Next Article in Journal
Designing a Multitemporal Analysis of Land Use Changes and Vegetation Indices to Assess the Impacts of Severe Forest Fires Before Applying Control Measures
Previous Article in Journal
Study on the Spatial–Temporal Variation of Groundwater Depth and Its Impact on Vegetation Coverage in Ejina Oasis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests

1
Institute of Forestry and Engineering, Estonian University of Life Sciences, Kreutzwaldi 5, 51006 Tartu, Estonia
2
Estonian State Forest Management Centre, Mõisa/3, Sagadi Village, 45403 Haljala Municipality, Estonia
3
Institute of Ecology and Earth Sciences, Faculty of Science and Technology, University of Tartu, J. Liivi tn 2, 50409 Tartu, Estonia
4
Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Fr. R. Kreutzwaldi 5, 51006 Tartu, Estonia
5
School of Environmental and Forest Sciences, University of Washington, Winkenwerder, Seattle, WA 98195-2100, USA
*
Author to whom correspondence should be addressed.
Forests 2024, 15(11), 2035; https://doi.org/10.3390/f15112035
Submission received: 28 August 2024 / Revised: 9 November 2024 / Accepted: 12 November 2024 / Published: 18 November 2024

Abstract

:
We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity.

1. Introduction

The concept of forest ecological memory includes variety of modifications (called forest legacies) generated by both natural disturbances and anthropogenic management along with temporal aspects [1,2]. Historically there has been a significant shift in forest ecosystems from natural disturbances to more intense human-driven management approaches such as clear-cutting, selective harvesting, plantings, and understory maintenance [3]. Nowadays more forest land is protected [4] and clear cutting is transitioning into selective cutting or continuous cover forestry (CCF) [5]. CCF is not a new idea in forest management but there has been renewed interest in it for sustainability requirements [6,7]. It has also been found that sustainable forest management needs more region- and forest site type-specific targets [4,8].
Using historical data to quantify environmental impacts continues to be controversial [9], because of uncertain spatial accuracy, dates, and low image quality [10]. On the other hand, implications of historical management on forest structure and biodiversity are undeniable [11,12]. For example, ecosystem management has been shown to lead to retrogressive succession [13,14] and a simplified forest structure [15] but historical (ancient) semi-natural habitats, such as woodlands or grasslands, can support diverse communities and are key elements for biodiversity [16,17,18].
Already two decades ago, researchers recommended that anthropogenic disturbances in mature and old forest stands should receive more attention compared to the more extensively studied stand replacement cutting and natural disturbances [19,20,21]. Intensive structural and compositional changes also occur during the (re)establishment and growth of the stand [22], overshadowing the minor effects of internal stand modification and maintenance activities [23]. The manipulation of forest density, tree species composition, and other structural properties impact forest ecosystem complexity and various vegetation layers [24,25], and these effects are forest type-specific [26]. Forest management activities not only decrease habitat quality through the reduction in deadwood -a critical structural element for many species [27]—but also alter the species composition of field layer vegetation, bryophytes, lichens, and wood-inhabiting polypore fungi [26,28,29].
Estonia’s forest ecosystems have undergone significant management transitions since the late 19th century. The study seeks to clarify the accumulating effect of forest management practices over the life-cycle time of an average stand in Estonia, tracing a cascade of management decisions from the period of the Russian Empire (beginning of the 20th century) to the current framework under the Republic of Estonia (Figure 1, Appendix A).
Furthermore, Estonia’s forest management has evolved from local collective farm and forest district management to a broader sector-based approach. For example, initially young forests (up to 20 years) undergo early tending and precommercial thinning [30], most systematically in planted and sowed conifer stands. This approach has started to evolve into more flexible cleaning and precommercial thinning of young stands, including those with naturally regenerated deciduous trees. Historical practices, such as seed tree harvest and selection thinning (developed by [31]), have been replaced by sanitation cutting across all ages and the more active use of clearcut and shelterwood silviculture in mature stands [32,33]. Recreational forest use has also gained prominence [34], with hiking and outdoor vacations complementing traditional activities such as berry/mushroom picking and herb gathering.
The Estonian Environmental Strategy 2030 indicates a complex turn towards sustainability, biodiversity conservation, and multifunctional forest use, marking a significant transformation need in Estonia’s forest stewardship. The implications of this transition are necessary, not just for the sustainability of forest resources but also for Estonia’s socio-economic landscape, setting a guideline for forest management in similar biogeographic contexts. Using a historical perspective, we examine the possibilities to shift from even-aged management to diverse, conservation-oriented strategies that align with modern ecological principles.
The first protected area in Estonia was established in 1910, the forest-oriented areas much later, and the establishment of new areas has continued through the 20th century [35]; however, in our study areas, the first conservation regulations began in 1981. Historically, forest protection zones were primarily managed at the district level, where forest land varied from 2000–4000 ha. Large nature preserves and landscape protection areas imposed various mild management restrictions. In addition to generic nature reserves and protection zones, specialized protected areas were established with the focus on water resources, natural maintenance, and key habitat protection. Since the 2000s, many areas have been reclassified as conservation zones or key habitat protection zones, bringing more specific regulations regarding cutting limitations and usage restrictions in sensitive areas such as road and water protection zones, recreational forests, and reserve coupes. The Nature Conservation Act (2004) categorizes protected areas into strict nature reserves, conservation zones, and limited management zones, specifying management restrictions in each.
In examining the long-term effects of forest management practices on the structure and biodiversity of Estonian hemiboreal forests, we categorize our study areas based on their stand origin, historical management, recent management, and conservation status (Figure 2).
Our study addresses the critical knowledge gap in understanding how long-term management practices, including historic and recent interventions, influence forest biodiversity and structure in Estonian hemiboreal forests. We hypothesize that multi-aged forests, as opposed to even-aged ones, will display higher biodiversity and structural complexity due to increased ecological continuity and varied habitat conditions. Additionally, we hypothesize that recent forest management will reduce biodiversity, while conservation practices will support higher deadwood volumes and species richness. We focused on the following research questions:
  • How does multi-aged forest management influence biodiversity and forest structure compared to even-aged forests?
  • What is the impact of historic management practices on current biodiversity and structural elements?
  • How do recent management interventions affect deadwood volumes and species composition?
  • How does conservation status contribute to biodiversity preservation in Estonian forests?
These objectives are essential for guiding future forest management strategies that balance production, conservation, and biodiversity goals.

2. Material and Methods

2.1. Study Region and Sample Plots

The study region is situated in eastern and southern Estonia (Figure 3). Estonia belongs to the hemiboreal vegetation zone [36]. The average annual precipitation is 550–750 mm per year−1, with average temperatures ranging from 17 °C in July to −5 °C in February [37]. These forests are characterized by a mix of deciduous and coniferous trees, often including species like spruce, pine, birch, and aspen. Hemiboreal forests support a diverse range of flora and fauna, offering habitats that blend species typical of both boreal (northern) and temperate zones. This transitional nature of hemiboreal forests makes them particularly sensitive to environmental changes, offering a valuable indicator of ecological shifts due to climate and land-use changes. A recent study (2015–2023) selected 150 forest sites (plots with 15–30 m radius) in multiple forest areas. They belong to the Estonian Network of Forest Research Plots [38]. Study plots were located within each forest compartment, representing specific forest site type and different combinations of management histories (Figure 2 and Figure 3). The recent study focused on three high-productivity forest site types: Oxalis (55 plots), Oxalis-Myrtillus (hereafter Ox-Myrt) (43 plots), and Oxalis-Rhodococcum (hereafter Ox-Rhod) (52 plots) [39,40]. Stands were limited to being at least 65 years old (average tree species age on plots varied from 65–177 years). The plots were then categorized by representation of differently managed forests and current conservation states (Figure 2).
In each plot, forest stands were characterized using the methodology of the Estonian Network of Forest Research Plots (ENFRP) [38]. Field works were carried out from June to September. Trees (including standing dead trees and broken dead trees at h > 1.3 m, i.e., snags) with a diameter at breast height (DBH) over 4 cm were recorded with the species, DBH, and height. In addition, all downed dead trees (logs) with a diameter over 10 cm at stump end were measured.
The sub-plot was positioned at the center of the stand plot. Pin-points were taken circularly extending from the center towards the perimeter at 1-m intervals. Ground-dwelling species of bryophytes and vascular plants were recorded. Their taxonomy followed the national reference textbooks [41,42]. Unidentified species were analyzed in the laboratory of the Estonian University of Life Sciences. Later, data on tree seedlings and bush species were excluded from the herb (field) layer data because they were also recorded in the forest understory data. In total, 199 vascular field layer plant species and 103 bryophyte species were identified.

2.2. Management History Assessment

Historical and recent forest management practices were assessed for the period from 1884 until recent survey (2015–2023 depending on plot). Management activities were categorized into binary variables to facilitate robust analysis (see Figure 2, Appendix A, Supplementary Materials). This approach reflects a simplification, acknowledging the continuum of management intensities; however, potential details would fall within the limits of main management steps, such as initiating the stand, maintenance of the stand for tree growth, and conservation. This historical assessment utilized a variety of sources (Appendix B), including historical maps, aerial photographs, and forest planning documents from the Estonian State Forest Center [43] and National Archives of Estonia [44]. Insights were improved by interviews with local forestry specialists (retired and working).
Management activities identified from State Forest maps and interviews ranged from early tending to selective cutting (Appendix A). For analysis, these activities were divided into the categories detectable and undetectable from aerial photographs. Activities such as large-scale cuttings were readily apparent on maps, contrasting with refined human interventions in forest stands that emerged from interview data. In our analysis, we focused on management actions detectable in aerial photographs, excluding the undetectable ones (Appendix A).
The timeline between historical to recent management was set to the 1990s to reflect sharp changes in the governmental system and forest management in Estonia. There was a significant shift from usage of clear-cuttings and wider use of forestry machines instead of manual labor. Alongside these shifts, forest conservation policy was revised following Estonia’s restoration of independence and joining the EU Natura 2000 legislation area, leading to an increase in strictly protected areas (Figure 1).
Management and conservation information was classified into categorical variables with four levels (Figure 2). Plots can be characterized either as commercial forests (55 plots) or protected sites (95 plots), including areas within the Natura 2000 network in Estonia. All the plots (96) in Natura 2000 forest sites represented the 9010* “Western Taiga” habitat type. Conservation information was categorized to reflect both commercial and protected areas. Protected sites are unmanaged according to the Nature Conservation Act (2004), which includes strict nature reserves and wilderness conservation areas. Commercial sites also include protected areas that permit some forms of forest management activities (limited management zones). We would like to note that the Natura 2000 habitat plots were surveyed in 2015, and by 2018, some of them (10 plots) were also managed as commercial forests. Currently, these Natura 2000 sites in Estonia are designated as Sites of Community Importance (SCI) but are expected to be reclassified as Special Areas of Conservation (SAC) [45]. The current management and protection statuses of the plots were used at the time (2015–2023) of recent survey. Classifications and main characteristics with found management histories for each plot can be found in Supplementary Materials.

2.3. Data Analysis

To explain the ecological requirements of vascular plants and bryophytes, we applied Ellenberg [46] indicator values. These values were estimated as community-weighted means for light and moisture requirements for both groups and for soil fertility value for vascular plants. Pin-point counts were used as abundances.
The structural characteristic of the forest stands and estimates of species richness were analyzed using a general linear mixed model (GLMM) [47]. The GLMM estimation using the Type I test was applied to test the cascading effect of factors, starting from the forest site type (as environmental envelope), stand origin, historic management, recent management, and ending with the present conservation status. Forestry region was included as a random factor to address spatial clustering of study sites and management styles within historic and present forest districts. Post hoc comparison analysis of mean estimates within factor were conducted using Tukey’s multiple comparison test [48]. Analyses were preformed using the MIXED procedure implemented in SAS version 9.2 (SAS Institute Inc, Cary, North Carolina). To ensure comparability across variables, all continuous variables were standardized before analysis. The standardization allowed us to scale the predictors and the response variable appropriately, and while this typically constrains the effect sizes to a range between −1 and 1, certain variables exhibited strong associations, resulting in effect sizes and error bars exceeding this range. These larger effect sizes reflect the strong biological relationships between key environmental and management factors and the forest structure metrics under study. We assessed multicollinearity among the predictor variables using the variance inflation factor (VIF). All VIF values were below 5, indicating no significant multicollinearity. This ensures that the predictor variables are sufficiently independent, allowing for reliable coefficient estimation.
Non-metric multidimensional scaling (NMDS) was chosen as the ordination method to elucidate patterns in species composition. The species dataset included pinpoint counts representing the abundances of each species. Species compositional patterns in relation to stand origin, management, and conservation regimes were investigated using a multi-response permutation procedure (MRPP) [49]. In both analyses, the Bray–Curtis dissimilarity distance was applied on raw data, but the Euclidean distance was used on the species-plot semi-residual matrix, where the effect of site type and region was removed. Indicator species analysis (ISA) [50] was utilized to detect differences between same factors, with indicator values assessed for statistical significance through Monte Carlo permutation tests (1000 runs).
The NMDS, ISA, and MRPP analyses were executed using PC-ORD version 7.1 [51].

2.4. Manuscript Preparation

Generative AI technology was used in the preparation of this manuscript to assist with language editing, grammar correction, and structural refinement. Specifically, OpenAI’s ChatGPT was employed to enhance the clarity and readability of the text, ensuring grammatical accuracy and consistency in terminology. No AI-generated content replaced the author’s original scientific insights, data interpretations, or conclusions. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

3. Results

3.1. Environmental Envelope

The NMDS ordination (stress factor = 14.62, p = 0.004; Figure 4) resulted in a two-dimensional solution, capturing a significant portion (I axis 78%, II axis 10%) of the variation in field layer using raw logarithm data of vascular plants and bryophytes with species frequency > 3 on the plot (n = 204) and 25 environmental variables for species composition. The first axis is correlated with the plants’ requirements for soil fertility and light availability and the second axis is correlated with the plants’ requirements for soil moisture—these are conditions well related to the studied site types. It points out that the effect of forest site type on the analyzed species and structure is stronger and should be taken into account in the interpretation of the effects of the stand origin, management, or conservation (Figure 5 and Figure 6; Appendix F).

3.2. Influence of Stand Origin on Forest Structure and Biodiversity

The results of General Linear Mixed Model (GLMM) analysis show that stand origin type predicts some forest structural and biodiversity features (Figure 5 and Figure 6). Specifically, mixed-aged forests had differences in average diameter of canopy trees (GLMM, p < 0.0001), a 16% smaller proportion of pine and 12% greater proportion of spruce in the stand, and a 17.8 m3/ha greater volume of lying deadwood (GLMM, p = 0.011) compared to even-aged stands (Appendix C, Figure 5). The basal area of canopy trees was 4.6 m2 higher in mixed-aged stands. Also, bryophyte species richness was 4.9 species smaller in even-aged stands (GLMM, p = 0.0003).
The MRPP test (Figure 7) also showed differences in species composition between mixed-aged and even-aged forests’ origin (T = −7.1, p < 0.001). ISA (Appendix D; Appendix E; Table 1), conducted for each site type separately, identified species such as Dicranum majus, Rhizomnium punctatum, and Dicranum heteromalla with higher frequency in multi-aged forests (ISA, p < 0.05). On the other hand, species such as Melampyrum pratense thrived in even-aged stands (ISA, p < 0.001).

3.3. Impact of Historical Management

Forests with historical management showed higher tree species richness (GLMM, p < 0.01) and lower deadwood volumes (GLMM, p < 0.05) relative to historically unmanaged forests.
The ISA (Table 1) revealed significant associations between historically managed forests and certain species. Notable indicator species (Table 1, Appendix D) for historically managed forests was Angelica sylvestris (ISA, p < 0.05) and Calamagrostis arundinacea (ISA, p < 0.01). The bryophyte Cirriphyllum piliferum also showed high indicator values for historically managed forests (ISA, p < 0.01). Conversely, Brachythecium oedipodium and Hypnum cupressiforme (ISA, p < 0.05) implied their preference for more undisturbed conditions.

3.4. Effects of Recent Management

Understory tree species richness and basal area of spruce in sub-canopy trees showed a significant increase due to recent management activities (GLMM, p < 0.01). All variables connected to tree volume or basal area obviously showed lowering effects (Figure 6) under recent management (GLMM, p < 0.01). Also, all deadwood volumes were decreasing within plots where management after the 1990s was detected (GLMM, p < 0.01) (Appendix C).
ISA (Table 1, Appendix D) for recent management showed several species with strong relationships to recently managed forests. This suggests that management activities such as thinning, selective logging, and other interventions have an impact on analyzed species distributions. For instance, Anemone nemorosa showed a strong preference for recently managed areas, with a frequency of occurrence in these plots of 83% (ISA, p < 0.001). In the case of bryophytes, Dicranum scoparium was also found lot in recently managed forests, with a frequency of 94% (ISA, p < 0.001).
Within the management, the disparity was similar between historically (MRPP, T= −3.2, p < 0.01) and recently (MRPP, T= −3.4, p < 0.01) managed vs. unmanaged forests (Figure 7).

3.5. Protected Areas Outcomes

Protected areas exhibited higher average diameter of canopy trees compared to commercial forests, indicative of the positive impact of these practices on preserving larger trees (GLMM, p < 0.01). The volume of lying deadwood was significantly higher (GLMM, p < 0.0001) in protected areas, averaging 59.1 m3 ha−1 (Figure 5, Appendix C) compared to much lower average 22.3 m3 ha−1 in commercial forests. Similarly (GLMM, p < 0.01), the total average volume of deadwood (77.6 m3 ha−1) in protected areas was 88% higher than in commercial forests (41.2 m3 ha−1). Bryophyte species richness was slightly higher in protected areas (GLMM, p < 0.01), contrary to the decrease in vascular plant species richness (GLMM, p < 0.05).
Indicator Species Analysis (ISA) also provided insights into how species that are indicative of conservation areas reflect the protective management regime’s impact on maintaining or increasing biodiversity within these forest ecosystems. Melampyrum pratense and Hylocomium splendens were significantly associated with conservation areas, showing a high frequency of 100% (ISA, p < 0.05). These species are quite usual in Estonian forest ecosystems and despite finding protected species on some protected sites they did not occur in our ISA results. Notably, the species composition of commercial forests exhibited significant divergence from protected forests (MRPP, T = −9.1, p < 0.001).

4. Discussion

Our study of Estonian hemiboreal forests shows how long-term management practices influence forest structure and biodiversity, which is crucial for designing effective forest management policies. Our findings correlate with previous research on living and dead tree densities [52,53]. Contrary to study [27], our protected areas exhibited significant differences in deadwood volumes compared to commercial forest areas, indicating positive effects associated with protection. Our hypothesis that mixed stands would exhibit greater structural diversity, and that managed stands would have lower levels of forest structures, particularly deadwood, was confirmed. Contrary to our initial assumptions, ground vegetation species richness was not significantly affected by management. Bryophyte species richness was higher in protected areas, though the richness of herb layer species decreased.
Previous studies in boreal forests [26] have detected a nonlinear change in species composition response, indicative of a significant resilience in medium productivity site types. Also, Ref. [54] found a likely indirect pathway of edge effects through overstorey loss which led to shrub cover loss in the long term. This resilience may be attributed to the dominance of shrub and moss species like Vaccinium myrtillus, Vaccinium vitis-idaea, and Hylocomium splendens. Because of their broad ecological niches, these species are more tolerant of disturbances, thus significantly contributing to the overall resilience of the forest stands [55,56]. These findings offer insights into the ecosystems’ natural resilience to anthropogenic impacts. Our results also support this viewpoint showing broad ecological niche species in different management regimes. For example, the presence of species like Orthilia secunda across various habitat conditions highlights their ecological adaptability, reflecting the intricate interplay between species and their environments. These dynamics are possibly influenced by the unique root systems of these plants, which engage in symbiotic relationships with mycorrhizal fungi, enhancing nutrient and water uptake. In turn, the fungi benefit from the carbohydrates produced by the plants through photosynthesis. The versatility of these species offers a valuable means to monitor diverse ecological states and assess the efficacy of various management approaches.
The transition in forestry processes has significantly impacted post-Soviet countries for decades [57,58,59,60]. Our research shows that forestry management actions such as sanitation, selection cutting, and thinning did not modify vascular and bryophyte species richness significantly. These findings do not suggest that recent management actions may become significant later, potentially being more substrate-based [61], or promoting vegetation growth [62]. Historical management actions conducted over 30 years ago have been shown to facilitate the restoration of vegetation composition in these stands, illustrating the resilience of historically and moderately managed forest stands [22]. This resilience is similar to our study observations in protected areas, where an increase in bryophyte richness and a decrease in vegetation richness indicate changes in substrate and light conditions.
Our findings about higher tree species richness in historical management sites suggest that these actions were more nature-based, creating more diverse species compositions. The scarcity of bryophytes and the presence of a larger scale of generalist species related to forest origin compared to historic management further endorse this idea [63]. Similarly to the findings of [64], we agree that in landscapes with long-term structures, forest species are less limited by dispersal and more by habitat characteristics. The species composition is influenced by the persistent presence of light, moisture, and fertility in the stand, determined by forest habitat type.
Our results show that in mature forests, the effect of forest age is more related to individual trees than to the entire stand. Individual trees are especially important for vascular plant species, which depend heavily on light conditions influenced by selected trees in past management actions. Selective cutting of canopy trees improves light availability, favoring regeneration and leading to a denser understory with an altered composition [65,66].
The significance of bryophyte richness from mixed-species origin and site protection has likely resulted from lower light access and different substrate base. This highlights the importance of considering the abundance, size, and decay stage distribution of coarse woody debris, which are key characteristics of natural forests [27,53] and support biodiversity [67,68]. The volume of deadwood increased under protection which suggests an enhancement of habitat complexity. Similar results, that forest protection increases deadwood volume and bryophyte species diversity, were also found by [69]. Like [70] we saw that management had the strongest negative effects on deadwood structures that occurred predominantly in the most productive forests like our study sites.
However, it is essential to acknowledge several limitations that merit consideration. The distinction between recent and historical management practices, influenced by the evolution of forestry machinery, introduces a variable that could influence the comparability of data across time. Although our study assumes ecological consistency across the research areas, aside from the effects of different habitat types and passive conservation measures, this simplification may not fully capture the complex interplay of ecological processes influencing forest dynamics. Moreover, our temporal overview, while comprehensive, may not capture the entirety of long-term ecological changes or the delayed effects of past management practices on the current composition and structure of forests. Future research should aim to incorporate more specific methodological approaches that can differentiate among various management practices over time and assess their individual impacts. Additionally, expanding the geographical and ecological scope of the study could enhance the applicability of future findings.

5. Conclusions

This study has systematically examined the long-term effects of different forest management practices on the biodiversity and structural complexity of Estonian hemiboreal forests. The results clearly demonstrate that multi-aged origin forests exhibit greater biodiversity and structural complexity compared to even-aged stands. This is driven by factors such as the higher average diameter of canopy trees, greater volumes of lying deadwood, and extended ecological continuity. These elements collectively support diverse plant communities, including a higher richness of bryophyte species and greater understory diversity, highlighting the critical role of habitat heterogeneity in promoting biodiversity.
Historically managed forests were found to have higher tree species richness but lower volumes of deadwood, suggesting a trade-off between species richness and structural complexity due to past disturbances. These findings show the importance of considering the long-lasting impacts of historical management when developing current forest management strategies.
Forests that have not undergone recent management interventions exhibited significantly higher levels of deadwood and understory diversity, confirming that recent management activities—particularly those implemented after the 1990s—tend to reduce tree volume and deadwood, impacting forest structure and biodiversity. In contrast, protected areas showed higher average diameters of canopy trees and greater volumes of both lying and standing deadwood. The bryophyte species richness was also higher in protected areas, although the richness of herb layer species decreased. These results bring out the importance of conservation-oriented management in maintaining habitat complexity and supporting ecological functions.
Our research offers several novel insights into the resilience and adaptability of forest ecosystems. For instance, species with broad ecological niches, such as Orthilia secunda, were prevalent across diverse management regimes, indicating their ability to thrive in various habitat conditions. These findings emphasize the need to manage multi-aged and protected forest stands with a focus on maintaining structural complexity and biodiversity.
In conclusion, successful forest management requires the integration of ecological insights and conservation priorities. By fostering landscapes that are productive, sustainable, and rich in biodiversity, forest management practices can better support ecosystem resilience and contribute to the long-term preservation of biodiversity in hemiboreal forests.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15112035/s1.

Author Contributions

Conceptualization, T.P. and J.L.; methodology, J.L. and T.P.; validation, T.P. and J.L.; formal analysis, J.L.; investigation, T.P. and M.L.; resources, D.L.; data curation, T.P.; writing—original draft preparation, T.P.; writing—review and editing, J.L., D.L., M.L., E.P., H.K. and J.F.F.; visualization, J.L. and T.P.; supervision, H.K. and J.F.F.; project administration, T.P.; funding acquisition, T.P. and H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Estonian Environmental Investment Center project number 9201. We express our sincere appreciation for this funding, which was essential for our data collection efforts.

Data Availability Statement

The datasets generated and analyzed during the current study are not publicly available due to institutional guidelines, but are available from the corresponding author on reasonable request.

Acknowledgments

We are appreciative of the contributions and support from our colleagues, which have been instrumental in the realization of this study. Special thanks are due to the dedicated students and fieldwork staff involved with the Estonian Network of Forest Research Plots. We also wish to acknowledge Toomas Kukk and Peedu Saar for their valuable input on species identification and classification. Andres Kiviste’s expert consultation on statistical analyses has been invaluable, and we are grateful for the constructive feedback from all the reviewers, which has significantly enhanced the quality of this manuscript. Our profound appreciation is extended to the forestry specialists we interviewed and questioned, including Leida & Ülo Vask, Pille & Peep Arold, Toomas Tulev, Rain Pint, Riho Barbo, Tõnis Leosk, Toomas Jüris, Mart Paadik, Riivo & Rein Rinne, Are Orion, Helle Michelson, Tauno Piho, Risto Sepp, Aivo Vaasa, Raivo Kuum, Aivi Miilits, Küllike Kuusik, Jüri Lattu, Uku Elken, Ants & Meelis Teder, Koit Kraav & Kaarel Tiganik. Their insights into historical and recent forest management in the study areas have been invaluable. We acknowledge the use of OpenAI’s ChatGPT in the preparation of this manuscript. The AI tool was utilized for language editing and stylistic improvements only, and all scientific content, analysis, and interpretations remain the sole responsibility of the authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Appendix A

Historical and recent forest management and protection. Conservation regimes that allow forest management are written in italic. Forest management practices are also described [30,31,33] & Estonian Forest Act (2006). Protection regimes in Estonian Nature Conservation Act (2004).
Forest Management Activities (Detectable)
Forest Age (Years)HISTORICALForest Age (Years)RECENT
up to 10Early tending (weed & release)up to 10Early tending (weed & release)
up to 10Early tending (cleaning)up to 20Precommercial thin
from 11–20Precommercial thinfrom 20-mature standCommercial thin
from 21–40Commercial thinall agesSanitation cut
from 41–…Selection thinin mature standSelective cut
from 60–…Sanitation cutin mature standClearcut
in mature standSelective cut (single tree)in mature standShelterwood cut
in mature standClearcutafter clearcutSeed tree harvest
after clearcutSeed tree harvest
Other Activities (Undetectable)
Forest Age (Years)HISTORICALForest Age (Years)RECENT
all agesbud pickingall ageshiking
all agesseed collectionall agesberry/mushroom picking
all agescone harvestingall agesherb picking
all agesgrazingall agesactive vacation
all agesfirewood stock
all agesHousehold facilities (stick cutting; bath broom; besom etc.)
all agesberry/mushroom picking
all agesherb picking
from 10–30Trimming (prune)
CONSERVATION
HISTORICALRECENT
reserve coupewater protection zone
road protection zoneprotection area (natural)
water protection zoneprotection area (maintenance)
esthetical/recreation forestsbuffer zone
landscape protection areasreservation area
nature conservation area/nature preservekey habitat protection

Appendix B

Used maps and aerial photos for the period of 1884–2022. Source: Estonian Land Board Web Map server since the 1880s (http://xgis.maaamet.ee/maps/XGis, accessed on 21 April 2024) and photo archives since the 1948s (https://fotoladu.maaamet.ee, accessed on 21 April 2024).
Map Types
Estonia/Rücker Livonia by Schmidt map (1884)
Verst map from the Russian Empire (1891–1912. scale 1:42,000)
Cadastral maps of the Estonian Republic (1930–1944. scale 1:42/50,000)
Topographic maps of Estonia (1923–1939. 1:50,000)
Soviet topographic maps (1942 reference system) in scales 1:10,000. 1:25,000. 1:50,000. 1:100,000. 1:200,000. 1: 300,000. 1:500,000. 1:1 000,000; all printed between 1946 and 1989. 1:100,000 printed between 1898 and 1989
Soviet topographic maps (1963 reference system) in scales 1:10,000 and 1:25,000 (printed between 1966 and 1987)
Estonian Base Map 1:50,000 (1994–1998)
Map of Estonia 1:50,000 (ordered by Estonian Defense Forces 1997–2003)
Estonian Basic Map 1:10,000 yearly versions (1996–2007 and since 2009 to nowadays)
Estonian Basic Map 1:20,000 (paper version. printed between 1994 and 2022)
Cadastral maps (schematic map 1930–1944. 1978–1989)
Soil map. Land Board 2001
Aerial photos and models
Arial photo archives (since the 1940s–1992)
Photo plans (1942–1991)
Land Board Orthophotos (2002–2022)
Historical satellite images (since 1965–1993)
Land Board Elevation Data 2017–2020 (height points. contours. depth points. depth contours)
Canopy Height Model—CHM
Digital Surface Model—DSM; visible in zoon scales 0 to 24,000)
Hillshading (2008–2012. 2012–2015)
Digital terrain model (2011–2014)

Appendix C

Plots (n = 150) variables and abbreviations.
AbbreviationVariablesUnitAverageStandard Dev.Lower QuartileMedianHigher Quartile
Tree Diamdiameter (DBH) of canopy treescm36.35.232.635.439.1
G.totalBasal area of trees over 5 m of heightm2 ha−136.98.531.737.242.1
Vol.totalVolume of trees over 5 m of heightm3 ha−1478.8127.7386.2472.8557.7
G.IBasal area of canopy treesm2 ha−130.98.126.430.835.9
Vol.IVolume of canopy treesm3 ha−1423.2123.0335.4420.3502.9
G.IIBasal area of sub-canopy trees (trees reaching height of 25–75% of canopy layer)m2 ha−16.03.13.85.38.0
Vol.IIVolume of sub-canopy trees (trees reaching height of 25–75% of canopy layer)m3 ha−155.630.531.949.074.0
All Decid%Percentage of deciduous trees by volume%18.824.41.48.525.6
Spruce%Percentage of Norway spruce by volume%31.924.313.025.744.2
Pine%Percentage of Scots pine by volume%49.234.47.658.078.3
Other dec.treesNumber of non-commercial decidious trees 0.30.50.00.01.0
Tree sp.richnessNumber of tree species 4.31.14.04.05.0
G.II spruceBasal area of spruce in sub-canopy trees (trees reaching height of 25–75% of canopy layer)m2 ha−15.23.04.57.32.9
Understory sp.rich.Number of tree species in forest understory (height under 4 m) 2.21.02.03.01.8
Tree recruit.countNumber of trees in forest understory (height under 4 m)N ha−1373.385.5268.8507.0610.7
Vol.lying DWVolume of lying dead wood (over 10 cm at stump end)m3 ha−145.642.213.137.464.9
Vol.stand.DWVolume of standing dead wood (over 4 cm DBH)m3 ha−118.618.45.713.926.6
Vol.total DWVolume of total dead wood (lying & standing)m3 ha−164.250.625.758.785.5
Vasc.sp.richnessVascular species richness on plotS22.010.414.021.029.0
Bryo.sp.richnessBryophytes species richness on plotS14.18.96.014.022.0
Vasc.Ell.LightHerb layer weighted average Ellenberg light value 4.40.83.94.75.1
Vasc.Ell.MoistHerb layer weighted average Ellenberg moisture value 5.40.35.25.35.6
Vasc.Ell.FertHerb layer weighted average Ellenberg nitrogen value 3.90.93.03.94.7
Bryo.Ell.LightBryophytes weighted average Ellenberg light value 5.50.95.45.75.9
Bryo.Ell.MoistBryophytes weighted average Ellenberg moisture value 4.40.84.14.34.7
Detailed GLMM analysis results (p-values) for each structural trait. The significant difference between sites was tested using Type I model for structural traits. Bold numbers indicate significant differences p < 0.01 in the analysis results.
Structural FeatureUnitSite TypeStand OriginHistoric
Management
Recent
Management
Conservation
Tree diametercm0.0533<0.00010.66840.13050.0053
Basal aream2 ha−10.1660.03720.58760.0010.6321
Total volumem3 ha−10.94020.41390.33920.00020.9685
Basal area of canopy treesm2 ha−10.87060.19910.61590.00380.4927
Volume of canopy treesm3 ha−10.85560.35780.60430.0020.718
Basal area of sub-canopy treesm2 ha−10.00580.6780.05940.01660.2311
Volume of sub-canopy treesm3 ha−10.03490.69960.13160.0050.1228
Pine%%<0.00010.00030.80630.11880.4911
Spruce%%<0.00010.00130.28830.08350.303
Deciduous trees%%0.00090.43840.32030.98150.4192
Number of other deciduous trees 0.0090.76240.24830.10590.3449
Tree species richness 0.0010.11020.01530.12150.1271
Basal area of spruce in sub-canopy treesm2 ha−10.00530.43470.41310.0170.2754
Understory tree species richness <0.00010.77690.6140.00250.7357
Tree recruitment countN ha−10.48420.07360.90820.35140.1374
Volume of lying deadwoodm3 ha−10.09960.01060.00280.0287<0.0001
Volume of standing deadwoodm3 ha−10.00260.50820.01160.00870.3901
Total deadwood volumem3 ha−10.01460.08170.00070.01230.0056
Vascular species richnessS<0.00010.52430.08540.18240.0204
Bryophyte species richnessS<0.00010.00030.05940.11930.0082
Vascular Ellenberg light valueH′<0.00010.32750.07380.24620.3264
Vascular Ellenberg moisture valueH′<0.00010.69320.4830.55280.0818
Vascular Ellenberg fertility value <0.00010.91210.39530.63610.4722
Bryophytes Ellenberg light value 0.00260.81480.98150.00210.6929
Bryophytes Ellenberg moisture value 0.00150.79080.99340.1050.3478

Appendix D

Detailed ISA analyses of classifying categories (stand origin—ORIGIN, historic management—HISTORIC, recent management—RECENT, Conservation—CONSERV.; Figure 2) and habitat (Ox-Myrtc, Oxalis, Ox-Rhod) species with frequency (FR) and indicator value (IV) to specific group (0/1) with significance (p*). Species and group abbreviations list with corresponding Latin names is given in Appendix E.
FeatureHabitatSpeciesFR0FR1IV0IV1p*Group
ORIGINOx-MyrtDicr maju59214650.0382Bryo
ORIGINOx-MyrtRhiz punc3103100.0374Bryo
ORIGINOx-MyrtDicr hete3403400.018Bryo
ORIGINOx-MyrtMela nemo3803800.0152Vasc
ORIGINOx-MyrtMela prat381008790.0002Vasc
ORIGINOx-MyrtDicr scop1008658360.0164Bryo
ORIGINOx-MyrtPlag aspl796455190.048Bryo
ORIGINOx-MyrtPtil pulc834353160.0352Bryo
ORIGINOxalisGale lute316510440.0112Vasc
ORIGINOxalisMili effu28588420.0142Vasc
ORIGINOxalisDaph meze17544420.0032Vasc
ORIGINOxalisDryo expa21545410.0074Vasc
ORIGINOxalisLath vern14502440.0012Vasc
ORIGINOxalisViol rivi21505380.0198Vasc
ORIGINOxalisGali odor7350320.0052Vasc
ORIGINOxalisPulm obsc14352290.0214Vasc
ORIGINOxalisStel nemo14352290.0462Vasc
ORIGINOxalisDicr mont663145100.015Bryo
ORIGINOxalisPtil cri-c48273670.0348Bryo
ORIGINOxalisVacc viti62274860.003Vasc
ORIGINOxalisPtil pulc59234170.0114Bryo
ORIGINOxalisMoeh trin45193160.049Vasc
ORIGINOxalisCirr pili52194330.0036Bryo
ORIGINOxalisImpa parv52154620.0008Vasc
ORIGINOxalisBrac oedi55153850.0148Bryo
ORIGINOxalisVero cham41123230.0234Vasc
ORIGINOxalisBrac sale3182510.0358Bryo
ORIGINOxalisOrth secu3483110.008Vasc
ORIGINOxalisTetr pell3883210.011Bryo
ORIGINOxalisNowe curv4143900.0008Bryo
ORIGINOxalisOxal acet10010047530.0048Vasc
ORIGINOxalisHylo sple907354290.0346Bryo
ORIGINOx-RhodCall vulg19445320.0412Vasc
ORIGINOx-RhodGood repe63125320.0002Vasc
ORIGINOx-RhodPtil pulc7887110.0002Bryo
ORIGINOx-RhodMela nemo3742810.03Vasc
ORIGINOx-RhodBrac oedi4143900.0016Bryo
ORIGINOx-RhodPtil cili4444100.0016Bryo
ORIGINOx-RhodTetr pell4444100.0012Bryo
ORIGINOx-RhodDicr maju4844500.0002Bryo
ORIGINOx-RhodLoph hete5244900.0002Vasc
ORIGINOx-RhodPlat laet5645200.0008Bryo
ORIGINOx-RhodPlat curv4104100.0006Bryo
ORIGINOx-RhodSani unci4404400.0004Bryo
ORIGINOx-RhodDicr mont6306300.0002Bryo
ORIGINOx-RhodNowe curv7407400.0002Bryo
ORIGINOx-RhodVacc myrt10010042580.0002Vasc
ORIGINOx-RhodPleu schr10010045550.0174Bryo
ORIGINOx-RhodHylo sple10010046540.0038Bryo
ORIGINOx-RhodVacc viti899635590.0122Vasc
ORIGINOx-RhodMela prat748023550.0236Vasc
ORIGINOx-RhodConv maja447610580.0012Vasc
ORIGINOx-RhodDicr poly1006056260.015Bryo
ORIGINOx-RhodDicr scop854454160.01Bryo
ORIGINOx-RhodPtil cri-c934460150.0006Bryo
HISTORICOx-MyrtCirr pili20732660.0022Bryo
HISTORICOx-MyrtBrac oedi30707530.047Bryo
HISTORICOx-MyrtLyco anno0640640.0022Vasc
HISTORICOx-MyrtConv maja0520520.0124Vasc
HISTORICOx-MyrtOrth secu10481440.0416Vasc
HISTORICOx-MyrtCare vagi0450450.02Vasc
HISTORICOx-MyrtAnge sylv0390390.0432Vasc
HISTORICOx-MyrtHypn cupr50124020.0224Bryo
HISTORICOx-MyrtMoli caer6095410.0004Vasc
HISTORICOx-MyrtCala arun9010036600.0016Vasc
HISTORICOx-MyrtRhyt triq607916570.0384Bryo
HISTORICOxalisPleu schr27837620.0018Bryo
HISTORICOxalisRubu idae27737550.01Vasc
HISTORICOxalisMyce mura13581520.0032Vasc
HISTORICOxalisPlag aspl20584450.032Bryo
HISTORICOxalisMoeh trin7431350.0456Vasc
HISTORICOxalisMela prat0300300.039Vasc
HISTORICOxalisLath vern47253660.0328Vasc
HISTORICOxalisCala arun10010044560.0136Vasc
HISTORICOxalisHylo sple609020600.0074Bryo
HISTORICOxalisVacc myrt678824560.0442Vasc
HISTORICOxalisFrag vesc477515510.0442Vasc
HISTORICOxalisRhyt triq33659480.0448Bryo
HISTORICOxalisAnem nemo936561220.0042Vasc
HISTORICOx-RhodLuzu pilo689728570.027Vasc
RECENTOx-MyrtAnem nemo32835690.0002Vasc
RECENTOx-MyrtSoli virg28728700.0002Vasc
RECENTOx-MyrtCrep palu12670500.0006Vasc
RECENTOx-MyrtAthy fili20674550.001Vasc
RECENTOx-MyrtPlag affi24612530.0008Bryo
RECENTOx-MyrtDicr maju20616120.0006Bryo
RECENTOx-MyrtLoph hete16614670.0238Vasc
RECENTOx-MyrtAnge sylv12562440.003Vasc
RECENTOx-MyrtDesc flex285658170.0122Vasc
RECENTOx-MyrtEqui prat0501340.008Vasc
RECENTOx-MyrtRhod rose205011610.0032Bryo
RECENTOx-MyrtDesc cesp0443310.0388Bryo
RECENTOx-MyrtCirr pili04410650.0006Bryo
RECENTOx-MyrtAego poda8392290.0364Vasc
RECENTOx-MyrtOrth secu8395470.006Vasc
RECENTOx-MyrtFrag vesc12398520.0046Vasc
RECENTOx-MyrtEqui sylv0399620.0014Vasc
RECENTOx-MyrtRubu saxa03912620.0014Vasc
RECENTOx-MyrtConv maja0336430.014Vasc
RECENTOx-MyrtPlag elli0281450.0018Bryo
RECENTOx-MyrtHepa nobi0285440.0088Vasc
RECENTOx-MyrtSpha russ0223720.0244Bryo
RECENTOx-MyrtPoly comm0224120.0104Bryo
RECENTOx-MyrtCare digi02219530.0292Vasc
RECENTOx-MyrtCare vagi44111580.0004Vasc
RECENTOx-MyrtCala arun9610041580.0022Vasc
RECENTOx-MyrtSpha girg36895060.0104Bryo
RECENTOx-MyrtTetr pell44834460.0262Bryo
RECENTOx-MyrtRhyt triq448323560.023Bryo
RECENTOx-MyrtCare glob60783910.016Vasc
RECENTOx-MyrtRubu idae44785370.0334Vasc
RECENTOx-MyrtGymn dryo80615450.0066Vasc
RECENTOxalisDryo fili32529380.0474Bryo
RECENTOxalisPlag elli14484360.0156Bryo
RECENTOxalisOrth secu32112520.0472Vasc
RECENTOxalisLuzu pilo899638550.0358Vasc
RECENTOxalisDryo cart869332580.0096Bryo
RECENTOxalisRubu saxa1008560340.001Vasc
RECENTOxalisPlag affi366713430.0372Bryo
RECENTOx-RhodDicr scop20944740.0002Bryo
RECENTOx-RhodPtil pulc0720720.0002Bryo
RECENTOx-RhodCall vulg22644510.005Vasc
RECENTOx-RhodNowe curv0630630.0002Bryo
RECENTOx-RhodDicr mont0530530.0002Bryo
RECENTOx-RhodPlat laet0500500.0002Bryo
RECENTOx-RhodLoph hete0470470.0006Vasc
RECENTOx-RhodDesc flex0440440.0016Vasc
RECENTOx-RhodDicr maju0440440.0014Bryo
RECENTOx-RhodPtil cili0410410.0018Bryo
RECENTOx-RhodTetr pell0410410.0022Bryo
RECENTOx-RhodBrac oedi0380380.0036Bryo
RECENTOx-RhodSani unci0380380.0046Bryo
RECENTOx-RhodPlat curv0340340.0028Bryo
RECENTOx-RhodCall vulg45223930.0106Vasc
RECENTOx-RhodFrag vesc50194520.002Vasc
RECENTOx-RhodFest ovin70136610.0002Vasc
RECENTOx-RhodGood repe4694210.039Vasc
RECENTOx-RhodPtil pulc5494710.0374Bryo
RECENTOx-RhodDicr mont4104100.0308Bryo
RECENTOx-RhodNowe curv4904900.016Bryo
RECENTOx-RhodVacc viti9010032640.0014Vasc
RECENTOx-RhodPleu schr10010043570.0108Bryo
RECENTOx-RhodVacc myrt10010044560.0034Vasc
RECENTOx-RhodHylo sple10010046540.0216Bryo
RECENTOx-RhodPleu schr10010056440.008Bryo
RECENTOx-RhodHylo sple10010057430.0002Bryo
RECENTOx-RhodVacc myrt10010057430.0002Vasc
RECENTOx-RhodVacc viti1008865310.0002Vasc
RECENTOx-RhodLuzu pilo908456310.0334Vasc
RECENTOx-RhodMela prat906965190.0002Vasc
RECENTOx-RhodConv maja705350150.0294Vasc
RECENTOx-RhodRubu saxa55344470.0164Vasc
CONSERV.Ox-MyrtHylo sple1009757420.0458Bryo
CONSERV.Ox-MyrtPlag aspl567913610.0288Bryo
CONSERV.Ox-MyrtSoli virg895060160.0116Vasc
CONSERV.Ox-MyrtMela prat1004776110.0002Vasc
CONSERV.Ox-MyrtMela sylv78356170.0018Vasc
CONSERV.OxalisRubu saxa8510036580.0094Vasc
CONSERV.OxalisMaia bifo9610041570.0358Vasc
CONSERV.OxalisConv maja588622530.023Vasc
CONSERV.OxalisDryo cart968357340.0242Vasc
CONSERV.OxalisPlag affi653843130.0348Bryo
CONSERV.OxalisStel holo733849120.0104Vasc
CONSERV.OxalisPter aqui27728510.004Vasc
CONSERV.OxalisOrth secu8342270.0334Vasc
CONSERV.OxalisEurh angu58284180.023Bryo
CONSERV.OxalisDryo fili58284370.0124Vasc
CONSERV.OxalisPlag elli46173440.0322Bryo
CONSERV.OxalisImpa parv54173950.0116Vasc
CONSERV.OxalisStel nemo35142920.0488Vasc
CONSERV.OxalisUrti dioi38142930.0336Vasc

Appendix E

Species abbreviations list with corresponding Latin names.
Vascular Plants
AbbervationName
Aego podaAegopodium podagraria
Anem nemoAnemone nemorosa
Ange sylvAngelica sylvestris
Athy filiAthyrium filix-femina
Cala arunCalamagrostis arundinacea
Call vulgCalluna vulgaris
Care digiCarex digitata
Care globCarex globularis
Care vagiCarex vaginata
Conv majaConvallaria majalis
Crep paluCrepis paludosa
Daph mezeDaphne mezereum
Desc cespDeschampsia cespitosa
Desc flexDeschampsia flexuosa
Dryo cartDryopteris carthusiana
Dryo expaDryopteris expansa
Dryo filiDryopteris filix-mas
Equi pratEquisetum pratense
Equi sylvEquisetum sylvaticum
Fest ovinFestuca ovina
Frag vescFragaria vesca
Gale luteGaleobdolon luteum
Gali odorGalium odoratum
Good repeGoodyera repens
Gymn dryoGymnocarpium dryopteris
Hepa nobiHepatica nobilis
Impa parvImpatiens parviflora
Lath vernLathyrus vernus
Luzu piloLuzula pilosa
Lyco annoLycopodium annotinum
Maia bifoMaianthemum bifolium
Mela nemoMelampyrum nemorosum
Mela pratMelampyrum pratense
Mela sylvMelampyrum sylvaticum
Mili effuMilium effusum
Moeh trinMoehringia trinervia
Moli caerMolinia caerulea
Myce muraMycelis muralis
Orth secuOrthilia secunda
Oxal acetOxalis acetosella
Pter aquiPteridium aquilinum
Pulm obscPulmonaria obscura
Rubu idaeRubus idaeus
Rubu saxaRubus saxatilis
Soli virgSolidago virgaurea
Stel holoStellaria holostea
Stel nemoStellaria nemorum
Urti dioiUrtica dioica
Vacc myrtVaccinium myrtillus
Vacc vitiVaccinium vitis-idaea
Vero chamVeronica chamaedrys
Viol riviViola riviniana
Bryophytes
AbbervationName
Brac oediBrachythecium oedipodium
Brac saleBrachythecium salebrosum
Cirr piliCirriphyllum piliferum
Dicr heteDicranum heteromalla
Dicr majuDicranum majus
Dicr montDicranum montanum
Dicr polyDicranum polysetum
Dicr scopDicranum scoparium
Eurh anguEurhynchium angustirete
Hylo spleHylocomium splendens
Hypn cuprHypnum cupressiforme
Loph heteLophocolea heterophylla
Nowe curvNowellia curvifolia
Plag asplPlagiochila asplenioides
Plag affiPlagiomnium affine
Plag elliPlagiomnium ellipticum
Plat curvPlagiothecium curvifolium
Plat laetPlagiothecium laetum
Pleu schrPleurozium schreberi
Poly commPolytrichum commune
Ptil ciliPtilidium ciliare
Ptil pulcPtilidium pulcherrimum
Ptil cri-cPtilium crista-castrensis
Rhiz puncRhizomnium punctatum
Rhod roseRhodobryum roseum
Rhyt triqRhytidiadelphus triquetrus
Sani unciSanionia uncinata
Spha girgSphagnum girgensohnii
Spha russSphagnum russowii
Tetr pellTetraphis pellucida

Appendix F

pNDMS Figure without site type and region effect.
Figure A1. First (40% of variance, p = 0.004) and second (23% of variance, p = 0.004) axes of the pNDMS varimax ordination for 150 sample plots (final stress= 17.49488) using vascular plants and bryophytes logarithm residuals data without site type and region effect with species frequency > 3 on plot (n = 204) and 25 environmental variables (App 3). The plots are classified after site type (blue—Ox-Myrt, red—Ox-Rhod, green—Oxalis).
Figure A1. First (40% of variance, p = 0.004) and second (23% of variance, p = 0.004) axes of the pNDMS varimax ordination for 150 sample plots (final stress= 17.49488) using vascular plants and bryophytes logarithm residuals data without site type and region effect with species frequency > 3 on plot (n = 204) and 25 environmental variables (App 3). The plots are classified after site type (blue—Ox-Myrt, red—Ox-Rhod, green—Oxalis).
Forests 15 02035 g0a1

References

  1. Peterson, G.D. Contagious disturbance, ecological memory, and the emergence of landscape pattern. Ecosystems 2002, 5, 329–338. [Google Scholar] [CrossRef]
  2. Ogle, K.; Barber, J.J.; Barron-Gafford, G.A.; Bentley, L.P.; Young, J.M.; Huxman, T.E.; Loik, M.E.; Tissue, D.T. Quantifying ecological memory in plant and ecosystem processes. Ecol. Lett. 2015, 18, 221–235. [Google Scholar] [CrossRef]
  3. Schelhaas, M.-J.; Nabuurs, G.-J.; Schuck, A. Natural disturbances in the European forests in the 19th and 20th centuries. Glob. Chang. Biol. 2003, 9, 1620–1633. [Google Scholar] [CrossRef]
  4. Lier, M.; Schuck, A. Criterion 4, Maintenance, Conservation and Appropriate Enhancement of Biological Diversity in Forest Ecosystems. In State of Europe’s Forests; Forest Europe: Bonn, Germany, 2020. [Google Scholar]
  5. Pommerening, A.; Murphy, S.T. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. For. Int. J. For. Res. 2004, 77, 27–44. [Google Scholar] [CrossRef]
  6. Pukkala, T.; Lähde, E.; Laiho, O. Continuous Cover Forestry in Finland—Recent Research Results. In Continuous Cover Forestry; Managing Forest Ecosystems; Pukkala, T., von Gadow, K., Eds.; Springer: Dordrecht, The Netherlands, 2012; Volume 23. [Google Scholar] [CrossRef]
  7. Kruse, L.; Erefur, C.; Westin, J.; Ersson, B.T.; Pommerening, A. Towards a benchmark of national training requirements for continuous cover forestry (CCF) in Sweden. Trees For. People 2023, 12, 100391. [Google Scholar] [CrossRef]
  8. Lõhmus, A.; Kohv, K.; Palo, A.; Viilma, K. Loss of old-growth, and the minimum need for strictly protected forests in Estonia. Ecol. Bull. 2004, 51, 401–411. [Google Scholar]
  9. Samojlik, T.; Rotherham, I.D.; Jędrzejewska, B. Quantifying Historic Human Impacts on Forest Environments: A Case Study in Białowieża Forest, Poland. Environ. Hist. 2013, 18, 576–602. [Google Scholar] [CrossRef]
  10. Vellend, M.; Brown, C.D.; Kharouba, H.M.; McCune, J.L.; Myers-Smith, I.H. Historical ecology: Using unconventional data sources to test for effects of global environmental change. Am. J. Bot. 2013, 100, 1294–1305. [Google Scholar] [CrossRef]
  11. Axelsson, A.-L.; Östlund, L. Retrospective gap analysis in a Swedish boreal forest landscape using historical data. For. Ecol. Manag. 2001, 147, 109–122. [Google Scholar] [CrossRef]
  12. Axelsson, A.-L. Forest Landscape Change in Boreal Sweden 1850–2000—A Multiscale Approach; Swedish University of Agricultural Sciences: Uppsala, Sweden, 2001; ISBN 91-576-6067-0. [Google Scholar]
  13. Rocha-Santos, L.; Pessoa, M.S.; Cassano, C.R.; Talora, D.C.; Orihuela, R.L.L.; Mariano-Neto, E.; Morante-Filho, J.C.; Faria, D.; Cazetta, E. The shrinkage of a forest: Landscape-scale deforestation leading to overall changes in local forest structure. Biol. Conserv. 2016, 196, 1–9. [Google Scholar] [CrossRef]
  14. Tabarelli, M.; Lopes, A.V.; Peres, C.A. Edge-effects Drive Tropical Forest Fragments Towards an Early-Successional System. Biotropica 2008, 40, 657–661. [Google Scholar] [CrossRef]
  15. Hedwall, P.O.; Brunet, J.; Nordin, A.; Bergh, J. Changes in the abundance of keystone forest floor species in response to changes of forest structure. J. Veg. Sci. 2013, 24, 296–306. [Google Scholar] [CrossRef]
  16. Aavik, T.; Püssa, K.; Roosaluste, E.; Moora, M. Vegetation change in boreonemoral forest during succession—trends in species composition, richness and differentiation diversity. Ann. Bot. Fenn. 2009, 46, 326–335. [Google Scholar] [CrossRef]
  17. Hietala-Koivu, R.; Järvenpää, T.; Helenius, J. Value of semi-natural areas as biodiversity indicators in agricultural landscapes. Agric. Ecosyst. Environ. 2004, 101, 9–19. [Google Scholar] [CrossRef]
  18. Duflot, R.; Aviron, S.; Ernoult, A.; Fahrig, L.; Burel, F. Reconsidering the role of ‘semi-natural habitat’ in agricultural landscape biodiversity: A case study. Ecol. Restor. 2015, 30, 75–83. [Google Scholar] [CrossRef]
  19. Niemelä, J. Management in relation to disturbance in the boreal forest. For. Ecol. Manag. 1999, 115, 127–134. [Google Scholar] [CrossRef]
  20. Angelstam, P. Maintaining and restoring biodiversity in European boreal forests by developing natural disturbance regimes. J. Veg. Sci. 1998, 9, 593–602. [Google Scholar] [CrossRef]
  21. Esseen, P.; Ehnström, B.; Ericson, L.; Sjöberg, K. Boreal Forests. Ecol. Bull. 1997, 46, 16–47. [Google Scholar]
  22. Baker, S.C.; Spies, T.A.; Wardlaw, T.W.; Balmer, J.; Franklin, J.F.; Jordan, G.J. The harvested side of edges: Effect of retained forests on the re-establishment of biodiversity in adjacent harvested areas. For. Ecol. Manag. 2013, 302, 107–121. [Google Scholar] [CrossRef]
  23. Pretzsch, H.; Biber, P. Size-symmetric versus size-asymmetric competition and growth partitioning among trees in forest stands along an ecological gradient in central Europe. Can. J. For. Res. 2010, 40, 370–384. [Google Scholar] [CrossRef]
  24. Bailey, J.D.; Mayrsohn, C.; Doescher, P.S.; St. Pierre, E.; Tappeiner, J.C. Understory vegetation in old and young Douglas-fir forests of western Oregon. For. Ecol. Manag. 1998, 112, 289–302. [Google Scholar] [CrossRef]
  25. Jalonen, J.; Vanha-Majamaa, I. Immediate effects of four different felling methods on mature boreal spruce forest understory vegetation in southern Finland. For. Ecol. Manag. 2001, 146, 25–34. [Google Scholar] [CrossRef]
  26. Kohv, K.; Zobel, M.; Liira, J. The resilience of the forest field layer to anthropogenic disturbances depends on site productivity. Can. J. For. Res. 2013, 43, 1040–1049. [Google Scholar] [CrossRef]
  27. Lõhmus, A.; Lõhmus, P.; Remm, J.; Vellak, K. Old-growth structural elements in a strict reserve and commercial forest landscape in Estonia. For. Ecol. Manag. 2005, 216, 201–215. [Google Scholar] [CrossRef]
  28. Fridman, J.; Walheim, M. Amount, structure, and dynamics of dead wood on managed forestland in Sweden. For. Ecol. Manag. 2000, 131, 23–36. [Google Scholar] [CrossRef]
  29. Kapusta, P.; Kurek, P.; Piechnik, L.; Szarek-Łukaszewska, G.; Zielonka, T.; Żywiec, M.; Holeksa, J. Natural and human-related determinants of dead wood quantity and quality in a managed European lowland temperate forest. For. Ecol. Manag. 2020, 459, 117845. [Google Scholar] [CrossRef]
  30. Grebner, D.L.; Bettinger, P.; Siry, J.P. Introduction to Forestry and Natural Resources; Academic Press: New York, NY, USA, 2013; 508p. [Google Scholar]
  31. Borggreve, B. Die Holzzucht: Ein Grundriss für Unterricht und Wirtschaft; P. Parey: Singhofen, Germany, 1891. [Google Scholar]
  32. Kiisel, M.; Remm, L. Continuous Cover Forestry Practitioners in a Clear-cutting-oriented System: Assessing the Potential to Foster the Practice. Small-Scale For. 2022, 21, 325–348. [Google Scholar] [CrossRef]
  33. Nyland, R. Silviculture: Concepts and Applications, 3rd ed.; Waveland Press: Long Grove, IL, USA, 2016; 680p. [Google Scholar]
  34. Rammo, M.; Karoles, K.; Maran, K.; Jansen, J.; Almik, A.; Rammo, R. Visitor surveys and visitor impact monitoring in recreational areas in state forests of Estonia. In Proceedings of the Second International Conference on Monitoring and Management of Visitor Flows in Recreational and Protected Areas, Rovaniemi, Finland, 16–20 June 2004; pp. 397–399. [Google Scholar]
  35. Tuvi, E.-L.; Vellak, A.; Reier, Ü.; Szava-Kovats, R.; Pärtel, M. Establishment of protected areas in different ecoregions, ecosystems, and diversity hotspots under successive political systems. Biol. Conserv. 2011, 144, 1726–1732. [Google Scholar] [CrossRef]
  36. Ahti, T.; Hämet-Ahti, L.; Jalas, J. Vegetation zones and their sections in northwestern Europe. Ann. Bot. Fenn. 1968, 5, 169–211. [Google Scholar]
  37. Kallis, A.; Rosin, K.; Pärnpuu, P.; Loodla, K.; Šišova, V. 100 Aastat Eesti Ilma (Teenistust); Keskkonnaagentuur: Tallinn, Estonia, 2019; 186p. (In Estonian) [Google Scholar]
  38. Kiviste, A.; Hordo, M.; Kangur, A.; Kardakov, A.; Laarmann, D.; Lilleleht, A.; Metslaid, S.; Sims, A.; Korjus, H. Monitoring and modelling of forest ecosystems: The Estonian Network of Forest Research Plots. For. Stud./Metsanduslikud Uurim. 2015, 62, 26–38. [Google Scholar] [CrossRef]
  39. Lõhmus, E. Forest Site Types of Estonia; Eesti Loodusfoto: Tartu, Estonia, 2004; 80p. (In Estonian) [Google Scholar]
  40. Cajander, A.K. Forest types and their significance. Acta For. Fenn. 1949, 56, 1–71. [Google Scholar] [CrossRef]
  41. Ingerpuu, N.; Kalda, A.; Kannukene, L.; Krall, H.; Leis, M.; Vellak, K. Eesti sammalde määraja. In Key-Book of Estonian Bryophytes; Eesti Loodusfoto: Tartu, Estonia, 1998; 239p. (In Estonian) [Google Scholar]
  42. Leht, M. Eesti taimede määraja. In Handbook of Estonian Vascular Plants; Estonian University of Life Sciences, Eesti Loodusfoto: Tartu, Estonia, 2010; 447p. (In Estonian) [Google Scholar]
  43. Estonian State Forest Center Archive. Sagadi Museum Archive, Mõisa/3, Haljala parish, Lääne-Viru county, Estonia & Antsla Office Archive, Haabsaare, Antsla parish, Võru County, Estonia. 2023. [Google Scholar]
  44. National Archives of Estonia. Register of the Maps. 2023. Available online: https://www.ra.ee/kaardid/ (accessed on 21 April 2024).
  45. EELIS. Nature Information System (Eesti Looduse Infosüsteem). Estonian Environmental Agency. 2024. Available online: http://loodus.keskkonnainfo.ee/eelis/ (accessed on 21 April 2024).
  46. Ellenberg, H.; Weber, H.E.; Dull, R.; Wirth, V.; Werner, W.; Paulissen, D. Ziegerwerte von Pflanzen in Mitteleuropa. Scr. Geobot. 1991, 18, 1–248. [Google Scholar]
  47. Littell, R.C.; Milliken, G.A.; Stroup, W.W.; Wolfinger, R.D. SAS® System for Mixed Models; SAS Publishing: Cary, NC, USA, 1996; 814p. [Google Scholar]
  48. Haynes, W. Tukey’s Test. In Encyclopedia of Systems Biology; Dubitzky, W., Wolkenhauer, O., Cho, K.H., Yokota, H., Eds.; Springer: New York, NY, USA, 2013. [Google Scholar]
  49. Mielke, P.W.; Berry, K.J.; Johnson, E.S. Multi-response permutation procedures for a priori classifications. Commun. Stat-Theor. 1976, 5, 1409–1424. [Google Scholar] [CrossRef]
  50. Dufrêne, M.; Legendre, P. Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecol. Monogr. 1997, 67, 345–366. [Google Scholar] [CrossRef]
  51. McCune, B.; Mefford, M.J. PC_ORD:Multivariate Analysis of Ecological Data; Version 7.1; MjM Software: Gleneden Beach, OR, USA, 2016. [Google Scholar]
  52. Nilsson, S.G.; Niklasson, M.; Hedin, J.; Aronsson, G.; Gutowski, J.M.; Linder, P.; Ljungberg, H.; Mikusinski, G.; Ranius, T. Densities of large living and dead trees in old-growth temperate and boreal forests. For. Ecol. Manag. 2002, 161, 189–204. [Google Scholar] [CrossRef]
  53. Põldveer, E.; Korjus, H.; Kiviste, A.; Kangur, A.; Paluots, T.; Laarmann, D. Assessment of spatial stand structure of hemiboreal conifer dominated forests according to different levels of naturalness. Ecol. Indic. 2020, 110, 105944. [Google Scholar] [CrossRef]
  54. Runnel, K.; Palo, A.; Reila, A.; Rosenvald, R.; Lõhmus, A. External management effects on the stand structure of protected forest patches. Appl. Veg. Sci. 2022, 25, e12655. [Google Scholar] [CrossRef]
  55. Hautala, H.; Kuuluvainen, T.; Hokkanen, T.J.; Tolvanen, A. Long-term spatial organization of understorey vegetation in boreal Pinus sylvestris stands with different fire histories. Community Ecol. 2005, 6, 119–130. [Google Scholar] [CrossRef]
  56. Rydgren, K.; De Kroon, H.; Økland, R.H.; Van Groenendael, J. Effects of fine-scale disturbances on the demography and population dynamics of the clonal moss Hylocomium splendens. J. Ecol. 2001, 89, 395–405. [Google Scholar] [CrossRef]
  57. Lawrence, A. Forestry in transition: Imperial legacy and negotiated expertise in Romania and Poland. For. Policy Econ. 2009, 11, 429–436. [Google Scholar] [CrossRef]
  58. Cashore, B.; Gale, F.; Meidinger, E.; Newsom, D. Confronting Sustainability: Forest Certification in Developing and Transitioning Countries; Forestry & Environmental Studies Publication Series; Yale University: New Haven, CT, USA, 2006; Volume 28, Available online: https://elischolar.library.yale.edu/fes-pubs/28 (accessed on 21 April 2024).
  59. Lazdinis, M.; Carver, A.; Tõnisson, K.; Silamikele, I. Innovative use of forest policy instruments in countries with economies in transition: Experience of the Baltic States. For. Policy Econ. 2005, 7, 527–537. [Google Scholar] [CrossRef]
  60. Eikeland, S.; Eythorsson, E.; Ivanova, L. From Management to Mediation: Local Forestry Management and the Forestry Crisis in Post-Socialist Russia. Environ. Manag. 2004, 33, 285–293. [Google Scholar] [CrossRef]
  61. Palm-Hellenurm, K.; Tullus, T.; Vodde, F.; Jõgiste, K. Delayed response of bryophytes to wind disturbance and salvage logging in hemiboreal mixed forests. For. Ecol. Manag. 2024, 555, 121718. [Google Scholar] [CrossRef]
  62. Zhang, H.; Liu, S.; Yu, J.; Li, J.; Shangguan, Z.; Deng, L. Thinning increases forest ecosystem carbon stocks. For. Ecol. Manag. 2024, 555, 121702. [Google Scholar] [CrossRef]
  63. Bourgouin, M.; Haughian, S.R.; Jean, M. The diversity of epixylic bryophytes in relation to dead wood properties and forest management in New Brunswick, Canada. For. Ecol. Manag. 2024, 554, 121646. [Google Scholar] [CrossRef]
  64. Lõhmus, K.; Paal, T.; Liira, J. Long-term colonization ecology of forest-dwelling species in a fragmented rural landscape—Dispersal versus establishment. Ecol. Evol. 2014, 4, 3113–3126. [Google Scholar] [CrossRef] [PubMed]
  65. Halpern, C.B.; McKenzie, D.; Evans, S.A.; Maguire, D.A. Initial responses of forest understories to varying levels and patterns of green-tree retention. Ecol. Appl. 2005, 15, 175–195. [Google Scholar] [CrossRef]
  66. Nelson, C.R.; Halpern, C.B. Short-term effects of timber harvest and forest edges on ground-layer mosses and liverworts. Can. J. Bot. 2005, 83, 610–620. [Google Scholar] [CrossRef]
  67. Jonsson, B.G.; Kruys, N. Ecology of wood debris in boreal forests. Ecol. Bull. 2001, 49, 279–281. [Google Scholar]
  68. Berg, A.; Ehnstrom, B.; Gustafsson, L.; Hallingback, T.; Jonselland, M.; Weslien, J. Threatened Plant, Animal, and Fungus Species in Swedish Forests: Distribution and Habitat Associations. Conserv. Biol. 1994, 8, 718–731. [Google Scholar] [CrossRef]
  69. Czerepko, J.; Gawryś, R.; Mańk, K.; Janek, M.; Tabor, J.; Skalski, Ł. The influence of the forest management in the Białowieża forest on the species structure of the forest community. For. Ecol. Manag. 2021, 496, 119363. [Google Scholar] [CrossRef]
  70. Hämäläinen, A.; Runnel, K.; Ranius, T.; Strengbom, J. Diversity of forest structures important for biodiversity is determined by the combined effects of productivity, stand age, and management. Ambio 2024, 53, 718–729. [Google Scholar] [CrossRef] [PubMed]
Figure 1. The timescale (state, year) and data (data sources) used for classifying (origin, management, conservation) study areas in Estonia.
Figure 1. The timescale (state, year) and data (data sources) used for classifying (origin, management, conservation) study areas in Estonia.
Forests 15 02035 g001
Figure 2. Study outline. Data (2015–2023) from 150 forest plots (blue) across various site types (white) and supplemented by historical data (orange) were used to classify plots into categories of stand origin (grey), historical management (brown), recent management (yellow), and conservation status (green).
Figure 2. Study outline. Data (2015–2023) from 150 forest plots (blue) across various site types (white) and supplemented by historical data (orange) were used to classify plots into categories of stand origin (grey), historical management (brown), recent management (yellow), and conservation status (green).
Forests 15 02035 g002
Figure 3. Location of the studied sample plots in Estonia. The map shows the geographic distribution of the 150 sample plots. Legend codes represent various management and conservation factors. O indicates stand historic origin, where 0 (solid fill) represents mixed-aged stands and 1 (pattern fill) represents even-aged stands. H represents historic management, with 0 (circle) for not managed and 1 (triangle) for managed. R stands for recent management, with 0 (green fill) for not managed and 1 (red fill) for managed. C represents conservation status, where 0 (red edge) indicates commercial forest and 1 (green edge) represents protected area.
Figure 3. Location of the studied sample plots in Estonia. The map shows the geographic distribution of the 150 sample plots. Legend codes represent various management and conservation factors. O indicates stand historic origin, where 0 (solid fill) represents mixed-aged stands and 1 (pattern fill) represents even-aged stands. H represents historic management, with 0 (circle) for not managed and 1 (triangle) for managed. R stands for recent management, with 0 (green fill) for not managed and 1 (red fill) for managed. C represents conservation status, where 0 (red edge) indicates commercial forest and 1 (green edge) represents protected area.
Forests 15 02035 g003
Figure 4. NDMS varimax ordination of 150 sample plots based on vascular plants and bryophytes. The first axis explains 78% of the variance (p = 0.004), while the second axis accounts for 10% of the variance (p = 0.004). The ordination was performed using raw logarithm data for species with a frequency >3 per plot (n = 204 species) and 25 environmental variables (see Appendix C for full list). Only environmental factors significantly related to the ordination axes (p < 0.05) are shown, with a cut-off of R2 = 0.2 for vector inclusion. Plots are color-coded by site type: blue for Ox-Myrt, red for Ox-Rhod, and green for Oxalis. The pNDMS ordination without the effects of site type and region is available in Appendix F. The final stress value of the ordination is 14.62225, indicating the goodness of fit.
Figure 4. NDMS varimax ordination of 150 sample plots based on vascular plants and bryophytes. The first axis explains 78% of the variance (p = 0.004), while the second axis accounts for 10% of the variance (p = 0.004). The ordination was performed using raw logarithm data for species with a frequency >3 per plot (n = 204 species) and 25 environmental variables (see Appendix C for full list). Only environmental factors significantly related to the ordination axes (p < 0.05) are shown, with a cut-off of R2 = 0.2 for vector inclusion. Plots are color-coded by site type: blue for Ox-Myrt, red for Ox-Rhod, and green for Oxalis. The pNDMS ordination without the effects of site type and region is available in Appendix F. The final stress value of the ordination is 14.62225, indicating the goodness of fit.
Forests 15 02035 g004
Figure 5. Heatmap showing GLMM analysis results (p-values) for each structural trait in different categories. The significant difference between sites was tested using the Type I model for structural traits. More detailed results in Appendix C.
Figure 5. Heatmap showing GLMM analysis results (p-values) for each structural trait in different categories. The significant difference between sites was tested using the Type I model for structural traits. More detailed results in Appendix C.
Forests 15 02035 g005
Figure 6. General Linear Mixed Model (GLMM) Type I tests to evaluate the effects of interventions (stand origin, historic and recent management, conservation) on the traits (structural features and biodiversity components) of forest stands). Each point indicates the mean effect size with its confidence interval, showing influence across traits. Variance inflation factor (VIF) values for all predictors were below 5, indicating no multicollinearity among the independent variables.
Figure 6. General Linear Mixed Model (GLMM) Type I tests to evaluate the effects of interventions (stand origin, historic and recent management, conservation) on the traits (structural features and biodiversity components) of forest stands). Each point indicates the mean effect size with its confidence interval, showing influence across traits. Variance inflation factor (VIF) values for all predictors were below 5, indicating no multicollinearity among the independent variables.
Forests 15 02035 g006
Figure 7. Multi-response permutation procedure (MRPP) results comparing species composition by different management regimes for stand origin, historic management, recent management, and conservation, with test statistic (T) and agreement (A) values.
Figure 7. Multi-response permutation procedure (MRPP) results comparing species composition by different management regimes for stand origin, historic management, recent management, and conservation, with test statistic (T) and agreement (A) values.
Forests 15 02035 g007
Table 1. Summary of ISA (Indicator Species Analysis) results (p < 0.05) for different forest management regimes. The table summarizes species significantly associated with different management regimes for each habitat type. Habitats are indicated with superscripts: M for Ox-Myrt, O for Oxalis, and R for Ox-Rhod. For more detailed results of the ISA, please refer to Appendix D.
Table 1. Summary of ISA (Indicator Species Analysis) results (p < 0.05) for different forest management regimes. The table summarizes species significantly associated with different management regimes for each habitat type. Habitats are indicated with superscripts: M for Ox-Myrt, O for Oxalis, and R for Ox-Rhod. For more detailed results of the ISA, please refer to Appendix D.
Factor: Historic Origin
Level 0: Multi-AgedLevel 1: Even-Aged
Vascular plants: Goodyera repensR, Impatiens parvifloraO, Melampyrum nemorosumMR, Moehringia trinerviaO, Orthilia secundaO, Vaccinium vitis-idaeaO, Veronica chamaedrysOVascular plants: Calluna vulgarisR, Daphne mezereumO, Dryopteris expansaO, Galeobdolon luteumO, Galium odoratumO, Lathyrus vernusO, Milium effusumO, Pulmonaria obscuraO, Stellaria nemorumO, Viola rivinianaO
Bryophytes: Brachythecium oedipodiumOR, Brachythecium salebrosumO, Cirriphyllum piliferumO, Dicranum heteromallaM, Dicranum majusMR, Dicranum montanumOR, Lophocolea heterophyllaR, Nowellia curvifoliaOR, Plagiothecium curvifoliumR, Plagiothecium laetumR, Ptilidium ciliare, Ptilium crista-castrensisO, Ptilidium pulcherrimumO,R, Rhizomnium punctatumM, Sanionia uncinataR, Tetraphis pellucidaO,RBryophytes: -
Factor: Historic Management
Level 0: Not ManagedLevel 1: Managed
Vascular plants: Molinia caeruleaM, Lathyrus vernusOVascular plants: Angelica sylvestrisM, Carex vaginataM, Convallaria majalisM, Lycopodium annotinumM, Melampyrum pratenseO, Moehringia trinerviaO, Mycelis muralisO, Orthilia secundaM, Rubus idaeusO
Bryophytes: Hypnum cupressiformeMBryophytes: Brachythecium oedipodiumM, Cirriphyllum piliferumM, Plagiochila asplenioidesO, Pleurozium schreberiO
Factor: Recent Management
Level 0: Not ManagedLevel 1: Managed
Vascular plants: Calluna vulgarisR, Deschampsia flexuosaM, Festuca ovinaR, Fragaria vescaR, Goodyera repensR, Orthilia secundaOVascular plants: Aegopodium podagrariaM, Anemone nemorosaM, Angelica sylvestrisM, Athyrium filix-feminaM, Calluna vulgarisR, Carex digitataM, Carex vaginataM, Convallaria majalisM, Crepis paludosaM, Deschampsia cespitosaM, Deschampsia flexuosaR, Dryopteris filix-masO, Equisetum pratenseM, Equisetum sylvaticumM, Fragaria vescaM, Hepatica nobilisM, Orthilia secundaM, Rubus saxatilisM, Solidago virgaureaM
Bryophytes: Dicranum majusM, Dicranum montanumR, Lophocolea heterophyllaM, Nowellia curvifoliaR, Polytrichum communeM, Ptilidium pulcherrimumR, Sphagnum russowiiMBryophytes: Brachythecium oedipodiumR, Cirriphyllum piliferumM, Dicranum majusR, Dicranum montanumR, Dicranum scopariumR, Lophocolea heterophyllaR, Nowellia curvifoliaR, Plagiomnium affineM, Plagiomnium ellipticumMO, Plagiothecium curvifoliumR, Plagiothecium laetumR, Ptilidium ciliareR, Ptilidium pulcherrimumR, Rhodobryum roseumM, Sanionia uncinataR, Tetraphis pellucidaR
Factor: Conservation
Level 1: ProtectedLevel 0: Commercial
Vascular plants: Pteridium aquilinumO, Orthilia secundaOVascular plants: Dryopteris filix-masO, Impatiens parvifloraO, Stellaria nemorumO, Urtica dioicaO
Bryophytes: -Bryophytes: Eurhynchium angustireteO, Plagiomnium ellipticumO
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Paluots, T.; Liira, J.; Leis, M.; Laarmann, D.; Põldveer, E.; Franklin, J.F.; Korjus, H. Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests. Forests 2024, 15, 2035. https://doi.org/10.3390/f15112035

AMA Style

Paluots T, Liira J, Leis M, Laarmann D, Põldveer E, Franklin JF, Korjus H. Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests. Forests. 2024; 15(11):2035. https://doi.org/10.3390/f15112035

Chicago/Turabian Style

Paluots, Teele, Jaan Liira, Mare Leis, Diana Laarmann, Eneli Põldveer, Jerry F. Franklin, and Henn Korjus. 2024. "Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests" Forests 15, no. 11: 2035. https://doi.org/10.3390/f15112035

APA Style

Paluots, T., Liira, J., Leis, M., Laarmann, D., Põldveer, E., Franklin, J. F., & Korjus, H. (2024). Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests. Forests, 15(11), 2035. https://doi.org/10.3390/f15112035

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop