Exploration of spatial biases in natural hardwood regeneration in conifer plantations in southwestern Japan

Exploration of spatial biases in natural hardwood regeneration in conifer plantations in southwestern Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploration of spatial biases in natural hardwood regeneration in conifer plantations in southwestern Japan Eriko Ito, Yoshiyuki Inagaki, Kimihiro Kida, Hitomi Furusawa, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7644359/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Japan has adopted biodiversity-oriented forest management, necessitating the diversification of extensive conifer plantations and the identification of geographic conditions that favor natural hardwood regeneration. The increasing availability of high-resolution airborne laser scanning (ALS) data provides new opportunities to analyze spatial patterns in forests. In this study, we applied exploratory approaches to quantify the prevalence of natural hardwood regeneration within mature conifer plantations in Kochi Prefecture, southwestern Japan. The study covered an area of approximately 250 km 2 , enabling spatial analyses at the landscape scale. Hardwood regeneration was defined as areas recorded as conifer plantations in forest registry data (2005–2009) but dominated by hardwoods based on ALS data collected in 2018. Across postwar afforestation sites (1949–1978 planting), hardwood regeneration consistently occupied 20–25% of the total area, regardless of the planting year. Using a logistic generalized additive model, we found that hardwood regeneration was favored on slopes steeper than 40° and in ridges and valleys. In the low-elevation zone (< 600 m a.s.l.), where evergreen Castanopsis and Quercus species were the dominant vegetation, the likelihood of finding hardwood regeneration increased with decreasing elevation and greater southern slope exposure. This trend was particularly evident within specific geologic zones. Spatial analyses to identify site characteristics that favor natural hardwood regeneration could be used to support biodiversity-oriented forest management. Furthermore, high-resolution ALS data that will soon be publicly available hold significant promise for uncovering geographic patterns and generating novel insights on forest ecosystem dynamics. ALS biodiversity-oriented forest management elevation geology slope aspect Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In recent decades, forest management has increasingly emphasized biodiversity, recognizing its vital role in sustaining ecosystem functions and productivity (Liang et al., 2016 ; Jactel et al., 2018 ; Ali, 2023 ; Chisholm and Dutta Gupta, 2023 ). In Japan, where monocultures of sugi cedar ( Cryptomeria japonica ) and hinoki cypress ( Chamaecyparis obtusa ) still dominate, such awareness has led to a policy shift. The 2023 National Biodiversity Strategy, aligned with the Kunming–Montreal Global Biodiversity Framework, promotes the diversification of these plantations through longer harvest rotations and increased use of mixedwood and hardwood stands (Japan Forestry Agency, 2022b ). A key challenge in implementing this strategy is identifying plantation sites suitable for natural hardwood regeneration, which can serve as candidates for conversion (Kunisaki, 2024 ). Understanding the geographic and environmental conditions that support such regeneration is essential for effective spatial planning and long-term forest sustainability (Yamagawa et al., 2010 ; Nagaike, 2012 ; Jafarzade et al., 2022 ; Lidman et al., 2024 ). Previous studies have examined various factors that influence the success of plantation conversion to mixed or hardwood stands (e.g., Sato, 2021 ). Silvicultural characteristics such as plantation species (Akai et al., 1983 ; Akai et al., 1986 ; Kurose, 2005 ; Koyama and Yamauchi, 2011 ), forest age (Ito et al., 2006 ; Yamagawa et al., 2009 ), forest patch size (Nagashima et al., 2009 ), thinning regime (Fukata et al., 2006 ; Noguchi et al., 2009 ; Hirata et al., 2011 ; Seiwa 2012; Noguchi et al., 2016 ; Negishi et al., 2020 ), and overall forest-management practices (Utsugi et al., 2006 ; Yamagawa et al., 2006 ; Nagashima et al. 2009 ; Igarashi et al., 2016 ) have all been shown to affect regeneration outcomes. In addition, site history—particularly land-use legacy and browsing pressure by wild deer—can significantly constrain natural regeneration (Ito et al., 2004 ; Sakai et al., 2005 ; Sakai et al., 2006 ; Yamagawa et al., 2006 ; Noguchi, 2009; Takafumi and Hiura, 2009 ). Another key factor is seed dispersal dynamics, including proximity to natural seed sources (Sakai et al., 2005 ; Ito et al., 2006 ; Sakai et al., 2006 ; Yamagawa et al., 2007 ; Igarashi and Masaki, 2018 ), natural regeneration sources (Yamagawa and Ito, 2006 ; Igarashi and Kiyono, 2008 ), and dispersal patterns (Nagaike, 2002 ; Sakai et al., 2006 ), which have received considerable attention in previous research. While topographic features such as slope and aspect are also known to influence regeneration following clear-cutting (Yamagawa et al., 2006 ), they have been less intensively studied. Although geological factors once received considerable attention in early forestry studies and were shown to be closely linked to areas of exceptional conifer growth (Yasuoka, 1935 ; Koide 1939 ), they have since been largely overlooked in research on hardwood regeneration; therefore, we included them in our analysis. The forestry sector is undergoing a massive shift in the utilization of widely available information technology and remote-sensing techniques. Japan’s National Forest Inventory (NFI)—a systematic network of approximately 16,000 permanent plots sampled every five years—has been used to identify spatial patterns of hardwood regeneration across plantations (Yamaura et al. 2019 ). Such exploratory, visualization‑based approaches, though not grounded in a priori hypotheses, help lay the groundwork for subsequent hypothesis testing and detailed analyses. Beyond inventory data, forest registry data (Japanese: shinrinbo )—the foundational basis for forest management in Japan—are increasingly standardized and transitioned to cloud-based Geographic Information Systems (GIS). These advances facilitate centralized management of forest age, stand type, species composition, monitoring records, and operational histories. Through GIS, regeneration success can be spatially related to proximity to “mother” hardwood stands and past land use (Oda et al., 2010 ; Forestry and Forest Products Research Institute, 2012 ). Light detection and ranging (LiDAR) technology represents another leap forward in data generation for forest management in Japan (Takahashi et al., 2005 ; Wulder et al., 2008 ; Hirata et al., 2009 ; Taylor et al., 2020 ), holding particular promise for biodiversity-oriented forest management (Camarretta et al., 2020 ). Since October 2023, high‑density airborne LiDAR scans (ALS) covering both public and privately owned forests have been released for select prefectures. As of August 2025, species‑polygon data are publicly available in 10 Japanese prefectures, enabling fine‑scale (10-m resolution) mapping of tree species distributions. This expanded accessibility marks a leap forward from earlier data releases that only included national forest boundaries. Combining forest registry data with ALS-derived forest metrics is expected to provide substantial insights for forest management. These “big data” would effectively support exploratory approaches to natural hardwood regeneration in mature conifer plantations, revealing spatial heterogeneity in regeneration patterns. Therefore, in this study, we quantified the spatial heterogeneity of hardwood regeneration across a study area of roughly 250 km² in the warm-temperate forest region of Kochi Prefecture, southwestern Japan, for which ALS data are now available. Specifically, we compared GIS-based forest registry data with ALS-derived tree species mapping using a pixel-counting approach for cedar and cypress plantations. We examined whether hardwood regeneration in these plantations exhibited geographic bias based on topographic and geologic factors. Adopting a discovery-focused approach, we evaluated its relationship with these environmental surface factors. A logistic generalized additive model (GAM) was used to analyze how topographic and geological factors contributed to the occurrence probability of canopy-reaching hardwood regeneration in conifer plantations. Materials and Methods Study area The study area encompassed Kami, Kochi Prefecture, Japan (Fig. 1 ), located in the warm-temperate zone and characterized by high precipitation and steep, mountainous terrain (Fig. 1 a). Forested land within the study area consists of > 70% cedar and cypress plantations (Fig. 1 b) and the average slope is 33°. Prior to the development of conifer plantations, vegetation at low elevations (< 600 m a.s.l., Sakai et al., 2006 ) was dominated by evergreen hardwood forests, mainly comprising evergreen Castanopsis and Quercus species (e.g., Quercus glauca ), and members of the laurel family ( Machilus and Neolitsea species). These species tend to germinate quickly following seed dispersal, often forming dense seedling stocks on the forest floor, particularly Quercus (Hashizume and Aikawa, 1978 ; Takeda, 2017 ; Sakai et al., 2010 ). At middle elevations (approximately 600–900 m a.s.l.), evergreen Castanopsis species are absent; this zone features a mix of evergreen hardwoods, including evergreen Quercus species (notably Quercus salicina , Quercus acuta , and Quercus sessilifolia ), Distylium racemosum , and conifers such as Abies firma and Tsuga sieboldii . The high-elevation zone (> 900 m a.s.l.) is dominated by deciduous Fagaceae species, including Fagus crenata , Fagus japonica , Quercus crispula , and Castanea crenata . These high-elevation species are accompanied by other deciduous hardwoods such as Carpinus spp. and Acer species as canopy trees. Additionally, conifers such as A. firma , T. sieboldii , and Abies homolepis sometimes grow in mixed stands (Ministry of the Environment, 1988 ). These three elevation zones largely correspond to categories defined by Fukata et al. ( 2005 ) based on understory vegetation: low-elevation zones dominated by the most common ferns and evergreen trees, middle-elevation zones characterized by evergreen Quercus species, and high-elevation zones with deciduous trees. The Shikoku Mountains in Kochi Prefecture are oriented along an approximate east–west gradient. The geology is characterized by belts of geological zones of different formation ages (Fig. 1 c). The northernmost formation is the Sambagawa Belt (metamorphic rock of the Cretaceous period, composed of crystalline schist), followed by the Chichibu Belt (Permian to Early Cretaceous accretionary complex), the Cretaceous system in the Chichibu Terrane (Cretaceous marine sedimentary rocks), and the Shimanto Belt (Cretaceous accretionary complex) (Moreno et al., 2016 ). Due to tectonic effects, accretionary ages tend to be younger in the southern part of the Shimanto Belt zone and older in the southern part of the Chichibu Belt (Endo and Yokoyama, 2019 ). We divided the study area into geologic zones based on rock formation ages and processes (Table S1 ). Broadly, elevation tends to be higher in the northern portion of the study area, such that geologic zones and elevation tend to be correlated. Forest registry data Spatially explicit forest registry data available from Kochi Prefecture include stand attributes such as age, type, tree species, and ownership boundaries. The database underwent revision between 2005 and 2009. Forest registry data were available for a 337.6-km 2 area in Kami, of which 40% comprised cedar plantations and 34% comprised cypress plantations. In 2018, the year of ALS acquisition in the study area, the cedar and cypress plantations had a median ages of 58 and 57 years, respectively. A small proportion of the study area (0.03%) included plantations older than 120 years. These reported plantations likely represent errors in data reporting or entry; therefore, only stands ≤ 120 years of age were included in our analysis pertaining to forest age. ALS data for Kochi Prefecture were collected in 2018 and made publicly available in 2023 as part of a Forest Agency initiative. Tree species were identified using ALS-derived reflectance intensity at 10 m resolution, concurrently collected aerial photographs, and field confirmation surveys ( https://www.geospatial.jp/ckan/dataset/tree_species_kochi ). The species classification targets overstory trees, but in areas where canopy closure had not yet occurred, understory trees may have been detected. Canopy closure in Kochi typically occurs within 15 years for cedar and 20 years for cypress plantations (Kochi Prefecture, 2019), though timing varies depending on growth rate and planting density (Irimajiri, 1982 ). Hardwood species were classified only as “hardwood”. The composition of hardwood species that regenerate naturally in conifer plantations is described in Tanimoto ( 1982 ) and Sakai et al. ( 2006 ). The equipment, collection protocols, and analytical methodology used in ALS data generation were described by Nakao et al. ( 2022 ) and the Forest GIS Forum (2022). Geographical data Elevation, slope angle (hereafter referred to as slope), slope aspect (hereafter referred to as aspect), and topographic wetness index (TWI) data were obtained for the study area based on a 25-m-resolution digital terrain model for Kochi Prefecture derived using ALS data collected in 2018 (Nakao et al., 2022 ). The terrain data were resampled to 10-m resolution for analyses. Of these topographic factors, aspect and TWI were chosen because they were highly correlated with cedar growth in a previous study conducted in Kami (Nakao et al., 2022 ). Although slope appeared to have minimal effect on cedar growth according to Nakao et al. ( 2022 ), it was included in our analysis because slope is a fundamental index linked to hardwood regeneration (Yamagawa et al., 2006 ) and may affect the feasibility of forest management actions (Kondo et al., 2004 ). The geology of each survey grid used in the analysis was classified based on a digital geologic map prepared by the Geological Survey of Japan (Geological Survey of Japan, AIST, 2023 ). Hardwood regeneration Natural regeneration of hardwood species was determined by comparing forest registry data with ALS data. First, the forest registry data were converted to a 10-m grid to align with the ALS data. For areas where the two datasets overlapped, any grid cell (hereafter, cell) containing a cedar or cypress planation in the forest registry data was included in the analysis. Each cell was classified into one of two categories: conifer–cell, where both datasets identified the cell as containing cedar or cypress plantations, and hardwood–cell, where the forest registry dataset identified the cell as containing cedar or cypress plantations, but the ALS dataset indicated the presence of hardwood trees. All hardwood–cells were interpreted as indicative of hardwood regeneration in conifer plantations in subsequent analyses. In this analysis, cedar and cypress trees are collectively referred to as “conifers.” Regardless of whether a management unit (Japanese: segyohan ) was registered as cedar or cypress, both species were often interplanted within the same unit (Table S2 ). Consequently, we could not determine whether the conifers in those hardwood–cells had originally been cedar or cypress. All GIS analyses were conducted using ArcGIS v10.6 (ESRI, 2018). ALS-based species classification was validated elsewhere (Table S3 ). Although it is possible that the forest registry data, which were updated during 2005–2009, contained errors, we were unable to validate the dataset. Statistical analysis We assessed the relationships between hardwood–cells and predictor variables. Preliminary analyses confirmed spatial autocorrelation in the hardwood–cell distribution from the ALS data (Moran’s I = 0.531), indicating that the probability of a cell being occupied by hardwood trees was higher when adjacent cells were also occupied by hardwoods (Table S4 ). The semivariogram stabilized at a range of < 200 m (Figure S1 ). To account for spatial autocorrelation, we conducted subsequent statistical analyses using systematic sampling, by dividing the main study area into 200-m 2 subareas and then consistently selecting a cell that occupied the same relative position within each subarea. This procedure resulted in 400 data subsets. A preliminary overall analysis was conducted to evaluate potential predictor variables, including forest age, elevation, slope, aspect, TWI, geology, and their interactions (Figure S3 ). This exploratory analysis did not account for spatial autocorrelation and used the entire dataset. A logistic GAM was used to explore variables that might predict the probability of hardwood regeneration, with a binary outcome variable, because our preliminary analysis indicated a non-linear relationship between slope or TWI, and the occurrence of hardwood–cells (see Overall trends ). Preliminary analysis also suggested an aspect effect in the north–south direction and an interaction between elevation zone and aspect. The logistic GAM was applied separately to the three elevation zones (low, middle, and high) following cosine transformation of the azimuth angle of slope aspect as a continuous variable. In this transformation, north and south were assigned values of 1 and − 1, respectively. The analysis focused on stands aged 41–70 years in 2018, which represented postwar afforestation from 1949 to 1978. Other minor age groups were excluded due to potential uncertainties related to stand history. The logistic GAM incorporated elevation, slope, aspect, TWI, and second-order interaction terms as explanatory variables. Initially, 15 models combining these four topographic variables were evaluated using 400 data subsets to identify the combinations yielding the lowest Akaike information criterion (AIC). Subsets containing fewer than 50 data points were excluded from the analysis, a condition observed exclusively in high-elevation zones. All variables used in the logistic GAM model were scaled, with the regularization parameter fixed at 10.0. Subsequent analyses were restricted to low-elevation zones. Based on our preliminary overall analysis, we tested a model that imposed a monotonicity constraint, such that the effects of elevation and aspect on hardwood–cell probability were set to decrease monotonically to prevent overfitting. These analyses included second-order interaction terms as explanatory variables. The most frequently selected variable combinations were refined by adding second-order interaction terms to further minimize the AIC. Identification of the best model was based on the area under the receiver operating characteristic curve (AUROC), a metric commonly used to evaluate the performance of binary classification models that measures the ability of the model to predict correct labels. The AUROC ranges from 0 to 1, with higher values indicating better performance. The AUROC was calculated for both the subset used in model fitting and the separate test data to evaluate overfitting. The predicted probability of hardwood occurrence under various combinations of elevation and aspect in the best model were visualized as a two-dimensional (2D) heat map, illustrating elevation–aspect interactions. The effects of geology were considered exclusively in low-elevation zones and restricted to cells within the top five geologic categories, thereby excluding 2.1% of all cells (Table S1 ). To visualize the effects of aspect within each geological category, as shown by the overall analysis, the 400 data subsets were divided according to five geologic categories, yielding a total of 2000 data subsets. The best model described above was re-fitted to each subset, and 2D partial dependence plots of elevation and aspect were created for each geological category. Modeling was conducted using LogisticGAM in Python v3.10.15, with GeoPandas used for spatial data processing, NumPy for numerical computations, and Matplotlib’s Pyplot module for visualization. Supplementary analyses were performed using JMP v10.0.2 (SAS Institute, Cary, NC, USA). Results Overall trends The forest registry dataset included approximately 250 km 2 of conifer (cedar or cypress) plantations. The ALS dataset included 53 km 2 of hardwood (21.4% of all), and 184 km 2 of conifer (73.8% of all). The remaining 4.4% in the ALS dataset were excluded from further analyses. Of all cells classified into the two change patterns (n = 2,373,113), 77.5% were conifer–cells and 22.5% were hardwood–cells. Considering stand age, both change patterns had similar age class distributions, with most stands aged 56–60 years as of 2018 (Fig. 2 ). Within the target cell, 81.0% of the total were 41–70 years old in 2018, and were therefore planted during the 30-year period 1949–1978. Forestry-related details of each period are presented in Table S5 . The proportion of hardwood–cells within the main postwar afforestation area consistently accounted for approximately 20–25% of all cells (Fig. 2 b). Generally, hardwood–cells were more prevalent in young stands (< 20 years of age) and very old stands (110–120 years). The former result may reflect observations of understory species before canopy closure, whereas the latter is limited by the small number of data points. Modeling outcomes Logistic GAMs incorporating all four explanatory topographic variables (elevation, slope, aspect, and TWI) most frequently yielded the lowest AIC (Model I, Table 1 ). Partial dependence plots with 95% confidence intervals (CIs) for each variable are shown in Fig. 3 . Higher hardwood–cell probability scores were associated with lower elevation in the low-elevation zone and higher elevation in the high-elevation zone (Fig. 3 a). However, CIs were wider in the high-elevation zone (Fig. 3 , right panels), likely due to the smaller sample size (Table S6 ). Plots of aggregated elevation values, independent of model estimates, are shown in Figure S2 . Table 1 Statistical details of the best model with the minimum AIC and combination of topographic variables achieving minimum AIC across subsets in logistic GAM models by elevation zone. The numbers are means ± SD, with the range indicated in parentheses. Model [variables] Description Elevation zone 1) [geological category] No. of subsets 2) No. of data points per subset 3) AUROC 4) Training subset Test data 5) Model I [ elevation + slope + aspect + TWI ] Without interactions Low 400 2759 ± 478 (1573–4029) 0.755 ± 0.036 0.568 ± 0.029 Middle 399 1505 ± 528 (226–2831) 0.805 ± 0.051 0.551 ± 0.027 High 385 561 ± 292 (50–1693) 0.887 ± 0.063 0.517 ± 0.035 Model II [ elevation + slope + aspect + TWI ] Without interactions, monotonicity constraints (elevation, aspect) Low 400 2759 ± 478 (1573–4029) 0.716 ± 0.045 0.596 ± 0.018 Model III [ elevation + slope + aspect + TWI + elevation * slope + elevation * aspect + elevation * TWI ] With interactions Low 400 2759 ± 478 (1573–4029) 0.782 ± 0.032 0.568 ± 0.027 Model IV [ elevation + slope + aspect + TWI ] Based on Model II, re-fitted by geological category Low [Jurassic Chichibu Belt] 396 722 ± 337 (63–1668) 0.800 ± 0.077 0.552 ± 0.033 Low [Shimanto Belt] 399 782 ± 262 (121–1487) 0.778 ± 0.070 0.586 ± 0.034 Low [Cretaceous system of the Chichibu Terrane] 396 698 ± 329 (50–1605) 0.779 ± 0.074 0.568 ± 0.032 1) Elevation zones: Low ( 900 m). 2) Number of subsets on which the model converged. 3) Subsets with fewer than 50 data were excluded from analysis. 4) Area under the receiver operating characteristic curve (AUROC), a metric commonly used to evaluate the performance of binary classification models that measures the ability of the model to predict correct labels. The AUROC ranges from 0 to 1, with higher values indicating better performance. 5) Data not used in training subsets. Higher hardwood–cell probability was clearly associated with south-facing slopes in the low-elevation zone; this relationship was weaker in the middle-elevation zone (Fig. 3 b). In the high-elevation zone, CIs were wider, and the effect of aspect was unclear (Fig. 3 b, right panel). Hardwood–cell probability scores tended to increase when the slope exceeded 40° (Fig. 3 c), while moderate TWI values were associated with lower hardwood–cell probability, regardless of the elevation zone (Fig. 3 d). Confidence intervals for extreme TWI values were wide, likely due to small sample sizes, making these associations less pronounced. Plots of aggregated values for aspect, slope, and TWI are shown in Figure S3 . In the low-elevation zone, imposing the monotonicity constraint on elevation and aspect further increased AUROC values for test data (Model II, Table 1 ). Model III, which included second-order interactions of elevation with slope, aspect, and TWI, yielded the lowest AIC in 92% of the total 400 data subsets. In contrast with the increasing AUROC values observed in the training data, AUROC values for the test data decreased (Table 1 ), which suggested model overfitting. Model II was considered to be the best model for the low-elevation zone (Table 1 ). A 2D heatmap depicting hardwood–cell probability in relation to elevation and aspect was generated from Model II (Fig. 4 ), which highlights the high probability associated with south-facing slopes at lower elevations. The best model (Model II) was re-fitted by each geological category (Model IV). Among the five geological categories, most subsets for the Sambagawa Belt and the Permian Chichibu Belt were insufficient data (< 50) or failed to converge (316 and 252 out of the 400 subsets, respectively). Therefore, partial dependence plots with 95% CIs are shown only for the three categories in which more than 95% of the subsets successfully converged (Fig. 5 ). These plots did not show clear differences among the three geological divisions. However, 2D heatmaps of hardwood–cell probability in relation to elevation and aspect revealed distinct differences among the geological divisions (Fig. 6 ). In the Shimanto Belt, a pronounced trend of higher hardwood–cell probability on south-facing slopes at lower elevations was observed (Fig. 6 b). A similar but less pronounced trend was evident in the Cretaceous system of the Chichibu Terrane (Fig. 6 c). In the Chichibu Belt (Jurassic accretionary complex), a trend of higher hardwood–cell probability on low-elevation, south-facing slopes was only faintly observed, with consistently low probability across all combinations (Fig. 6 a). Plots of aggregate aspect, slope, TWI values for each of the five geological categories and all elevation zones are shown in Figure S4 . Discussion Elevation and aspect influence on hardwood regeneration The predictive strength of elevation and aspect for the probability of hardwood regeneration within conifer plantations varied with the elevation zone. The association between lower elevation and hardwood regeneration was evident in the low-elevation zone, but less distinct in the middle-elevation zone (Fig. 3 a). The frequent occurrence of hardwood regeneration in this low elevation zone in Kochi Prefecture aligns with the findings of Sakai et al. ( 2006 ) and Yamaura et al. ( 2019 ), who demonstrated that hardwood regeneration tends to be more vigorous in warmer, lower-elevation areas, based on the relationship with elevation or the warm index, which is generally correlated with elevation. In the high-elevation zones, higher elevation was associated with increased hardwood regeneration (Fig. 3 a). Natural hardwood regeneration in plantation forests is often associated with plantation failure, particularly at high elevations characterized by heavy snowfall, dense bamboo stands, and increased risk of weather-related damage (Kodani, 1990 ; Yokoi and Yamaguchi, 1998 ; Niiyama et al., 2010 ; Aiura et al., 2017 ). However, the CIs for these relationships were notably wide, suggesting the need for additional data to better evaluate these trends. Aspect has been identified as the most significant topographic parameter in predicting the growth of sugi cedar in this study area, with particularly poor height growth observed on southwest-facing slopes (Nakao et al., 2022 ). In contrast, the influence of aspect on hardwood regeneration differed across elevation zones (Fig. 3 b). In the low-elevation zone, south-facing slopes were clearly associated with hardwood regeneration, a trend that became less distinct in the middle-elevation zone. By contrast north-facing slopes were weakly associated with hardwood regeneration in high-elevation zones, albeit with substantial estimate uncertainty. The effect of aspect on vegetation dynamics can vary with climate, as north-facing slopes may benefit vegetation establishment and growth in dry areas but hinder it where temperatures are limiting (Yin et al., 2023 ). Although our study site lies within the warm-temperate zone, the observed patterns suggest a clear shift in the aspect–hardwood regeneration relationship—from a south-facing preference at low elevations to a north-facing preference at high elevations—with this trend becoming less distinct in the middle-elevation zone. Effects of slope and TWI on hardwood regeneration Both slope and TWI were effective predictors of hardwood regeneration probability, showing consistent but non-monotonic trends across elevation zones. Hardwood regeneration probability increased sharply on slopes exceeding 40° (Figs. 3 c). In contrast, TWI indicated higher regeneration likelihood in ridges and valleys, and lower probability in moderate terrains (Figs. 3 d). This pattern partially aligns with Yamagawa et al. ( 2006 ), who reported greater post-clearcutting vegetation-recovery on steep or convex slopes and attributed it to factors such as soil moisture, surface soil stability, and the pre-harvest distribution of understory trees. Our findings, which demonstrate a topographic bias in hardwood regeneration within conifer plantations prior to logging, support the importance of pre-existing hardwood vegetation in explaining these recovery patterns. However, this study does not provide sufficient evidence to confirm or refute the influence of soil moisture or surface soil stability. Trends in slope gradient may also be linked to forest management practices. Historical forest management was not considered in this study due to data availability limitations that allowed only simple comparisons between ALS and forest registry data. However, forest management history should not be overlooked in future localized studies (Yamaura et al., 2019 ). Early weeding operations are critical to afforestation success, yet their efficiency declines on slopes steeper than 40° (Kondo et al., 2004 ). Notably, Kondo et al. ( 2004 ) based their findings on surveys of workers using engine-powered brush cutters, whereas weeding in the study area was historically conducted with scythes, potentially leading to different efficiency patterns on steep slopes. Insufficient silvicultural practices on steep slopes during this period may also have contributed to the observed trends. Natural hardwood regeneration on low-elevation, south-facing slopes A key result of our analysis was that the probability of hardwood regeneration within conifer plantations was higher at lower elevations, particularly on south-facing slopes (Fig. 4 ) in the low-elevation zone (< 600 m), which was dominated by evergreen Castanopsis and Quercus . Lower elevations are typically associated with lower soil moisture content in warm-temperate forests (Inoue et al., 1973 ), presumably due to higher evaporation rates resulting from higher temperatures. Additionally, vegetation is affected by varied microclimates along slopes, and south-facing slopes tend to be drier due to increased sun exposure (Yin et al., 2023 ). An investigation of slope aspect and soil type distribution in Kochi Prefecture found that dry soils were most prevalent on south-facing slopes (Nagamori and Irimajiri, 1974 ), and low soil moisture has been documented in south-facing cypress plantations in Shikoku (Inoue et al., 1973 ; Inagaki et al., unpublished data). Water deficiency associated with dry conditions is well known to hinder plant growth. Highly permeable soils in the Shikoku region are considered ideal for sugi cedar plantations. However, optimal growth can be achieved only in the absence of drought, with adequate rainfall, low temperatures, and favorable wind conditions (Asada, 1936 ; Yasuoka, 1935 ). Although Japanese hardwood species may not achieve peak growth rates under dry conditions (Tanimoto, 1990 ), they are likely more tolerant of dry conditions than planted conifers, particularly cedars, which prefer wetter soil conditions (Nagakura et al., 2004 ). Indeed, a spatial analysis of cedar height in Kami based on ALS data found that cedar growth was significantly reduced on south- and southwest-facing slopes (Nakao et al., 2022 ). Furthermore, dry conditions can lead to nutrient deficiency. Nitrogen availability is influenced by soil moisture, and nitrogen levels on dry, south-facing slopes may limit conifer growth. Noguchi et al. ( 2011 ) reported a growth advantage for hardwoods over hinoki cypress in nitrogen-deficient soils in Kochi Prefecture. Reduced nitrogen concentrations in litter and soil have also been observed at low elevations in conifer plantations (Inagaki et al., 2010 ; Inagaki et al., 2011 ). Low soil nitrogen concentrations, driven by limited water availability, may contribute to greater hardwood regeneration in low-elevation conifer plantations in this region. The observed patterns of hardwood regeneration in relation to slope and TWI partly support the hypothesis that dry soil conditions promote regeneration on south-facing slopes in low-elevation zones. Increased regeneration on steep slopes or ridges (low TWI) is consistent with this idea (Fig. 3 c, d). However, higher regeneration in valleys (high TWI) likely reflects other factors, such as reduced surface stability from frequent sediment deposition, which may facilitate pioneer species (Yamagawa et al., 2006 ). Geological variation in elevation- and aspect-dependent hardwood regeneration Hardwood regeneration on low-elevation, south-facing slopes varied by geologic zones, with probabilities decreasing from the Cretaceous Shimanto Belt to the Cretaceous system in the Chichibu Terrane, and lowest in the Jurassic Chichibu Belt (Fig. 6 ). These differences may reflect geological influences on drought conditions, even at similar elevations and aspects. In these outer-belt mountains of southwestern Japan, steep slopes and gravelly soils promote rapid drainage and soil drying (Shinomiya and Yoshinaga, 2008 ). Soil permeability is influenced by geological factors such as the type and age of parent material related to weathering (Koide, 1952 ) and the inclination of the stratum surface (Yasuoka, 1935 ). Geology also affects the water-holding capacity of the A and B horizons in mineral soils (Fujieda, 2007 ). Notably, water retention in Mesozoic geologic zones of Kochi Prefecture spans the full range observed across Japan (Shimizu, 1998). Differences in water retention, particularly between the Jurassic Chichibu Belt and the Cretaceous Shimanto Belt, may help explain the observed variation in hardwood regeneration probability across geologic zones. Soil surveys have shown Shimanto Belt soils have high bulk density and low moisture content (Inoue et al., 1973 ), although comparisons were limited to the Sambagawa Belt. The lack of soil data for the Jurassic Chichibu Belt and Cretaceous Chichibu Terrane constrains further analysis. Implications for forest management Following World War II, the Japanese government encouraged the monoculture planting of sugi cedar and hinoki cypress to meet increasing lumber demands. This initiative led to a significant expansion of plantation forests, with the total area growing from 4.93 million ha in 1951 (Ministry of Agriculture, Forestry, and Fisheries, 1952 ) to 10.01 million ha in 2022 and now accounting for 40% of the total forest area (Japan Forestry Agency, 2022a ). The study area exemplifies this expansion, with its age class structure reflecting the history of postwar afforestation (Table S5 ). Throughout the main postwar afforestation period (defined in this study as 41–70 years old in 2018), the proportion of hardwood–cells remained relatively stable (Fig. 2 ). Although afforestation incentives might be dampened by events such as the liberalization of timber imports in 1964 and declining timber prices from 1975, early silvicultural practices, including weeding, appear to have been largely upheld. Our analysis of publicly accessible ALS datasets revealed that hardwood regeneration is more prevalent on low-elevation, south-facing slopes, particularly in the Cretaceous Shimanto Belt (Figs. 4 , 6 ). This suggests that elevation and aspect, along with conventional geomorphic factors such as slope and TWI, have a marked influence on hardwood regeneration. Incorporating these findings into forest management could improve site selection for future planting. With future plantations projected to shrink to 60% of the current area (Japan Forestry Agency, 2021 ), selecting suitable sites will be essential for efficient re-afforestation. Although traditional forestry prioritized high -growth areas, future strategies may need to avoid sites prone to natural hardwood regeneration, as labor-intensive practices like manual weeding and clearing become less feasible (Sato, 2021 ). As public access to ALS datasets expands, exploratory analyses such as those employed in the present study are essential for advancing analytical frameworks that support biodiversity-oriented forest management. In summary, high-resolution spatial analysis using publicly accessible ALS data can pave the way for biodiversity-oriented forest management strategies. By uncovering key patterns in hardwood regeneration and validating established knowledge, this study demonstrates how such data can inform future forest management efforts and facilitate more sustainable practices. Declarations Conflict of interest The authors declare that they have no conflicts of interest. Funding Declaration This work was supported by the Ministry of Agriculture, Forestry, and Fisheries commissioned project, “Development of a method for evaluating profitability in forestry across Japan using a two-axis matrix of site quality and location” (grant no. JPJ012043). Author Contribution E.I. and Y.I. wrote the main manuscript text. All other authors contributed to data collection and manuscript preparation. K.K. prepared Table S3, while all other figures and tables were prepared by E.I. Spatial analyses were conducted by E.I. and K.N. All authors reviewed and approved the final manuscript. Acknowledgement ALS data and forest GIS data were provided by Kochi Prefectural Technology Center, Kochi Prefectural Government. Data Availability ALS data that support the findings of this study were collected in 2018 and released in 2023 as part of a Forest Agency initiative. The dataset, which includes tree species identified using ALS-derived reflectance intensity at 10 m resolution, aerial photographs, and field confirmation surveys, is publicly available at https://www.geospatial.jp/ckan/dataset/tree_species_kochi. References Aiura H, Nakajima H, Kato A (2017) Regeneration of broad-leaved trees and evaluation as plantations for wood production in weather-damaged Sugi ( Cryptomeria japonica ) stands. Bull Toyama Forestry Res Inst 9:1–10 (in Japanese with English abstract) Akai T, Yoshimura K, Manabe I, Ueda S, Honjyo T (1983) Structure and silvicultural system of multi-storied mixture forests (1): On stratified mixture of hinoki [ Chamaecyparis obtusa ], akamatsu [ Pinus densiflora ] and broad-leaf trees. Bull Kyoto Univ Forests (Japan) 55:63–79 (in Japanese with English abstract) Akai T, Yoshimura K, Manabe I, Ueda S, Honjyo T (1986) Structure and silvicultural system of multi-storied mixed forests (2): On stratified mixtures of planted sugi ( Cryptomeria japonica ) and natural sugi, hinoki ( Chamaecyparis obtusa ), akamatsu ( Pinus densiflora ) and broad leaved trees. Bull Kyoto Univ Forests (Japan) 58:105–124 (in Japanese with English abstract) Ali A (2023) Biodiversity–ecosystem functioning research: brief history, major trends and perspectives. Biol Conserv 285:110210. 10.1016/j.biocon.2023.110210 Asada S (1936) A study on the occurrence, growth, and regeneration of sugi cedar saplings in the Yanase district. Kochi Rinyu Kochi Reg Forestry Bureau 191:6–24 (in Japanese, tentative translation) Camarretta N, Harrison PA, Bailey T, Potts B, Lucieer A, Davidson N, Hunt M (2020) Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches. New Forest 51:573–596. 10.1007/s11056-019-09754-5 Chisholm RA, Dutta Gupta T (2023) A critical assessment of the biodiversity–productivity relationship in forests and implications for conservation. Oecologia 201:887–900. 10.1007/s00442-023-05363-4 Endo S, Yokoyama S (2019) Geology of the Motoyama District. Quadrangle Series, 1:50,000, Kochi (13) No. 53, Geological Survey of Japan, AIST, 100 p. (in Japanese with English abstract, 100 p.) Forest GIS (2022) Forum Forest Resource Data Analysis and Management Standard Specifications Ver. 2.0 (tentative translation, in Japanese) https://fgis.jp/cloud [accessed on 7 June 2024] Forestry and Forest Products Research Institute (2012) Handbook for Conversion of Plantation Forests to Broadleaf Forests 2010, Second Midterm Plan Outcome 22 (Forestry and Wood Use – 5) (tentative translation, in Japanese) ISBN: 978-4-902606-78-2. https://www.ffpri.affrc.go.jp/pubs/chukiseika/documents/2nd-chuukiseika22.pdf [accessed on 7 June 2024] Fujieda M (2007) Water-holding capacity and basin storage at forest catchments in Japan. Bull Forestry For Prod Res Inst 403:101–110 (in Japanese with English abstract) Fukata H, Watanabe N, Kajihara N, Tsukamoto J (2005) Altitudinal zoning of understory vegetation in planted Hinoki cypress ( Chamaecyparis obtusa Endt.) forest from the view of surface soil erosion control. Japanese J For Environ 47:77–84. https://www.jstage.jst.go.jp/article/jjfe/47/2/47_KJ00005292004/_pdf (in Japanese with English abstract) Fukata H, Watanabe N, Kajihara N, Tsukamoto J (2006) Dynamics of undergrowth and its application to vegetation control of planted Chamaecyparis obtusa Endl. forests with special reference to mitigation of surface soil loss. J Japanese For Soc 88:231–239. http://www.jstage.jst.go.jp/article/jjfs/88/4/88_4_231/_pdf (in Japanese with English abstract) Geological Survey of Japan, AIST (2023) Seamless digital geological map of Japan V2 1:200,000, Original edition. https://gbank.gsj.jp/seamless/ [accessed on 7 Oct 2024] Hashizume H, Aikawa T (1978) Characteristics of seed germination in four Fagaceae species. Bull Fac Agric Tottori Univ 30:128–133 (in Japanese with English abstract) Hirata Y, Furuya N, Suzuki M, Yamamoto H (2009) Airborne laser scanning in forest management: Individual tree identification and laser pulse penetration in a stand with different levels of thinning. For Ecol Manag 258:752–760. 10.1016/j.foreco.2009.05.017 Hirata A, Sakai T, Takahashi K, Sato T, Tanouchi H, Sugita H, Tanaka H (2011) Effects of management, environment and landscape conditions on establishment of hardwood seedlings and saplings in central Japanese coniferous plantations. For Ecol Manag 262:1280–1288. 10.1016/j.foreco.2011.06.025 Igarashi T, Kiyono Y (2008) The potential of hinoki ( Chamaecyparis obtusa [Sieb. et Zucc.] Endlicher) plantation forests for the restoration of the original plant community in Japan. For Ecol Manag 255:183–192. 10.1016/j.foreco.2007.09.003 Igarashi T, Masaki T (2018) Species diversity of woody recruits within Japanese cedar ( Cryptomeria japonica ) plantations established on grasslands: the effects of site conditions and landscape. J For Res 23:156–165. 10.1080/13416979.2018.1432302 Igarashi T, Masaki T, Nagaike T, Tanaka H (2016) Species richness of the understory woody vegetation in Japanese cedar plantations declines with increasing number of rotations. J For Res 21:291–299. 10.1007/s10310-016-0537-2 Inagaki Y, Okuda S, Sakai A, Nakanishi A, Shibata S, Fukata H (2010) Leaf-litter nitrogen concentration in hinoki cypress forests in relation to the time of leaf fall under different climatic conditions in Japan. Ecol Res 25:429–438. 10.1007/s11284-009-0672-8 Inagaki Y, Nakanishi A, Fukata H (2011) Soil properties and nitrogen utilization of hinoki cypress as affected by strong thinning under different climatic conditions in the Shikoku and Kinki districts in Japan. J For Res 16:405–413. 10.1007/s10310-011-0271-8 Inoue K, Iwagawa O, Yoshida K (1973) Studies on the productivity of the forest soil in Shikoku Province. Bull Government Forestry Exp Stn 258:61–148. 10.11501/1772146 (in Japanese with English abstract) Irimajiri K (1982) Soil, silvicultural systems and thinning. Bulletin of the Kochi Prefectural Forest Experiment Station , Showa 57, 101–167. (in Japanese, tentative translation) Ito S, Nakayama R, Buckley GP (2004) Effects of previous land-use on plant species diversity in semi-natural and plantation forests in a warm-temperate region in southeastern Kyushu, Japan. For Ecol Manag 196:213–225. 10.1016/j.foreco.2004.02.050 Ito S, Ishigami S, Mitsuda Y, Buckley GP (2006) Factors affecting the occurrence of woody plants in understory of sugi ( Cryptomeria japonica D. Don.) plantations in a warm-temperate region in Japan. J For Res 11:243–251. 10.1007/s10310-005-0183-6 Jactel H, Gritti ES, Drössler L, Forrester DI, Mason WL, Morin X, Pretzsch H, Castagneyrol B (2018) Positive biodiversity–productivity relationships in forests: climate matters. Biol Lett 14:20170747. 10.1098/rsbl.2017.0747 Jafarzade M, Ravanbakhsh H, Moshki A, Mollashahi M (2022) Recolonization by Indigenous broadleaved species of a conifer plantation ( Cupressus spp.) in Northern Iran after 25 years. Ann For Sci 79:15. 10.1186/s13595-022-01131-1 Japan Forestry Agency (2006) Annual report on trends in forest and forestry. Fiscal Year 2006. http://www.rinya.maff.go.jp/new/hakusyoeigo/english18/index.html [accessed on 4 April 2024] Japan Forestry Agency (2021) Basic Plan for Forest and Forestry (revised in June 2021). https://www.rinya.maff.go.jp/j/kikaku/plan/ Japan Forestry Agency (2022a) Current status of forest resources (as of March 31, 2022) (tentative translation, in Japanese). https://www.rinya.maff.go.jp/j/keikaku/genkyou/r4/index.html [accessed on 7 June 2024] Japan Forestry Agency (2022b) Annual report on trends in forest and forestry. Fiscal Year 2022. https://www.maff.go.jp/e/data/publish/attach/pdf/index-193.pdf [accessed on 4 April 2024] Kochi P (2019) Kochi Regional Forest Plan (Established in 2019, 2020–2030). https://www.pref.kochi.lg.jp/doc/chiikikeikaku/file_contents/file_202176211251_4.pdf (in Japanese) [accessed on 4 June 2024] Kodani J (1990) Technology for creating and improving hardwood forests in snowy areas. Bull Ishikawa-Ken For Exp Stn 21:1–13 (in Japanese, tentative translation) Koide H (1939) Geological and topographical zones and cedar forestry in southwestern Japan. J Japanese For Soc 21:165–180 (in Japanese, tentative translation) Koide H (1952) Applied Geology —Rock Weathering and Forest Location— . Tokyo: Kokon. pp.177. (in Japanese, tentative translation) Kondo K, Yoshida C, Oka M, Kashima J, Imatomi Y, Inoue M (2004) Factors affecting work efficiency in underbrush clearing operations. Transactions of the annual meeting in Kanto branch of the Japanese forest society , 55, 299–300. (in Japanese, tentative translation) Koyama Y, Yamauchi M (2011) Development of renewal technology for mixed needle and broadleaf forest creation (tentative translation). Bull Nagano Prefectural Forestry Res Cent 25:29–44 (in Japanese) Kunisaki T (2024) Transformation from conifer plantations to mixed-species stands: Research challenges associated with the perspective of classical forest management planning. Jpn J Plann press. 10.20659/jjfp.A20240401 (in Japanese) Kurose K (2005) Studies for regeneration technology for a broadleaf forest from artificial forest of Cryptomeria japonica and Chamaecyparis obtusa. Bulletin . Okayama Prefectural For Exp Stn 21:1–13 (in Japanese) Liang J, Crowther TW, Picard N, Wiser S, Zhou M, Alberti G, Reich PB (2016) Positive biodiversity-productivity relationship predominant in global forests. Science 354(6309):aaf8957. 10.1126/science.aaf8957 Lidman FD, Karlsson M, Lundmark T, Sängstuvall L, Holmström E (2024) Birch establishes anywhere! So, what is there to know about natural regeneration and direct seeding of birch? New Forest 55:157–171. 10.1007/s11056-023-09971-z Ministry of Agriculture, Forestry, and Fisheries (1952) Statistical Table of the Ministry of Agriculture and Forestry, 28th, 1951 (tentative translation, in Japanese). http://www.library-archive.maff.go.jp/index/500205935_0001 [accessed on 7 June 2024] Ministry of the Environment (1988) Third National Survey on the Natural Environment, Vegetation Survey Report, Kochi Prefecture (tentative translation, in Japanese). http://www.biodic.go.jp/reports2/3rd/vgt_39/3_vgt_39.pdf [accessed on 26 November 2024] Moreno T, Wallis SR, Kojima T, Gibbons W (2016) The geology of Japan. Geological Society, London. pp522 Nagaike T (2002) Differences in plant species diversity between conifer ( Larix kaempferi ) plantations and broad-leaved ( Quercus crispula ) secondary forests in central Japan. For Ecol Manag 168:111–123. 10.1016/S0378-1127(01)00734-4 Nagaike T (2012) Review of plant species diversity in managed forests in japan. ISRN Forestry, 2012 , Article ID 629523. 10.5402/2012/629523 Nagakura J, Shigenaga H, Akama A, Takahashi M (2004) Growth and transpiration of Japanese cedar ( Cryptomeria japonica ) and Hinoki cypress ( Chamaecyparis obtusa ) seedlings in response to soil water content. Tree Physiol 24:1203–1208. 10.1093/treephys/24.11.1203 Nagamori M, Irimajiri K (1974) Study on the connection of the distribution of the forest soil type with topography in South-eastern Shikoku, Japan. Res Rep Kochi Univ Agric 23:107–114 (in Japanese with English abstract) Nagashima K, Yoshida S, Hosaka T (2009) Patterns and factors in early-stage vegetation recovery at abandoned plantation clearcut sites in Oita, Japan: possible indicators for evaluating vegetation status. J For Res 14:135–146. 10.1007/s10310-009-0118-8 Nakao K, Kabeya D, Awaya Y, Yamasaki S, Tsuyama I, Yamagawa H, Miyamoto K, Araki MG (2022) Assessing the regional-scale distribution of height growth of Cryptomeria japonica stands using airborne LiDAR, forest GIS database and machine learning. For Ecol Manag 506:119953. 10.1016/j.foreco.2021.119953 Negishi Y, Eto Y, Hishita M, Negishi S, Suzuki M, Masaka K, Seiwa K (2020) Role of thinning intensity in creating mixed hardwood and conifer forests within a Cryptomeria japonica conifer plantation: A 14-year study. For Ecol Manag 468:118184. 10.1016/j.foreco.2020.118184 Niiyama K, Ogawa M, Kushima H, Takahashi K, Sato T, Sakai T, Tanouchi H (2010) Collection and evaluation of literatures related to broadleaf tree regeneration for leading artificial coniferous forests to broadleaf forests. J Japanese For Soc 92:292–296. 10.4005/jjfs.92.292 (in Japanese with English abstract) Noguchi M, Sakai A, Okuda S, Inagaki Y, Fukata H (2009) Six-year responses vegetation to thinning in hinoki cypress plantations in Shikoku islands, Japan. Japanese J For Environ 51:127–136. 10.18922/jjfe.51.2_127 (in Japanese with English abstract) Noguchi M, Okuda S, Miyamoto K, Itou T, Inagaki Y (2011) Composition, size structure and local variation of naturally regenerated broadleaved tree species in hinoki cypress plantations: a case study in Shikoku, south-western Japan. Forestry 84:493–504. 10.1093/forestry/cpr027 Noguchi M, Miyamoto K, Okuda S, Itou T, Sakai A (2016) Heavy thinning in hinoki plantations in Shikoku (southwestern Japan) has limited effects on recruitment of seedlings of other tree species. J For Res 21:131–142. 10.1007/s10310-016-0522-9 Oda M, Mitsugi Y, Hirata Y (2010) GIS to extract forests suitable for conversion to hardwood forests. Shinrin Kagaku 59:9–12 (tentative translation, in Japanese) Sakai A, Sato S, Sakai T, Kuramoto S, Tabuchi R (2005) A soil seed bank in a mature conifer plantation and establishment of seedlings after clear-cutting in southwest Japan. J For Res 10:295–304. 10.1007/s10310-004-0138-3 Sakai A, Hirayama T, Oshioka S, Hirata Y (2006) Effects of elevation and postharvest disturbance on the composition of vegetation established after the clear-cut harvest of conifer plantations in southern Shikoku, Japan. J For Res 11:253–265. 10.1007/s10310-005-0182-7 Sakai A, Sakai T, Kuramoto S, Sato S (2010) Soil seed banks in a mature Hinoki ( Chamaecyparis obtusa Endl.) plantation and initial process of secondary succession after clearcutting in southwestern Japan. J For Res 15:316–327. 10.1007/s10310-010-0187-8 Sato T (2021) Municipal forest management practices aimed towards mixed conifer–hardwood forests . Tokyo: ZENRINKYOU. Forestry Improvement and Extension Series, No. 197. pp.173. ISBN978-4-88138-397-1 (in Japanese) Seiwa K, Eto Y, Hishita M, Masaka K (2012) Effects of thinning intensity on species diversity and timber production in a conifer ( Cryptomeria japonica ) plantation in Japan. J For Res 17:468–478. 10.1007/s10310-011-0316-z Shimizu T (1980) Relation between scanty runoff from mountainous watershed and geology, slope and vegetation. Bull Forestry For Prod Res Inst 310:109–128 (in Japanese with English abstract) Shinomiya Y, Yoshinaga S (2008) Behavior of water during large scale rainfall events on the slope of natural forest in a mountainous headwater catchment in the Shimanto river basin. J Japan Soc Hydrology Water Resour 21:126–139 (in Japanese with English abstract) Takafumi H, Hiura T (2009) Effects of disturbance history and environmental factors on the diversity and productivity of understory vegetation in a cool-temperate forest in Japan. For Ecol Manag 257:843–857. 10.1016/j.foreco.2008.10.020 Takahashi T, Yamamoto K, Senda Y, Tsuzuku M (2005) Estimating individual tree heights of sugi ( Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. J For Res 10:135–142. 10.1007/s10310-004-0125-8 Takeda H (2017) Germination characteristics of Machilus thunbergii in northern Niigata Prefecture. Bull Niigata Prefectural For Res Inst 58:1–3 (tentative translation, in Japanese) Tanimoto T (1982) Fundamental studies on release cutting in forest plantation (I) Analysis of growth of sugi ( Cryptomeria japonica ) and natural vegetation in the young sugi plantation. Bull Forestry For Prod Res Inst 320:53–121 (in Japanese with English abstract) Tanimoto T (1990) Ecology of Hardwood Practices . Tokyo: Soubun. p. 245. (tentative translation, in Japanese) Taylor R, Davis C, Brandt J, Parker M, Stäuble T, Said Z (2020) The rise of big data and supporting technologies in keeping watch on the world's forests. Int Forestry Rev 22:129–141. 10.1505/146554820829523880 Utsugi E, Kanno H, Ueno N, Tomita M, Saitoh T, Kimura M, Kaniu K, Seiwa K (2006) Hardwood recruitment into conifer plantations in Japan: effects of thinning and distance from neighboring hardwood forests. For Ecol Manag 237:15–28. 10.1016/j.foreco.2006.09.011 Wulder MA, Bater CW, Coops NC, Hilker T, White JC (2008) The role of LiDAR in sustainable forest management. forestry Chron 84:807–826. 10.5558/tfc84807-6 Yamagawa H, Ito S (2006) The role of different sources of tree regeneration in the initial stages of natural forest recovery after logging of conifer plantation in a warm-temperate region. J For Res 11:455–460. 10.1007/s10310-006-0229-4 Yamagawa H, Ito S, Mitsuda Y, Fukuzato K (2006) Effects of topography and management history on natural forest recovery in abandoned forest after clear-cutting in Miyazaki, Japan. J For Res 11:99–106. 10.1007/s10310-005-0192-5 Yamagawa H, Ito S, Nakao T (2007) Edge effects from a natural evergreen broadleaved forest patch on advanced regeneration and natural forest recovery after clear-cutting of a sugi ( Cryptomeria japonica ) plantation. Japanese J For Environ 49:111–122. 10.18922/jjfe.49.2_111 (in Japanese with English abstract) Yamagawa H, Ito S, Sakuta K, Mizoue N, Nakao T (2009) Effects of small-scale clearcutting management on species diversity and vertical structure of understory vegetation of a conifer plantation comprising uneven-aged stands, in Kyushu, Southern Japan. J Japanese For Soc 91:277–284. 10.4005/jjfs.91.277 (in Japanese with English abstract) Yamagawa H, Ito S, Nakao T (2010) Restoration of semi-natural forest after clearcutting of conifer plantations in Japan. Landscape Ecol Eng 6:109–117. 10.1007/s11355-009-0088-1 Yamaura Y, Lindenmayer D, Yamada Y, Gong H, Matsuura T, Mitsuda Y, Masaki T (2019) A spatially-explicit empirical model for assessing conservation values of conifer plantations. For Ecol Manag 444:393–404. 10.1016/j.foreco.2019.04.038 Yasuoka H (1935) Study of sugi cedar forest in Yanase National Forest . Kochi: Kochi Regional Forestry Bureau. pp56. (in Japanese, tentative translation) Yin G, Xie J, Ma D, Xie Q, Verger A, Descals A, Filella I, Peñuelaset J (2023) Aspect matters: Unraveling microclimate impacts on mountain greenness and greening. Geophys Res Lett 50:e2023GL105879. https://doi.org/10.1029/2023GL105879 Yokoi S, Yamaguchi K (1998) Growth patterns of cedar and broadleaf trees in a non-achieving cedar plantation in a snow-covered area. Japanese J For Environ 40:91–96 (in Japanese, tentative translation) Additional Declarations No competing interests reported. Supplementary Files SITable.docx FigureS1.tif FigureS2.tif FigureS3.tif FigureS4a.tif FigureS4b.tif FigureS4c.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 18 Jan, 2026 Reviewers invited by journal 16 Jan, 2026 Editor assigned by journal 18 Sep, 2025 Submission checks completed at journal 18 Sep, 2025 First submitted to journal 17 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7644359","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576409915,"identity":"3115f0bb-e23c-4db7-9a2b-7dd25e816970","order_by":0,"name":"Eriko Ito","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie2RsWrDMBCGfyFQFxOvCi7NKygInBT8MPLiqc2SpUMJKYZkCWTVw3Q4MHjKS4RC6ahuHjJUbgikUDsZS9G3CHH3obtfQCDwBxHHQyrEayKX3QH8vPCbIk6K3OV7W+jLyqmkAKN11CqXGIxKwufrVMegIsmMmk3W4B8Og1n3YMIw+y7TYflSJw9GzW8riHsLMe9WIsUjkpki/8pjc8gth9ARRL7sUw7fikmTqVFXKiCZekVrHBX+1q8Uhm1I6qH1IW/8LpKzFbOqe5dRWREaWoy37Vc2PjEZV5VzT3VnYp6b5vxmALaSULXpUX7SdnIHPF+vBAKBwH/nC8OwTBP8dXtoAAAAAElFTkSuQmCC","orcid":"","institution":"Kansai Research Center, FFPRI","correspondingAuthor":true,"prefix":"","firstName":"Eriko","middleName":"","lastName":"Ito","suffix":""},{"id":576409916,"identity":"d80e4b89-ecd9-4e1d-a7ab-1d512c969234","order_by":1,"name":"Yoshiyuki Inagaki","email":"","orcid":"","institution":"Shikoku Research Center, FFPRI","correspondingAuthor":false,"prefix":"","firstName":"Yoshiyuki","middleName":"","lastName":"Inagaki","suffix":""},{"id":576409917,"identity":"54ba258f-c21e-4fd1-8579-284210e1f6d7","order_by":2,"name":"Kimihiro Kida","email":"","orcid":"","institution":"Tohoku Research Center, FFPRI","correspondingAuthor":false,"prefix":"","firstName":"Kimihiro","middleName":"","lastName":"Kida","suffix":""},{"id":576409918,"identity":"f98a1fee-fc56-4999-b8c6-bf476c618b9b","order_by":3,"name":"Hitomi Furusawa","email":"","orcid":"","institution":"Forestry and Forest Products Research Institute (FFPRI)","correspondingAuthor":false,"prefix":"","firstName":"Hitomi","middleName":"","lastName":"Furusawa","suffix":""},{"id":576409919,"identity":"03f48b15-4acf-42db-8696-308070b3a20f","order_by":4,"name":"Tatsuya Tsurita","email":"","orcid":"","institution":"Forestry and Forest Products Research Institute (FFPRI)","correspondingAuthor":false,"prefix":"","firstName":"Tatsuya","middleName":"","lastName":"Tsurita","suffix":""},{"id":576409920,"identity":"5d253c65-6f99-4967-ac88-c8823b7ae5fb","order_by":5,"name":"Takuya Manaka","email":"","orcid":"","institution":"Forestry and Forest Products Research Institute (FFPRI)","correspondingAuthor":false,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Manaka","suffix":""},{"id":576409921,"identity":"b5fa1d64-f0f2-49d2-93df-83df57abf416","order_by":6,"name":"Nanae Hosokawa","email":"","orcid":"","institution":"Shikoku Research Center, FFPRI","correspondingAuthor":false,"prefix":"","firstName":"Nanae","middleName":"","lastName":"Hosokawa","suffix":""},{"id":576409922,"identity":"b90a0b6a-6a85-4cc9-b818-0ccffe1b312e","order_by":7,"name":"Katsuhiro Nakao","email":"","orcid":"","institution":"Kansai Research Center, FFPRI","correspondingAuthor":false,"prefix":"","firstName":"Katsuhiro","middleName":"","lastName":"Nakao","suffix":""},{"id":576409923,"identity":"8d76938a-e6b0-4a94-9075-1fd133335f49","order_by":8,"name":"Atsushi Sakai","email":"","orcid":"","institution":"Tohoku Research Center, FFPRI","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Sakai","suffix":""}],"badges":[],"createdAt":"2025-09-18 02:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7644359/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7644359/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100702526,"identity":"585a9bc0-328c-4c3c-a79d-80644bd6b0c9","added_by":"auto","created_at":"2026-01-20 16:21:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86603,"visible":true,"origin":"","legend":"","description":"","filename":"Maintextrevisever2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/f6239a9479e3a908edefa8f4.docx"},{"id":100702585,"identity":"4bbb46a0-689d-445e-a0c2-d986b5a1f889","added_by":"auto","created_at":"2026-01-20 16:21:56","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20825626,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/cafbd24fda0aefaae35951a4.tif"},{"id":100702784,"identity":"4f6eab25-9c44-40f6-b684-cb90a293be9c","added_by":"auto","created_at":"2026-01-20 16:22:55","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1193798,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/6c7cf0604c7699bb8ecb8e72.tif"},{"id":100702595,"identity":"2967e1ec-6486-4843-a7be-96bb7109aaf2","added_by":"auto","created_at":"2026-01-20 16:22:00","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":18226,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/98207e667e10e97967ca1de3.docx"},{"id":100702541,"identity":"f2d8dc43-996c-4bc0-a0c4-c67455034b24","added_by":"auto","created_at":"2026-01-20 16:21:32","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3181494,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/3d55291f3023dbe161271a90.tif"},{"id":100702494,"identity":"b87e8695-0b06-4990-b278-c4703ea72144","added_by":"auto","created_at":"2026-01-20 16:21:01","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1258330,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/cc79d50e3da74ac604d42250.tif"},{"id":100702846,"identity":"c8f0f7b3-9388-49ce-bd96-9a3493dc1d9f","added_by":"auto","created_at":"2026-01-20 16:23:12","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3115764,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/c351206200771baeed20fc69.tif"},{"id":100703136,"identity":"1f5c131c-3b1f-411b-99a2-e3b672878644","added_by":"auto","created_at":"2026-01-20 16:26:41","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1768884,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/9b926f182d7ba2811ca84596.tif"},{"id":100703131,"identity":"e8fbb671-12b4-4077-9030-0ef36153dca8","added_by":"auto","created_at":"2026-01-20 16:26:37","extension":"json","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10295,"visible":true,"origin":"","legend":"","description":"","filename":"7e9a06fcfc4a4940ae49c91899417ffa.json","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/3d0339d253821d2914891835.json"},{"id":100702568,"identity":"2e945ca8-a211-4f63-a55e-12a32f5ca6d1","added_by":"auto","created_at":"2026-01-20 16:21:43","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2938314,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/b737d2595dfd2c85440776ae.tif"},{"id":100702735,"identity":"e8f11c61-d664-4176-8641-d52bb2a0443e","added_by":"auto","created_at":"2026-01-20 16:22:30","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1210788,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/075eee4ddf00a3fea1a22c32.tif"},{"id":100702781,"identity":"9f4c3299-6776-47ee-96dd-1b15fa198397","added_by":"auto","created_at":"2026-01-20 16:22:54","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1507240,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/e0e964fb0aa0b04ba54599b8.tif"},{"id":100703600,"identity":"1776f35b-1529-46af-8d51-aab57b85336f","added_by":"auto","created_at":"2026-01-20 16:30:49","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1421800,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4a.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/57e331228e63d4da3023e585.tif"},{"id":100703035,"identity":"3c2b6f54-bf42-4485-8b0c-f45c42f006c6","added_by":"auto","created_at":"2026-01-20 16:25:23","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1452678,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4b.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/bfeb09c1080067ce1eb5d815.tif"},{"id":100703132,"identity":"1b010e2b-febd-42d0-8893-150f5b2a40d5","added_by":"auto","created_at":"2026-01-20 16:26:39","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1436726,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4c.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/96d177a0be40a10550fd047e.tif"},{"id":100702519,"identity":"f5fffa8f-1ada-4ed8-a0fe-d823eba031de","added_by":"auto","created_at":"2026-01-20 16:21:20","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69470,"visible":true,"origin":"","legend":"","description":"","filename":"SITable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/666a96c90799df101c99cc13.docx"},{"id":100703364,"identity":"5de90041-2962-47ee-9e55-5c90afe1a037","added_by":"auto","created_at":"2026-01-20 16:28:17","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182211,"visible":true,"origin":"","legend":"","description":"","filename":"7e9a06fcfc4a4940ae49c91899417ffa1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/2126b30855cd92678946257e.xml"},{"id":100702517,"identity":"c0e8978c-2048-4dc2-b3af-7b4e1b951341","added_by":"auto","created_at":"2026-01-20 16:21:19","extension":"tif","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":20825626,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/e78b1f851943768f0f95bf2f.tif"},{"id":100703101,"identity":"0f77fc2e-31a4-4d2d-959c-5f03e03c77e9","added_by":"auto","created_at":"2026-01-20 16:26:08","extension":"tif","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1193798,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/26ca144815d826b970811ca5.tif"},{"id":100702909,"identity":"de9d5114-866d-498f-aef4-e1e410ed9032","added_by":"auto","created_at":"2026-01-20 16:23:33","extension":"tif","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3181494,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/4fd66c056871725aee2930b6.tif"},{"id":100703279,"identity":"11d01c64-e311-42e4-8b74-7475426d1075","added_by":"auto","created_at":"2026-01-20 16:27:49","extension":"tif","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1258330,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/2c3a914274fa592c21558527.tif"},{"id":100703324,"identity":"5cfed921-37fa-42cd-9990-e05f6ca3a4b7","added_by":"auto","created_at":"2026-01-20 16:28:15","extension":"tif","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3115764,"visible":true,"origin":"","legend":"","description":"","filename":"Figure5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/f328395d49f1a5a96d0c07c2.tif"},{"id":100703039,"identity":"189df934-1333-4527-8db8-3e9b556aec7a","added_by":"auto","created_at":"2026-01-20 16:25:30","extension":"tif","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1768884,"visible":true,"origin":"","legend":"","description":"","filename":"Figure6.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/6c2828518b9f42b64ef04c2b.tif"},{"id":100703141,"identity":"6e1eed33-8dfd-4f54-a8fa-926f5afb8650","added_by":"auto","created_at":"2026-01-20 16:26:45","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80406,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/50fdc2ba9745e983500a3d8d.png"},{"id":100703003,"identity":"ec985f8a-8812-41d2-bf01-3b6d3f396516","added_by":"auto","created_at":"2026-01-20 16:24:34","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100693,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/2440eb2ef0aa420fda364408.png"},{"id":100703002,"identity":"1e6f29b1-c174-4f77-aeff-20482d1e9276","added_by":"auto","created_at":"2026-01-20 16:24:32","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":181587,"visible":true,"origin":"","legend":"","description":"","filename":"7e9a06fcfc4a4940ae49c91899417ffa1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/04053b2a7d4dd2f6eb3a8bca.xml"},{"id":100702660,"identity":"4daa14c1-e7a4-4770-b337-9b1f3d25e7b4","added_by":"auto","created_at":"2026-01-20 16:22:15","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":196172,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/0eefd2624d2374f929564299.html"},{"id":100702671,"identity":"6438571b-313a-4e7e-9570-b6f0a30752df","added_by":"auto","created_at":"2026-01-20 16:22:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":250042,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area in Kochi Prefecture, Japan, showing (a) elevation, (b) vegetation and (c) geology (Geological Survey of Japan, AIST, 2023). See Table S1 for detailed geological classifications.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/efec66142ee495dfca0e8b4d.png"},{"id":100703290,"identity":"23094e97-0ca5-4f5a-9ddc-90257bf4b719","added_by":"auto","created_at":"2026-01-20 16:28:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153411,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of stand age for (a) conifer–cells and (b) hardwood–cells. Line in (b) indicates the proportion of hardwood–cells. Gray bars represent stands aged 41–70 years in 2018, which form the core of postwar afforestation efforts. White circles indicate points with limited data (\u0026lt; 10000 observations).\u003c/p\u003e","description":"","filename":"OnlineFigure24.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/c3099963419005184b6f1a51.png"},{"id":100703263,"identity":"dcc664fe-e360-4c5c-a93d-d3b4c4cefead","added_by":"auto","created_at":"2026-01-20 16:27:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":578358,"visible":true,"origin":"","legend":"\u003cp\u003ePartial dependence plots illustrating the predicted probability of hardwood–cells based on a logistic generalized additive model (GAM) without interaction terms (Model I). The plots depict the average predicted probability and their 95% confidence intervals (CIs) for (a) elevation, (b) aspect, (c) slope, and (d) topographic wetness index (TWI) across elevation zones. Aspect was determined as the cosine-transformed value of the azimuth angle of aspect, where north south are represented by 1 and –1, respectively. Predicted probability was calculated by holding all variables constant at neutral values, except for the focal univariate predictor, whose assigned values are shown in the graph. Neutral elevation values were calculated separately for each elevation zone (318, 750, and 1191m for low-, middle- and high-elevation zones, respectively), whereas neutral values for aspect (0.00), slope (36.7), and TWI (1.92 as of ln(1 + TWI)) were constant values across the entire study area, regardless of elevation zone.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/121a35675170b7d9e5e1bfe0.png"},{"id":100702646,"identity":"62d032fd-8d82-488e-8fe1-13a3004edb57","added_by":"auto","created_at":"2026-01-20 16:22:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192234,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-dimensional heatmap showing the predicted probability of hardwood–cells based on a logistic GAM (Model II). The heatmap illustrates the average predicted probability for combinations of elevation and aspect in low-elevation zones. Predictions were generated by holding slope and TWI constant at their neutral values (slope = 36.7° and ln(1 + TWI) = 1.92), ensuring that their scaled values were set to 0.\u003c/p\u003e","description":"","filename":"OnlineFigure43.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/eef46f48af42814abae37d02.png"},{"id":100703128,"identity":"236bbe86-e689-4414-988b-39ec981800ff","added_by":"auto","created_at":"2026-01-20 16:26:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":568447,"visible":true,"origin":"","legend":"\u003cp\u003ePartial dependence plots illustrating the predicted probability of hardwood–cells in low-elevation zones based on Model II, fitted separately for each geological category (Model IV). The plots show the average predicted probability and their 95% CIs for (a) elevation, (b) aspect, (c) slope, and (d) TWI for each geological category. Predicted probability was calculated by holding all variables constant at neutral values, except for the focal univariate predictor. Neutral values—elevation (319 m), aspect (0.00), slope (36.7°), and TWI (1.92, expressed as ln(1 + TWI))—were uniformly applied across the three geological categories.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/31f1ec69de5606f6251992e0.png"},{"id":100702640,"identity":"aea1b78d-7bf6-48e8-9348-eb6f5f8a69c6","added_by":"auto","created_at":"2026-01-20 16:22:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":289474,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-dimensional heatmaps showing the predicted probability of hardwood–cells based on a logistic GAM, fitted separately for each geological category. Heatmaps illustrate the average predicted probability for combinations of elevation and aspect in the low-elevation zones. (a) Chichibu Belt (Jurassic accretionary complex), (b) Shimanto Belt (Cretaceous accretionary complex), and (c) the Cretaceous system of the Chichibu Terrane. Elevation values (m) are scaled between –1.0 and 1.0. The elevation range differs across geological categories, leading to some predicted values falling outside the observed range. The extrapolated range is below the dotted line in the figure, with the threshold marked in bold font. These probability values were calculated by substituting neutral values for slope (36.7°) and TWI (1.92, expressed as ln(1 + TWI)), ensuring that scaled values for these variables were set to 0 (see also Figure 5).\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/fa2736d9f4b2b4774089cc38.png"},{"id":100712345,"identity":"135772b9-fa47-437c-9f34-08fc84f79234","added_by":"auto","created_at":"2026-01-20 18:12:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4325030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/c84f8c82-c111-4b79-8f96-69b58d060aa1.pdf"},{"id":100702594,"identity":"c44eb225-cd06-430c-8667-6b9a38c06060","added_by":"auto","created_at":"2026-01-20 16:22:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69470,"visible":true,"origin":"","legend":"","description":"","filename":"SITable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/dc87949ec8f930e664337f84.docx"},{"id":100703322,"identity":"246f4728-f4cc-42da-9d00-8b7c5c125e9c","added_by":"auto","created_at":"2026-01-20 16:28:14","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2938314,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/2bfd2732be3dbba2bacd0b5f.tif"},{"id":100703129,"identity":"d92ab3a1-e666-4a44-afb3-4338a035dbf7","added_by":"auto","created_at":"2026-01-20 16:26:35","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1210788,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/81bfb8844a36160cf213bdfb.tif"},{"id":100702529,"identity":"0e760ebd-5eed-4038-b0c3-5576177c6e3c","added_by":"auto","created_at":"2026-01-20 16:21:27","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1507240,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/1bf8d4889051e72b173944f0.tif"},{"id":100702730,"identity":"be3f6020-cafb-4e2a-8965-b49937258c3a","added_by":"auto","created_at":"2026-01-20 16:22:28","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1421800,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4a.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/cc98d5dce123a85487d1d4f8.tif"},{"id":100702805,"identity":"608e916c-9ebe-401c-8799-22b40b091bf7","added_by":"auto","created_at":"2026-01-20 16:23:05","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1452678,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4b.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/195debf652cbe3cf54bb79ed.tif"},{"id":100702863,"identity":"aa798b40-e359-45f8-a6e4-1aea942c01d2","added_by":"auto","created_at":"2026-01-20 16:23:29","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1436726,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS4c.tif","url":"https://assets-eu.researchsquare.com/files/rs-7644359/v1/59bbfd3f029d4eb370849002.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration of spatial biases in natural hardwood regeneration in conifer plantations in southwestern Japan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, forest management has increasingly emphasized biodiversity, recognizing its vital role in sustaining ecosystem functions and productivity (Liang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jactel et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ali, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chisholm and Dutta Gupta, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Japan, where monocultures of \u003cem\u003esugi\u003c/em\u003e cedar (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) and \u003cem\u003ehinoki\u003c/em\u003e cypress (\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e) still dominate, such awareness has led to a policy shift. The 2023 National Biodiversity Strategy, aligned with the Kunming\u0026ndash;Montreal Global Biodiversity Framework, promotes the diversification of these plantations through longer harvest rotations and increased use of mixedwood and hardwood stands (Japan Forestry Agency, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key challenge in implementing this strategy is identifying plantation sites suitable for natural hardwood regeneration, which can serve as candidates for conversion (Kunisaki, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding the geographic and environmental conditions that support such regeneration is essential for effective spatial planning and long-term forest sustainability (Yamagawa et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nagaike, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jafarzade et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lidman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have examined various factors that influence the success of plantation conversion to mixed or hardwood stands (e.g., Sato, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Silvicultural characteristics such as plantation species (Akai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Akai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Kurose, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Koyama and Yamauchi, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), forest age (Ito et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yamagawa et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), forest patch size (Nagashima et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), thinning regime (Fukata et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Noguchi et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hirata et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Seiwa 2012; Noguchi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Negishi et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and overall forest-management practices (Utsugi et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yamagawa et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nagashima et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Igarashi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) have all been shown to affect regeneration outcomes. In addition, site history\u0026mdash;particularly land-use legacy and browsing pressure by wild deer\u0026mdash;can significantly constrain natural regeneration (Ito et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sakai et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Sakai et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yamagawa et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Noguchi, 2009; Takafumi and Hiura, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Another key factor is seed dispersal dynamics, including proximity to natural seed sources (Sakai et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ito et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sakai et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yamagawa et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Igarashi and Masaki, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), natural regeneration sources (Yamagawa and Ito, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Igarashi and Kiyono, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and dispersal patterns (Nagaike, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sakai et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), which have received considerable attention in previous research. While topographic features such as slope and aspect are also known to influence regeneration following clear-cutting (Yamagawa et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), they have been less intensively studied. Although geological factors once received considerable attention in early forestry studies and were shown to be closely linked to areas of exceptional conifer growth (Yasuoka, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1935\u003c/span\u003e; Koide \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1939\u003c/span\u003e), they have since been largely overlooked in research on hardwood regeneration; therefore, we included them in our analysis.\u003c/p\u003e \u003cp\u003eThe forestry sector is undergoing a massive shift in the utilization of widely available information technology and remote-sensing techniques. Japan\u0026rsquo;s National Forest Inventory (NFI)\u0026mdash;a systematic network of approximately 16,000 permanent plots sampled every five years\u0026mdash;has been used to identify spatial patterns of hardwood regeneration across plantations (Yamaura et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such exploratory, visualization‑based approaches, though not grounded in a priori hypotheses, help lay the groundwork for subsequent hypothesis testing and detailed analyses. Beyond inventory data, forest registry data (Japanese: \u003cem\u003eshinrinbo\u003c/em\u003e)\u0026mdash;the foundational basis for forest management in Japan\u0026mdash;are increasingly standardized and transitioned to cloud-based Geographic Information Systems (GIS). These advances facilitate centralized management of forest age, stand type, species composition, monitoring records, and operational histories. Through GIS, regeneration success can be spatially related to proximity to \u0026ldquo;mother\u0026rdquo; hardwood stands and past land use (Oda et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Forestry and Forest Products Research Institute, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Light detection and ranging (LiDAR) technology represents another leap forward in data generation for forest management in Japan (Takahashi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wulder et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hirata et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), holding particular promise for biodiversity-oriented forest management (Camarretta et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Since October 2023, high‑density airborne LiDAR scans (ALS) covering both public and privately owned forests have been released for select prefectures. As of August 2025, species‑polygon data are publicly available in 10 Japanese prefectures, enabling fine‑scale (10-m resolution) mapping of tree species distributions. This expanded accessibility marks a leap forward from earlier data releases that only included national forest boundaries.\u003c/p\u003e \u003cp\u003eCombining forest registry data with ALS-derived forest metrics is expected to provide substantial insights for forest management. These \u0026ldquo;big data\u0026rdquo; would effectively support exploratory approaches to natural hardwood regeneration in mature conifer plantations, revealing spatial heterogeneity in regeneration patterns. Therefore, in this study, we quantified the spatial heterogeneity of hardwood regeneration across a study area of roughly 250 km\u0026sup2; in the warm-temperate forest region of Kochi Prefecture, southwestern Japan, for which ALS data are now available. Specifically, we compared GIS-based forest registry data with ALS-derived tree species mapping using a pixel-counting approach for cedar and cypress plantations. We examined whether hardwood regeneration in these plantations exhibited geographic bias based on topographic and geologic factors. Adopting a discovery-focused approach, we evaluated its relationship with these environmental surface factors. A logistic generalized additive model (GAM) was used to analyze how topographic and geological factors contributed to the occurrence probability of canopy-reaching hardwood regeneration in conifer plantations.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study area encompassed Kami, Kochi Prefecture, Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), located in the warm-temperate zone and characterized by high precipitation and steep, mountainous terrain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Forested land within the study area consists of \u0026gt;\u0026thinsp;70% cedar and cypress plantations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) and the average slope is 33\u0026deg;. Prior to the development of conifer plantations, vegetation at low elevations (\u0026lt;\u0026thinsp;600 m a.s.l., Sakai et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) was dominated by evergreen hardwood forests, mainly comprising evergreen \u003cem\u003eCastanopsis\u003c/em\u003e and \u003cem\u003eQuercus\u003c/em\u003e species (e.g., \u003cem\u003eQuercus glauca\u003c/em\u003e), and members of the laurel family (\u003cem\u003eMachilus\u003c/em\u003e and \u003cem\u003eNeolitsea\u003c/em\u003e species). These species tend to germinate quickly following seed dispersal, often forming dense seedling stocks on the forest floor, particularly \u003cem\u003eQuercus\u003c/em\u003e (Hashizume and Aikawa, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Takeda, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sakai et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). At middle elevations (approximately 600\u0026ndash;900 m a.s.l.), evergreen \u003cem\u003eCastanopsis\u003c/em\u003e species are absent; this zone features a mix of evergreen hardwoods, including evergreen \u003cem\u003eQuercus\u003c/em\u003e species (notably \u003cem\u003eQuercus salicina\u003c/em\u003e, \u003cem\u003eQuercus acuta\u003c/em\u003e, and \u003cem\u003eQuercus sessilifolia\u003c/em\u003e), \u003cem\u003eDistylium racemosum\u003c/em\u003e, and conifers such as \u003cem\u003eAbies firma\u003c/em\u003e and \u003cem\u003eTsuga sieboldii\u003c/em\u003e. The high-elevation zone (\u0026gt;\u0026thinsp;900 m a.s.l.) is dominated by deciduous Fagaceae species, including \u003cem\u003eFagus crenata\u003c/em\u003e, \u003cem\u003eFagus japonica\u003c/em\u003e, \u003cem\u003eQuercus crispula\u003c/em\u003e, and \u003cem\u003eCastanea crenata\u003c/em\u003e. These high-elevation species are accompanied by other deciduous hardwoods such as \u003cem\u003eCarpinus\u003c/em\u003e spp. and \u003cem\u003eAcer\u003c/em\u003e species as canopy trees. Additionally, conifers such as \u003cem\u003eA. firma\u003c/em\u003e, \u003cem\u003eT. sieboldii\u003c/em\u003e, and \u003cem\u003eAbies homolepis\u003c/em\u003e sometimes grow in mixed stands (Ministry of the Environment, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). These three elevation zones largely correspond to categories defined by Fukata et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) based on understory vegetation: low-elevation zones dominated by the most common ferns and evergreen trees, middle-elevation zones characterized by evergreen \u003cem\u003eQuercus\u003c/em\u003e species, and high-elevation zones with deciduous trees.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Shikoku Mountains in Kochi Prefecture are oriented along an approximate east\u0026ndash;west gradient. The geology is characterized by belts of geological zones of different formation ages (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The northernmost formation is the Sambagawa Belt (metamorphic rock of the Cretaceous period, composed of crystalline schist), followed by the Chichibu Belt (Permian to Early Cretaceous accretionary complex), the Cretaceous system in the Chichibu Terrane (Cretaceous marine sedimentary rocks), and the Shimanto Belt (Cretaceous accretionary complex) (Moreno et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to tectonic effects, accretionary ages tend to be younger in the southern part of the Shimanto Belt zone and older in the southern part of the Chichibu Belt (Endo and Yokoyama, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We divided the study area into geologic zones based on rock formation ages and processes (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Broadly, elevation tends to be higher in the northern portion of the study area, such that geologic zones and elevation tend to be correlated.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eForest registry data\u003c/h3\u003e\n\u003cp\u003eSpatially explicit forest registry data available from Kochi Prefecture include stand attributes such as age, type, tree species, and ownership boundaries. The database underwent revision between 2005 and 2009. Forest registry data were available for a 337.6-km\u003csup\u003e2\u003c/sup\u003e area in Kami, of which 40% comprised cedar plantations and 34% comprised cypress plantations. In 2018, the year of ALS acquisition in the study area, the cedar and cypress plantations had a median ages of 58 and 57 years, respectively. A small proportion of the study area (0.03%) included plantations older than 120 years. These reported plantations likely represent errors in data reporting or entry; therefore, only stands\u0026thinsp;\u0026le;\u0026thinsp;120 years of age were included in our analysis pertaining to forest age.\u003c/p\u003e \u003cp\u003eALS data for Kochi Prefecture were collected in 2018 and made publicly available in 2023 as part of a Forest Agency initiative. Tree species were identified using ALS-derived reflectance intensity at 10 m resolution, concurrently collected aerial photographs, and field confirmation surveys (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geospatial.jp/ckan/dataset/tree_species_kochi\u003c/span\u003e\u003cspan address=\"https://www.geospatial.jp/ckan/dataset/tree_species_kochi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The species classification targets overstory trees, but in areas where canopy closure had not yet occurred, understory trees may have been detected. Canopy closure in Kochi typically occurs within 15 years for cedar and 20 years for cypress plantations (Kochi Prefecture, 2019), though timing varies depending on growth rate and planting density (Irimajiri, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Hardwood species were classified only as \u0026ldquo;hardwood\u0026rdquo;. The composition of hardwood species that regenerate naturally in conifer plantations is described in Tanimoto (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and Sakai et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The equipment, collection protocols, and analytical methodology used in ALS data generation were described by Nakao et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the Forest GIS Forum (2022).\u003c/p\u003e\n\u003ch3\u003eGeographical data\u003c/h3\u003e\n\u003cp\u003eElevation, slope angle (hereafter referred to as slope), slope aspect (hereafter referred to as aspect), and topographic wetness index (TWI) data were obtained for the study area based on a 25-m-resolution digital terrain model for Kochi Prefecture derived using ALS data collected in 2018 (Nakao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The terrain data were resampled to 10-m resolution for analyses. Of these topographic factors, aspect and TWI were chosen because they were highly correlated with cedar growth in a previous study conducted in Kami (Nakao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although slope appeared to have minimal effect on cedar growth according to Nakao et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it was included in our analysis because slope is a fundamental index linked to hardwood regeneration (Yamagawa et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and may affect the feasibility of forest management actions (Kondo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The geology of each survey grid used in the analysis was classified based on a digital geologic map prepared by the Geological Survey of Japan (Geological Survey of Japan, AIST, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eHardwood regeneration\u003c/h3\u003e\n\u003cp\u003eNatural regeneration of hardwood species was determined by comparing forest registry data with ALS data. First, the forest registry data were converted to a 10-m grid to align with the ALS data. For areas where the two datasets overlapped, any grid cell (hereafter, cell) containing a cedar or cypress planation in the forest registry data was included in the analysis. Each cell was classified into one of two categories: conifer\u0026ndash;cell, where both datasets identified the cell as containing cedar or cypress plantations, and hardwood\u0026ndash;cell, where the forest registry dataset identified the cell as containing cedar or cypress plantations, but the ALS dataset indicated the presence of hardwood trees. All hardwood\u0026ndash;cells were interpreted as indicative of hardwood regeneration in conifer plantations in subsequent analyses. In this analysis, cedar and cypress trees are collectively referred to as \u0026ldquo;conifers.\u0026rdquo; Regardless of whether a management unit (Japanese: \u003cem\u003esegyohan\u003c/em\u003e) was registered as cedar or cypress, both species were often interplanted within the same unit (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Consequently, we could not determine whether the conifers in those hardwood\u0026ndash;cells had originally been cedar or cypress.\u003c/p\u003e \u003cp\u003eAll GIS analyses were conducted using ArcGIS v10.6 (ESRI, 2018). ALS-based species classification was validated elsewhere (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Although it is possible that the forest registry data, which were updated during 2005\u0026ndash;2009, contained errors, we were unable to validate the dataset.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe assessed the relationships between hardwood\u0026ndash;cells and predictor variables. Preliminary analyses confirmed spatial autocorrelation in the hardwood\u0026ndash;cell distribution from the ALS data (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.531), indicating that the probability of a cell being occupied by hardwood trees was higher when adjacent cells were also occupied by hardwoods (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). The semivariogram stabilized at a range of \u0026lt;\u0026thinsp;200 m (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To account for spatial autocorrelation, we conducted subsequent statistical analyses using systematic sampling, by dividing the main study area into 200-m\u003csup\u003e2\u003c/sup\u003e subareas and then consistently selecting a cell that occupied the same relative position within each subarea. This procedure resulted in 400 data subsets. A preliminary overall analysis was conducted to evaluate potential predictor variables, including forest age, elevation, slope, aspect, TWI, geology, and their interactions (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). This exploratory analysis did not account for spatial autocorrelation and used the entire dataset.\u003c/p\u003e \u003cp\u003eA logistic GAM was used to explore variables that might predict the probability of hardwood regeneration, with a binary outcome variable, because our preliminary analysis indicated a non-linear relationship between slope or TWI, and the occurrence of hardwood\u0026ndash;cells (see \u003cem\u003eOverall trends\u003c/em\u003e). Preliminary analysis also suggested an aspect effect in the north\u0026ndash;south direction and an interaction between elevation zone and aspect. The logistic GAM was applied separately to the three elevation zones (low, middle, and high) following cosine transformation of the azimuth angle of slope aspect as a continuous variable. In this transformation, north and south were assigned values of 1 and \u0026minus;\u0026thinsp;1, respectively. The analysis focused on stands aged 41\u0026ndash;70 years in 2018, which represented postwar afforestation from 1949 to 1978. Other minor age groups were excluded due to potential uncertainties related to stand history.\u003c/p\u003e \u003cp\u003eThe logistic GAM incorporated elevation, slope, aspect, TWI, and second-order interaction terms as explanatory variables. Initially, 15 models combining these four topographic variables were evaluated using 400 data subsets to identify the combinations yielding the lowest Akaike information criterion (AIC). Subsets containing fewer than 50 data points were excluded from the analysis, a condition observed exclusively in high-elevation zones. All variables used in the logistic GAM model were scaled, with the regularization parameter fixed at 10.0.\u003c/p\u003e \u003cp\u003eSubsequent analyses were restricted to low-elevation zones. Based on our preliminary overall analysis, we tested a model that imposed a monotonicity constraint, such that the effects of elevation and aspect on hardwood\u0026ndash;cell probability were set to decrease monotonically to prevent overfitting. These analyses included second-order interaction terms as explanatory variables. The most frequently selected variable combinations were refined by adding second-order interaction terms to further minimize the AIC. Identification of the best model was based on the area under the receiver operating characteristic curve (AUROC), a metric commonly used to evaluate the performance of binary classification models that measures the ability of the model to predict correct labels. The AUROC ranges from 0 to 1, with higher values indicating better performance. The AUROC was calculated for both the subset used in model fitting and the separate test data to evaluate overfitting. The predicted probability of hardwood occurrence under various combinations of elevation and aspect in the best model were visualized as a two-dimensional (2D) heat map, illustrating elevation\u0026ndash;aspect interactions.\u003c/p\u003e \u003cp\u003eThe effects of geology were considered exclusively in low-elevation zones and restricted to cells within the top five geologic categories, thereby excluding 2.1% of all cells (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). To visualize the effects of aspect within each geological category, as shown by the overall analysis, the 400 data subsets were divided according to five geologic categories, yielding a total of 2000 data subsets. The best model described above was re-fitted to each subset, and 2D partial dependence plots of elevation and aspect were created for each geological category.\u003c/p\u003e \u003cp\u003eModeling was conducted using LogisticGAM in Python v3.10.15, with GeoPandas used for spatial data processing, NumPy for numerical computations, and Matplotlib\u0026rsquo;s Pyplot module for visualization. Supplementary analyses were performed using JMP v10.0.2 (SAS Institute, Cary, NC, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOverall trends\u003c/h2\u003e \u003cp\u003eThe forest registry dataset included approximately 250 km\u003csup\u003e2\u003c/sup\u003e of conifer (cedar or cypress) plantations. The ALS dataset included 53 km\u003csup\u003e2\u003c/sup\u003e of hardwood (21.4% of all), and 184 km\u003csup\u003e2\u003c/sup\u003e of conifer (73.8% of all). The remaining 4.4% in the ALS dataset were excluded from further analyses. Of all cells classified into the two change patterns (n\u0026thinsp;=\u0026thinsp;2,373,113), 77.5% were conifer\u0026ndash;cells and 22.5% were hardwood\u0026ndash;cells.\u003c/p\u003e \u003cp\u003eConsidering stand age, both change patterns had similar age class distributions, with most stands aged 56\u0026ndash;60 years as of 2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Within the target cell, 81.0% of the total were 41\u0026ndash;70 years old in 2018, and were therefore planted during the 30-year period 1949\u0026ndash;1978. Forestry-related details of each period are presented in Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e. The proportion of hardwood\u0026ndash;cells within the main postwar afforestation area consistently accounted for approximately 20\u0026ndash;25% of all cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Generally, hardwood\u0026ndash;cells were more prevalent in young stands (\u0026lt;\u0026thinsp;20 years of age) and very old stands (110\u0026ndash;120 years). The former result may reflect observations of understory species before canopy closure, whereas the latter is limited by the small number of data points.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModeling outcomes\u003c/h3\u003e\n\u003cp\u003eLogistic GAMs incorporating all four explanatory topographic variables (elevation, slope, aspect, and TWI) most frequently yielded the lowest AIC (Model I, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Partial dependence plots with 95% confidence intervals (CIs) for each variable are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Higher hardwood\u0026ndash;cell probability scores were associated with lower elevation in the low-elevation zone and higher elevation in the high-elevation zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). However, CIs were wider in the high-elevation zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, right panels), likely due to the smaller sample size (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Plots of aggregated elevation values, independent of model estimates, are shown in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical details of the best model with the minimum AIC and combination of topographic variables achieving minimum AIC across subsets in logistic GAM models by elevation zone. The numbers are means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, with the range indicated in parentheses.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003e[variables]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation zone\u003csup\u003e1)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e[geological category]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. of subsets\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of data points per subset\u003csup\u003e3)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAUROC\u003csup\u003e4)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTraining subset\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTest data\u003csup\u003e5)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModel I\u003c/p\u003e \u003cp\u003e[\u003cem\u003eelevation\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eslope\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003easpect\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eTWI\u003c/em\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2759\u0026thinsp;\u0026plusmn;\u0026thinsp;478\u003c/p\u003e \u003cp\u003e(1573\u0026ndash;4029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.755\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.568\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1505\u0026thinsp;\u0026plusmn;\u0026thinsp;528\u003c/p\u003e \u003cp\u003e(226\u0026ndash;2831)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.805\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.551\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e561\u0026thinsp;\u0026plusmn;\u0026thinsp;292\u003c/p\u003e \u003cp\u003e(50\u0026ndash;1693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.887\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.517\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel II\u003c/p\u003e \u003cp\u003e[\u003cem\u003eelevation\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eslope\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003easpect\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eTWI\u003c/em\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout interactions, monotonicity constraints (elevation, aspect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2759\u0026thinsp;\u0026plusmn;\u0026thinsp;478\u003c/p\u003e \u003cp\u003e(1573\u0026ndash;4029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.716\u0026thinsp;\u0026plusmn;\u0026thinsp;0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.596\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel III\u003c/p\u003e \u003cp\u003e[\u003cem\u003eelevation\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eslope\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003easpect\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eTWI\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eelevation\u003c/em\u003e * \u003cem\u003eslope\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eelevation\u003c/em\u003e * \u003cem\u003easpect\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eelevation\u003c/em\u003e * \u003cem\u003eTWI\u003c/em\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith interactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2759\u0026thinsp;\u0026plusmn;\u0026thinsp;478\u003c/p\u003e \u003cp\u003e(1573\u0026ndash;4029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.782\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.568\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel IV\u003c/p\u003e \u003cp\u003e[\u003cem\u003eelevation\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eslope\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003easpect\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eTWI\u003c/em\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBased on Model II, re-fitted by geological category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e[Jurassic Chichibu Belt]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e722\u0026thinsp;\u0026plusmn;\u0026thinsp;337\u003c/p\u003e \u003cp\u003e(63\u0026ndash;1668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.800\u0026thinsp;\u0026plusmn;\u0026thinsp;0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.552\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e[Shimanto Belt]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e782\u0026thinsp;\u0026plusmn;\u0026thinsp;262\u003c/p\u003e \u003cp\u003e(121\u0026ndash;1487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.778\u0026thinsp;\u0026plusmn;\u0026thinsp;0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.586\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003e[Cretaceous system of the Chichibu Terrane]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e698\u0026thinsp;\u0026plusmn;\u0026thinsp;329\u003c/p\u003e \u003cp\u003e(50\u0026ndash;1605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.779\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.568\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1)\u003c/sup\u003e Elevation zones: Low (\u0026lt;\u0026thinsp;600 m), Middle (600\u0026ndash;900 m), High (\u0026gt;\u0026thinsp;900 m).\u003c/p\u003e \u003cp\u003e\u003csup\u003e2)\u003c/sup\u003e Number of subsets on which the model converged.\u003c/p\u003e \u003cp\u003e\u003csup\u003e3)\u003c/sup\u003e Subsets with fewer than 50 data were excluded from analysis.\u003c/p\u003e \u003cp\u003e\u003csup\u003e4)\u003c/sup\u003e Area under the receiver operating characteristic curve (AUROC), a metric commonly used to evaluate the performance of binary classification models that measures the ability of the model to predict correct labels. The AUROC ranges from 0 to 1, with higher values indicating better performance.\u003c/p\u003e \u003cp\u003e\u003csup\u003e5)\u003c/sup\u003e Data not used in training subsets.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHigher hardwood\u0026ndash;cell probability was clearly associated with south-facing slopes in the low-elevation zone; this relationship was weaker in the middle-elevation zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In the high-elevation zone, CIs were wider, and the effect of aspect was unclear (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, right panel). Hardwood\u0026ndash;cell probability scores tended to increase when the slope exceeded 40\u0026deg; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), while moderate TWI values were associated with lower hardwood\u0026ndash;cell probability, regardless of the elevation zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Confidence intervals for extreme TWI values were wide, likely due to small sample sizes, making these associations less pronounced. Plots of aggregated values for aspect, slope, and TWI are shown in Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the low-elevation zone, imposing the monotonicity constraint on elevation and aspect further increased AUROC values for test data (Model II, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Model III, which included second-order interactions of elevation with slope, aspect, and TWI, yielded the lowest AIC in 92% of the total 400 data subsets. In contrast with the increasing AUROC values observed in the training data, AUROC values for the test data decreased (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which suggested model overfitting. Model II was considered to be the best model for the low-elevation zone (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A 2D heatmap depicting hardwood\u0026ndash;cell probability in relation to elevation and aspect was generated from Model II (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which highlights the high probability associated with south-facing slopes at lower elevations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe best model (Model II) was re-fitted by each geological category (Model IV). Among the five geological categories, most subsets for the Sambagawa Belt and the Permian Chichibu Belt were insufficient data (\u0026lt;\u0026thinsp;50) or failed to converge (316 and 252 out of the 400 subsets, respectively). Therefore, partial dependence plots with 95% CIs are shown only for the three categories in which more than 95% of the subsets successfully converged (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These plots did not show clear differences among the three geological divisions. However, 2D heatmaps of hardwood\u0026ndash;cell probability in relation to elevation and aspect revealed distinct differences among the geological divisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the Shimanto Belt, a pronounced trend of higher hardwood\u0026ndash;cell probability on south-facing slopes at lower elevations was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). A similar but less pronounced trend was evident in the Cretaceous system of the Chichibu Terrane (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). In the Chichibu Belt (Jurassic accretionary complex), a trend of higher hardwood\u0026ndash;cell probability on low-elevation, south-facing slopes was only faintly observed, with consistently low probability across all combinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Plots of aggregate aspect, slope, TWI values for each of the five geological categories and all elevation zones are shown in Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eElevation and aspect influence on hardwood regeneration\u003c/h2\u003e \u003cp\u003eThe predictive strength of elevation and aspect for the probability of hardwood regeneration within conifer plantations varied with the elevation zone. The association between lower elevation and hardwood regeneration was evident in the low-elevation zone, but less distinct in the middle-elevation zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The frequent occurrence of hardwood regeneration in this low elevation zone in Kochi Prefecture aligns with the findings of Sakai et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Yamaura et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who demonstrated that hardwood regeneration tends to be more vigorous in warmer, lower-elevation areas, based on the relationship with elevation or the warm index, which is generally correlated with elevation. In the high-elevation zones, higher elevation was associated with increased hardwood regeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Natural hardwood regeneration in plantation forests is often associated with plantation failure, particularly at high elevations characterized by heavy snowfall, dense bamboo stands, and increased risk of weather-related damage (Kodani, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Yokoi and Yamaguchi, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Niiyama et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Aiura et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the CIs for these relationships were notably wide, suggesting the need for additional data to better evaluate these trends.\u003c/p\u003e \u003cp\u003eAspect has been identified as the most significant topographic parameter in predicting the growth of \u003cem\u003esugi\u003c/em\u003e cedar in this study area, with particularly poor height growth observed on southwest-facing slopes (Nakao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, the influence of aspect on hardwood regeneration differed across elevation zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In the low-elevation zone, south-facing slopes were clearly associated with hardwood regeneration, a trend that became less distinct in the middle-elevation zone. By contrast north-facing slopes were weakly associated with hardwood regeneration in high-elevation zones, albeit with substantial estimate uncertainty. The effect of aspect on vegetation dynamics can vary with climate, as north-facing slopes may benefit vegetation establishment and growth in dry areas but hinder it where temperatures are limiting (Yin et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although our study site lies within the warm-temperate zone, the observed patterns suggest a clear shift in the aspect\u0026ndash;hardwood regeneration relationship\u0026mdash;from a south-facing preference at low elevations to a north-facing preference at high elevations\u0026mdash;with this trend becoming less distinct in the middle-elevation zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffects of slope and TWI on hardwood regeneration\u003c/h2\u003e \u003cp\u003eBoth slope and TWI were effective predictors of hardwood regeneration probability, showing consistent but non-monotonic trends across elevation zones. Hardwood regeneration probability increased sharply on slopes exceeding 40\u0026deg; (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In contrast, TWI indicated higher regeneration likelihood in ridges and valleys, and lower probability in moderate terrains (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). This pattern partially aligns with Yamagawa et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), who reported greater post-clearcutting vegetation-recovery on steep or convex slopes and attributed it to factors such as soil moisture, surface soil stability, and the pre-harvest distribution of understory trees. Our findings, which demonstrate a topographic bias in hardwood regeneration within conifer plantations prior to logging, support the importance of pre-existing hardwood vegetation in explaining these recovery patterns. However, this study does not provide sufficient evidence to confirm or refute the influence of soil moisture or surface soil stability.\u003c/p\u003e \u003cp\u003eTrends in slope gradient may also be linked to forest management practices. Historical forest management was not considered in this study due to data availability limitations that allowed only simple comparisons between ALS and forest registry data. However, forest management history should not be overlooked in future localized studies (Yamaura et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Early weeding operations are critical to afforestation success, yet their efficiency declines on slopes steeper than 40\u0026deg; (Kondo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Notably, Kondo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) based their findings on surveys of workers using engine-powered brush cutters, whereas weeding in the study area was historically conducted with scythes, potentially leading to different efficiency patterns on steep slopes. Insufficient silvicultural practices on steep slopes during this period may also have contributed to the observed trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNatural hardwood regeneration on low-elevation, south-facing slopes\u003c/h2\u003e \u003cp\u003eA key result of our analysis was that the probability of hardwood regeneration within conifer plantations was higher at lower elevations, particularly on south-facing slopes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) in the low-elevation zone (\u0026lt;\u0026thinsp;600 m), which was dominated by evergreen \u003cem\u003eCastanopsis\u003c/em\u003e and \u003cem\u003eQuercus\u003c/em\u003e. Lower elevations are typically associated with lower soil moisture content in warm-temperate forests (Inoue et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), presumably due to higher evaporation rates resulting from higher temperatures. Additionally, vegetation is affected by varied microclimates along slopes, and south-facing slopes tend to be drier due to increased sun exposure (Yin et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An investigation of slope aspect and soil type distribution in Kochi Prefecture found that dry soils were most prevalent on south-facing slopes (Nagamori and Irimajiri, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1974\u003c/span\u003e), and low soil moisture has been documented in south-facing cypress plantations in Shikoku (Inoue et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Inagaki et al., unpublished data).\u003c/p\u003e \u003cp\u003eWater deficiency associated with dry conditions is well known to hinder plant growth. Highly permeable soils in the Shikoku region are considered ideal for \u003cem\u003esugi\u003c/em\u003e cedar plantations. However, optimal growth can be achieved only in the absence of drought, with adequate rainfall, low temperatures, and favorable wind conditions (Asada, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1936\u003c/span\u003e; Yasuoka, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1935\u003c/span\u003e). Although Japanese hardwood species may not achieve peak growth rates under dry conditions (Tanimoto, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), they are likely more tolerant of dry conditions than planted conifers, particularly cedars, which prefer wetter soil conditions (Nagakura et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Indeed, a spatial analysis of cedar height in Kami based on ALS data found that cedar growth was significantly reduced on south- and southwest-facing slopes (Nakao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, dry conditions can lead to nutrient deficiency. Nitrogen availability is influenced by soil moisture, and nitrogen levels on dry, south-facing slopes may limit conifer growth. Noguchi et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported a growth advantage for hardwoods over \u003cem\u003ehinoki\u003c/em\u003e cypress in nitrogen-deficient soils in Kochi Prefecture. Reduced nitrogen concentrations in litter and soil have also been observed at low elevations in conifer plantations (Inagaki et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Inagaki et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Low soil nitrogen concentrations, driven by limited water availability, may contribute to greater hardwood regeneration in low-elevation conifer plantations in this region.\u003c/p\u003e \u003cp\u003eThe observed patterns of hardwood regeneration in relation to slope and TWI partly support the hypothesis that dry soil conditions promote regeneration on south-facing slopes in low-elevation zones. Increased regeneration on steep slopes or ridges (low TWI) is consistent with this idea (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d). However, higher regeneration in valleys (high TWI) likely reflects other factors, such as reduced surface stability from frequent sediment deposition, which may facilitate pioneer species (Yamagawa et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGeological variation in elevation- and aspect-dependent hardwood regeneration\u003c/h2\u003e \u003cp\u003eHardwood regeneration on low-elevation, south-facing slopes varied by geologic zones, with probabilities decreasing from the Cretaceous Shimanto Belt to the Cretaceous system in the Chichibu Terrane, and lowest in the Jurassic Chichibu Belt (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These differences may reflect geological influences on drought conditions, even at similar elevations and aspects. In these outer-belt mountains of southwestern Japan, steep slopes and gravelly soils promote rapid drainage and soil drying (Shinomiya and Yoshinaga, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Soil permeability is influenced by geological factors such as the type and age of parent material related to weathering (Koide, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1952\u003c/span\u003e) and the inclination of the stratum surface (Yasuoka, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1935\u003c/span\u003e). Geology also affects the water-holding capacity of the A and B horizons in mineral soils (Fujieda, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Notably, water retention in Mesozoic geologic zones of Kochi Prefecture spans the full range observed across Japan (Shimizu, 1998). Differences in water retention, particularly between the Jurassic Chichibu Belt and the Cretaceous Shimanto Belt, may help explain the observed variation in hardwood regeneration probability across geologic zones. Soil surveys have shown Shimanto Belt soils have high bulk density and low moisture content (Inoue et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), although comparisons were limited to the Sambagawa Belt. The lack of soil data for the Jurassic Chichibu Belt and Cretaceous Chichibu Terrane constrains further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplications for forest management\u003c/h2\u003e \u003cp\u003eFollowing World War II, the Japanese government encouraged the monoculture planting of \u003cem\u003esugi\u003c/em\u003e cedar and \u003cem\u003ehinoki\u003c/em\u003e cypress to meet increasing lumber demands. This initiative led to a significant expansion of plantation forests, with the total area growing from 4.93\u0026nbsp;million ha in 1951 (Ministry of Agriculture, Forestry, and Fisheries, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1952\u003c/span\u003e) to 10.01\u0026nbsp;million ha in 2022 and now accounting for 40% of the total forest area (Japan Forestry Agency, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The study area exemplifies this expansion, with its age class structure reflecting the history of postwar afforestation (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Throughout the main postwar afforestation period (defined in this study as 41\u0026ndash;70 years old in 2018), the proportion of hardwood\u0026ndash;cells remained relatively stable (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although afforestation incentives might be dampened by events such as the liberalization of timber imports in 1964 and declining timber prices from 1975, early silvicultural practices, including weeding, appear to have been largely upheld.\u003c/p\u003e \u003cp\u003eOur analysis of publicly accessible ALS datasets revealed that hardwood regeneration is more prevalent on low-elevation, south-facing slopes, particularly in the Cretaceous Shimanto Belt (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This suggests that elevation and aspect, along with conventional geomorphic factors such as slope and TWI, have a marked influence on hardwood regeneration. Incorporating these findings into forest management could improve site selection for future planting. With future plantations projected to shrink to 60% of the current area (Japan Forestry Agency, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), selecting suitable sites will be essential for efficient re-afforestation. Although traditional forestry prioritized high -growth areas, future strategies may need to avoid sites prone to natural hardwood regeneration, as labor-intensive practices like manual weeding and clearing become less feasible (Sato, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As public access to ALS datasets expands, exploratory analyses such as those employed in the present study are essential for advancing analytical frameworks that support biodiversity-oriented forest management.\u003c/p\u003e \u003cp\u003eIn summary, high-resolution spatial analysis using publicly accessible ALS data can pave the way for biodiversity-oriented forest management strategies. By uncovering key patterns in hardwood regeneration and validating established knowledge, this study demonstrates how such data can inform future forest management efforts and facilitate more sustainable practices.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eDeclaration\u003c/p\u003e \u003cp\u003eThis work was supported by the Ministry of Agriculture, Forestry, and Fisheries commissioned project, \u0026ldquo;Development of a method for evaluating profitability in forestry across Japan using a two-axis matrix of site quality and location\u0026rdquo; (grant no. JPJ012043).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE.I. and Y.I. wrote the main manuscript text. All other authors contributed to data collection and manuscript preparation. K.K. prepared Table S3, while all other figures and tables were prepared by E.I. Spatial analyses were conducted by E.I. and K.N. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eALS data and forest GIS data were provided by Kochi Prefectural Technology Center, Kochi Prefectural Government.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eALS data that support the findings of this study were collected in 2018 and released in 2023 as part of a Forest Agency initiative. The dataset, which includes tree species identified using ALS-derived reflectance intensity at 10 m resolution, aerial photographs, and field confirmation surveys, is publicly available at https://www.geospatial.jp/ckan/dataset/tree_species_kochi.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAiura H, Nakajima H, Kato A (2017) Regeneration of broad-leaved trees and evaluation as plantations for wood production in weather-damaged Sugi (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) stands. Bull Toyama Forestry Res Inst 9:1\u0026ndash;10 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkai T, Yoshimura K, Manabe I, Ueda S, Honjyo T (1983) Structure and silvicultural system of multi-storied mixture forests (1): On stratified mixture of hinoki [\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e], akamatsu [\u003cem\u003ePinus densiflora\u003c/em\u003e] and broad-leaf trees. Bull Kyoto Univ Forests (Japan) 55:63\u0026ndash;79 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkai T, Yoshimura K, Manabe I, Ueda S, Honjyo T (1986) Structure and silvicultural system of multi-storied mixed forests (2): On stratified mixtures of planted sugi (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) and natural sugi, hinoki (\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e), akamatsu (\u003cem\u003ePinus densiflora\u003c/em\u003e) and broad leaved trees. Bull Kyoto Univ Forests (Japan) 58:105\u0026ndash;124 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli A (2023) Biodiversity\u0026ndash;ecosystem functioning research: brief history, major trends and perspectives. Biol Conserv 285:110210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.biocon.2023.110210\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2023.110210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsada S (1936) A study on the occurrence, growth, and regeneration of sugi cedar saplings in the Yanase district. Kochi Rinyu Kochi Reg Forestry Bureau 191:6\u0026ndash;24 (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamarretta N, Harrison PA, Bailey T, Potts B, Lucieer A, Davidson N, Hunt M (2020) Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches. New Forest 51:573\u0026ndash;596. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11056-019-09754-5\u003c/span\u003e\u003cspan address=\"10.1007/s11056-019-09754-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChisholm RA, Dutta Gupta T (2023) A critical assessment of the biodiversity\u0026ndash;productivity relationship in forests and implications for conservation. Oecologia 201:887\u0026ndash;900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00442-023-05363-4\u003c/span\u003e\u003cspan address=\"10.1007/s00442-023-05363-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndo S, Yokoyama S (2019) Geology of the Motoyama District. Quadrangle Series, 1:50,000, Kochi (13) No. 53, Geological Survey of Japan, AIST, 100 p. (in Japanese with English abstract, 100 p.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForest GIS (2022) Forum Forest Resource Data Analysis and Management Standard Specifications Ver. 2.0 (tentative translation, in Japanese) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fgis.jp/cloud\u003c/span\u003e\u003cspan address=\"https://fgis.jp/cloud\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 7 June 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForestry and Forest Products Research Institute (2012) Handbook for Conversion of Plantation Forests to Broadleaf Forests 2010, Second Midterm Plan Outcome 22 (Forestry and Wood Use \u0026ndash;\u0026thinsp;5) (tentative translation, in Japanese) ISBN: 978-4-902606-78-2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ffpri.affrc.go.jp/pubs/chukiseika/documents/2nd-chuukiseika22.pdf\u003c/span\u003e\u003cspan address=\"https://www.ffpri.affrc.go.jp/pubs/chukiseika/documents/2nd-chuukiseika22.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 7 June 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujieda M (2007) Water-holding capacity and basin storage at forest catchments in Japan. Bull Forestry For Prod Res Inst 403:101\u0026ndash;110 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukata H, Watanabe N, Kajihara N, Tsukamoto J (2005) Altitudinal zoning of understory vegetation in planted Hinoki cypress (\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e Endt.) forest from the view of surface soil erosion control. Japanese J For Environ 47:77\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jstage.jst.go.jp/article/jjfe/47/2/47_KJ00005292004/_pdf\u003c/span\u003e\u003cspan address=\"https://www.jstage.jst.go.jp/article/jjfe/47/2/47_KJ00005292004/_pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukata H, Watanabe N, Kajihara N, Tsukamoto J (2006) Dynamics of undergrowth and its application to vegetation control of planted \u003cem\u003eChamaecyparis obtusa\u003c/em\u003e Endl. forests with special reference to mitigation of surface soil loss. J Japanese For Soc 88:231\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.jstage.jst.go.jp/article/jjfs/88/4/88_4_231/_pdf\u003c/span\u003e\u003cspan address=\"http://www.jstage.jst.go.jp/article/jjfs/88/4/88_4_231/_pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeological Survey of Japan, AIST (2023) Seamless digital geological map of Japan V2 1:200,000, Original edition. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gbank.gsj.jp/seamless/\u003c/span\u003e\u003cspan address=\"https://gbank.gsj.jp/seamless/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 7 Oct 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashizume H, Aikawa T (1978) Characteristics of seed germination in four Fagaceae species. Bull Fac Agric Tottori Univ 30:128\u0026ndash;133 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirata Y, Furuya N, Suzuki M, Yamamoto H (2009) Airborne laser scanning in forest management: Individual tree identification and laser pulse penetration in a stand with different levels of thinning. For Ecol Manag 258:752\u0026ndash;760. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2009.05.017\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2009.05.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirata A, Sakai T, Takahashi K, Sato T, Tanouchi H, Sugita H, Tanaka H (2011) Effects of management, environment and landscape conditions on establishment of hardwood seedlings and saplings in central Japanese coniferous plantations. For Ecol Manag 262:1280\u0026ndash;1288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2011.06.025\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2011.06.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgarashi T, Kiyono Y (2008) The potential of hinoki (\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e [Sieb. et Zucc.] Endlicher) plantation forests for the restoration of the original plant community in Japan. For Ecol Manag 255:183\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2007.09.003\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2007.09.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgarashi T, Masaki T (2018) Species diversity of woody recruits within Japanese cedar (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) plantations established on grasslands: the effects of site conditions and landscape. J For Res 23:156\u0026ndash;165. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13416979.2018.1432302\u003c/span\u003e\u003cspan address=\"10.1080/13416979.2018.1432302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIgarashi T, Masaki T, Nagaike T, Tanaka H (2016) Species richness of the understory woody vegetation in Japanese cedar plantations declines with increasing number of rotations. J For Res 21:291\u0026ndash;299. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-016-0537-2\u003c/span\u003e\u003cspan address=\"10.1007/s10310-016-0537-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInagaki Y, Okuda S, Sakai A, Nakanishi A, Shibata S, Fukata H (2010) Leaf-litter nitrogen concentration in hinoki cypress forests in relation to the time of leaf fall under different climatic conditions in Japan. Ecol Res 25:429\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11284-009-0672-8\u003c/span\u003e\u003cspan address=\"10.1007/s11284-009-0672-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInagaki Y, Nakanishi A, Fukata H (2011) Soil properties and nitrogen utilization of hinoki cypress as affected by strong thinning under different climatic conditions in the Shikoku and Kinki districts in Japan. J For Res 16:405\u0026ndash;413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-011-0271-8\u003c/span\u003e\u003cspan address=\"10.1007/s10310-011-0271-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInoue K, Iwagawa O, Yoshida K (1973) Studies on the productivity of the forest soil in Shikoku Province. Bull Government Forestry Exp Stn 258:61\u0026ndash;148. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11501/1772146\u003c/span\u003e\u003cspan address=\"10.11501/1772146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIrimajiri K (1982) Soil, silvicultural systems and thinning. \u003cem\u003eBulletin of the Kochi Prefectural Forest Experiment Station\u003c/em\u003e, Showa 57, 101\u0026ndash;167. (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto S, Nakayama R, Buckley GP (2004) Effects of previous land-use on plant species diversity in semi-natural and plantation forests in a warm-temperate region in southeastern Kyushu, Japan. For Ecol Manag 196:213\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2004.02.050\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2004.02.050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIto S, Ishigami S, Mitsuda Y, Buckley GP (2006) Factors affecting the occurrence of woody plants in understory of sugi (\u003cem\u003eCryptomeria japonica\u003c/em\u003e D. Don.) plantations in a warm-temperate region in Japan. J For Res 11:243\u0026ndash;251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-005-0183-6\u003c/span\u003e\u003cspan address=\"10.1007/s10310-005-0183-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJactel H, Gritti ES, Dr\u0026ouml;ssler L, Forrester DI, Mason WL, Morin X, Pretzsch H, Castagneyrol B (2018) Positive biodiversity\u0026ndash;productivity relationships in forests: climate matters. Biol Lett 14:20170747. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsbl.2017.0747\u003c/span\u003e\u003cspan address=\"10.1098/rsbl.2017.0747\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJafarzade M, Ravanbakhsh H, Moshki A, Mollashahi M (2022) Recolonization by Indigenous broadleaved species of a conifer plantation (\u003cem\u003eCupressus\u003c/em\u003e spp.) in Northern Iran after 25 years. Ann For Sci 79:15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13595-022-01131-1\u003c/span\u003e\u003cspan address=\"10.1186/s13595-022-01131-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapan Forestry Agency (2006) Annual report on trends in forest and forestry. Fiscal Year 2006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rinya.maff.go.jp/new/hakusyoeigo/english18/index.html\u003c/span\u003e\u003cspan address=\"http://www.rinya.maff.go.jp/new/hakusyoeigo/english18/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 4 April 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapan Forestry Agency (2021) Basic Plan for Forest and Forestry (revised in June 2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rinya.maff.go.jp/j/kikaku/plan/\u003c/span\u003e\u003cspan address=\"https://www.rinya.maff.go.jp/j/kikaku/plan/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapan Forestry Agency (2022a) Current status of forest resources (as of March 31, 2022) (tentative translation, in Japanese). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rinya.maff.go.jp/j/keikaku/genkyou/r4/index.html\u003c/span\u003e\u003cspan address=\"https://www.rinya.maff.go.jp/j/keikaku/genkyou/r4/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 7 June 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJapan Forestry Agency (2022b) Annual report on trends in forest and forestry. Fiscal Year 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maff.go.jp/e/data/publish/attach/pdf/index-193.pdf\u003c/span\u003e\u003cspan address=\"https://www.maff.go.jp/e/data/publish/attach/pdf/index-193.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 4 April 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKochi P (2019) Kochi Regional Forest Plan (Established in 2019, 2020\u0026ndash;2030). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pref.kochi.lg.jp/doc/chiikikeikaku/file_contents/file_202176211251_4.pdf\u003c/span\u003e\u003cspan address=\"https://www.pref.kochi.lg.jp/doc/chiikikeikaku/file_contents/file_202176211251_4.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (in Japanese) [accessed on 4 June 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKodani J (1990) Technology for creating and improving hardwood forests in snowy areas. Bull Ishikawa-Ken For Exp Stn 21:1\u0026ndash;13 (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoide H (1939) Geological and topographical zones and cedar forestry in southwestern Japan. J Japanese For Soc 21:165\u0026ndash;180 (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoide H (1952) \u003cem\u003eApplied Geology \u0026mdash;Rock Weathering and Forest Location\u0026mdash;\u003c/em\u003e. Tokyo: Kokon. pp.177. (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondo K, Yoshida C, Oka M, Kashima J, Imatomi Y, Inoue M (2004) Factors affecting work efficiency in underbrush clearing operations. \u003cem\u003eTransactions of the annual meeting in Kanto branch of the Japanese forest society\u003c/em\u003e, 55, 299\u0026ndash;300. (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoyama Y, Yamauchi M (2011) Development of renewal technology for mixed needle and broadleaf forest creation (tentative translation). Bull Nagano Prefectural Forestry Res Cent 25:29\u0026ndash;44 (in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunisaki T (2024) Transformation from conifer plantations to mixed-species stands: Research challenges associated with the perspective of classical forest management planning. Jpn J Plann press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20659/jjfp.A20240401\u003c/span\u003e\u003cspan address=\"10.20659/jjfp.A20240401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurose K (2005) Studies for regeneration technology for a broadleaf forest from artificial forest of \u003cem\u003eCryptomeria japonica\u003c/em\u003e and \u003cem\u003eChamaecyparis obtusa. Bulletin\u003c/em\u003e. Okayama Prefectural For Exp Stn 21:1\u0026ndash;13 (in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang J, Crowther TW, Picard N, Wiser S, Zhou M, Alberti G, Reich PB (2016) Positive biodiversity-productivity relationship predominant in global forests. Science 354(6309):aaf8957. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.aaf8957\u003c/span\u003e\u003cspan address=\"10.1126/science.aaf8957\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLidman FD, Karlsson M, Lundmark T, S\u0026auml;ngstuvall L, Holmstr\u0026ouml;m E (2024) Birch establishes anywhere! So, what is there to know about natural regeneration and direct seeding of birch? New Forest 55:157\u0026ndash;171. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11056-023-09971-z\u003c/span\u003e\u003cspan address=\"10.1007/s11056-023-09971-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Agriculture, Forestry, and Fisheries (1952) Statistical Table of the Ministry of Agriculture and Forestry, 28th, 1951 (tentative translation, in Japanese). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.library-archive.maff.go.jp/index/500205935_0001\u003c/span\u003e\u003cspan address=\"http://www.library-archive.maff.go.jp/index/500205935_0001\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 7 June 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of the Environment (1988) Third National Survey on the Natural Environment, Vegetation Survey Report, Kochi Prefecture (tentative translation, in Japanese). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.biodic.go.jp/reports2/3rd/vgt_39/3_vgt_39.pdf\u003c/span\u003e\u003cspan address=\"http://www.biodic.go.jp/reports2/3rd/vgt_39/3_vgt_39.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [accessed on 26 November 2024]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno T, Wallis SR, Kojima T, Gibbons W (2016) The geology of Japan. Geological Society, London. pp522\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagaike T (2002) Differences in plant species diversity between conifer (\u003cem\u003eLarix kaempferi\u003c/em\u003e) plantations and broad-leaved (\u003cem\u003eQuercus crispula\u003c/em\u003e) secondary forests in central Japan. For Ecol Manag 168:111\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0378-1127(01)00734-4\u003c/span\u003e\u003cspan address=\"10.1016/S0378-1127(01)00734-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagaike T (2012) Review of plant species diversity in managed forests in japan. \u003cem\u003eISRN Forestry, 2012\u003c/em\u003e, Article ID 629523. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5402/2012/629523\u003c/span\u003e\u003cspan address=\"10.5402/2012/629523\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagakura J, Shigenaga H, Akama A, Takahashi M (2004) Growth and transpiration of Japanese cedar (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) and Hinoki cypress (\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e) seedlings in response to soil water content. Tree Physiol 24:1203\u0026ndash;1208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/treephys/24.11.1203\u003c/span\u003e\u003cspan address=\"10.1093/treephys/24.11.1203\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagamori M, Irimajiri K (1974) Study on the connection of the distribution of the forest soil type with topography in South-eastern Shikoku, Japan. Res Rep Kochi Univ Agric 23:107\u0026ndash;114 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagashima K, Yoshida S, Hosaka T (2009) Patterns and factors in early-stage vegetation recovery at abandoned plantation clearcut sites in Oita, Japan: possible indicators for evaluating vegetation status. J For Res 14:135\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-009-0118-8\u003c/span\u003e\u003cspan address=\"10.1007/s10310-009-0118-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakao K, Kabeya D, Awaya Y, Yamasaki S, Tsuyama I, Yamagawa H, Miyamoto K, Araki MG (2022) Assessing the regional-scale distribution of height growth of \u003cem\u003eCryptomeria japonica\u003c/em\u003e stands using airborne LiDAR, forest GIS database and machine learning. For Ecol Manag 506:119953. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2021.119953\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2021.119953\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegishi Y, Eto Y, Hishita M, Negishi S, Suzuki M, Masaka K, Seiwa K (2020) Role of thinning intensity in creating mixed hardwood and conifer forests within a \u003cem\u003eCryptomeria japonica\u003c/em\u003e conifer plantation: A 14-year study. For Ecol Manag 468:118184. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2020.118184\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2020.118184\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiiyama K, Ogawa M, Kushima H, Takahashi K, Sato T, Sakai T, Tanouchi H (2010) Collection and evaluation of literatures related to broadleaf tree regeneration for leading artificial coniferous forests to broadleaf forests. J Japanese For Soc 92:292\u0026ndash;296. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4005/jjfs.92.292\u003c/span\u003e\u003cspan address=\"10.4005/jjfs.92.292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoguchi M, Sakai A, Okuda S, Inagaki Y, Fukata H (2009) Six-year responses vegetation to thinning in hinoki cypress plantations in Shikoku islands, Japan. Japanese J For Environ 51:127\u0026ndash;136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18922/jjfe.51.2_127\u003c/span\u003e\u003cspan address=\"10.18922/jjfe.51.2_127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoguchi M, Okuda S, Miyamoto K, Itou T, Inagaki Y (2011) Composition, size structure and local variation of naturally regenerated broadleaved tree species in hinoki cypress plantations: a case study in Shikoku, south-western Japan. Forestry 84:493\u0026ndash;504. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/forestry/cpr027\u003c/span\u003e\u003cspan address=\"10.1093/forestry/cpr027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoguchi M, Miyamoto K, Okuda S, Itou T, Sakai A (2016) Heavy thinning in hinoki plantations in Shikoku (southwestern Japan) has limited effects on recruitment of seedlings of other tree species. J For Res 21:131\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-016-0522-9\u003c/span\u003e\u003cspan address=\"10.1007/s10310-016-0522-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOda M, Mitsugi Y, Hirata Y (2010) GIS to extract forests suitable for conversion to hardwood forests. Shinrin Kagaku 59:9\u0026ndash;12 (tentative translation, in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakai A, Sato S, Sakai T, Kuramoto S, Tabuchi R (2005) A soil seed bank in a mature conifer plantation and establishment of seedlings after clear-cutting in southwest Japan. J For Res 10:295\u0026ndash;304. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-004-0138-3\u003c/span\u003e\u003cspan address=\"10.1007/s10310-004-0138-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakai A, Hirayama T, Oshioka S, Hirata Y (2006) Effects of elevation and postharvest disturbance on the composition of vegetation established after the clear-cut harvest of conifer plantations in southern Shikoku, Japan. J For Res 11:253\u0026ndash;265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-005-0182-7\u003c/span\u003e\u003cspan address=\"10.1007/s10310-005-0182-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakai A, Sakai T, Kuramoto S, Sato S (2010) Soil seed banks in a mature Hinoki (\u003cem\u003eChamaecyparis obtusa\u003c/em\u003e Endl.) plantation and initial process of secondary succession after clearcutting in southwestern Japan. J For Res 15:316\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-010-0187-8\u003c/span\u003e\u003cspan address=\"10.1007/s10310-010-0187-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSato T (2021) \u003cem\u003eMunicipal forest management practices aimed towards mixed conifer\u0026ndash;hardwood forests\u003c/em\u003e. Tokyo: ZENRINKYOU. Forestry Improvement and Extension Series, No. 197. pp.173. ISBN978-4-88138-397-1 (in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeiwa K, Eto Y, Hishita M, Masaka K (2012) Effects of thinning intensity on species diversity and timber production in a conifer (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) plantation in Japan. J For Res 17:468\u0026ndash;478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-011-0316-z\u003c/span\u003e\u003cspan address=\"10.1007/s10310-011-0316-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimizu T (1980) Relation between scanty runoff from mountainous watershed and geology, slope and vegetation. Bull Forestry For Prod Res Inst 310:109\u0026ndash;128 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShinomiya Y, Yoshinaga S (2008) Behavior of water during large scale rainfall events on the slope of natural forest in a mountainous headwater catchment in the Shimanto river basin. J Japan Soc Hydrology Water Resour 21:126\u0026ndash;139 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakafumi H, Hiura T (2009) Effects of disturbance history and environmental factors on the diversity and productivity of understory vegetation in a cool-temperate forest in Japan. For Ecol Manag 257:843\u0026ndash;857. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2008.10.020\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2008.10.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi T, Yamamoto K, Senda Y, Tsuzuku M (2005) Estimating individual tree heights of sugi (\u003cem\u003eCryptomeria japonica\u003c/em\u003e D. Don) plantations in mountainous areas using small-footprint airborne LiDAR. J For Res 10:135\u0026ndash;142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-004-0125-8\u003c/span\u003e\u003cspan address=\"10.1007/s10310-004-0125-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeda H (2017) Germination characteristics of \u003cem\u003eMachilus thunbergii\u003c/em\u003e in northern Niigata Prefecture. Bull Niigata Prefectural For Res Inst 58:1\u0026ndash;3 (tentative translation, in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanimoto T (1982) Fundamental studies on release cutting in forest plantation (I) Analysis of growth of sugi (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) and natural vegetation in the young sugi plantation. Bull Forestry For Prod Res Inst 320:53\u0026ndash;121 (in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanimoto T (1990) \u003cem\u003eEcology of Hardwood Practices\u003c/em\u003e. Tokyo: Soubun. p. 245. (tentative translation, in Japanese)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor R, Davis C, Brandt J, Parker M, St\u0026auml;uble T, Said Z (2020) The rise of big data and supporting technologies in keeping watch on the world's forests. Int Forestry Rev 22:129\u0026ndash;141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1505/146554820829523880\u003c/span\u003e\u003cspan address=\"10.1505/146554820829523880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUtsugi E, Kanno H, Ueno N, Tomita M, Saitoh T, Kimura M, Kaniu K, Seiwa K (2006) Hardwood recruitment into conifer plantations in Japan: effects of thinning and distance from neighboring hardwood forests. For Ecol Manag 237:15\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2006.09.011\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2006.09.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWulder MA, Bater CW, Coops NC, Hilker T, White JC (2008) The role of LiDAR in sustainable forest management. forestry Chron 84:807\u0026ndash;826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5558/tfc84807-6\u003c/span\u003e\u003cspan address=\"10.5558/tfc84807-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamagawa H, Ito S (2006) The role of different sources of tree regeneration in the initial stages of natural forest recovery after logging of conifer plantation in a warm-temperate region. J For Res 11:455\u0026ndash;460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-006-0229-4\u003c/span\u003e\u003cspan address=\"10.1007/s10310-006-0229-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamagawa H, Ito S, Mitsuda Y, Fukuzato K (2006) Effects of topography and management history on natural forest recovery in abandoned forest after clear-cutting in Miyazaki, Japan. J For Res 11:99\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10310-005-0192-5\u003c/span\u003e\u003cspan address=\"10.1007/s10310-005-0192-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamagawa H, Ito S, Nakao T (2007) Edge effects from a natural evergreen broadleaved forest patch on advanced regeneration and natural forest recovery after clear-cutting of a sugi (\u003cem\u003eCryptomeria japonica\u003c/em\u003e) plantation. Japanese J For Environ 49:111\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18922/jjfe.49.2_111\u003c/span\u003e\u003cspan address=\"10.18922/jjfe.49.2_111\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamagawa H, Ito S, Sakuta K, Mizoue N, Nakao T (2009) Effects of small-scale clearcutting management on species diversity and vertical structure of understory vegetation of a conifer plantation comprising uneven-aged stands, in Kyushu, Southern Japan. J Japanese For Soc 91:277\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4005/jjfs.91.277\u003c/span\u003e\u003cspan address=\"10.4005/jjfs.91.277\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e(in Japanese with English abstract)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamagawa H, Ito S, Nakao T (2010) Restoration of semi-natural forest after clearcutting of conifer plantations in Japan. Landscape Ecol Eng 6:109\u0026ndash;117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11355-009-0088-1\u003c/span\u003e\u003cspan address=\"10.1007/s11355-009-0088-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamaura Y, Lindenmayer D, Yamada Y, Gong H, Matsuura T, Mitsuda Y, Masaki T (2019) A spatially-explicit empirical model for assessing conservation values of conifer plantations. For Ecol Manag 444:393\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.foreco.2019.04.038\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2019.04.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYasuoka H (1935) \u003cem\u003eStudy of sugi cedar forest in Yanase National Forest\u003c/em\u003e. Kochi: Kochi Regional Forestry Bureau. pp56. (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin G, Xie J, Ma D, Xie Q, Verger A, Descals A, Filella I, Pe\u0026ntilde;uelaset J (2023) Aspect matters: Unraveling microclimate impacts on mountain greenness and greening. Geophys Res Lett 50:e2023GL105879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023GL105879\u003c/span\u003e\u003cspan address=\"10.1029/2023GL105879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYokoi S, Yamaguchi K (1998) Growth patterns of cedar and broadleaf trees in a non-achieving cedar plantation in a snow-covered area. Japanese J For Environ 40:91\u0026ndash;96 (in Japanese, tentative translation)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"new-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nefo","sideBox":"Learn more about [New Forests](http://link.springer.com/journal/11056)","snPcode":"11056","submissionUrl":"https://submission.nature.com/new-submission/11056/3","title":"New Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ALS, biodiversity-oriented forest management, elevation, geology, slope aspect","lastPublishedDoi":"10.21203/rs.3.rs-7644359/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7644359/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJapan has adopted biodiversity-oriented forest management, necessitating the diversification of extensive conifer plantations and the identification of geographic conditions that favor natural hardwood regeneration. The increasing availability of high-resolution airborne laser scanning (ALS) data provides new opportunities to analyze spatial patterns in forests. In this study, we applied exploratory approaches to quantify the prevalence of natural hardwood regeneration within mature conifer plantations in Kochi Prefecture, southwestern Japan. The study covered an area of approximately 250 km\u003csup\u003e2\u003c/sup\u003e, enabling spatial analyses at the landscape scale. Hardwood regeneration was defined as areas recorded as conifer plantations in forest registry data (2005\u0026ndash;2009) but dominated by hardwoods based on ALS data collected in 2018. Across postwar afforestation sites (1949\u0026ndash;1978 planting), hardwood regeneration consistently occupied 20\u0026ndash;25% of the total area, regardless of the planting year. Using a logistic generalized additive model, we found that hardwood regeneration was favored on slopes steeper than 40\u0026deg; and in ridges and valleys. In the low-elevation zone (\u0026lt;\u0026thinsp;600 m a.s.l.), where evergreen \u003cem\u003eCastanopsis\u003c/em\u003e and \u003cem\u003eQuercus\u003c/em\u003e species were the dominant vegetation, the likelihood of finding hardwood regeneration increased with decreasing elevation and greater southern slope exposure. This trend was particularly evident within specific geologic zones. Spatial analyses to identify site characteristics that favor natural hardwood regeneration could be used to support biodiversity-oriented forest management. Furthermore, high-resolution ALS data that will soon be publicly available hold significant promise for uncovering geographic patterns and generating novel insights on forest ecosystem dynamics.\u003c/p\u003e","manuscriptTitle":"Exploration of spatial biases in natural hardwood regeneration in conifer plantations in southwestern Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 13:31:53","doi":"10.21203/rs.3.rs-7644359/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T18:55:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T12:12:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30516845781444389147211578051839182632","date":"2026-01-18T22:10:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T21:23:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T06:34:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T06:34:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"New Forests","date":"2025-09-18T02:20:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"new-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nefo","sideBox":"Learn more about [New Forests](http://link.springer.com/journal/11056)","snPcode":"11056","submissionUrl":"https://submission.nature.com/new-submission/11056/3","title":"New Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b111f892-3542-4e09-b3dd-64f91afb9ac8","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T02:09:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 13:31:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7644359","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7644359","identity":"rs-7644359","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}