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Article

Establishment, Survival, and Growth of Beech, Oak, and Spruce Seedlings During Unassisted Forest Recovery in Post-Mining Sites

by
Jakub Černý
1,2,
Tereza Daňková
3,
Ondřej Mudrák
3,
Veronika Spurná
3 and
Jan Frouz
3,*
1
Department of Silviculture, Forestry and Game Management Research Institute, Na Olivě 550, 517 73 Opočno, Czech Republic
2
Department of Silviculture, Faculty of Forestry and Wood Technology, Mendel University in Brno, Zemědělská 3, 613 00 Brno, Czech Republic
3
Institute for Environmental Sciences, Faculty of Science, Charles University, Benátská 2, 128 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Forests 2025, 16(11), 1651; https://doi.org/10.3390/f16111651
Submission received: 26 September 2025 / Revised: 22 October 2025 / Accepted: 28 October 2025 / Published: 29 October 2025
(This article belongs to the Section Forest Ecology and Management)
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Versions Notes

Abstract

A previous study demonstrated that spontaneous forest recovery can result in the development of functional mixed forests in post-mining areas. A critical step in this process is the establishment of climax woody species in the understory of pioneer trees. In this case study, we utilise repeated sampling to evaluate the establishment, initial survival, and growth of pedunculate oak (Quercus robur) and European beech (Fagus sylvatica) seedlings, and to newly assess Norway spruce (Picea abies) during unassisted forest recovery on a post-mining site after coal mining near Sokolov in North Bohemia. Detailed mapping of beech and oak seedlings was conducted in 2009 and 2012 (i.e., 14 and 11 years after the site was reclaimed). Now, we have resurveyed these seedlings, which has allowed us to evaluate their survival and growth. We have also mapped spruce seedlings and estimated their age from annual branch whorls. In the original study, most seedlings were found on the northern site near the edge of the post-mining area and the surrounding landscape, which serve as seed sources. Beech shows the best survival and growth on the northern site, where the greatest number of new seedlings also appear. In contrast, oaks demonstrate much higher mortality than beech overall, with the highest mortality observed on the northern site and the highest survival on the southern site, where most of the new seedlings also appeared. Interestingly, however, surviving oaks grew faster on the northern site. Across microtopography, seedlings of all three tree species were most frequent on the slopes of micro-undulations. Beech individuals were taller in depressions, whereas oaks did not consistently demonstrate a size advantage across microhabitats. Spruce colonised vigorously and was the most abundant of the three species across microhabitats. Age-frequency analyses suggest an annual mortality rate of 3%–9%. Browsing damage was observed on 19% of beech seedlings and 9% of oak seedlings. The study shows that pioneer tree stands are suitable nursing sites for studied climax tree species, which can colonise these sites several kilometres away from mature trees, and their establishment involves a complex interplay between distance to seed source and local microclimatic conditions. Our resurvey indicates that later successional stages may increasingly be shaped by shade-tolerant beech and spruce under the developing canopy.

1. Introduction

Surface mining is among the most impactful human activities on the environment [1,2], despite its key role in the global economy and export revenues [3]. It degrades soil and vegetation, alters water regimes, causes air pollution, leads to habitat fragmentation and biodiversity loss [2,4,5,6]. Furthermore, it emits greenhouse gases and toxic substances that can harm ecosystems and human health [7,8]. Although mining brings benefits such as jobs and infrastructure [9], the environmental costs often prevail. Soil, water and air contamination, along with microclimate changes, challenge site reclamation [10,11]. Restoring post-mining sites is thus crucial for long-term ecological and socio-economic stability [12]. Afforestation is a frequent approach to post-mining sites, which allows for cost-efficient restoration of functional ecosystems and the restoration of ecosystem services provisioning [1,13,14,15]. Despite substantial effort being paid to the development of afforestation technologies, recent studies show that post-mining sites can be successfully colonised by pioneer tree species, which can eventually allow establishment of climax species (e.g., Fagus sylvatica, Quercus robur [16]). Yet, this often produces species-poor and ecologically unstable stands [17]. Conversely, spontaneous succession can lead to a functional forest ecosystem [14,15,18,19].
Late-successional (climax) tree species such as oak (Quercus spp.) and European beech (Fagus sylvatica) play a key role in restoring stable forest ecosystems thanks to their ability to form closed canopy layers, enhance humus accumulation, and improve soil structure and microclimate [20,21,22]. Beech typically prefers acidic to neutral soils with sufficient water availability [23,24], whereas oak tolerates a broader pH range, including slightly acidic to alkaline soils [25]. Their ecological significance lies in their long life span (e.g., [26,27,28]), high shade tolerance in the case of beech [29,30], and the ability to support a wide range of forest biodiversity [28,31]. In addition, both tree species are of high silvicultural and economic value [22,32] and are often considered as target species for close-to-nature forest management in Europe [33,34]. We also include Norway spruce (Picea abies) as an economically and silviculturally significant tree species in Central Europe [35,36].
Although the dynamics of pioneer tree species on spoil heaps have been extensively studied [14,18,19], the process by which climax tree species become established and developed remains comparatively underexplored [16]. This knowledge gap, therefore, poses a critical challenge for the effective planning and execution of reclamation projects aimed at achieving the long-term, sustainable restoration of areas affected by mining [15,16,19]. Understanding the fate of late-successional tree species under spontaneous succession scenarios is particularly important for evaluating whether such passive approaches can lead to functionally and compositionally stable forest ecosystems.
The presented study builds on long-term monitoring of understory tree regeneration, specifically focusing on beech and oak seedlings established within post-mining sites colonised spontaneously by pioneer wood species. The resurvey of the same sites allows us to monitor the survival of the previously recorded seedlings and the establishment of new seedlings [16].
This study aims to (i) assess the survival, mortality and colonisation rates of European beech, pedunculate oak and Norway spruce on spoil heaps, (ii) evaluate growth rates of these species in natural succession, (iii) analyse changes in mortality over time (for beech and oak) and effects of spatial heterogeneity on the establishment, survival and growth across microhabitats, and (iv) assess the impact of stand canopy closure on the growth and survival of seedlings based on permanent plot resurvey data.
Based on these objectives, the following hypotheses were formulated and tested:
H1. 
Wind-dispersed spruce will show higher colonisation than both broadleaf species, and oak will exhibit higher colonisation rates on spoil heaps than European beech due to the closer proximity of seed sources.
H2. 
Shade-tolerant European beech and spruce will have higher survival and growth rates than light-demanding oaks.
H3. 
Seedling mortality has decreased between the historical and recent surveys (especially in beech), and overall mortality on the heap will be lower than the values reported for natural regeneration in a forest stand.
H4. 
Surface spatial heterogeneity will have a more important effect on European beeches than oaks, whereas the growth and survival of oaks will be more influenced by canopy closure and the amount of available light.

2. Materials and Methods

2.1. Study Site

The study site is part of the 1957-hectare Velká Podkrušnohorská spoil heap (Figure 1), the largest in the Karlovy Vary region and one of the most extensive in the Czech Republic. It was formed by the gradual accumulation of overburden rocks extracted from nearby open-pit mines and several smaller spoil heaps. This process resulted in a homogeneous anthropogenic formation developed on remnants of Oligocene sediments. These clays have a high nutrient content, and the initial pH of the substrates ranges from 8 to 9. This gradually decreases over time as succession progresses, with pH levels ranging from 5 to 6 in study sites. The main minerals present in these materials are kaolinite, illite, calcium carbonate and quartz.
The surface of the spoil heaps is uneven and characterised by rows of elongated depressions and ridges, which were formed during the dumping process. These “undulations” originated from the heaping process, whereby the conveyor dumps material in this shape. The undulations are oriented in an east–west direction, range in height from one to two metres. They are spaced of approximately six metres apart, meaning that each studied area consists of ten terrain undulations. The part of the heap which was used in this study was created in late 1980 and has been left uninterrupted since then.
Spontaneous succession, involving the gradual recovery of vegetation and soil organisms, is occurring in some parts of the spoil heaps in the Sokolov basin. During the first 14 years, only sporadic herbs and grasses, such as coltsfoot (Tussilago farfara) and wood-small reeds (Calamagrostis epigejos), appeared in these areas. In the later stages of succession, shrubs such as goat willow (Salix caprea) also become established. Long-term research has shown that spontaneous succession can be more effective than technical reclamation, as areas undergoing natural recovery tend to develop more diverse plant and animal communities after 30–40 years than artificially reclaimed areas [16]. Winds coming from the west prevail here. The nearest trees that could serve as a source of seedlings are located to the north or northwest of the studied area, at distances of approximately 1 km for oak, 1.5 km for spruce, and 2.5 km for beech (measured from the centre of the studied area).

2.2. Field Data Collection

The study was conducted in the same area used by Frouz et al. [16], who carried out a census of beech and oak seedlings. The resurvey of beech and oak and the survey of spruce were performed in three study areas, each with a size of 1.5 to 2.5 ha, located in the southern, middle and northern parts of the post-mining heap. In November and December 2023, seedlings were surveyed within each zone. This period was considered optimal due to the strong contrast between the orange-brown leaves of oak and European beech seedlings and the bare ground surface.
The following data were recorded for each located seedling: species (oak or beech), accurate GPS coordinates using a Trimble GeoXT locator (Trimble Inc., Sunnyvale, CA, USA) with an accuracy of 0.5 m in horizontal distance, height, and position on the terrain undulation (top, bottom and slope of the undulation). To avoid duplicate records, the seedlings were marked using a non-toxic temporary marking spray.
Contrary to oak and beach for which we have historical data bout seedling position, no such data are available for spruce. To assess the survival and growth of spruce seedlings we benefited from the fact that spruce form a whorl of lateral branches from the second year of age, which allows us to estimate the age of spruce seedlings. Seedlings were mapped at the same time in the areas of the heap by the same approach as described above for beech and oak seedlings. For each seedling, we recorded geographic position, position on the terrain undulation, tree height and number of whorls of branches. The age was determined as the number of whorls plus 1; seedlings without lateral branches were assumed to be one year old.
To quantify changes in overstory and understory vegetation, we benefited from a long-term botanical survey conducted using nine permanent plots (5 × 5 m) randomly distributed over the study area. In these plots, plant species were listed, and the cover of individual plant species in individual vegetation layers was visually estimated. Here, we present data from the 2014 and 2024 surveys; see for more details.

2.3. Data Processing

When comparing the current distribution of beech and oak seedlings with historical data, the potential inaccuracy of the GPS measurements had to be considered [16]. Therefore, when a seedling in a recent survey was found inside a circle with a radius of 1.5 m and historical seedlings in the centre, and based on annual increment zones, it was as old as they could be present on-site at the time of the previous census, it was considered the same individual, hereafter called a survivor. In the cases of multiple seedlings that fit this definition, only the closest specimen was counted as the survivor. The other seedlings found in this census were assumed to be newcomers.
For spruce, we estimate mortality by plotting the relationship between estimated seedling age and number of seedlings of that age and fitting this relationship with linear and exponential functions, which allow us to estimate annual mortality from the whole dataset. Alternatively, we consider that spruce seedlings may come in seeding years which form peaks of seedling frequency. We compare these peaks to the number of seedlings in the youngest available cohort to estimate the survival of seedlings with the age of the frequency peak. As a frequency peak was taken for any year which have a higher number of seedlings than the previous year. If the number increased in several consecutive years, the year with the highest value was taken.
The distribution of seedlings among individual study areas was compared against an even distribution (the assumption that each area contains an equal number of seedlings) using the Χ2 test. One-way ANOVA was used to compare the number of seedlings of individual species across all microhabitats and the study plots. Two-way ANOVA was used to test the effect of the area on the heap (south, middle, north), and the position on the terrain undulations (top or bottom of the slope). All computations were performed in Statistica 13.0 (TIBCO, Palo Alto, CA, USA).

3. Results

The composition of overstory vegetation was similar in 2014 and 2024. Cover of the tree and shrub layer varies from 53% to 60% without significant changes between years. Salix caprea and Betula pendula dominate in the shrub and tree layer. Populus tremula in the tree layer and Picea abies in the shrub layer significantly increased between 2014 and 2024. The total number of plant species per plot varies around 20 without a significant difference between years (Table 1).
In both beech and oak distribution of historical seedlings significantly differs from even distribution between the studied areas (χ2 test, p < 0.001), with apparently more seedlings occurring in northern study sites, which were located closer to the surrounding landscape (Figure 2). Survivors represent 50% of seedlings found in 2009. The spatial distribution of surviving beech seedlings between individual study areas also significantly differs from even distribution (χ2 test, p = 0.006) and has a similar pattern as the number of historical seedlings with the highest number of survivors occurring in the north site (Figure 2). A similar pattern, with the highest occurrence in the northern site, also applies to European beech newcomers. Additionally, the distribution of beech newcomers differs significantly from the even distribution (χ2 test, p < 0.001).
Oak survival was much lower than beech, with only 3% of seedlings in 2012 surviving. The distribution of survivors between the studied areas was significantly uneven (χ2 test, p < 0.001), with survivors occurring only in the south site, where the survival rate reaches 18%, and absent in the others. Likewise, the distribution of newcomers differs significantly from an even distribution between areas (χ2 test, p < 0.001), with the highest number of newcomers in the southern site and the lowest in the northern site, which is the opposite pattern to the seedling distribution observed in the historical survey.
In all tree species, the distribution of seedlings found in this survey (both newcomers and survivors together) between individual areas was significantly uneven (χ2 test, p < 0.001), with the highest number of seedlings found on slopes or the terrain undulations, and lower in the tops and bottoms of the undulations (Figure 3). Using the same data, spruce seedlings were significantly more abundant across all microsites than beech which is more abundant than oak (one-way ANOVA, LSD post hoc test).
Concerning seedling growth, a significant effect of position in terrain undulations on tree growth was found in beech, where seedlings in depressions were bigger than in other microhabitats (Table 2). In spruce, we compare only 14-year-old seedlings; seedlings in the central part of the heap were bigger than those in other locations. Interestingly, in beech and spruce, no difference in size was found between survivors and newcomers. In total, about 19% of beech seedlings and 9% of oak seedlings show some sign of recent browsing damage.
In spruce, it is not possible to distinguish between newcomers and survivors as because no historical surveys were conducted; instead, tree ages were examined. Plotting seedling numbers in individual age classes, we can relate seedling numbers to seedling age and predict mortality from the relationship between these two parameters. However, both linear and exponential fits were only marginally significant (Figure 4a). When we, however, used these equations, predicted mortality will vary from 3% to 9% a year for the linear and exponential models, respectively. When we compare the number of seedlings in peaks of seedling occurrence with the numbers of youngest seedlings, this proportion in 8–14-year-old seedlings varies broadly around 50% (Figure 4a). When individually studied areas were plotted separately (Figure 4b), then not even a marginally significant relationship was found, but the age frequency distribution varies between areas. The number of seedlings found in the north, central and south areas was 209, 73 and 290 seedlings, respectively, which significantly differ from even distribution (χ2 test, p < 0.001). However, the numbers in the northern part, which is closer to the spruce forest in the surrounding landscape, are not higher than in the southern part, which is further from the source of seeds. In spruce seedlings, height correlates with seedling age (Figure 5).

4. Discussion

This study shows high survival of beech and oak seedlings that spontaneously colonise post-mining heaps. Based on previous results of Frouz et al. [16], these colonists represent the tail of seed distribution located one to several kilometres away from the closest mother tree. Our data suggest that the survival of beech seedlings after more than 10 years is about 50%, while the survival of oak is patchy, but can reach 18% in suitable patches. Although direct comparison is difficult due to substantial ecological differences, these values appear higher than those typically observed in natural forest regeneration, where dense seed rain forms dense seedling cover and ten-year survival is usually only a few percent [37].
The relatively high survival rate observed in this study may be partly explained by the low density of seedlings on the spoil heaps, which likely reduces competition for light and resources. It is also possible that the sparse distribution of seedlings limited the spread of host-specific pathogens and herbivores, thereby decreasing the risk of mortality. These mechanisms could help to explain the differences observed compared to natural forest regeneration, where dense layers of seedlings can increase competition and pathogen pressure.
A similar situation is in spruce, where exact survival is difficult to estimate because there is not good fit between seedling number and seedling age, in some periods seedling number even increase with age, but estimates we have suggested may reach 50% after 10 years, which again is higher than reported from dense seedling stands of natural forest regeneration [38]. Most research tends to underestimate the role of seeds, which are on the tail of seed distribution, but our results suggest that despite a low number of migrants, those seeds can be ecologically important due to a high survival rate. This corroborates some previous results in other plants [39]. The fact that seedlings that emerge from seeds that were transported from the mother tree is well-known in tropical rain forests and is represented by the basic idea of the Janzen-Connell hypothesis explaining the high diversity of trees in tropical forests [40,41]. Also, data from temperate forests suggest that seedlings emerging at a larger distance from the mother tree have better survival than those closer to the mother tree, although underlying mechanisms may differ from the Janzen-Connell hypothesis [42,43]. Our study shows that this is true even for very distant migrants. We believe that the reason for better survival of distant migrants is the absence of density-dependent distribution between seedlings as density is very low, lack of competition from mother tree and also escape from species-specific pathogens and herbivores, as suggested by the Janzen-Connell hypothesis. Suitable conditions formed by pioneer trees, namely coat willow, birch and aspen, may contribute to the success of beech, spruce and oak in the understory. The pioneer trees form suitable soil conditions [16] and, as shown in Table 1, they form stable overstory conditions with shrub and tree cover of about 60% which may provide a suitable buffer of microclimatic conditions and, at the same time, enough light for understorey seedlings.
This study suggests that the mid-successional stages of spoil heaps in the studied post-mining areas are mainly colonised by two dominant climax tree species (European beech, pedunculate oak), characteristic of deciduous forests in Central Europe, as well as by Norway spruce, which is the most commonly planted tree species in the Czech Republic. This aligns with the findings of Badraghi et al. [15] and indicates that areas of the spoil heap that have grown back spontaneously have the potential to develop into diverse mixed forests dominated by oak with the presence of spruce. Our study indicates higher survival rates for European beech and Norway spruce seedlings, suggesting that they may dominate in later successional stages. However, Badraghi et al. [15] reported that late successional stages are dominated by oaks. This discrepancy can be due to local variability in seed sources and differences in site-specific conditions. Besides spatial variability, temporal variability may be important. In the previous study on the same sites [16], the number of incoming oak seedlings was much higher than beech seedlings, which corresponds to the fact that oak trees are numerous on the edge of the heap, while close beech stands are several kilometres apart from the edge of the heap. However, in this study, we see a massive increase in beech seedlings, suggesting that interannual variation in seed production and other factors may play an important role. Consequently, the site may also be dominated by oak or beech, as the time window suitable for colonisation may have coincided with several years of high seed production for one species or the other, and higher tree survival, as is explained below. This finding also confirms the ability of beech, oak and spruce to colonise new sites several kilometres away from the parent tree.
In this study, oak seedlings performed better on the southern slope, whereas European beech seedlings showed higher survival rates on the northern slope. This pattern reflects the distinct ecological demands of the two studied tree species [44,45]. Although these tree species often coexist within the same ecosystem, their ecological niches differ significantly [15]. The European beech is a shade-tolerant tree species that prefers moist and humus-rich soils and a temperate climate [24]. Its ability to regenerate in shaded conditions enables it to utilise the space below the canopies of older trees effectively and gradually outcompete them [46]. In contrast, the pedunculate oak is a light-demanding tree species [47] that requires ample sunlight for successful regeneration [24]. Light availability becomes more important during later oak developmental stages, primarily because of increasing competition for light, whereas the number of oaks in the overstory is important during early development stages due to protection and microclimate regulation by mature stands [48]. It is also more tolerant to drought and nutrient-poor soils than the European beech [49]. The different ecological requirements of beech and oak have a fundamental influence on their spatial distribution and the species composition of forest communities. The present analysis of historical and current distribution data from the Velká Podkrušnohorská spoil heap has revealed significant shifts in the spatial representation of these tree species. Specifically, a substantial decline in the proportion of pedunculate oak in northern sites was observed, accompanied by a notable expansion of European beech. This trend supports the hypothesis that oak tends to grow faster than beech during the early stages of succession [50]. In this study, the beech growth was highest in the northern part of the heap, where there was also the largest density of beech seedlings. In oak, only annual growth differs between sites. Interestingly, it was also highest in the northern part of the heap, where oak has low survival; however, annual growth reflects growth conditions only in one year and may not be representative of long-term growth conditions. In spruce, survival was estimated solely from the frequency distribution of seedlings of various ages, which was too coarse to find any difference between various slope positions.
Spruce colonised the site very vigorously, as visible from the fact that it was absent in the 2014 survey and formed an important part of the shrub layer in 2024. Spruce is also the most abundant of all three studied species. This is likely due to the large prevalence of spruce forests in the surrounding areas and wind dispersal. Wind dispersal is also likely the reason why the number of seedlings in the southern part of the heaps did not decrease in comparison to the northern part, which is closer to the source of seeds. In the initial stages of succession, distance from a source of diaspora was the principal factor for beech and oak [16]. In the latter stages, tree establishment and their survival are driven mainly by light competition becomes more important. It agrees with other studies showing that the importance of individual ecological factors can change during succession. While in the initial stages, abiotic factors such as the distance of the diaspora source, nutrient availability and water regime may be decisive, in the later stages, the importance of biotic interactions, namely competition for light, increases. Similar conclusions about the change in the importance of individual factors during succession have also been obtained in several studies (e.g., [17,51,52]).
Mortality of beech and pedunculate oak seedlings in the first years of their life can be associated with intensive browsing by game [53], which had free access to the dump from the surrounding stands. Many seedlings showed extensive browsing, which in some cases made any measurement of annual growth impossible. Woś et al. [54] cite game browsing as a significant factor influencing species composition and vegetation cover. However, this conclusion contrasts with the results of a previous study [55], which, when examining the influence of browsing using an experiment with fences, found no connection with growth. Therefore, the question is what specifically plays the largest role in increased early mortality.
Besides the better establishment of late succession species documented in this study, many studies (e.g., [17,52,56,57,58,59]) show that spontaneous succession can lead to the development of rich and diverse plant and invertebrate communities. Leaving areas for spontaneous succession appears to be an effective strategy for supporting biodiversity and restoring ecosystems in disturbed areas.

5. Conclusions

This study confirms that mid-successional stages of spoil heaps are extensively colonised by European beech and pedunculate oak, which represent two dominant climax tree species of temperate European forests. This study suggests that far-dispersed seeds can be ecologically important, and low seed numbers are partly compensated by higher seed survival. Our findings suggest that spruce and beech tend to dominate later successional stages due to their higher survival rates. Our results indicate that pioneer tree stands can act as effective nurse crops for climax species, facilitating the establishment of spruce, beech and oak seedlings several kilometres away from mature stands. This finding has significant implications for post-mining landscape restoration, as it supports the use of natural succession as a viable strategy for promoting biodiversity and ecosystem recovery.

Author Contributions

Conceptualisation, J.F. and J.Č.; methodology, J.F. and J.Č.; software, J.F.; validation, J.Č., T.D. and J.F.; formal analysis, T.D.; investigation, T.D., V.S. and O.M.; resources, J.F. and J.Č.; data curation, T.D. and J.F.; writing—original draft preparation, J.Č. and J.F.; writing—review and editing, J.Č., T.D., O.M., V.S. and J.F.; visualisation, J.F.; supervision, J.F.; project administration, J.Č.; funding acquisition, J.Č. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Technological Agency of the Czech Republic, grant number TQ03000234 and the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO0123.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors are grateful to Sokolovská uhelná a.s. for permitting this research on the Podkrušnohorská spoil heap managed by them. J. Jongepier is thanked for proofreading the manuscript and linguistic improvement.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Bradshaw, A. Restoration of Mined Lands—Using Natural Processes. Ecol. Eng. 1997, 8, 255–269. [Google Scholar] [CrossRef]
  2. Worlanyo, A.S.; Jiangfeng, L. Evaluating the Environmental and Economic Impact of Mining for Post-Mined Land Restoration and Land-Use: A Review. J. Environ. Manag. 2021, 279, 111623. [Google Scholar] [CrossRef]
  3. Zine, H.; Hakkou, R.; Papazoglou, E.G.; Elmansour, A.; Abrar, F.; Benzaazoua, M. Revegetation and Ecosystem Reclamation of Post-Mined Land: Toward Sustainable Mining. Int. J. Environ. Sci. Technol. 2024, 21, 9775–9798. [Google Scholar] [CrossRef]
  4. Sena, K.; Barton, C.; Hall, S.; Angel, P.; Agouridis, C.; Warner, R. Influence of Spoil Type on Afforestation Success and Natural Vegetative Recolonization on a Surface Coal Mine in Appalachia, United States. Restor. Ecol. 2015, 23, 131–138. [Google Scholar] [CrossRef]
  5. Sonter, L.J.; Ali, S.H.; Watson, J.E.M. Mining and Biodiversity: Key Issues and Research Needs in Conservation Science. Proc. R. Soc. B 2018, 285, 20181926. [Google Scholar] [CrossRef]
  6. Sonter, L.J.; Dade, M.C.; Watson, J.E.M.; Valenta, R.K. Renewable Energy Production Will Exacerbate Mining Threats to Biodiversity. Nat. Commun. 2020, 11, 4174. [Google Scholar] [CrossRef] [PubMed]
  7. Chen, Y.; Zhao, J.; Chen, X.; Zheng, L. Analysis of the Source Apportionment and Pathways of Heavy Metals in Soil in a Coal Mining Area Based on Machine Learning and an APCS-MLR Model. Minerals 2023, 14, 54. [Google Scholar] [CrossRef]
  8. Da Silva, M.G.; Muniz, A.R.C.; Hoffmann, R.; Lisbôa, A.C.L. Impact of Greenhouse Gases on Surface Coal Mining in Brazil. J. Clean. Prod. 2018, 193, 206–216. [Google Scholar] [CrossRef]
  9. Fleming-Muñoz, D.A.; Poruschi, L.; Measham, T.; Meyers, J.; Moglia, M. Economic Vulnerability and Regional Implications of a Low Carbon Emissions Future. Aust. J. Agric. Resour. Econ. 2020, 64, 575–604. [Google Scholar] [CrossRef]
  10. Doležalová, J.; Vojar, J.; Smolová, D.; Solský, M.; Kopecký, O. Technical Reclamation and Spontaneous Succession Produce Different Water Habitats: A Case Study from Czech Post-Mining Sites. Ecol. Eng. 2012, 43, 5–12. [Google Scholar] [CrossRef]
  11. Matýsek, D.; Jirásek, J. Mineralogy of the Coal Waste Dumps from the Czech Part of the Upper Silesian Basin: Emphasized Role of Halides for Element Mobility. Int. J. Coal Geol. 2022, 264, 104138. [Google Scholar] [CrossRef]
  12. Frantál, B.; Nováková, E.A. Curse of Coal? Exploring Unintended Regional Consequences of Coal Energy in the Czech Republic. Morav. Geogr. Rep. 2014, 22, 55–65. [Google Scholar] [CrossRef]
  13. Macdonald, S.E.; Landhäusser, S.M.; Skousen, J.; Franklin, J.; Frouz, J.; Hall, S.; Jacobs, D.F.; Quideau, S. Forest restoration following surface mining disturbance: Challenges and solutions. New For. 2015, 46, 703–732. [Google Scholar] [CrossRef]
  14. Woś, B.; Pietrzykowski, M.; Józefowska, A. Reclaimed Mine Soil Substrates and Tree Stands vs. Successional Forest Floor Vegetation: A Case Study of Developing Ecosystems on Afforested Mine Sites. Ecol. Eng. 2018, 120, 504–512. [Google Scholar] [CrossRef]
  15. Badraghi, A.; Krůček, M.; Král, K.; Reitschmiedová, E.; Šálek, V.; Kotápišová, M.; Novotná, B.; Frouz, J. Woody Species Succession and Spontaneous Forest Development in Post-Mining Sites after Coal Mining in the Czech Republic. Ecol. Eng. 2023, 194, 107051. [Google Scholar] [CrossRef]
  16. Frouz, J.; Vobořilová, V.; Janoušová, I.; Kadochová, Š.; Matějíček, L. Spontaneous Establishment of Late Successional Tree Species English Oak (Quercus robur) and European Beech (Fagus sylvatica) at Reclaimed Alder Plantation and Unreclaimed Post Mining Sites. Ecol. Eng. 2015, 77, 1–8. [Google Scholar] [CrossRef]
  17. Šálek, M.; Hendrychová, M.; Řehoř, M. Breeding Habitat of Sparrowhawks, Accipiter Nisus on 22. Spoil Heaps after Coal Mining. Acta Oecologica 2010, 36, 197–201. [Google Scholar] [CrossRef]
  18. Wiegleb, G.; Felinks, B. Primary Succession in Post-Mining Landscapes of Lower Lusatia—Chance or Necessity. Ecol. Eng. 2001, 17, 199–217. [Google Scholar] [CrossRef]
  19. Frouz, J.; Dvorščík, P.; Vávrová, A.; Doušová, O.; Kadochová, Š.; Matějíček, L. Development of Canopy Cover and Woody Vegetation Biomass on Reclaimed and Unreclaimed Post-Mining Sites. Ecol. Eng. 2015, 84, 233–239. [Google Scholar] [CrossRef]
  20. Mihók, B.; Kenderes, K.; Kirby, K.J.; Paviour-Smith, K.; Elbourn, C.A. Forty-Year Changes in the Canopy and the Understorey in Wytham Woods. Forestry 2009, 82, 515–527. [Google Scholar] [CrossRef]
  21. Trap, J.; Laval, K.; Akpa-Vinceslas, M.; Gangneux, C.; Bureau, F.; Decaëns, T.; Aubert, M. Humus Macro-Morphology and Soil Microbial Community Changes along a 130-Yr-Old Fagus sylvatica Chronosequence. Soil. Biol. Biochem. 2011, 43, 1553–1562. [Google Scholar] [CrossRef]
  22. Baumbach, L.; Niamir, A.; Hickler, T.; Yousefpour, R. Regional adaptation of European beech (Fagus sylvatica) to drought in Central European conditions considering environmental suitability and economic implications. Reg. Environ. Change 2019, 19, 1159–1174. [Google Scholar] [CrossRef]
  23. Leuschner, C.; Meier, I.C.; Hertel, D. On the Niche Breadth of Fagus Sylvatica: Soil Nutrient Status in 50 Central European Beech Stands on a Broad Range of Bedrock Types. Ann. For. Sci. 2006, 63, 355–368. [Google Scholar] [CrossRef]
  24. Ellenberg, H. Vegetation Ecology of Central Europe; Cambridge University Press: Cambridge, UK, 1988; ISBN 0521236428. [Google Scholar]
  25. Ducousso, A.; Bordacs, S. EUFORGEN Technical Guidelines for Genetic Conservation and Use for Pedunculate and Sessile Oaks (Quercus robur) and (Quercus petraea); Bioversity International: Rome, Italy, 2003; ISBN 9290436603. [Google Scholar]
  26. Von Wuhlisch, G. European Beech (Fagus sylvatica). In EUFORGEN Technical Guidelines for Genetic Conservation and Use; Bioversity International: Rome, Italy, 2008. [Google Scholar]
  27. Norby, R.J.; Loader, N.J.; Mayoral, C.; Ullah, S.; Curioni, G.; Smith, A.R.; Reay, M.K.; Van Wijngaarden, K.; Amjad, M.S.; Brettle, D. Enhanced Woody Biomass Production in a Mature Temperate Forest Under Elevated CO2. Nat. Clim. Change 2024, 14, 983–988. [Google Scholar] [CrossRef]
  28. Jansone, D.; Liepiņa, A.A.; Barone, I.; Elferts, D.; Lībiete, Z.; Matisons, R. The Older, the Richer? A Comparative Study of Tree-Related Microhabitats and Epiphytes on Champion and Planted Mature Oaks. Diversity 2025, 17, 484. [Google Scholar] [CrossRef]
  29. Emborg, J. Understorey Light Conditions and Regeneration with Respect to the Structural Dynamics of a Near-Natural Temperate Deciduous Forest in Denmark. For. Ecol. Manag. 1998, 106, 83–95. [Google Scholar] [CrossRef]
  30. Collet, C.; Lanter, O.; Pardos, M. Effects of Canopy Opening on Height and Diameter Growth in Naturally Regenerated Beech Seedlings. Ann. For. Sci. 2001, 58, 127–134. [Google Scholar] [CrossRef]
  31. Brunet, J.; Fritz, Ö.; Richnau, G. Biodiversity in European Beech Forests—A Review with Recommendations for Sustainable Forest Management. Ecol. Bull. 2010, 53, 77–94. [Google Scholar]
  32. Blanc-Jolivet, C.; Liesebach, M. Tracing the Origin and Species Identity of Quercus robur and Quercus petraea in Europe: A Review. Silvae Genet. 2015, 64, 182–193. [Google Scholar] [CrossRef]
  33. Cambi, M.; Mariotti, B.; Fabiano, F.; Maltoni, A.; Tani, A.; Foderi, C.; Laschi, A.; Marchi, E. Early Response of Quercus robur Seedlings to Soil Compaction Following Germination. Land Degrad. Dev. 2018, 29, 916–925. [Google Scholar] [CrossRef]
  34. Fuchs, Z.; Vacek, Z.; Vacek, S.; Cukor, J.; Šimůnek, V.; Štefančík, I.; Brabec, P.; Králíček, I. European Beech (Fagus sylvatica L.): A Promising Candidate for Future Forest Ecosystems in Central Europe amid Climate Change. Cent. Eur. For. J. 2024, 70, 62–76. [Google Scholar] [CrossRef]
  35. Kržišnik, D.; Lesar, B.; Thaler, N.; Humar, M. Performance of bark beetle damaged Norway spruce wood against water and fungal decay. Bioresources 2018, 13, 3473–3486. [Google Scholar] [CrossRef]
  36. Bosela, M.; Tumajer, J.; Cienciala, E.; Dobor, L.; Kulla, L.; Marčiš, P.; Popa, I.; Sedmák, R.; Sedmáková, D.; Sitko, R. Climate warming induced synchronous growth decline in Norway spruce populations across biogeographical gradients since 2000. Sci. Total Environ. 2021, 752, 141794. [Google Scholar] [CrossRef]
  37. Szwagrzyk, J.; Szewczyk, J.; Bodziarczyk, J. Dynamics of seedling banks in beech forest: Results of a 10-year study on germination, growth and survival. For. Ecol. Manag. 2001, 141, 237–250. [Google Scholar] [CrossRef]
  38. Dobrovolny, L. Density and spatial distribution of beech (Fagus sylvatica L.) regeneration in Norway spruce (Picea abies (L.) Karsten) stands in the central part of the Czech Republic. iForest-Biogeosci. For. 2016, 9, 666. [Google Scholar] [CrossRef]
  39. Van Diggelen, R. Landscape: Spatial interactions. In Restoration Ecology: The New Frontier; Blackwell Publishing: Oxford, UK, 2006; pp. 31–44. [Google Scholar]
  40. Clark, D.A.; Clark, D.B. Spacing dynamics of a tropical rainforest tree: Evaluation of the Janzen-Connell model. Am. Nat. 1984, 124, 769–788. [Google Scholar] [CrossRef]
  41. Comita, L.S.; Queenborough, S.A.; Murphy, S.J.; Eck, J.L.; Xu, K.; Krishnadas, M.; Beckman, N.; Zhu, Y. Testing predictions of the Janzen–Connell hypothesis: A meta-analysis of experimental evidence for distance-and density-dependent seed and seedling survival. J. Ecol. 2014, 102, 845–856. [Google Scholar] [CrossRef]
  42. Deniau, M.; Jung, V.; Le Lann, C.; Morra, T.; Murray, P.J.; Prinzing, A. Janzen–Connell patterns are not the result of Janzen–Connell process: Oak recruitment in temperate forests. Perspect. Plant Ecol. Evol. Syst. 2017, 24, 72–79. [Google Scholar] [CrossRef]
  43. Millerón, M.; López de Heredia, U.; Lorenzo, Z.; Alonso, J.; Dounavi, A.; Gil, L.; Nanos, N. Assessment of spatial discordance of primary and effective seed dispersal of European beech (Fagus sylvatica L.) by ecological and genetic methods. Mol. Ecol. 2013, 22, 1531–1545. [Google Scholar] [CrossRef]
  44. Eaton, E.; Caudullo, G.; Oliveira, S.; de Rigo, D. Quercus robur and Quercus petraea in Europe. In European Atlas of Forest Tree Species; Publication Official EU: Luxembourg, 2016; pp. 160–163. [Google Scholar]
  45. Antonucci, S.; Santopuoli, G.; Marchetti, M.; Tognetti, R.; Chiavetta, U.; Garfì, V. What is known about the management of European beech forests facing climate change? A review. Curr. For. Rep. 2021, 7, 321–333. [Google Scholar] [CrossRef]
  46. Wilkens, J.F.; Schlicht, R.; Wagner, S. Resource-Based Growth Models Reveal Opportunities to Mitigate Climate Change Effects on Beech Regeneration by Silvicultural Measures. For. Ecol. Manag. 2023, 532, 120815. [Google Scholar] [CrossRef]
  47. Van de Peer, T.; Verheyen, K.; Kint, V.; Van Cleemput, E.; Muys, B. Plasticity of Tree Architecture through Interspecific and Intraspecific Competition in a Young Experimental Plantation. For. Ecol. Manag. 2017, 385, 1–9. [Google Scholar] [CrossRef]
  48. Annighöfer, P.; Beckschäfer, P.; Vor, T.; Ammer, C. Regeneration Patterns of European Oak Species (Quercus petraea (Matt.) Liebl., Quercus robur L.) in Dependence of Environment and Neighborhood. PLoS ONE 2015, 10, e0134935. [Google Scholar] [CrossRef] [PubMed]
  49. Plomion, C.; Aury, J.; Amselem, J.; Alaeitabar, T.; Barbe, V.; Belser, C.; Bergès, H.; Bodénès, C.; Boudet, N.; Boury, C. Decoding the Oak Genome: Public Release of Sequence Data, Assembly, Annotation and Publication Strategies. Mol. Ecol. Resour. 2016, 16, 254–265. [Google Scholar] [CrossRef] [PubMed]
  50. Wagner, S.; Collet, C.; Madsen, P.; Nakashizuka, T.; Nyland, R.D.; Sagheb-Talebi, K. Beech Regeneration Research: From Ecological to Silvicultural Aspects. For. Ecol. Manag. 2010, 259, 2172–2182. [Google Scholar] [CrossRef]
  51. Brom, J.; Nedbal, V.; Procházka, J.; Pecharová, E. Changes in Vegetation Cover, Moisture Properties and Surface Temperature of a Brown Coal Dump from 1984 to 2009 Using Satellite Data Analysis. Ecol. Eng. 2012, 43, 45–52. [Google Scholar] [CrossRef]
  52. Dulias, R. Landscape Planning in Areas of Sand Extraction in the Silesian Upland, Poland. Landsc. Urban. Plan. 2010, 95, 91–104. [Google Scholar] [CrossRef]
  53. Brabec, P.; Cukor, J.; Vacek, Z.; Vacek, S.; Skoták, V.; Ševčík, R.; Fuchs, Z. Wildlife Damage to Forest Stands in the Context of Climate Change—A Review of Current Knowledge in the Czech Republic. Cent. Eur. For. J. 2024, 70, 207–221. [Google Scholar] [CrossRef]
  54. Woś, B.; Nezhad, M.T.K.; Mustafa, A.; Pietrzykowski, M.; Frouz, J. Soil Carbon Storage in Unreclaimed Post Mining Sites Estimated by a Chronosequence Approach and Comparison with Historical Data. Catena 2023, 220, 106664. [Google Scholar] [CrossRef]
  55. Janoušová, I. Faktory Ovlivňující Šíření Dubu Letního Na Výsypce Po Těžbě Uhlí. Master’s Thesis, Univerzita Karlova, Prague, Czech Republic, 2013. [Google Scholar]
  56. Antwi, E.K.; Krawczynski, R.; Wiegleb, G. Detecting the Effect of Disturbance on Habitat Diversity and Land Cover Change in a Post-Mining Area Using GIS. Landsc. Urban Plan. 2008, 87, 22–32. [Google Scholar] [CrossRef]
  57. Hendrychová, M.; Kabrna, M. An Analysis of 200-Year-Long Changes in a Landscape Affected by Large-Scale Surface Coal Mining: History, Present and Future. Appl. Geogr. 2016, 74, 151–159. [Google Scholar] [CrossRef]
  58. Kompała-Bąba, A.; Sierka, E.; Dyderski, M.K.; Bierza, W.; Magurno, F.; Besenyei, L.; Błońska, A.; Ryś, K.; Jagodziński, A.M.; Woźniak, G. Do the Dominant Plant Species Impact the Substrate and Vegetation Composition of Post-Coal Mining Spoil Heaps? Ecol. Eng. 2020, 143, 105685. [Google Scholar] [CrossRef]
  59. Vojar, J.; Doležalová, J.; Solský, M.; Smolova, D.; Kopecký, O.; Kadlec, T.; Knapp, M. Spontaneous Succession on Spoil Banks Supports Amphibian Diversity and Abundance. Ecol. Eng. 2016, 90, 278–284. [Google Scholar] [CrossRef]
Forests 16 01651 g001
Figure 1. Distribution of study sites in the post-mining heap. All sites are located in one large forest stand formed by unassisted spontaneous forest recovery dominated by birch, aspen and coat willow. The position of study sites in forest stands and their elevations in metres above sea level are depicted, and their position relative to the surrounding landscape, which starts immediately after the northern edge of the figure. The edge between heap and surrounding landscape is marked by bold yellow line.
Figure 1. Distribution of study sites in the post-mining heap. All sites are located in one large forest stand formed by unassisted spontaneous forest recovery dominated by birch, aspen and coat willow. The position of study sites in forest stands and their elevations in metres above sea level are depicted, and their position relative to the surrounding landscape, which starts immediately after the northern edge of the figure. The edge between heap and surrounding landscape is marked by bold yellow line.
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Forests 16 01651 g002
Figure 2. Distribution of beech (a) and oak (b) seedlings in individual study locations during the historical and recent surveys. The recent survey is divided into seedlings that survived from the previous survey (survivors) and new seedlings that have appeared since the last survey (newcomers). The data represent the census of seedlings in each study area.
Figure 2. Distribution of beech (a) and oak (b) seedlings in individual study locations during the historical and recent surveys. The recent survey is divided into seedlings that survived from the previous survey (survivors) and new seedlings that have appeared since the last survey (newcomers). The data represent the census of seedlings in each study area.
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Forests 16 01651 g003
Figure 3. The total number of beech (Fagus sylvatica), oak (Quercus robur), and spruce (Picea abies) seedlings (labelled as seedlings) in individual microsite related to the position on the terrain undulations.
Figure 3. The total number of beech (Fagus sylvatica), oak (Quercus robur), and spruce (Picea abies) seedlings (labelled as seedlings) in individual microsite related to the position on the terrain undulations.
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Figure 4. The number of spruce (Picea abies) seedlings of individual ages, (a) for all studied areas together, (b) individually studied areas plotted separately. Relationship between seedling age and number of seedlings of the age was fitted by linear and exponential equations; the formula r, and p values are in the inserted tables. A large number marks the percentage of seedlings of a given age in relation to the youngest cohort of seedlings. N—northern; S—southern; M—central area.
Figure 4. The number of spruce (Picea abies) seedlings of individual ages, (a) for all studied areas together, (b) individually studied areas plotted separately. Relationship between seedling age and number of seedlings of the age was fitted by linear and exponential equations; the formula r, and p values are in the inserted tables. A large number marks the percentage of seedlings of a given age in relation to the youngest cohort of seedlings. N—northern; S—southern; M—central area.
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Figure 5. Relationship between the height of spruce (Picea abies) seedlings and their estimated ages.
Figure 5. Relationship between the height of spruce (Picea abies) seedlings and their estimated ages.
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Table 1. Mean cover of individual vegetation layers and species, and number of species per plot (mean from 9 plots ± sd) in 2014 and 2024; a t-test was used to test the difference between 2014 and 2024.
Table 1. Mean cover of individual vegetation layers and species, and number of species per plot (mean from 9 plots ± sd) in 2014 and 2024; a t-test was used to test the difference between 2014 and 2024.
Parameter-Year20142024p-Value
Betula pendula cover %3.0 ± 7.89.4 ± 17.70.366
Populus tremula cover %0.4 ± 1.37.8 ± 8.90.048
Salix caprea cover %49.4 ± 26.929.4 ± 12.30.082
Picea abies cover %0.0 ± 0.011.0 ± 9.30.010
Herb cover %22.4 ± 18.813.3 ± 14.30.292
Shrub cover %2.8 ± 7.912.1 ± 13.90.123
Tree cover %51.1 ± 27.147.8 ± 17.70.775
Number of plant species20.6 ± 5.719.3 ± 6.90.706
Table 2. Mean size (±sd) of beech, oak and spruce seedlings found in individually studied areas and specific positions on the terrain undulations. Only 14-year-old spruce seedlings were considered. Total N for beech is 282, for oak 157 and for spruce 63 seedling.
Table 2. Mean size (±sd) of beech, oak and spruce seedlings found in individually studied areas and specific positions on the terrain undulations. Only 14-year-old spruce seedlings were considered. Total N for beech is 282, for oak 157 and for spruce 63 seedling.
Factor/SpeciesBeechOakSpruce
Position on the heap
North61.5 ± 86.626.3 ± 19.7196.2 ± 63.9
Central41.6 ± 42.613.4 ± 3.7280.1 ± 0.7
South89.0 ± 104.014.8 ± 24.1178.0 ± 51.5
Position in terrain undulation
Bottom130.2 ± 140.216.2 ± 10.2205.2 ± 72.0
Slope51.8 ± 61.613.6 ± 10.8203.9 ± 57.3
Top72.7 ± 78.219.1 ± 38.8167.1 ± 67.1
Survival
Newcomers81.6 ± 96.916.0 ± 26.4n/a
Survivors84.5 ± 107.715.1 ± 5.6n/a
ANOVA F p values
Position on heap12.0, 0.14791.6, 0.827110.7, 0.0001
Position on terrain undulation1.8, 0.00010.6, 0.47770.1, 0.9084
Newcomers-survivors t-test, p0.54710.8927n/a
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MDPI and ACS Style

Černý, J.; Daňková, T.; Mudrák, O.; Spurná, V.; Frouz, J. Establishment, Survival, and Growth of Beech, Oak, and Spruce Seedlings During Unassisted Forest Recovery in Post-Mining Sites. Forests 2025, 16, 1651. https://doi.org/10.3390/f16111651

AMA Style

Černý J, Daňková T, Mudrák O, Spurná V, Frouz J. Establishment, Survival, and Growth of Beech, Oak, and Spruce Seedlings During Unassisted Forest Recovery in Post-Mining Sites. Forests. 2025; 16(11):1651. https://doi.org/10.3390/f16111651

Chicago/Turabian Style

Černý, Jakub, Tereza Daňková, Ondřej Mudrák, Veronika Spurná, and Jan Frouz. 2025. "Establishment, Survival, and Growth of Beech, Oak, and Spruce Seedlings During Unassisted Forest Recovery in Post-Mining Sites" Forests 16, no. 11: 1651. https://doi.org/10.3390/f16111651

APA Style

Černý, J., Daňková, T., Mudrák, O., Spurná, V., & Frouz, J. (2025). Establishment, Survival, and Growth of Beech, Oak, and Spruce Seedlings During Unassisted Forest Recovery in Post-Mining Sites. Forests, 16(11), 1651. https://doi.org/10.3390/f16111651

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