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Article

Natural Regeneration of Sand Quarries Supports Oligotrophic Boreal Forest Vegetation Development Within Three Decades: A Case Study

by
Austra Zuševica
1,*,
Viktorija Vendina
1,2,
Dagnija Lazdiņa
1,
Roberts Matisons
1,
Toms Artūrs Štāls
1,
Kārlis Dūmiņš
1 and
Santa Celma
1
1
Latvian State Forest Research Institute ‘Silava’, 111 Rigas Str., LV-2169 Salaspils, Latvia
2
Faculty of Forestry, Latvia University of Life Sciences and Technologies, 11 Akadēmijas Str., LV-3001 Jelgava, Latvia
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(8), 3989; https://doi.org/10.3390/su18083989
Submission received: 5 February 2026 / Revised: 1 April 2026 / Accepted: 13 April 2026 / Published: 17 April 2026
(This article belongs to the Section Sustainable Forestry)
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Abstract

Sand extraction drastically alters ecosystem structure and initiates conditions for primary succession development. Forest stands aged 9, 16, 19, and 28 years were surveyed to assess understory vegetation and epiphytic lichen communities in post-mining sand and gravel quarries in eastern Latvia. Community structure and functional traits were analyzed. Younger stands (9–19 years) exhibited the highest understory species diversity, dominated by hemicryptophytes, open-habitat grasses, and low-to-moderate ecological value lichens, while older stands (28 years) supported high-value epiphytic lichens and understory species typical of oligotrophic boreal forests. In 9-year-old stands, high-value epiphytic lichens comprised, on average, 5.7% (SE = 1.6) total lichen cover, while in 28-year-old stands it was 24.8% (SE = 1.9). Species with animal-mediated seed dispersal were more prevalent in younger stands, reflecting indications of animal presence based on vegetation composition and observed animal damage on trees. No invasive species were recorded, likely due to quarry isolation (≥1 km closest edge of the forest ecosystem) and proximity to mature forest margins. Our results highlight the multidimensionality of biodiversity by integrating two taxonomic groups and indicate high potential for passive natural regeneration toward Western Taiga 9010 habitat conditions under an oligotrophic environment.

1. Introduction

Sand and gravel are widely distributed in Quaternary deposits in Latvia, and in terms of extraction volume, they represent the most exploited mineral resources in the country, with an average annual production of 3.6 million m3 of sand–gravel and 2.7 million m3 of sand between 2021 and 2024 (Latvijas Geospatial Information Agency, 2025) [1]. Sand extraction fundamentally alters the pre-existing ecosystem, eliminating its structure and functions, and initiates conditions for new successional development [2]. In the European Union, legal frameworks such as the Nature Restoration Law [3] require Member States to ensure the rehabilitation and ecological restoration of areas affected by resource extraction. Beyond their restoration potential, quarries may also provide important ecosystem services. Although mines and quarries have historically been regarded as biodiversity sinks, increasing evidence highlights their potential as valuable natural assets under post-mining conditions, particularly when restoration objectives incorporate biodiversity conservation [4]. These areas can contribute to regulating services such as carbon sequestration, greenhouse gas regulation, and nature-based recreation when restoration is planned with ecosystem services in mind [2,5].
The promoted actions can be divided into natural regeneration, assisted regeneration, and reconstruction [6,7]. Although assisted regeneration and reconstruction typically accelerate habitat recovery and allow for more targeted outcomes, they are considerably more costly than natural regeneration approaches [8]. The costs of active restoration in sand and gravel quarries vary widely depending on the targeted outcomes and can include activities such as soil engineering through slope reduction for afforestation or waterbody creation, soil stabilization for waterbody construction, soil fertilization to support planting or initiate natural revegetation, and the control of invasive species. These costs are also strongly influenced by site-specific conditions, including soil properties and quarry morphology [5]. For instance, the application of erosion-control blankets is generally justified only on steep slopes, where erosion risks are high [9]. Similarly, the addition of soil amendments may enhance restoration success in some cases, but their use should be carefully evaluated due to high costs and potential negative environmental impacts, and therefore cannot be justified universally [5,9]. Furthermore, targeted planting is consistently more expensive than sowing, with costs reported to exceed those of sowing by more than fivefold [9]. Natural regeneration is generally the slowest pathway and provides limited direct control over successional trajectories, but it involves comparatively low costs and requires little subsequent anthropogenic intervention [10]. However, not all post-mining areas are suitable for natural restoration; alternative approaches should be applied when damage is too severe and natural recovery is unlikely [8].
Natural restoration can be implemented when soil and substrate conditions are suitable for habitat establishment, when there is minimal risk of invasive species occupation that could negatively affect surrounding ecosystems, and when there is no significant threat of adverse environmental impacts on the site or adjacent areas [11,12]. Passive restoration could also be carried out in the quarries that are surrounded by margins of the target ecosystem, therefore initiating natural ecosystem restoration with minimal threat of going to ruderal and low-value land direction [13]. Mining usually completely destroys soil, leaving bare substrate and substantially reducing the density and diversity of the native seed bank, thereby prolonging the process of natural vegetation establishment [14]. Studies in these territories can provide fundamental bases for species distribution strategies. Large, uncovered areas are at high risk of creating zones where invasive and expansive species can thrive [11]. Seeds of invasive species can travel long distances even in locations far removed from major sources, including highways, railways, industrial areas, and populated places [15].
Oligotrophic forests often rely on disturbances for their preservation [16]. Specific disturbances, such as wildfires, grazing, and grass cutting, can help to provide habitat to some rare species [16]. Lack of these disturbances often leads to natural eutrophication of these habitats, and the environment becomes less favorable for oligotrophic species [17]. Previous studies suggest that after natural regeneration of sand mines, vegetation composition is dominated by dry and mesic open-habitat species under dry conditions [13,18]; therefore, post-mining territories in boreal zones could create islands for oligotrophic flora and fauna.
At the same time, restoration approaches that involve soil amendments and fertilization may significantly alter these oligotrophic conditions. Active reclamation treatments can accelerate soil formation and nutrient accumulation, resulting in faster development of humus horizons and increased carbon and nitrogen accumulation compared to natural succession [19]. While such measures promote rapid vegetation establishment and ecosystem recovery, they may also change habitat suitability for species adapted to nutrient-poor environments. For example, research on sand-dwelling beetles demonstrated that restored quarry areas with fertilized soils and dense herbaceous vegetation supported lower abundance and reduced dispersal ability compared to untreated sandy habitats [20,21].
Despite increasing interest in quarry restoration, there is still a limited understanding of how natural regeneration in sand quarries develops over time in boreal regions and whether such sites can support oligotrophic forest habitats without active restoration interventions. In particular, little is known about the balance between natural successional processes, the potential spread of invasive species, and the development of forest characteristics typical of oligotrophic boreal ecosystems. Moreover, ecological studies often face challenges in capturing the multidimensionality of biodiversity, as most assessments focus on a single aspect of diversity (e.g., species richness), whereas different taxonomic groups can respond differently under the same environmental conditions, potentially obscuring important ecological patterns [22].
We conducted research in a post-mining sand quarry containing forest stands of varying ages to examine ground vegetation and epiphytical lichen succession dynamics. Specifically, we tested: (1) whether natural regeneration over 9–28 years produces understory and epiphytic communities resembling Western Taiga 9010 reference conditions; (2) if proximity to mature forest (>1 km from major disturbance sources) limits invasive species establishment; and (3) how functional trait composition (dispersal modes, life forms) shifts during succession. We hypothesize that the distance of the studied sand quarries (≥1 km from major invasive species sources) is sufficient to limit invasive species establishment. Moreover, we expect that the surrounding old-growth boreal forest and the continuous forest margins along quarry edges will facilitate the colonization of vascular plants and epiphytic lichens on newly established trees, thereby accelerating succession toward mature forest.
This study demonstrates the potential for passive restoration of sand and gravel quarries. As the Land Degradation Neutrality is a key component of Sustainable Development Goals (SDGs), it is important to evaluate quarry restoration approaches under local environmental conditions [23]. The dominance of invasive and ruderal species following restoration represents a potential threat that may reduce the ecological value of these areas [12]. Therefore, it is essential to identify the conditions under which such dominance may occur and to determine the factors that can limit it.

2. Materials and Methods

2.1. Study Area

The study area is located in the eastern part of Latvia (the centroid of the study area: 57.507445, 26.724092) at the sand excavation site “Gaileņu karjers”, where medium coarse-to-fine sand is extracted (Figure 1). The area is characterized by relatively cold winters and hot summers for the region due to its remoteness from the Baltic Sea. The 20-year mean annual air temperature is 5.8 °C, with the warmest month being July (18 °C) and the coldest month being February (−4 °C) [24]. The 10-year mean for precipitation was 797 mm, with 276 mm during the summer period (June–August) and 159 mm during the winter period (December–February) [24]. Climatic data was gathered from the closest meteorological observation station 20 km away. The total quarry area is 17 ha. Active sand extraction is still ongoing in the northwestern part of the quarry, whereas the eastern part of the site has naturally undergone reclamation. Active quarry areas are characterized by sandy deposits with interlayers of sandy loam and clayey sand occurring both above and below the groundwater table, which ranges from 7.5 to 17.7 m depth [25]. The stratigraphic units correspond to artificial fill (f), sandy loam (lg), Upper Quaternary deposits (Q3), and locally limnoglacial varved clays (ltv), with filtration coefficients ranging from 0.01 to 5.23 [25]. In the reclaimed part of the quarry, the substrate consists mainly of medium-to-fine-grained sand with occasional dusty sand interlayers (f, Q3, ltv), where groundwater levels vary from −1.9 to −13.6 m, and the average filtration coefficient is approximately 2.2 [25]. In the reclaimed quarry part, extraction took place at different time periods, with a total of 215.811 thousand m3 extracted, resulting in regenerated forest stands of varying ages, ranging from 9 to 28 years. Stand ages were obtained from forest inventory data provided by Latvijas valsts meži (LVM) and accessed through the LVM GEO platform (https://www.lvmgeo.lv, accessed on 26 February 2026) [26]. Reclamation must be completed within one year of the stand, as legislation requires that reclamation begin no later than one year after the end of extraction [27].

2.2. Field Sampling and Data Analysis

The study area covers 5.4 ha and is divided into four regenerated forest stands, representing one stand per age class (9, 16, 19, and 28 years), ranging in size from 0.48 to 2.02 ha. Dendrometric and lichen data was gathered in January 2023, while vegetation data was surveyed in July 2023. In the stands larger than 1 ha, 9 round sampling plots with a radius of 3.99 m (50 m2, total of 450 m2) were established, but in the stands with an area smaller than 1 ha, 5 round sampling plots with a radius of 5.64 m (100 m2, total of 500 m2) were established (Table 1). This design was chosen to ensure adequate spatial coverage of each stand while avoiding overlap between sampling plots, which would have been likely in the smallest stands if smaller plots were used in higher numbers. Different plot sizes (50 m2 vs. 100 m2) were applied to account for the variation in stand sizes and to ensure sufficient spatial coverage within each stand. Although this resulted in slightly different total sampled areas relative to stand size, sampling intensity was kept comparable across stands by distributing plots randomly and proportionally within each stand area. While larger plots in smaller stands slightly increase sampling area, this approach maintained comparable sampling intensity relative to stand area and minimized bias in stand-level comparisons. Dendrometric measurements of the dominant tree species were recorded in the sample plots, including diameter at breast height (DBH) and tree height. In the 9-year-old stand (stand No. 18), tree damage caused by ungulates was recorded in the youngest and most affected site, including branches and stem damage, top breakage, and mortality (Table A1).
Although Scots pine (Pinus sylvestris Linnaeus) is the dominant tree species, associated species are present: silver birch (Betula pendula Roth), Norway spruce (Picea abies (L.) H. Karst.), and European aspen (Populus tremula (L.)). The proportion of P. sylvestris in the 9-year-old stand is 94%. In older stands, the proportion of P. sylvestris decreases as the share of P. abies increases. It is observed that older stands exhibit higher tree species diversity—in stands aged 16 years and older, B. pendula and individual trees of Alnus glutinosa (L.) Gaertn and Quercus robur (L.) are present. In the 28-year-old stand, P. abies comprised 62% of the stand composition and exceeded P. sylvestris.
To assess vegetation, 9 sampling plots (1 × 1 m) in each forest stand were established. Vascular plants, epigeal moss, and lichen species were observed during the survey. All species and their projective cover in each sampling plot were recorded. Species richness and the Shannon–Wiener diversity index were calculated. LEDA traitbase data was used to structure vegetation data based on plant traits [29]. Two plant traits were included—plant life form (chamaephyte, geophyte, hemicryptophyte, phanerophyte, and therophyte) and dispersal type (anemochory, autochory, endozoochory, and epizoochory). We chose these traits because they are important for primary succession research in this study and show how succession occurs and its intensity. Because plant life form is a trait applicable only to vascular plants, we added an epigeal category that included all moss and lichen species observed in this study. We included a habitat affiliation category, classifying species occurring in the study area that are characteristic of the European Union Habitats Directive Western taiga 9010 habitat type: Calluna vulgaris (L.) Hull, Vaccinium myrtillus (L.), Vaccinium vitis-idaea L., Cladonia deformis (L.) Hoffm., Cladonia stellaris (Opiz) Pouzar & Vězda, Dicranum majus Sm., Hylocomium splendens (Hedw.) Schimp., Pleurozium schreberi (Brid.) Mitt., Trientalis europaea (L.), Agrostis capillaris (L.), Equisetum sylvaticum (L.), B. pendula, P. abies, P. sylvestris, and P. tremula [30]. We selected this specific habitat type, as it represents the target habitat towards which succession in this habitat is expected to develop.
We calculated the Shannon–Wiener index based on richness and cover data and weighted average values for each category for plant life form and dispersal type traits, as well as for EU habitat species, according to formula Equation (1), where Fi is the presence of the trait category for species i, Wi is the relative cover of species i, and n is the total number of species in the sample:
W h e i g h t e d   a v e r a g e   v a l u e s = ∑ i = 1 , n F i W i ∑ i = 1 , n W i
Three sample plots were established in each stand, in which three trees per plot were selected randomly. As a result, data on lichen species richness and cover were obtained from nine trees per stand. Lichen surveys were conducted at two observation heights on each selected tree: 0.5–0.9 m and 1.5–1.9 m above the root collar on the north-facing and south-facing exposures of the stem. At both heights, lichens were recorded in 40 × 10 cm sampling quadrats on the tree stem.
Lichen species were classified into ecological value groups based on functional traits indicative of successional development towards Western taiga 9010 conditions. Because no single classification covers our study region, we combined indicator values from multiple European studies to create a composite ecological index. This approach allows inclusion of all relevant traits, including poleotolerance, and provides a robust, region-independent assessment of lichen habitat preferences. We used three studies of European flora: “Ecological indicator values of some lichen species noted in Poland” [31], “Ecological indicator values of lichens—enlarged and updated species list” [32] in Central Europe, and “The Lichens of Italy. A Second Annotated Catalogue” [33].
We first identified the traits most relevant for this habitat: low tolerance to anthropogenic disturbances, dependence on forest continuity and microclimate, sensitivity to eutrophication and pollution, and preference for acidic [34] and oligotrophic substrates [30,35]. From the available parameters, we selected those describing these characteristics: poleotolerance (lower = better), eutrophication (lower = better), reaction (lower = better), moisture (higher = better), and light (lower and intermediate = better). Eutrophication, moisture, reaction, and light indicator values had different ranges depending on the study: 1–9 in Central Europe [32], and 1–5 in Poland and Italy [31,33] (Table A2, Table A3, Table A4 and Table A5). Poleotolorence as a variable was only used in Nimis study, providing range from 0 to 3, where: 0—species which exclusively occur on old trees in ancient, undisturbed forests; 1—species mostly occurring in natural or semi-natural habitats; 2—species occurring in moderately disturbed areas (agricultural areas, small settlements, etc.); and 3—species occurring also in heavily disturbed areas, incl. large towns [33]. Based on the importance of determining habitat characteristics, we gave weight to each trait: poleotolerance: 0.30, eutrophication: 0.20, reaction: 0.20, moisture: 0.20, and light: 0.10. Species were assigned to three ecological value groups (high, moderate, and low) by dividing the continuous ecological value index into terciles based on its empirical distribution (Table A6). The relative importance of each trait was chosen based on the habitat and research objectives. Since the main disturbances in this area are anthropogenic, poleotolerance was assigned the highest weight in the index [36].
Weighted average values for each ecological value group were calculated for each data collection sample point. Shannon–Wiener and Simpson diversity indices were calculated based on species richness and cover data. We analyzed all plant growth forms separately; therefore, we assessed the normality of each growth form across stands of different ages. Normality was tested with visual inspection first and the Shapiro–Wilk test afterwards, and based on the results, none of the plant growth form groups was normally distributed across stands of different ages, and therefore, nonparametric methods were chosen. We used the Kruskal–Wallis rank-sum test with stand age as a factor and pairwise Wilcoxon rank-sum tests with the Bonferroni method as a post hoc test. Parametric methods were used for traits that did not significantly differ from a normal distribution (EU habitat species, seed dispersal groups, and Shannon–Wiener index), combined with Tukey’s HSD test as a post hoc test.
To check if observation height and exposure had an effect on the results for lichen cover and species richness, we created a global linear model (glm):
Glm = (Species_richness ~ h × exposure + age, data = data, family = Poisson)
Glm = (Species_cover ~ h × exposure + age, data = data, family = Gamma)
For the species richness model, we chose the Poisson distribution, as these are discrete values, but for continuous and positive cover data, we chose the Gamma distribution. We also used the Gamma distribution for both biodiversity indices—Shannon–Wiener and Simpson, because the data significantly differed from the normal distribution. Overdispersion in the Poisson GLMM was evaluated using Pearson residuals and simulation-based diagnostics implemented in the DHARMa (version 0.4.6) package. For the Gamma GLM, model assumptions were assessed using simulated residual diagnostics, confirming an adequate fit of the Gamma distribution with a log link. For further epiphytic lichen analyses, we used the Kruskal–Wallis rank-sum test and pairwise Wilcoxon rank-sum tests as post hoc tests.
An RDA ordination analysis was conducted to examine the effects of stand age on observed plant traits. Stand age was used as the explanatory variable (site scores), while the response values (species scores) included: understory vegetation characteristics (Shannon–Wiener index, EU habitat species, and plant growth forms: chamaephyte, epigeal, geophyte, hemicryptophyte, phanerophyte, and therophyte), dominant dispersal strategies (anemochory, autochory, endozoochory, epizoochory, and vegetative propagation), tree diameter, epiphytic species richness, cover, Shannon–Wiener and Simpson diversity indices, and ecological value groups (high, moderate, and low). Because the traits were measured on different scales, they were standardized to z-scores prior to analysis, so that each trait had a mean of 0 and a standard deviation of 1. Redundancy analysis (RDA) was used to assess the relationship between lichen functional traits and stand age. The significance of the constraints and individual canonical axes was tested using permutation tests with 999 permutations.
All statistical analyses and visualizations were performed in R (version 4.5.2) [37]. We used base R functions together with the packages ‘vegan’ (version 2.6-6) for ordination analyses, ‘lme4’ (version 1.1-35) for generalized linear mixed-effects models, ‘ggplot2’ (version 3.5.1) and ‘cowplot’ (version 1.1.3) for data visualization, ‘emmeans’ (version 1.10.0) for post hoc comparisons, and ‘dplyr’ (version 1.1.4) for data manipulation [38,39,40,41,42,43].

3. Results

3.1. Understory Vegetation

Based on plant growth form indices, the only significant difference that we found, on stand age was in the proportion of epigeal–non-vascular species, which includes all epigeal moss and lichen species (Kruskal–Wallis test: χ2 = 16.66, df = 3, and p = 0.0008). Post hoc pairwise Wilcoxon tests with Bonferroni correction indicated significant differences between the 9- and 16-year stands (p = 0.0021) and between the 9- and 28-year stands (p = 0.0383) (Figure 2(1)). Similar patterns were present for typical EU habitat species: lower value is in the youngest stand, but all older stands have higher values, with the highest in the 28-year-old (Figure 2(2)). The Shannon index was significantly lower in older stands compared to younger stands (Figure 2(3)). No significant differences were found between younger stands (9 to 19 years old), and also no difference occurred between the two older stands: 19 and 28 years old.
Seed dispersion had no significant differences between values, because the data was unevenly distributed with limited sample plots, due to the limited study area (ANOVA: Anemochory F = 0.83, p = 0.488; Autochory F = 1.06, p = 0.381; Endozoochory F = 1.13, p = 0.354; Epizoochory F = 1.76, p = 0.178). Even so, when observing the mean values, decreasing autochory and increasing anemochory with increasing stand age can be seen (Figure 3).

3.2. Epiphytic Lichen

In total, 15 epiphytical lichen species were observed. The lichen community was dominated by Hypogymnia physodes (L.) Nyl., which occurred across all stand ages and in nearly all sampling plots. Other frequently recorded species included Hypogymnia tubulosa, Bryoria fuscescens, and Lepraria incana, representing taxa with high-to-moderate tolerance to air pollution. In contrast, Vulpicida pinastri and Platismatia glauca, a species with lower pollution tolerance, were also common in the study area. Moreover, the species typical of older forests were not found in the 9-year-old stand, but were present later in succession.
Based on the results of the GLM model, species richness was not affected by observation height (β = −0.17 ± 0.11 SE, p = 0.116), but was influenced by tree exposure and stand age. The corresponding incidence rate ratio (IRR = e−0.3856 ≈ 0.68) indicates that richness on the south-facing side was, on average, about 32% lower than on the north-facing side, showing the most significant differences in the youngest pine stands (Wilcoxon W = 266, p = 0.0007 for 9-year-old stands; W = 224, p = 0.0435 for 19-year-old stands; W = 219–179, p = 0.06–0.592 for other ages) (Figure 4). Pairwise comparisons showed that richness increased with age, with significant differences between the youngest (9 years) and all other age classes (16, 19, and 28 years; adjusted p < 0.001), as well as between 16- and 28-year-old stands (adjusted p = 0.0014) and 19- and 28-year-old stands (adjusted p = 0.0118) (Figure 4).
Total lichen cover increases with age (β = 0.039 ± 0.018 SE, p = 0.0334) on the north side of the tree (β = −1.19 ± 0.50 SE, p = 0.0183), and also shows a small interaction between observation height and exposure. The north side of the tree was more covered in lichens compared to the south side of the tree, which was observed only closer to ground, but no significant difference occurred higher on the stem (β = 0.82 ± 0.45 SE, p = 0.0677) (Figure 5(1)).
Unlike the cover, both biodiversity indices—Shannon–Wiener and Simpson—were unaffected by observation height of the sampling (all p > 0.05). Based on these results, we excluded observation height from further richness and biodiversity analyses. Both the Shannon–Wiener and Simpson indices were lowest in the youngest stands and increased with stand age, reaching the highest Shannon–Wiener values in the 28-year-old stand. Exposure affected biodiversity only in the youngest stand, and differences disappeared as diversity increased (Figure 6).
In the youngest stand, species with low ecological value dominated the community, accounting for 83.6 ± 4.3% of the total cover, while medium- and high-value species represented 10.7 ± 4.3% and 5.7 ± 1.6%, respectively. With increasing stand age, the proportion of low-value species generally decreased, while the share of species with medium and high ecological value increased. In the 16-year-old stand, low-value species comprised 49.2 ± 2.3%, medium 32.6 ± 3.2%, and high 18.1 ± 2.4% of total cover. The 19-year-old stand was again dominated by low-value species (70.5 ± 2.6%), whereas medium- and high-value species accounted for 3.2 ± 1.1% and 26.3 ± 2.6%, respectively. In the oldest stand, the cover distribution was more balanced, with 52.9 ± 1.8% low-value, 22.3 ± 2.0% medium-value, and 24.8 ± 2.0% high-value species. This classification allowed assessment of how the ecological quality of the lichen community changed along the stand age gradient.

3.3. Ecosystem Trends

The RDA model explained 41.3% of the total variance in the data (adjusted R2 = 0.36). The first three canonical axes explained 71.5%, 17.2%, and 11.3% of the constrained variance, respectively. The overall model was significant according to permutation tests (F = 7.49, p = 0.001), and all canonical axes were significant (Figure 7). Envfit analysis confirmed that stand age was significantly correlated with the ordination of ecosystem traits (r2 = 0.74, p = 0.001), indicating that age strongly structures community composition. Based on ordination analyses, the 9-year-old pine stand differed the most from the other stands across all ecosystem traits. Geophytes and Hemicryptophytes and epiphytic lichens from the low-ecological-value group were more present in the 9-year-old stand, but the overall higher tree stem lichen cover and biodiversity, and presence of understory lichen, moss, and EU habitat species, as well as a higher presence of high-ecological-value group lichens were more present in the oldest of the observed stands—28 years old. While epiphytic lichen diversity tended to increase in more mature stands, the Shannon–Wiener index for understory species was higher in stands aged 9–19 years. These contrasting patterns between taxonomic groups indicate that different components of biodiversity respond differently to successional development.

4. Discussion

4.1. Succession and Understory Vegetation

Study area in an excavated sand quarry, with sandy, well-drained but nutrient-poor soil left, promoted oligotrophic boreal forest succession with Pinus sylvestris, Picea abies, and Betula pendula dominance in the first forest floor. In total, 57 understory species were observed, of which 48 were herbaceous, six bryophyte, and two lichen species. Other studies have reported a variable number of herbaceous species during the first 21 years after extraction, changing from 15 to 37 species [44]. A relatively high proportion of grasses and open-habitat plant species is observed in the quarry part with a 9-year-old naturally regenerated pine forest, similar to the pattern often observed in the young growth of boreal forests [18,45,46]. The vegetation composition is dominated by species such as Phragmites australis (Cav.) Trin. ex Steud., Poa annua (L.), Equisetum sylvaticum, Vicia cracca (L.), Achillea millefolium (L.), and Artemisia campestris (L.), but the moss layer contains Dicranum majus, Hylocomium splendens (Hedw.) Schimp., and Pleurozium schreberi (Brid.) Mitt., which are characteristic of pine forests’ Vaccinio-Piceetea communities [47]. Species included in our study that are typical of boreonemoral forests, such as Equisetum sylvaticum, are known to mostly occur in mature stands, while early-successional species, including Viola canina L., Dactylis glomerata L., and Anthriscus sylvestris (L.) Hoffm., are usually found in younger forests [48]. Species commonly associated with human disturbances, such as clear-cut areas, like Chamaenerion angustifolium and Taraxacum officinale F.H. Wigg., were also present in the species list, reflecting their ability to colonize disturbed habitats [48]. Three decades after the start of succession, the percentage of open-habitat species, such as P. australis, is no longer observed in the sample plots, but in addition to the previously mentioned moss species characteristic of oligotrophic pine forests, Cladonia spp. and Caluna vulgaris were present, which were not observed in other younger stands, suggesting succession in the Vaccinio-Piceetea direction [49]. The Shannon diversity index declined with increasing stand age, reflecting lower species richness of Vaccinio-Picetea forests compared to most dry grassland or open habitats [50]. The increased species richness in early boreal succession stages is usually connected to suitable conditions for the coexistence of early- and late-succession species [51]. Although understory diversity decreased with stand age, epiphytic lichen diversity increased in older stands. This contrasting response between taxonomic groups highlights the multidimensional nature of biodiversity, where different organism groups respond differently to the same environmental gradients and successional processes.
There were no invasive species recorded. The only species with expansive characteristics was P. australis, which was observed in all stand ages except the oldest one. Epilobium angustifolium (L.) was recorded in one plot; however, given the stand age (9-year-old), the site may already be beyond the successional stage suitable for its expansion, as the species is characteristic of early succession [46]. Quarry location in the middle of forested lands, with the closest habitat margin (highway with agricultural land) within 1 km, could influence the low number of ruderal and anthropogenic plant species present during ecosystem succession. Similar patterns have been described earlier, with disturbances occurring in less-altered landscapes showing less occupation by ruderal species than more-altered landscapes [52]. The only true ruderal species we observed were Tussilago farfara (L.), Taraxacum officinale, E. angustifolium, and A. campestris, all of which are typical species occurring in forest disturbances and are native to Latvia’s flora [17].

4.2. Species Dispersal

Epizoochory and endozoochory are animal-mediated seed dispersal mechanisms involving either external attachment to animals or seed ingestion. The absence of zoochorous species in the oldest stand may indicate that animal activity is concentrated in the youngest stands, potentially reflecting that forest ungulates preferentially use more open habitats during active feeding periods [53]. Visual assessments, including excrement, browsing traces, and tree damage, indicated stronger ungulate presence in the youngest stands (Table A1). On average, 83% of trees in the 9-year-old stand exhibited damage caused by ungulates, including branch breakage, top damage, and bark stripping (Table A1). Scots pine remains an accessible food source for ungulates for approximately 20 years, with the most intensive damage typically occurring before trees reach 3 m in height [54,55]. Recent studies report increasing ungulate populations across Europe, with a notable rise also documented in eastern Latvia (approximately 100 km from the study area) [56]. Changes in large herbivore population dynamics in recent decades may therefore explain the higher browsing pressure observed in younger stands of the quarry. At the same time, the absence of zoochorous herbaceous species in older stands likely reflects reduced availability of preferred food resources and diminished animal-mediated dispersal. Other studies have similarly emphasized the importance of large animals for natural regeneration in early forest stages and in degraded environments, such as quarries, where animal-mediated zoochory can provide a continuous source of viable seeds that support passive restoration [57,58]. While damage observations on trees support the possibility of animal-mediated dispersal, they do not directly demonstrate it, as dispersal events were not tracked. Alternative explanations for the observed distribution patterns include environmental filters, such as light, moisture, or substrate availability, and biotic interactions, including competition among early colonizers, which could also shape species establishment and abundance [59,60].

4.3. Epiphytic Lichens as Indicators of Forest Continuity

The increase in stand age promoted the colonization of epiphytic lichen species, resulting in higher species richness and cover in the 28-year-old stand. Within the increase in stand age, the availability of structures suitable for lichen growth—such as thicker stems, larger branch diameters, and thick fissured bark—increases, creating diverse microhabitats for epiphytic communities [61]. This is further supported by the results of this study and other studies, suggesting that lichens with high ecological value were more abundant in older stands but absent in the youngest ones, reflecting the time required for forest succession to develop [62]. The continued increase in species richness in the study area indicates that epiphytic lichen succession is still ongoing and that additional species may be established in the coming years [63].
The early presence of lichens with high ecological value may be facilitated by the proximity of the quarry to large forested land. For species that primarily disperse via vegetative propagation, as is typical for the main epiphytic lichens in this region, connectivity to surrounding forests can be a critical factor influencing natural colonization, as many epiphytic lichens are dispersal-limited [64]. Studies have shown that lichens have restricted dispersal, with most falling within 10–50 m of the source tree, while long-distance dispersal events are rare [64,65,66]. The findings of this study support the hypothesis that when quarries are surrounded by relatively undisturbed landscapes, natural regeneration can be an effective reclamation strategy, minimizing the risk of the site developing into low-value land dominated by invasive vegetation [52].
The pattern of higher cover and species richness in stands aged 9–19 years at a height of 0.5 m may be influenced by understory vegetation. In younger stands, a denser understory dominated by grasses, as opposed to the moss and lichen groundcover characteristic of 28-year-old stands, could create a more favorable microclimate for lichen establishment during early developmental stages [67]. The absence of this effect at 1.5 m height, where the influence of understory vegetation on microclimate is weaker, further supports this idea [68]. The observed differences in epiphytic lichen species richness depending on bark exposure highlight the importance of survey methodology, particularly the need to examine different parts of the tree. The limited sample size, constrained by the size of the case study area, should be considered when interpreting the results, and future studies with more extensive sampling are recommended to confirm these patterns.

4.4. Successional Ecosystem Dynamics

Given that study is based on four time points (9, 16, 19, and 28 years), the observed patterns reflect space-for-time substitution rather than true temporal replication, and thus should be interpreted with caution regarding actual successional dynamics. When analyzed together, the studied traits indicated that the most species-diverse forest stand was the 9-year-old stand, characterized by a higher proportion of hemicryptophytes—mainly grassland species—and lichens of moderate ecological value. The increasing importance of epiphytic lichen cover and richness, and ecological value, alongside the rising dominance of epigeal species and the competition decline of open-habitat hemicryptophytes, suggests a potential successional trajectory toward the Western taiga 9010 habitat type (Western Taiga). The Western Taiga 9010 habitat is a critically important boreal forest type in the Baltic region, forming one of the largest forest habitats in Estonia and contributing substantially to Natura 2000 protected areas [69]. In Latvia, Western taiga forests are predominantly found on oligotrophic-to-mesotrophic mineral soils and include several ecological subtypes distinguished by dominant tree species such as spruce, pine, or mixed stands. These forests support key biodiversity elements, including old-growth structures and diverse bryophyte and lichen communities [30]. Despite their ecological significance, Western taiga forests are increasingly threatened by modern forestry practices and the disruption of natural disturbance regimes, which can reduce old-growth features and habitat quality for species dependent on heterogeneous stand structures [30,69].
Additionally, the opposite trends observed for diversity indices of understory vegetation and epiphytic lichens further illustrate the multidimensionality of biodiversity, emphasizing that different taxonomic groups can respond differently under the same environmental conditions. Such patterns highlight the importance of considering multiple organism groups in ecological assessments and support the need for a site-specific approach in ecological research. This also implies that, beyond the scope of the present case study, future research would benefit from incorporating additional components of biodiversity, such as pollinator and bird communities, as well as soil biota and other functional groups, to achieve a more comprehensive ecological assessment.
Changes in forest management systems across Europe during the last century have substantially reduced the frequency and spatial extent of natural disturbances such as fire, windthrow, and pest outbreaks [70]. As a consequence, disturbance-dependent oligotrophic forest types, including the Western Taiga 9010 habitat, have become increasingly rare in the European context [71]. In the absence of disturbance, oligotrophic pine forests tend to follow successional pathways toward more productive forest types dominated by shade-tolerant species, leading to a gradual loss of light-demanding and nutrient-poor habitat specialists [72]. Although sand quarries do not replicate natural gap dynamics or fire regimes, they create open, high-light, and nutrient-poor conditions that contrast with the surrounding managed forest matrix [4]. These conditions resemble disturbance effects such as windthrow, where tree removal resets successional trajectories [73]. In intensively managed boreal landscapes, post-extraction quarries may therefore function as habitat islands that support oligotrophic species otherwise underrepresented in closed-canopy forests [4]. Similar roles have been documented in other disturbance-dependent habitats, such as dry grasslands, dunes, and alvar systems, where open conditions and nutrient-poor substrates maintain communities of stress-tolerant and light-demanding species that decline after canopy closure [74]. Post-industrial habitats, including sand pits and quarries, can therefore contribute significantly to regional biodiversity by providing refugia for early-successional species and increasing landscape heterogeneity within intensively managed environments [75,76].
Climate represents an important factor influencing the restoration of sand and gravel quarries in boreal regions, as changes in temperature and precipitation can alter species composition and distribution, affecting both plant productivity and functional diversity [77,78,79]. Climate change, together with human-mediated processes, may facilitate the spread of invasive species and modify traditional ecosystem processes, potentially slowing recovery and altering successional trajectories [80,81]. In this context, maintaining high functional diversity can enhance ecosystem resilience and productivity, as species with different responses to environmental stressors can sustain key ecosystem functions under changing climatic conditions [78,82].

5. Conclusions

Younger stands (9–19 years) showed the highest understory species diversity, driven by hemicryptophytes, grasses, and epiphytic lichens of low-to-moderate ecological value, while older stands (28 years old) supported epiphytic lichens of high ecological value and increasing epiphytic lichen cover and species richness, reflecting ongoing forest succession and habitat continuity. Plant species with animal-mediated seed dispersal types, including epizoochory and endozoochory, were concentrated in younger stands, highlighting the role of animals in shaping forest regeneration. The opposite trends for diversity indices for understory vegetation and epiphytic lichen species justify the multidimensionality of biodiversity concepts and indicate the need for a site-specific approach for ecological research. The observed successional patterns indicate a potential trajectory toward Western Taiga 9010 habitat in quarries surrounded by mature forests.
Post-sand-mining areas located near undisturbed forested landscapes show high potential for natural regeneration. This is supported by high-ecological-value species occupying the area only a few decades after restoration initiation. This case study shows that natural regeneration of sand quarries is particularly applicable under boreal forest conditions and at a sufficient distance from sources of invasive species propagules, creating islands for oligotrophic flora and fauna in boreal forests. Active restoration may not be required in such contexts. We recommend monitoring for invasive species and maintaining forested buffers around quarry sites to facilitate natural regeneration and support the development of Western Taiga habitats. As a pilot case study, these results indicate potential successional trends in post-mining ecosystems of this region but may also reflect local environmental conditions. Future multi-scale studies should also consider the multidimensionality of biodiversity to achieve a more comprehensive understanding. Nevertheless, the findings provide insight into succession and species colonization processes in boreal post-mining landscapes and support evidence-based restoration strategies for degraded lands. In the context of European environmental policy and global sustainability frameworks such as Sustainable Development Goal 15, studies of this type help identify conditions under which passive quarry restoration can be applied while minimizing the risk of establishing invasive species.

Author Contributions

Conceptualization, D.L. and V.V.; methodology, D.L., A.Z. and V.V.; software, A.Z.; formal analysis, A.Z.; investigation, A.Z., V.V., T.A.Š., K.D. and S.C.; data curation, D.L., A.Z. and V.V.; writing—original draft preparation, A.Z.; writing—review and editing, V.V., D.L., R.M., K.D., T.A.Š. and S.C.; visualization, A.Z. and T.A.Š.; supervision, D.L. and R.M.; project administration, D.L. and R.M.; funding acquisition, D.L. and R.M. All authors have read and agreed to the published version of the manuscript.

Funding

The European Union Cohesion Policy Program 2021–2027, specific support objective 1.1.1 “Strengthening research and innovation capacity and introducing advanced technologies into the common R&D system,” measure 1.1.1.8 “Doctoral grants” (agreement No. 1.1.1.8/1/24/I/002) for Viktorija Vendina.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available within the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We acknowledge Latvia council of Science project “Forest4LV—Innovation in Forest Management and Value Chain for Latvia’s Growth: New Forest Services, Products and Technologies” (VPP-ZM-VRIIILA-2024/2-0002) for financial support including supplementary data collection. We also thank Sintija Engelsa, a bachelor’s student from the Latvia University of Life Sciences and Technologies, for her assistance with data gathering.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Tree damage by ungulates in a 9-year-old stand.
Table A1. Tree damage by ungulates in a 9-year-old stand.
Sample Plot NumberTotal Number of TreesDamage Trees (%)Type of Damage Tree (%)
BranchesStemTop BreakageMortality
15184.3730146
22910090730
32310091440
420907015150
517824135618
613100693100
7108050102020
8189450281111
96100336700
Total20783631385
Table A2. Eutrophication indicator values for lichens across different regional classification systems.
Table A2. Eutrophication indicator values for lichens across different regional classification systems.
ValueCentral Europe [19]Poland [18]Italy [20]
1No/almost no eutrophicationExtremely poorNot resistant to eutrophication
2No/almost no eutrophicationPoorResistant to very weak eutrophication
3Weak eutrophicationModerately richResistant to weak eutrophication
4Weak eutrophicationRichRather eutrophicated
5Moderate eutrophicationVery richHighly eutrophicated
6Moderate eutrophication––
7Fairly strong eutrophication––
8Strong eutrophication––
9Very strong eutrophication––
Table A3. Moisture indicator values for lichens across different regional classification systems.
Table A3. Moisture indicator values for lichens across different regional classification systems.
ValueCentral Europe [19]Poland [18]Italy [20]
1Restricted to driest areasVery dry habitatsHydro-/hygrophytic (aquatic, foggy sites)
2Prefers low precipitationDry habitatsRather hygrophytic
3Tolerates dry, often humidModerately moistMesophytic
4Dry sites only with high humidityVery moistXerophytic (not extremely arid)
5Avoids dry areas (>700 mm)Wet and aquaticVery xerophytic
6Precipitation > 800 mm––
7Precipitation > 1000 mm––
8>1400 mm, tolerates desiccation––
9>1400 mm, very humid––
Table A4. Reaction indicator values for lichens across different regional classification systems.
Table A4. Reaction indicator values for lichens across different regional classification systems.
ValueCentral Europe [19]Poland [18]Italy [20]
1Extremely acidic (pH < 3.4)Highly acidic (pH < 4)Very acidic (lignum, conifer bark)
2Very acidic (3.4–4.0)Acidic (pH 4–5)Acidic (non-eutrophicated oak bark)
3Fairly acidic (4.1–4.8)Moderately acidic (pH 5–6)Subacid–subneutral (e.g., Sambucus bark)
4Moderately acidic (4.5–5.2)Subneutral–neutral (pH 6–7)Slightly basic (dust-covered bark)
5Moderately acidic (4.9–5.6)Alkaline (pH > 7)Basic (limestone)
6Weakly acidic (5.3–6.1)––
7Subneutral (5.7–6.5)––
8Neutral (6.6–7.5)––
9Basic (pH > 7.5)––
Table A5. Light indicator values for lichens across different regional classification systems.
Table A5. Light indicator values for lichens across different regional classification systems.
ValueCentral Europe [19]Poland [18]Italy [20]
1Deep shade (<1% R.L.)Deep shadeVery shaded (deep gorges, closed forests)
21–3% R.L.Moderate shadeShaded (north side of boles)
3Shade plant (<5% R.L.)Half-shadeDiffuse light, little direct sun
43–5% R.L.Moderate lightSun-exposed, avoids extremes
5Partial shade (>10% R.L.)Full lightVery high direct irradiation
65–7––
7Partial light––
8Light plant––
9Full light––
Table A6. Indicator values for observed lichen species.
Table A6. Indicator values for observed lichen species.
SpeciesEcological Value GroupPTEutrophicationMoistureReactionLight
CEITAPOLCEPOLITACEPOLITACEPOLITA
Pseudevernia furfuracea (L.)High1–221–23533–4311–2843–5
Usnea hirta (L.)High141–21542–3321–2744–5
Vulpicida pinastri (L.)High1–2212733211–2643–5
Tuckermannopsis chlorophylla (Willd.) HaleHigh131–22633321–2643–4
Platismatia glauca (L.) W.L. Culb. & C.F. CulbHigh1–221–23533221–2743–5
Chaenotheca brunneola (Ach.) Müll. Arg.Moderate1112631–2321–2321–3
Bryoria fuscescens (Gyeln.) Brodo & D. Hawksw.Moderate141–21631–2321–3743–5
Usnea subfloridana Stirt.Moderate1–221–21642–3521–3743–5
Cladonia coniocraea (Flörke) Spreng.Moderate1–231–3NANA32–3422–3533–4
Lepraria incana (L.) Ach.Moderate151–25332–4321–2432–4
Arthonia radiata (Pers.) Ach.Low1–341–33432–3532–3333–4
Parmelia sulcata TaylorLow1–371–34332–3531–3743–5
Lecanora conizaeoides Nyl. ex Cromb. (1885)Low2–351–35322–3211–2733–5
Hypogymnia physodes (L.) Nyl. (1896)Low1–331–25332–3321–3743–4
Hypogymnia tubulosa (Schaer.) Hav.Low1–241–23332–3531–2743
PT—Poleotolerance, CE—Central Europe [30], ITA—Italy [31], POL—Poland [29], and indicator value ranges across Table A2, Table A3, Table A4 and Table A5.

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Figure 1. Study site location and orthophoto (source: Latvian Geospatial Information Agency, 7th-cycle orthophoto, and aerial imagery acquired 2021) of natural regeneration forest stands (9 years old, 16 years old, 19 years old, and 28 years old), including the quarry area with active extraction in the northeastern part of the photo [28].
Figure 1. Study site location and orthophoto (source: Latvian Geospatial Information Agency, 7th-cycle orthophoto, and aerial imagery acquired 2021) of natural regeneration forest stands (9 years old, 16 years old, 19 years old, and 28 years old), including the quarry area with active extraction in the northeastern part of the photo [28].
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Figure 2. Effect of stand age on understory vegetation traits: (1) epigeal species cover, (2) EU habitat species cover, and (3) Shannon–Wiener index. The different letters indicate significant differences (p < 0.05).
Figure 2. Effect of stand age on understory vegetation traits: (1) epigeal species cover, (2) EU habitat species cover, and (3) Shannon–Wiener index. The different letters indicate significant differences (p < 0.05).
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Figure 3. Weighted average plant cover by dispersal type.
Figure 3. Weighted average plant cover by dispersal type.
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Figure 4. Species richness of epiphytical lichen species in relation to stand age and exposure. The different letters indicate significant differences between stand ages groups (p < 0.05). Asterisks indicate levels of statistical significance between different exposure within one stand age group: * p < 0.05, *** p < 0.001.
Figure 4. Species richness of epiphytical lichen species in relation to stand age and exposure. The different letters indicate significant differences between stand ages groups (p < 0.05). Asterisks indicate levels of statistical significance between different exposure within one stand age group: * p < 0.05, *** p < 0.001.
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Figure 5. Cover of epiphytic lichens in relation to stand age and exposure at two observation heights: (1) 0.5 m and (2) 1.5 m. The different letters indicate significant differences between stand ages groups (p < 0.05). Asterisks indicate levels of statistical significance between different exposure within one stand age group: * p < 0.05.
Figure 5. Cover of epiphytic lichens in relation to stand age and exposure at two observation heights: (1) 0.5 m and (2) 1.5 m. The different letters indicate significant differences between stand ages groups (p < 0.05). Asterisks indicate levels of statistical significance between different exposure within one stand age group: * p < 0.05.
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Figure 6. Shannon–Wiener and Simpson’s biodiversity indices depending on stand age and exposure. The different letters indicate significant differences between stand ages groups (p < 0.05). Asterisks indicate levels of statistical significance between different exposure within one stand age group: * p < 0.05.
Figure 6. Shannon–Wiener and Simpson’s biodiversity indices depending on stand age and exposure. The different letters indicate significant differences between stand ages groups (p < 0.05). Asterisks indicate levels of statistical significance between different exposure within one stand age group: * p < 0.05.
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Figure 7. Ordination analyses grouped by stand age responding to vegetation and epiphytic lichen traits (plant: EU_hab—EU habitat species cover; Shan_div_p—Shannons-Wiener biodiversity index for plant species: Epizoochory, Endozoochory, Anemochory, Autochory, Therophyte, Phanerophyte, Geophyte, and Hemicryptophyte; Diameter—tree diameter at the breast height; lichen: Low_lev—Low lichen ecological value group, Mod_lev—moderate lichen ecological value group, and High_lev—High lichen ecological value group; Shan_div_L—Shannon–Wiener biodiversity index for epiphytical lichen; Lich_rich—epiphytical lichen richness; and Lich_cov—epiphytical lichen cover).
Figure 7. Ordination analyses grouped by stand age responding to vegetation and epiphytic lichen traits (plant: EU_hab—EU habitat species cover; Shan_div_p—Shannons-Wiener biodiversity index for plant species: Epizoochory, Endozoochory, Anemochory, Autochory, Therophyte, Phanerophyte, Geophyte, and Hemicryptophyte; Diameter—tree diameter at the breast height; lichen: Low_lev—Low lichen ecological value group, Mod_lev—moderate lichen ecological value group, and High_lev—High lichen ecological value group; Shan_div_L—Shannon–Wiener biodiversity index for epiphytical lichen; Lich_rich—epiphytical lichen richness; and Lich_cov—epiphytical lichen cover).
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Table 1. Study site description.
Table 1. Study site description.
Year Extraction EndedAge of Recultivation Area (Years)Pinus sylvestris Occurrence (%)Number of Sample PlotsArea (ha)Mean Height (m)DBH (cm)
201199491.8533.8
2004165291.0410.110.2
2001195892.0211.112.2
1994282850.489.510.7
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Zuševica, A.; Vendina, V.; Lazdiņa, D.; Matisons, R.; Štāls, T.A.; Dūmiņš, K.; Celma, S. Natural Regeneration of Sand Quarries Supports Oligotrophic Boreal Forest Vegetation Development Within Three Decades: A Case Study. Sustainability 2026, 18, 3989. https://doi.org/10.3390/su18083989

AMA Style

Zuševica A, Vendina V, Lazdiņa D, Matisons R, Štāls TA, Dūmiņš K, Celma S. Natural Regeneration of Sand Quarries Supports Oligotrophic Boreal Forest Vegetation Development Within Three Decades: A Case Study. Sustainability. 2026; 18(8):3989. https://doi.org/10.3390/su18083989

Chicago/Turabian Style

Zuševica, Austra, Viktorija Vendina, Dagnija Lazdiņa, Roberts Matisons, Toms Artūrs Štāls, Kārlis Dūmiņš, and Santa Celma. 2026. "Natural Regeneration of Sand Quarries Supports Oligotrophic Boreal Forest Vegetation Development Within Three Decades: A Case Study" Sustainability 18, no. 8: 3989. https://doi.org/10.3390/su18083989

APA Style

Zuševica, A., Vendina, V., Lazdiņa, D., Matisons, R., Štāls, T. A., Dūmiņš, K., & Celma, S. (2026). Natural Regeneration of Sand Quarries Supports Oligotrophic Boreal Forest Vegetation Development Within Three Decades: A Case Study. Sustainability, 18(8), 3989. https://doi.org/10.3390/su18083989

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