 0 1020304050 0 1020304050 0 1020304050 Number of specimens on sampling plot Number of specimens on sampling plot Number of specimens on sampling plot first quality recruits low−grade recruits number o observed specimens = 167 number o observed specimens = 221 average density = 70 pcs./ha average density = 93 pcs./ha zeroes proportion = 76.67 % zeroes proportion = 72.50 % 0 1020304050 0 1020304050 Number of specimens on sampling plot Number of specimens on sampling plot Fig. 1 Specimens counts on sampling plots distributions. The non-consideration of this excess of statistical models interaction terms were added to assess zeroes might reduce the ability to detect relevant rela- potential influence of stand characteristics on the reac- tionships and make inaccurate inferences. Three add- tions of young oaks on the change of soil enzyme activ- itional types of zero-inflated mixture models were built: ity level. For building models concerning recruit counts, one based on a Poisson distribution (GLMM ZI Poisson) variables concerning vegetation soil coverage were not and two based on a negative binomial distribution with included because it is rather unlikely that the number of linear (GLMM ZI nbinom1) and quadratic (GLMM ZI trees with a DBH greater than 2 cm is governed by low nbinom2) relations between mean and variance. Finally vegetation. for each regeneration stage, six additional mixed models At the end of the model fitting process for each oak for each oak regeneration stage were built (GLMM Pois- regeneration stage, six competing models were built that son, GLMM nbinom1, GLMM nbinom2, GLMM ZI address the problems of autocorrelation, overdispersion Poisson, GLMM ZI nbinom1, and GLMM ZI nbinom2). and zero inflation. The final model describing the influ- All the possible explanatory variables collected during ence of important variables on the particular oak regen- field work are shown in Additional file 1: Table A1. In eration stage was chosen based on the lowest AIC value the first stage of modelling simple generalized linear from 6 models (Zuur et al. 2009, 2012; Crotteau et al. Poisson models containing only one explanatory variable 2014; Peters and Visscher 2019). After the identification were build. About twenty variables which gave the best of the best model, its residuals were inspected to con- models were selected to the next modelling stage in firm that errors were homogeneous. which all of them were included in a model belonging to All analyses were performed with R language (R ver- one of the previously mentioned model types (e.g. sion 3.5.0, R Core Team 2018), and models were fitted GLMM nbinom1). In the case of collinearity, the vari- using a template model builder (TMB) via maximum able with less explanatory power were removed from ini- likelihood estimation using the R package “glmmTMB” tial models based on their inflation factors (VIFs) (Zuur (Brooks et al. 2017). For the final models containing ran- et al. 2009). In the refined set of explanatory variables dom effects, it was possible to characterize their explana- no variable had VIFs value above 3. During building of tory power by the calculation of the marginal R-squared Frequency Frequency 0 50 100 150 200 250 0 50 100 150 200 Frequency Frequency 0 50 100 150 200 250 0 50 100 150 200 Frequency 0 50 100 150 200 Dorota et al. Forest Ecosystems (2021) 8:43 Page 6 of 17 value considering only the variance of the fixed effects unequivocal dominance of this distribution form among and conditional R-squared, taking both the fixed and the the highest ranked models. random effects into account according to the formulas Only for seedling model the assumption concerning proposed by (Nakagawa et al. 2017).The R values for zero inflation was proven by the AIC comparison of the selected models were calculated with the function competing models. The best mixed model build for from the “performance” package (Lüdecke et al. 2019). seedlings counts namely GLMM ZI nbinom2 turned out to have variance of random component equal zero. This problem was solved in the way described by Pasch et al. Results (2013) by the reduction of random component of the The general information of specimen counts belonging model. The parameters of the final model were recalcu- to different species and developmental stages are given lated back from logit form and provided in Table 1. The in the Additional file 1 in Tables A3–A5. The frequen- only explanatory variable proven to be useful for predict- cies of different classes of oak regeneration on sampling ing zero excess was bilberry (Vaccinium myrtillus L.) plots and information on the overall density of oak ground cover. The ground cover of raspberry (Rubus spontaneous regeneration are presented in Fig. 1.We idaeus L.) growing on the sample plot and the birch found that the density of oak regeneration decreased crown projection area had a positive influence on the from the seedling class to larger classes. The total dens- observed number of oak seedlings. A negative effect was ity of oak seedlings was almost two times higher than cast by the ground cover with litter and ferns (Pteridium the sapling density (2043 vs. 801 individuals per ha). The aquilinum (L.) Kuhn). number of recruits was lower than 200 individuals per From analysed soil enzymes only activity of dehydro- ha. We compared the density of first quality (straight genase correlated positively and in statistically important stems) and lower quality (curved stems and bushy shape) manner with numbers of seedlings on sampling plots recruits and found that the density of lower quality re- (Fig. 2b). If the dehydrogenase activity increased to 0.4, cruits was greater than that of better quality recruits. the predicted average count of oak seedlings was greater In this section, we refer only to the final models. Com- than 3. The increase in the raspberry cover to 25% and peting model details and comparisons can be found in birch crown projection area to 105 m (the highest ob- the Additional file 1 (Tables A6–A10). The assumption served levels of those factors) strongly positively influ- that spatial autocorrelation connected with plot co- enced oak seedling counts (Fig. 2a, c). The negative occurrence in the same stand was important was sup- impact of fern and litter was presented on Fig. 2e, d. ported by lower AIC values of models containing stand Plots without litter and fern cover had markedly higher random effects for almost all final models but not for seedling counts. the best model describing seedling counts. The second Four predictors had a negative influence on the num- best competing model for seedlings was the GLMM with ber of oak saplings (Table 2). There were: ground cover a comparable log likelihood value, but its AIC value was by litter, basal area of pedunculate oak, European horn- higher by 2 due to a larger degrees of freedom value, beam and Norway spruce. A positive relationship was which means that including random effects in this case observed for phosphatase activity in the organic soil did not improve the model. In no final models inter- horizon. As shown in Figs. 3a–d the negative influencing action term between soil enzyme activity and other inde- factors have a very strong diminishing influence on the pendent variables was proven to be statistically saplings counts. The highest oak sapling density was important. predicted if the basal area of oak, spruce and hornbeam The usefulness of the Poisson distribution for model- was zero. The higher crown projection area of these spe- ling oak regeneration was very low. In none of the ana- cies decreased the oak sapling count. Compering the ef- lysed regeneration stages were competing models based fect of these species, the strongest impact was observed on the Poisson distribution found among the three high- for hornbeam. We found that if the litter cover was est rated models (Additional file 1: Tables A6–A10). In 100% the count of oak saplings was 0.6. As shown in the majority of cases, Poisson models occupied the end Fig. 3e if the phosphatase activity is greater than 4.5 the of the ranking table. The over-dispersion value for the expected count of oak was 7. best models ranged from 1.29 (in the recruit model) to Three statistical models describing counts of different 3.29 (in the sapling model). Three of the five best types of oak recruit were built. The first (Table 3) de- models were built on the assumption that the depend- scribed general counts of recruits. This model contained ence between the mean and variance of the specimen only two statistically important variables (the activity of count had a nonlinear form (nbinom2), but the examin- phosphatase in the humus soil horizon and total crown ation of competing model ranking tables (Additional file projection area of all trees growing on sample plots) that 1: Tables A6–A10) suggested that there was no negatively reacted with the predicted recruit counts. The Dorota et al. Forest Ecosystems (2021) 8:43 Page 7 of 17 Table 1 Model of seedlings count Predictors Seedlings count Incidence rate ratios std. Error CI z Statistic p-value (Intercept) 1.46 0.18 1.04–2.06 2.18 0.029 Raspberry 1.06 0.03 1.01–1.12 2.40 0.016 Fern 0.95 0.02 0.91–0.99 −2.21 0.027 Litter 0.98 0.01 0.97–0.99 −3.14 0.002 Birch.crown.m 1.01 0.00 1.00–1.02 2.04 0.041 Dehydrogenase.A 9.18 0.68 2.40–35.13 3.24 0.001 Zero-Inflated Model (Intercept) 0.08 0.89 0.01–0.46 −2.84 0.005 Bilberry 1.03 0.01 1.01–1.05 2.97 0.003 Observations 240 predicted count of all recruits were presented in Fig. 4a counts was 2.5. A similar pattern could be observed and b. If the total crown area was 400 m the count of when only the first quality (in terms of their potential oaks was close to zero. As shown in Fig. 4b, only when silvicultural use) recruits were analysed (Table 4, Fig. 4c the phosphatase activity in the soil was close to the low- and d). The same factors influenced recruit counts in a est observed levels (0.23 mg per 10 g of soil) all recruit similar manner, but the average count of this class of a) b) c) 10.0 7.5 5.0 2.5 0.1 0.2 0.3 0.4 0 25 50 75 100 0 5 10 15 20 25 Dehydrogenase activity in soil Birch crown projection [m ] [mg of triphenylformazan per 10 g of soil] Raspberry cover [%] d) e) 2 2 1 1 0 0 00 2255 5507 0755 110000 00 2255 5507 0755 110000 Fern cover [%] Litter cover [%] Fig. 2 Variables influencing oak seedlings counts Predicted avaerage count of oak seedlings Predicted avaerage count of oak seedlings Predicted avaerage count of oak seedlings Predicted avaerage count of oak seedlings Predicted avaerage count of oak seedlings Dorota et al. Forest Ecosystems (2021) 8:43 Page 8 of 17 Table 2 Model of saplings count Predictors Saplings count Incidence rate ratios std. Error CI z Statistic p-value (Intercept) 0.27 1.32 0.02–3.63 −0.99 0.325 Litter 0.98 0.01 0.96–0.99 −3.26 0.001 Oak.basal.m 0.01 1.13 0.00–0.08 −4.16 < 0.001 Hornbeam.basal.m 0.00 3.93 0.00–0.02 −2.91 0.004 Spruce.basal.m 0.08 0.70 0.02–0.31 −3.64 < 0.001 Phosphatase.O 2.30 0.31 1.25–4.23 2.68 0.007 Random Effects Residual variance 0.52 Random intercept variance 0.17 Intraclass correlation coefficient 0.25 Number of forest stands 13 Observations 240 2 2 Marginal R / Conditional R 0.494 / 0.619 a) b) c) 9 9 9 6 6 6 3 3 3 0 0 0 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 2 2 2 2 2 2 Oak basal area [m Oak basal area [m ]] Spruce basal area [m Spruce basal area [m ]] Hornbeam basal area [m Hornbeam basal area [m ]] d) e) 3.0 3.5 4.0 4.5 0 25 50 75 100 Phosphatases activity in litter Litter cover [%] [mg of p-nitro phenol per 10 g of litter] Fig. 3 Variables influencing oak saplings counts Predicted avaerage count of oak saplings Predicted avaerage count of oak saplings Predicted avaerage count of oak saplings Predicted avaerage count of oak saplings Predicted avaerage count of oak saplings Dorota et al. Forest Ecosystems (2021) 8:43 Page 9 of 17 Table 3 Model of recruits count Predictors Seedlings count Incidence rate ratios std. Error CI z Statistic p-value (Intercept) 353.85 2.34 3.61–34,679.99 2.51 0.012 All.crown.m 1.00 0.00 0.99–1.00 −2.63 0.008 Phosphatase.A 0.00 6.07 0.00–0.00 −3.09 0.002 Random Effects Residual variance 2.52 Random intercept variance 4.48 Intraclass correlation coefficient 0.64 Number of forest stands 13 Observations 240 2 2 Marginal R / Conditional R 0.448 / 0.801 recruits was two times lower than that of all recruits. In spontaneous oak regeneration can play an important the case of the lower quality recruit counts (Table 5, role in their transformation to future stands that are Figs. 4e–g), a statistically important negative impact of more stable and species reach. three predictors was detected: the activity of phosphatase in the humus soil horizon, spruce basal area and oak Density of oak regeneration volume in the sample plot. We observed oak regeneration of all development phases (from seedlings to recruits) in Scots pine-dominated Discussion stands (Additional file 1: Tables A3–A5). Half of all oaks Today’s challenge in forestry goes further than to were included in the smallest height group (up to 0.5 m achieve timber production with social requirements and height). We would like to stress that in the selection of to ensure maintenance of forests as part of our heritage important variables in the model building process, we (Schütz 1999;O’Hara 2016). Expected climate change did not show that the age of Scots pine stands is an im- will influence vast forest areas simultaneously and in a portant variable. relatively short time in comparison to the longevity of The average density of oak recruits was not high, but the classical forest production cycle. The gradual adjust- the importance of their presence was potentially great, ment of species composition during the process of util- especially if they were present in the relatively young isation of mature forest stands and establishment of new Scots pine-dominated stands (Additional file 1: Table better adapted generation could be too slow to react on A5; Fig. 1). Although their direct economic meaning is fast occurring climate changes (Schelhaas et al. 2015). rather constrained (they could be utilized at most for Near-natural silviculture approaches emphasizing the fuel wood during final cuttings), their impact on the bio- utilization of natural spontaneous processes may have logical stabilization of Scots pine stands is well docu- the potential to develop complex and sustainable forests mented (Steckel et al. 2019). The conversion of pure that are adapted to our changing world (Brang et al. Scots pine into mixed stands with understory oaks 2014). The idea that biological processes rather than soundly increased the number and species composition silvicultural efforts might be relied upon are especially of parasitoid wasps, which could mitigate the outbreaks promising given the size of the challenges ahead. Bio- of folivore insects (Jäkel and Roth 2004). This goal could logical automation might accommodate reduced re- be achieved in artificial way by underplanting oaks in source inputs/effort into forest management operations 40–50 year old Scots pine stands which is costly, or in (Pretzsch and Zenner 2017). Our investigation referred natural way by jays activity which we demonstrated in to the idea of creating mixed stands that are well our results. adapted to changing environments. Moreover, most Some modelling results suggested that even recruits studies have focused on the most popular 2-species that were not abundant could potentially have economic combinations (e.g., spruce-beech, oak-beech), while importance because they are not randomly but rather other important combinations, such as pine-oak, have contagiously distributed in the stand. In the beginning received scant attention (Pretzsch et al. 2017). Because stage of final model selection, initial statistical models climate change may negatively impact growing condi- assuming different types of individual distributions (e.g., tions for Scots pine monocultures situated on dry, sandy Poisson, negative binomial and their zero inflated coun- soils in central Europe (Slodicak et al. 2011), terparts) were compared for each regeneration stage Dorota et al. Forest Ecosystems (2021) 8:43 Page 10 of 17 a) b) 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0.3 0.4 0.5 0.6 0 100 200 300 400 Phosphatases activity in soil total crown projection area [m ] [mg of p-nitro phenol per 10 g of soil] c) d) 12.5 12.5 0.6 0.6 10.0 10.0 0.4 0.4 7.5 7.5 5.0 5.0 0.2 0.2 2.5 2.5 0.0 0.0 0.0 0.0 0.3 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0 0 100 100 200 200 300 300 400 400 Phosphatases activity in soil Phosphatases activity in soil 2 2 total crown projection area [m total crown projection area [m ]] [mg of p-nitro phenol per 10 g of soil] [mg of p-nitro phenol per 10 g of soil] e) f) g) 8 8 0.4 0.4 6 6 0.4 0.4 0.3 0.3 4 4 0.2 0.2 0.2 0.2 2 2 0.1 0.1 0 0 0.0 0.0 0.0 0.0 0.3 0.4 0.5 0.6 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 02468 02468 Phosphatases activity in soil 2 2 3 3 Spruce basal area [m Spruce basal area [m ]] Oak volume [m Oak volume [m ]] [mg of p-nitro phenol per 10 g of soil] Fig. 4 Variables influencing count of all recruits (a, b), first quality recruits (c, d) and low quality recruits (e, f, g) (Additional file 1: Tables A6–A10). As in many other in- distribution of counts on randomly chosen sampling vestigations (Fyllas et al. 2008; Li et al. 2011; Zhang et al. quadrats could be seen as evidence that the spatial point 2012; Crotteau et al. 2014; Vickers et al. 2017), models pattern formed by specimens is completely random that did not assume Poisson distributions of young re- (Diggle 2003; Wiegand and Moloney 2014). The even- generating individual counts proved to be better. Al- tual superiority of Poisson-based models describing oak though the better performance of models based on a regeneration counts on sampling plots would suggest negative binomial count distribution is not a general pat- that factors influencing oak regeneration spatial place- tern in analysing spontaneous regeneration (Vickers and ment were acting in a random manner on the area of in- Palmer 2000; Fyllas et al. 2008; Peters and Visscher vestigated stands. The superiority of models based on 2019), this was an important finding from the manage- different forms of negative binomial distributions sug- ment (silviculture) point of view. The Poisson gested that regenerating oaks were not placed Predicted avaerage count of oak recruits Predicted avaerage count of oak recruits Predicted avaerage count of oak recruits Predicted avaerage count of oak recruits Predicted avaerage count of oak recruits Predicted avaerage count of oak recruits Predicted avaerage count of oak recruits Dorota et al. Forest Ecosystems (2021) 8:43 Page 11 of 17 Table 4 Model of first quality recruits count Predictors First quality recruits count Incidence rate ratios std. Error CI z Statistic p-value (Intercept) 220.56 2.57 1.43–34,031.33 2.10 0.036 Phosphatase.A 0.00 6.99 0.00–0.00 −2.75 0.006 All.crown.m 0.99 0.00 0.99–1.00 −2.59 0.010 Random Effects Residual variance 3.64 Random intercept variance 4.68 Intraclass correlation coefficient 0.56 Number of forest stands 13 Observations 240 2 2 Marginal R / Conditional R 0.420 / 0.746 completely randomly. Based only on this information, it chance that their stems will become crooked. When was not possible to note the name of a particular spatial planning the conversion of Scots pine to oak, full over- point process “responsible” for creating oak spatial dis- story light should be provided as early as possible, but tribution (Diggle and Milne 1983; Coly et al. 2016), but no later than 20 years after the regeneration is estab- it was safe to assume that the spatial distributions of oak lished (Skrzyszewski and Pach 2015). were heterogeneous (Velázquez et al. 2015) or even grouped (Krebs 1999) in the space of investigated stands. Soil factors influencing oak spontaneous regeneration Grouped distribution of different stages of bird- We found that enzymatic activity is one of the most im- dispersed oak regeneration was also found in other stud- portant factors correlating with oak spontaneous regen- ies (Mosandl and Kleinert 1998; Frost and Rydin 2000). eration in Scots pine stands. Our statistical models From a practical point of view, this result means that lo- suggest that the correlation with soil enzyme activity de- cally (in some fragments of a forest stand), the density of pends on the stage of oak regeneration. The number of oak saplings or recruits could be large enough to be use- oak seedlings was positively related to the soil dehydro- ful for silvicultural goals, e.g., in some forest districts in genase activity. This is often interpreted as an indicator Poland, clumps of well-shaped oak recruits are used to of increased microbial activity in the soil, particularly create the oak admixture in the next generation of Scots mycorrhizal fungal activity (Buée et al. 2005). Yguel pine stands (Gniot 2007; Skrzyszewski and Pach 2015). et al. (2014) found that oaks surrounded by phylogenet- The possibility of successfully incorporating understory ically distant neighbours had increased abundance and oaks as a good quality admixture into the next gener- enzymatic activity of ectomycorrhizal fungi in the litter. ation is limited by the amount of time young oaks spend This suggests that reduced nutrient availability in a in the understory. The longer the time, the greater the phylogenetically distant litter was partially compensated Table 5 Model of lower quality recruits count Predictors Lower quality recruits count Incidence rate ratios std. Error CI z Statistic p-value (Intercept) 108.90 2.06 1.91–6221.84 2.27 0.023 Phosphatase.A 0.00 5.85 0.00–0.01 −2.79 0.005 Spruce.basal.m 0.00 4.07 0.00–0.98 −1.96 0.049 Oak.volume.m 0.56 0.27 0.33–0.95 −2.14 0.032 Random Effects Residual variance 3.29 Random intercept variance 2.65 Intraclass correlation coefficient 0.45 Number of forest stands 13 Observations 240 2 2 Marginal R / Conditional R 0.500 / 0.723 Dorota et al. Forest Ecosystems (2021) 8:43 Page 12 of 17 for by increased litter decomposition by ectomycorrhizal 2013). Moreover, the mineralization rate is higher in fungal activity. The research conducted by Showalter soils under broadleaved trees than under Norway spruce et al. (2010) directly shows that dehydrogenase activity is and Scots pine Smolander and Kitunen (2002). For oak positively correlated with tree growth, which, according younger regeneration growing in soil covered by almost to the cited author, indicates that a well-established pure Scots pine litter the higher activity of phosphatase mycorrhiza is increasing nutrient availability for host is needed to improve the supply of phosphorus. Oak re- tree. cruits add substantial quantity of leaves to the litter so The density of oak saplings and recruits was related to availability of phosphorus could be improved by the the phosphatase activity in soils. The increase in phos- higher ratio of litter decomposition and the increased phatase activity in organic soil horizon corresponded to activity of phosphatase is not needed. increased oak sapling density but the density of oak re- 2) The second mechanism in which the presence of cruits was negatively correlated with phosphatase activity oak recruits could negatively influence the activity of in the humus soil horizon. phosphatase could be connected with their influence on Soil phosphatase plays critical roles in phosphorus cy- soil moisture. Oaks have different strategy than Scots cles and the metabolic state of soil microorganisms pine in terms of soil water usage. In the case of drought (Watts et al. 2010) and its activity is positively correlated Scots pines strongly reduce their transpiration but oaks with soil-extractable phosphorus and with high produc- in Central Europe tend to keep high rate of transpiration tion capacity, stand biomass and/or plant cover (Carreira as long as possible (Toïgo et al. 2015). This difference in et al. 2000). Phosphorus availability is essential for plant water usage strategy was observed in Europe-wide exper- growth and may be a limiting factor in some forest eco- iments (Steckel et al. 2020) and could lead to faster systems (Attiwill and Adams 1993). This constraint water depletion under oaks than pines. Augusto et al. could be especially important for young oaks (Collet (2003) observed that vascular plants growing under oaks et al. 1997), with small and relatively shallow root system in mixed stands have lower moisture requirements than growing in relative poor site condition in our experi- under Scots pine. The presence of oak recruits in inves- ment. The higher activity of the enzyme increases the tigated stands could locally diminish soil moisture and nutrient uptakes from organic soil horizon by oak regen- diminish activity of phosphatase, which depends strongly eration and could promote their survival and increase on this soil property (Baldrian 2014). the density of oak saplings. We hypothesise that young oaks depend positively on Impact of stand features on the density of oak soil enzyme activity, but for the older ones the cause- regeneration effect relationship is reversed, so that soil enzyme activ- Oak seedlings establishment ity depends negatively on the presence of recruits. The We found that the cover of bilberry negatively influ- size of the recruits is much larger than that of the other enced the establishment of oak seedlings. However, oak regeneration classes studied. These relatively large Drössler et al. (2017) observed more oaks in blueberry organisms could have a more significant effect on soil patches and suggested that the Eurasian jay (Garrulus microbial activity than smaller ones. There are at least glandarius) prefers to hide acorns under dwarf shrub two ways in which oak recruits may reduce phosphatase vegetation. A negative impact on oak seedlings was ob- activity in Scots pine-dominated stands: served from fern cover. Jensen et al. (2011) also sug- 1) Oak trees took up phosphorus mainly from 15 cm gested a negative effect of dense herbaceous ground soil depth, where the greater amount of roots and exter- vegetation on oak regeneration. Bilberry and fern, nal mycorrhizal mycelia were found (Göransson et al. especially common bracken, created dense ground cover 2006). The phosphatase activity is correlated with the that was not preferred by oak seedlings in pine stands. availability of phosphorus in the soil. The increase in Humphrey and Swaine (1997) showed that competition phosphorus in soil typically leads to a decrease in the ac- from bracken (Pteridium aquilinum (L.) Kuhn) restricted tivity of this enzyme (Olander and Vitousek 2000). The the growth of oak seedlings. Competition for nutrients additional amount of available phosphorus in soil com- and moisture may also be important, especially in nutri- ing from the decomposition of litter composed not only ent poor or drier areas (Löf 2000; Brudvig and Asbjorn- with pine needles but also oak leaves could decreases sen 2007). However, we also found that litter cover the activity of phosphatase. Soils under Quercus typically created inappropriate conditions for oak regeneration. In showed low enzyme activity (Šnajdr et al. 2013). Al- previous studies (Kurek and Dobrowolska 2016; Kurek though the amount of phosphorus is similar in pine and et al. 2018), it was observed that jays deposited the oak litter and soil (Šnajdr et al. 2013), the rate of their acorns in small patches of the litter. Moreover, litter af- litter decomposition is different. Oak litter is rapidly fected soil humidity and the amplitude of diurnal decomposed compared to other litter (Šnajdr et al. temperature fluctuations. The forest floor can act as a Dorota et al. Forest Ecosystems (2021) 8:43 Page 13 of 17 physical barrier and can possibly release toxic metab- a pioneer tree (Götmark and Kiffer 2014), it survives as a olites (Sayer 2006). This process depends on the seedling/sapling in relatively dark understories (our amount of litter present and the environmental condi- results). tions (Donath and Eckstein 2008). The positive im- pact of raspberry cover on oak seedling density was Oak recruits observed in our study. Raspberry does not create as The count of oak recruits showed a clear relation to dense of ground cover as bilberry, and it protects crown projection of all trees. The density of oak recruits oaks against damage caused by biotic (herbivory) and increased with decreasing crown projection of all trees. abiotic (drought, insolation, lack of humidity) factors Crown size is an indicator of space occupancy because it (Donoso and Nyland 2006). Cuttings that remove is correlated with the photosynthetic capacity (Kamler more than 40% of the forest canopy create environ- et al. 2016). Similar results were achieved by Annighöfer mental conditions that promote the establishment of et al. (2015), who found that sapling quantity decreased raspberry. In such places, oak finds good conditions with increasing basal area of other species. The situation for regeneration (Donoso and Nyland 2006). when the number of recruits is low and the crown pro- Overstory species composition affected oak seedling jection of old tree is high suggests water limitations at density. We recognized the positive impact of birch small scales. On the Fig. 4, it could be seen that at the (birch crown projection area) on oak density. Light same level of local crown projection, the density of high transmission was found to be higher in dense birch- quality recruits was lower than the total density of re- dominated stands than in dense pine-dominated cruits. Recruits is the category of young oaks that have stands because of the higher total foliage area and the the greatest age and longer growth history under canopy higher location of foliage in the pine canopy (Lintu- pressure. Observation from other studies (Skrzyszewski nen et al. 2013). Because pedunculate oak is a light- and Pach 2015) indicate that prolonged period of growth demanding species (Savill 2019), it requires at least under the canopy (more than 20 years) reduces the qual- 20% full sunlight to avoid severe growth depression ity of young oaks. This explanation is suitable also for (Ligot et al. 2013). Light is not a requirement for ger- the first-quality recruits. In the case of low-quality re- mination (Ligot et al. 2013), as seedlings largely rely cruits, more factors had a negative impact on their dens- on energy from the acorn during the first season. ity. These factors included spruce basal area and oak Paluch and Bartkowicz (2004) also found that oaks volume. Norway spruce and oak utilise more water than occurred more frequently in the vicinity of birches. It Scots pine and may locally diminish water reserves in is possible that the neighbourhood of birch trees soil and also transmit less light through their canopy. could facilitate the establishment of oak by reducing With the lack of light, oaks create a shrubby crown. Tall the competition of vegetation. and slender oaks reflect a priority for shoot growth, which is a common strategy employed by plants in re- Oak saplings sponse to shading (Jensen et al. 2011). We recognized that hornbeam, Norway spruce and ped- unculate oak basal area negatively influenced the num- New dimension of oak regeneration niche ber of oak saplings. All of these species have dense In our study, we explored the oak regeneration niche, crowns that transmit less light. We think that light con- i.e., the set of environmental requirements potentially ditions were the key factor, as the light requirement of important for germination and establishment of its re- oak increases with increasing tree age and size (von generation. Much research is devoted to exploring vari- Lüpke and Hauskeller-Bullerjahn 1999; Vizoso-Arribe ables that constrain oak regeneration, and indeed they et al. 2014). Annighöfer et al. (2015) also showed that explore various dimensions of the oak regeneration the occurrence of oak saplings was related to light con- niche, but rarely do authors directly state that they are ditions and that abundance increased with increasing studying this phenomenon (Collins and Good 1987). light availability. Moreover, the negative impact of litter The concept of the regeneration niche (Grubb 1977)is cover on sapling density suggested more demands of ad- based on the idea of the ecological niche, which was de- vanced oak regeneration for light. Our results were op- fined by Hutchinson (1957) as a region in a multidimen- posite to those of Lithuanian investigators, who observed sional space of environmental factors that influence the that the abundance of oak undergrowth was largest well-being of a species. The results of our study reveal where spruces and beeches were predominant in the new, potentially important dimensions of the oak regen- overstory (Jurkšienė and Baliuckas 2018). However, their eration niche. The relatively high coefficients of deter- results confirmed our results regarding the negative in- mination of models describing the number of young fluence of hornbeam on oak regeneration (Jurkšienė and oaks in the stands studied suggest that soil conditions, Baliuckas 2018). Although pedunculate oak can grow as represented by soil enzyme activity, play an important Dorota et al. Forest Ecosystems (2021) 8:43 Page 14 of 17 role in the establishment and growth of oak spontaneous Poison distribution; GLMM ZI Poisson: Zero-inflated mixture model based on a Poisson distribution; GLMM nbinom1: Mixture model based on a negative regeneration under the canopy of Scots pine stands. This binomial distribution with linear relations between mean and variance; factor, relatively rarely studied in the context of oak re- GLMM ZI nbinom1: Zero-inflated mixture model based on a negative generation, may be important in explaining the failure of binomial distribution with linear relations between mean and variance; GLMM nbinom2: Mixture model based on a negative binomial distribution oak regeneration in some central European forests. It is with quadratic relations between mean and variance; GLMM ZI well known that the importance of a particular dimen- nbinom2: Zero-inflated mixture model based on a negative binomial sion of ecological niche may change in different parts of distribution with quadratic relations between mean and variance a species’ distribution range, e.g. in a colder region the availability of direct sunlight might be more important Supplementary Information than in a warmer one (Peterson et al. 2011). It is likely The online version contains supplementary material available at https://doi. org/10.1186/s40663-021-00317-9. that the importance of soil enzyme activity for oak re- generation establishment could change with changes in Additional file 1: Table A1. Expalanatory variables collected during other environmental variables, especially beyond the field work. Table A2. Species composition of the investigated stands. boundaries examined in our study. It is difficult to give Table A3. Density of seedlings (H ≤ 0.5 m) in the investigated pine dominated stands. Table A4. Density of saplings (H > 0.5 m and DBH ≤ 2 simple instructions on how to regulate the level of en- cm) in the investigated pine dominated stands. Table A5. Density of zyme activity on an economic scale. However, we believe recruits (DBH < 7 cm) in the investigated pine dominated stands. Table that the results of our study can be used to some extent A6. Comparison of competing models for seedlings on Akaike Information Criterion (AIC). GLM = generalized linear model; GLMM = to diagnose the proper site conditions for oak generalized linear mixed model; ZI = zero inflated; Family = family of error regeneration. distribution; negbin1 = negative binomial (variance that increases linearly with the mean); negbin2 = negative binomial (variance that increases quadratically with the mean); Df = degrees of freedom; The optimal Conclusions model is placed on the top of table. dLogLik and dAIC are the difference Successful regeneration of pedunculate oak under Scots between subsequent models and the best one in term of AIC and log pine-dominated stands of different ages is possible, as likelihood (logLik). Table A7. Comparison of competing models for saplings on Akaike Information Criterion (AIC). GLM = generalized linear shown by the presence of all stages of oak regeneration model; GLMM = generalized linear mixed model; ZI = zero inflated; from seedling to recruit. We found that oak regeneration Family = family of error distribution; negbin1 = negative binomial density depended on a combination of several variables, (variance that increases linearly with the mean); negbin2 = negative binomial (variance that increases quadratically with the mean); Df = but the activity of two soil enzymes played a major role degrees of freedom; The optimal model is placed on the top of table. in oak establishment and advancement. Soil enzyme ac- dLogLik and dAIC are the difference between subsequent models and tivity can be considered not only a predictor of site con- the best one in term of AIC and log likelihood (logLik). Table A8. Comparison of competing models for recruits on Akaike Information ditions, but also a predictor of establishment and Criterion (AIC). GLM = generalized linear model; GLMM = generalized advancement of oak regeneration. The results of our linear mixed model; ZI = zero inflated; Family = family of error distribution; study reveal new, potentially important dimensions of negbin1 = negative binomial (variance that increases linearly with the mean); negbin2 = negative binomial (variance that increases quadratically the oak regeneration niche. with the mean); Df = degrees of freedom; NA = not applicable to The spatial distribution of oak saplings and recruits computational/convergence issues. The optimal model is placed on the was heterogeneous or even grouped in some fragments top of table. dLogLik and dAIC are the difference between subsequent models and the best one in term of AIC and log likelihood (logLik). of a studied forest stand; therefore, it can be used in fu- Table A9. Comparison of competing models for first quality recruits on ture conversion of pine-dominated stands into mixed Akaike Information Criterion (AIC). GLM = generalized linear model; stands. Even if oak cannot be considered an important GLMM = generalized linear mixed model; ZI = zero inflated; Family = family of error distribution; negbin1 = negative binomial (variance that tree species in the upper stand layer now, it will play an increases linearly with the mean); negbin2 = negative binomial (variance important role in the future forest ecosystem because that increases quadratically with the mean); Df = degrees of freedom; existing groups of good quality oaks (especially saplings) NA = not applicable to computational/convergence issues. The optimal model is placed on the top of table. dLogLik and dAIC are the difference could be used as admixture when creating the next gen- between subsequent models and the best one in term of AIC and log eration of forest stands. It could be particularly useful if likelihood (logLik). Table A10. Comparison of competing models for possible climate change forces us to convert large areas lower quality recruits on Akaike Information Criterion (AIC). GLM = generalized linear model; GLMM = generalized linear mixed model; ZI = of Scots pine monocultures in Central Europe into zero inflated; Family = family of error distribution; negbin1 = negative mixed forest stands. Even if it will not be necessary, the binomial (variance that increases linearly with the mean); negbin2 = spontaneous spread of oaks in Scots pines monocultures negative binomial (variance that increases quadratically with the mean); Df = degrees of freedom; NA = not applicable to computational/ could increase the biological stability and resilience of convergence issues. The optimal model is placed on the top of table. these forests (resistance to outbreaks of folivorous dLogLik and dAIC are the difference between subsequent models and insects). the best one in term of AIC and log likelihood (logLik). Abbreviations DBH: Diameter at breast height; TTC: 2,3,5-triphenyltetrazolium chloride; Acknowledgements TPF: Triphenyl formazan; pNP: p-nitro phenol; GLMMs: Generalized linear We would like to thank Bogdan Pawlak technician employed in Forest mixed effect models; GLMM Poisson: Negative binomial distribution of the Research Institute for his engagement in collecting data. Special thanks to Dorota et al. Forest Ecosystems (2021) 8:43 Page 15 of 17 Grzegorz Wanat - deputy manager of Nowe Ramuki Forest District for his Bojarczuk K, Kieliszewska-Rokicka B (2010) Effect of ectomycorrhiza on cu and Pb comments and help during stand measurements. accumulation in leaves and roots of silver birch (Betula pendula Roth.) seedlings grown in metal-contaminated soil. Water Air Soil Pollut 207(1-4): 227–240. https://doi.org/10.1007/s11270-009-0131-8 Authors’ contributions Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White JS (2009) DD: literature review, goal and scope determination, method development, Generalized linear mixed models: a practical guide for ecology and evolution. data preparation, result interpretation, critical revision, conclusion Trends Ecol Evol 24(3):127–135. https://doi.org/10.1016/j.tree.2008.10.008 formulation, and manuscript drafting. LB: literature review, data preparation, Bossema I (1979) Jays and oaks: an eco-ethological study of a symbiosis. data analysis, result interpretation, table and graph generation. PK: Behaviour 70(1-2):1–116. https://doi.org/10.1163/156853979X00016 manuscript revision, and consulting. GO: literature review, data preparation, Brang P, Spathelf P, Larsen JB, Bauhus J, Boncčìna A (2014) Suitability of close-to- data processing, data analysis. The author(s) read and approved the final nature silviculture for adapting temperate European forests to climate manuscript. change. Forestry 87(4):492–503. https://doi.org/10.1093/forestry/cpu018 Brooks ME, Kristensen K, Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug Funding HJ, Mächler M, Bolker BM (2017) glmmTMB balances speed and flexibility This study was performed under the research project entitled “The ability of among packages for zero-inflated generalized linear mixed modeling. R J 9: used oak natural regeneration in Scots pine stand conversion – the role of 378. https://doi.org/10.32614/RJ-2017-066 birds in forest regeneration process”, which was financially supported by the Brudvig LA, Asbjornsen H (2007) Stand structure, composition, and regeneration the Ministry of Science and Higher Education (Grant No. 240–110). dynamics following removal of encroaching woody vegetation from Midwestern oak savannas. For Ecol Manag 244(1-3):112–121. https://doi.org/1 Availability of data and materials 0.1016/j.foreco.2007.03.066 The data that support the findings of this study are available from the Forest Buée M, Vairelles D, Garbaye J (2005) Year-round monitoring of diversity and Research Institute (Poland), but restrictions apply to the availability of these potential metabolic activity of the ectomycorrhizal community in a beech data, which were used under licence for the current study and are thus not (Fagus silvatica) forest subjected to two thinning regimes. Mycorrhiza 15(4): publicly available. The data are, however, available from the authors upon 235–245. https://doi.org/10.1007/s00572-004-0313-6 reasonable request and with permission from the Forest Research Institute Burke DJ, Weintraub MN, Hewins CR, Kalisz S (2011) Relationship between soil (Poland). emzyme activities, nutrient cycling and soil funagal communities in a northern hardwood forest. 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Journal
"Forest Ecosystems" – Springer Journals
Published: Jun 30, 2021
Keywords: Forest stand conversion; Spontaneous regeneration; Regeneration niche; Dehydrogenase; Phosphatase