Assessing the relationship between canopy density and understory vegetation in planted forest by using constraint line methodology

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Therefore, determining whether a high canopy density results in reduced growth of understory vegetation and associated thresholds may guide the reasonable planting density of planted forest. In this study, we selected an artificial coniferous forest planting area in Hubei Province, China as the research area, and adopted a constraint line to explore the relationship between the canopy density of different species, including Chinese fir ( Cunninghamia lanceolata ), cypress ( Cupressus funebris ), and Masson pine ( Pinus massoniana ), and the height and coverage of shrubs and herbs in the forests. The upper constraint lines, lower constraint lines, and mean lines were extracted by segmented quantile regression derived from the maximums, means, and minimums in the scatter clounds, which represented the relationships under the best, worst, and general environmental conditions, respectively. The results showed that for different species, the upper constraint lines were almost hump-shaped with thresholds, indicating that regardless of how good the environmental conditions were, the indicators of understory shrubs and herbs first increased and then decreased with an increase in canopy density. The canopy density thresholds for Chinese fir, cypress, and Masson pine were approximately 50%, 30%~40%, and 50%~60%, respectively. Overall, the thresholds of canopy density for herb indicators were greater than those of shrubs. Planning reasonable canopy density may enhance the growth of understory vegetation. planted forest constraint line understory herb understory shrub humus thickness Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Planted forests are forests formed by planting and/or intentional seeding of native or introduced tree species (FAO 2005 ). Planted forests have become an important part of terrestrial ecosystems, and bring considerable economic and ecological benefits (Stevens et al. 2015 , Chandler and McGraw 2017 , Spicer et al. 2020 ). Meanwhile, planted forests have also been recognized as an effective way to mitigate climate change (Cheng et al. 2023 ). China has been implementing afforestation and reforestation projects since the 1970s, establishing more than a quarter of the world's planted forest area (FAO 2021 ). The results of China's eighth consecutive national forest resources inventory (2009–2013) show that the planted forests cover an area of 69.33 million hectares, accounting for 36% of the country's forested land. Compared with natural forests, the planted forests tend to have fewer species, younger stands, and a higher forest management intensity (Rollinson et al. 2020 , Cheng et al. 2023 ). As a result, the forest ecosystems dominated by the planted forests are prone to a series of ecological problems, especially affecting the healthy growth of understory vegetation (Fig. 1 ). Understory vegetation is an important part of forest ecosystems (Scott et al. 2000 , Chandler and McGraw 2017 ). Although understory vegetation accounts for a small proportion of the total biomass of the forest ecosystem, it plays an important role in maintaining forest diversity and conserving water and soil (Bohlman 2015 , Bell 2016, Benes and Carpenter 2016 ; Zhao et al. 2016 ). Increased shrub and herb coverage or diversity may benefit various species groups, such as birds and invertebrates (Díaz 2006 ). Therefore, the growth of understory vegetation is of great significance to the sustainable management of the ecosystems in the planted forest. Some studies have suggested that understory vegetation does not grow due to allelopathy, that is, coniferous forests secrete tannins that inhibit the growth of understory vegetation (e.g. Benes and Carpenter 2016 ). However, other studies have reported that the excessive canopy density of artificial coniferous forests hinders the growth of understory vegetation (Scott et al. 2000 , Ligot et al. 2014 , Benes and Carpenter 2016 , Chandler and McGraw 2017 ). Canopy density is a key factor that affects the microclimatic like light transmission and forest precipitation, and it has an important impact on the development of understory vegetation (e.g. Bell et al. 2016 ). However, the relationship between the canopy density and the understory vegetation is very variable and depending in climate conditions, and the thresholds of the relationship have not been clearly determined. Previous studies have been performed using different thinning intensities or retention densities to analyze the relationship between the canopy density and the understory vegetation (Li et al. 2009 , Chandler and McGraw 2017 ). It is difficult to ensure the continuous change of the control variables in the quantitative control experiments, which leads to different results in different studies, thus making it difficult to obtain an accurate threshold of canopy density (Li et al. 2009 , Stevens et al. 2015 , Cao et al. 2018 ). In addition, understory vegetation is possibly affected not only by the canopy density but also by forest age, topography, and soil fertility (Ligot et al. 2014 , Stevens et al. 2015 , Spicer et al. 2020 ). Researchers have used correlation analysis and regression analysis to study the relationship between canopy density and understory vegetation, for example, Zhao et al. ( 2016 ) found that the diversity of understory plants increase with the decreasing canopy density by the regression analysis. These statistical methods obtain only the average relationship between the variables; however, factors other than canopy density could influence understory, and the statistical results mixed the effect of other factors may elicit the wrong result (Laiho et al. 2014 , Chandler and McGraw 2017 ). Additionally, the result of correlation analysis represents a monotonic relationship, but the relationships between ecological variables are nonlinear and have thresholds (Bohlman 2015 ). For example, canopy density of Masson pine ( Pinus massoniana ) that is too low ( 0.8) is not conducive to soil conservation, and a canopy density of 0.6 has the best effect in terms of preventing soil erosion (Zhao et al. 2016 , Cao et al. 2018 , Li et al. 2020 ). The constraint line provides a useful method to analyze the complex nonlinear relationships between ecological variables based on massive data (Hao et al., 2017 ). The concept of the constraint line was first proposed and used to represent the best state of population growth (Holling 1987 , Guo and Rundel 1998 , Hao et al. 2017 ). Due to the interaction of multiple factors in a complex ecological process, the two variables studied in the real situation commonly show scattered point clouds, but the ecological processes represented by different parts of the cloud-shaped data are not the same and contain rich ecological information (Thomson et al. 1996 , Cade and Noon 2003 ). When the supply of all factors, except the measurement factor, is at a free level, it is considered an ideal situation. In this case, only the measurement factor has a restrictive effect on the related relationship, which could be represented by the upper constraint lines (Evanylo and Sumner 1987 , Schnug et al. 2008 ). Therefore, the upper constraint line represents the boundary at which the response variable is primarily limited by the constraint variable, rather than by other factors (Guo and Rundel 1998 ). In contrast, the lower constraint line represents the change trend of the response factor when the other impacting factors are at their worst levels (Cade and Noon 2003 ). The mean line represents the general relationship between the measurement factor and the response factor in the real situation. The selection of planted forest species varies according to the region, climate and other conditions (Li et al. 2009 ). In the hilly areas of southern China, planted forest species are selected from Chinese fir ( Cunninghamia lanceol ata), cypress ( Cupressus funebris ), Masson pine ( Pinus massoniana ), and eucalyptus ( Eucalyptus sp ), etc.; in the plains of northern China, poplar ( Populus ) and paulownia ( Paulownia fortunei ) are the main species; in the mountains of northeastern China, larch ( Larix gmelinii ) is the main species (Cao et al. 2018 , Li et al. 2020 ). In the early 1990s, to speed up the progress of wasteland eradication, aerial seeding and high-density artificial afforestation were adopted (Tan et al. 2016 ). Several dense forests have been formed with an average density of 3000 trees per hectare (Li et al. 2009 , Cao et al. 2018 ). Most of the planted forests are pure forests with sparse understory vegetation and single biodiversity metrics, resulting in serious understory ecological problems (Fig. 1 ), such as severe soil erosion and nonpoint source pollution (Paletto and Tosi 2009 , Li et al. 2009 , Cao et al. 2018 , Li et al. 2020 ). Especially, under the planted forests of pine and cypress, because pine needles are hard and unpalatable, shrubs and herbs do not grow well in the forest understory, resulting in poor stability of the entire ecosystem, which is not conducive to the sustainability of the regional ecosystem (Li et al. 2009 ). In this study, Hubei Province, located in the southern China, was chosen as the study area. The three planted tree species (Chinese fir, cypress, and Masson pine), commonly found in the hilly areas of southern China, were selected to analyze the effects of canopy density on the understory vegetation, meanwhile, the thresholds of canopy density were explored. The analysis results of this paper would provide suggestions for the plantation density and management of the planted forest in the hilly areas of southern China. The research methods and ideas in this paper could also be used in the decision analysis of optimal canopy density for the planted forests in China and around the world. Specifically, this paper has the following two objectives: 1) explore the relationships between the canopy density and the indicators of understory vegetation by using the mean lines, upper constraint lines, and lower constraint lines; 2) reveal the optimal canopy density values of the three planted tree species that are conducive to the growth of understory shrubs and herbs. 2. Materials and Methods 2.1 Study area Hubei Province is located between 29°05'–33°20'N and 108°21'–116°07'E, comprising a total area of 186,000 km 2 (Fig. 2 ). During 2010–2020, nearly 14,000 km 2 of planted forests had been completed, making it one of the largest provinces in China in terms of planted forests. In 2015, the forest accounted for 50% of the entire area of Hubei Province and became the largest land use type in terms of area covered (Fig. 3 ). The forest is mainly distributed in the eastern and western Hubei Province where the elevation is high. The terrain of Hubei Province is rough and surrounded by mountains in the east, west, and north with a low elevation. In particular, the areas of elevation above 600 m account for 30% of the entire area, and they are mainly in the western area. Hubei Province is in the subtropical zone in a typical monsoon zone. Except for the high mountainous areas, most areas have a humid subtropical monsoon climate with sufficient light energy, abundant heat, a long frost-free period, and abundant precipitation. 2.2 Sampling We established 415 plots on the woodland land use type for the survey, and the sample size is 20m×20m (Fig. 2 ). In each sample, species covered more than 80% of the area were considered to be the dominant species. Three species of Chinese fir, cypress, and Masson pine were included in the plots, and the sample numbers were 95, 26, and 294, respectively. In each plot, five small squares of 1m×1m were set up along the diagonal. The heights of shrubs and herbs in the five samples were determined by using a ruler, and the average heights were taken as the corresponding indicators in that plot. The photos obtained by camera in the five small samples were used to calculate the vegetation cover and the canopy density, and the average values were also as the corresponding indicators in that plot. The vegetation coverage includes the overall vegetation cover, herb vegetation cover, and shrub vegetation cover. In the study, the understory vegetation coverage is a combination of shrub and herb coverage. The survey time was between June and August in 2014. The samples of Masson pine were distributed in the western, eastern, and northern parts of Hubei Province (Fig. 2 ). The samples of Chinese fir were mainly distributed in the western and southeastern parts of Hubei Province. The samples of cypress were distributed in the western region. 2.3 Extraction of the constraint lines from scatter plots We found that the sample data was in the form of scatter clouds and showed clear boundary lines. Therefore, we used the constraint line method to analyze the relationships between the canopy density and the indicators representing understory vegetation. In this study, segmented quantile regression (Mills et al., 2009 ; Medinski et al., 2010 ; Hao et al., 2017 ) was employed to obtain the constraint lines, which are characterized in Fig. 4 . The value range of the X variable was equally divided into 100 parts in the columns (Fig. 4 ). We calculated the maximum, mean, and minimum in each column as the upper boundary points, mean points, and lower boundary points, respectively. Then, the upper boundary points, mean points, and lower boundary points in all columns were fitted to obtain the upper constraint lines, mean lines, and lower constraint lines, respectively (Hao et al., 2017 ). The line types were evaluated based on the shapes of the scatter cloud and the goodness-of-fit values (R 2 ). We analyzed the upper constraint lines, mean lines, and lower constraint lines of canopy density and the understory shrub height, understory herb height, understory shrub coverage, understory shrub coverage, and understory vegetation coverage with all samples and with samples of Chinese fir, cypress, and Masson pine separately. Finally, the thresholds for the mean lines, upper constraint lines, and lower constraint lines were extracted by solving their derivative function (Hao et al. , 2022; Li et al. , 2023). 3. Results 3.1 Constraint effect of canopy density on the indicators of understory vegetation by all samples Overall, the fitting equations significantly characterized the relationship between canopy density and the indicators of understory vegetation with very high goodness-of-fit on the mean lines (Table 1 ). The means of understory shrub height, understory herb height, and understory shrub coverage first increased and then decreased with the increasing canopy density (Figs. 5 a, 5 b, and 5 c). Except for the understory vegetation coverage, the relationship between the canopy density and other indicators of understory vegetation had an obvious upper constraint line (Table 1 ). However, only the understory herb coverage and understory vegetation coverage had obviously lower constraint lines with canopy density (Table 1 ). For all significant upper and lower constraint relationships between the canopy density and the indicators of understory vegetation, the corresponding lines were hump-shaped or inverse hump-shaped with a threshold (Fig. 5 ). Table 1 The goodness-of-fit (R 2 ) of mean lines, upper constraint lines, and lower constraint lines between canopy density and indicators of understory vegetation Line types Species The indicators whose relationship with canopy density understory shrub height understory herb height understory shrub coverage understory herb coverage understory vegetation coverage Mean line All samples 0.63** 0.85** 0.93** 0.53* 0.60** Chinese fir 0.83** 0.68** 0.75** 0.74** 0.82** Cypress 0.10 0.56* 0.61** 0.03 0.32* Masson pine 0.69** 0.51* 0.95** 0.74** 0.69** Upper constraint line All samples 0.61** 0.62** 0.86** 0.48* 0.14 Chinese fir 0.79** 0.88** 0.95** 0.81** 0.95** Cypress 0.97** 0.88** 0.99** 0.91** 0.99** Masson pine 0.85** 0.58* 0.90** 0.72** 0.77** Lower constraint line All samples 0.10 0.17 0.11 0.73** 0.87** Chinese fir 0.51* 0.57* 0.35* 0.56* 0.99** Cypress 0.94** 0.88** 0.60** 0.70** 0.94** Masson pine 0.22 0.01 0.04 0.31* 0.63** Note: ** and * indicate that the relationship is significant at the levels of 0.01 and 0.05, respectively. 3.2 Constraint effect of canopy density on the indicators of understory vegetation by different species 3.2.1 Samples of Chinese fir All the lines between the canopy density of Chinese fir and the indicators of understory vegetation, including the mean lines, upper constraint lines, and lower constraint lines, were significant (Table 1 ). They were nonmonotonic and generally presented a trend of first increasing and then decreasing (Fig. 6 ). Compared with the mean lines and lower constraint lines, the corresponding curvatures of the upper constraint lines were greater (Fig. 6 ). Even though the understory vegetation coverage of all the scatter clusters was between 50% and 100%, the upper constraint line and the lower constraint line described the relationship well (Fig. 6 e and Table 1 ). 3.2.2 Samples of Cypress Overall, the upper and lower constraint lines of the Cypress samples were fitted better with higher R 2 values than the mean lines (Table 1 ). For the cypress species, the shapes and trends of the upper constraint lines and lower constraint lines were almost consistent. For example, on the upper constraint line and lower constraint line, the maximum and minimum understory shrub coverage sharply decreased with increasing cypress canopy density (Fig. 7 c). However, the trend of the mean lines was diverse and varied with the understory vegetation indicators, and most of them were different with the upper and lower constraint lines (Fig. 7 ). For example, the mean line of canopy density and understory herb coverage only increased, but the mean line of canopy density and understory shrub coverage first decreased and then increased (Figs. 7 d and 7 c, respectively). 3.2.3 Samples of Masson pine For the Masson pine species, all of the mean lines and upper constraint lines characterized the relationships between the canopy density and the understory indicators with statistically significant fitting equations (Table 1 ). The trends of the lower constraint lines of canopy density and understory shrub height, herb height, and understory shrub coverage were not obvious, but the upper constraint lines between the Masson pine canopy density and the understory vegetation indicators were all hump-shaped with an obvious threshold (Fig. 8 ). Except for the mean line between the canopy density and the understory herb coverage, which had a decreasing trend, the other mean lines were nonmonotonic (Fig. 8 ). 3.3 Thresholds of the relationship between canopy density and the indicators of understory vegetation Overall, the threshold ranges of the canopy density on the lower constraint lines were wider than those on the mean lines and the upper constraint lines (Fig. 9 ). The thresholds ranged from 32–61% on the mean lines, from 31–66% on the upper constraint lines, and from 28–89% on the lower constraint lines (Fig. 9 ). Except for the thresholds on the mean lines of Cypress, other thresholds indicated that when the canopy density was greater than the thresholds, the indicators of understory vegetation decreased with increasing canopy density (Fig. 8 ). On the upper constraint lines, the thresholds for Cypress were small and clustered between 32% and 38%, while those for Masson pine and Chinese fir were larger, being clustered between approximately 49–61% and 46–58%, respectively (Fig. 9 ). On the lower constraint lines, the thresholds for Cypress were smaller and clustered at approximately 30%, while the thresholds for Chinese fir were larger and evenly distributed between 44% and 65% (Fig. 9 ). 4. Discussion In Hubei Province, since the 1970s, the density of plantations has gradually increased, and the proportion of natural forests in some forest areas has been significantly reduced. Coniferous forest is the main woodland type in Hubei Province, and the main plantation tree species are Chinese fir and Masson pine (Tan et al., 2016 ). Reasonable planting and management of existing planted forests are key issues to ensure the healthy development of the forest ecosystem in Hubei Province. Researchers commonly use the means of samples to analyze the relationships between variables (Cade and Noon, 2003 ). However, in this study, the results of the constraint lines showed that the relationships of mean values may interfere with complicated ecological processes, and it was difficult to clearly identify only the relationship between the two variables studied (Mills et al., 2009 ). The results regarding the constraint effect between the canopy density and the indicators of understory vegetation could guide rational management of planted forests. 4.1 Interpretation of the constraint effect between the canopy density of planted forests and the understory vegetation The canopy density is thought to affect the understory light environment, air permeability, rain, and temperature, which jointly influence the growth of understory vegetation (Tomita and Seiwa, 2004; Ligot et al., 2014 ; Cao et al., 2018 ; Rollinson et al., 2020 ). When the canopy density is low and the temperature and light are high, the conditions enhance the decomposition of the thin humus under forest (Tomita and Seiwa, 2004; Ligot et al., 2014 ). We analyzed the lower constraint lines between humus thicknesses and the understory vegetation coverage, and found that all the lines are with high goodness-of-fit values (R 2 ) (Fig. 10 ). The four lower constraint lines with upward trends indicated that even in an environment with poor growth in Hubei Province, the understory vegetation coverage increased with increasing humus thickness. Cao et al. ( 2018 ) found that soil thickness and soil nutrients are important factors affecting the growth of understory vegetation. Humus is rich in nutrients, generally accounting for 85–90% of the total soil organic matter, and humus provides abundant energy for plant growth (Szwaluk and Strong, 2003 ). A higher canopy density reduces rainfall erosion of understory vegetation (Spicer et al., 2020 ; Li et al., 2020 ). Therefore, a suitable canopy density may create a good growth environment for understory vegetation. However, when the canopy density is higher than the thresholds, a high canopy density reduces the light transmittance and air permeability under the coniferous forest, which may be the main reasons for the reduced growth of understory vegetation (Thrippleton et al., 2017 ). The thresholds of canopy density for shrubs were larger than those for herbs, which was similar to the result of Tan et al. ( 2016 ). The understory light was most influenced by canopy density and could be modified by forest height and stand structure (Scott et al., 2000 ; Laiho et al., 2014 ). Chinese fir is a large evergreen tree with an average height of 30 to 40 meters and a diameter at breast height greater than 3 meters, and Cypress is generally up to 20 meters high. It is that taller, wider trees such as Chinese fir would block out more light and thus inhibit understory growth in comparison to the smaller cypress. Therefore, this may be one of the main reasons why Chinese fir had a nonmonotonic relationship between the canopy density and the indicators of understory vegetation, while the increase in cypress canopy density monotonically inhibited the growth of the understory shrubs and herbs. Additionally, Cypress is densely branched with many thin and weak branches as well as dense leaves and the crown is completely surrounded by branches and leaves (Tan et al., 2016 ), therefore, the light transmission and air permeability under such dense foliage are poor. The reason of diverse relationships for Masson pine as the mean lines and the lower constraint lines showed were with various environmental conditions. Masson pine is widely distributed in Hubei Province, and it can reach 45 meters in height and 1.5 meters in diameter at breast height. Possible reasons for the lack of hump-shaped relationship for lower constraint lines of Masson pine is intolerant to shade and prefers high light and temperature (Zhao et al., 2016 ; Cao et al., 2018 ). Additionally, the soil requirement of Masson pine is not strict, and it can grow in gravelly soil, sandy soil, clay, and on steep rocky mountains (Zhao et al., 2016 ). Therefore, the constraint lines of Masson pine performed hump-shaped in the upper constraint lines and inverse hump-shaped in the lower constraint lines. 4.2 Management implications of the thresholds of canopy density The canopy density thresholds can guide planners to arrange the planting density of artificial coniferous forests. The upper constraint lines, lower constraint lines, and mean lines represent the relationships between the canopy density and the indicators of understory vegetation under the best, worst, and general environmental conditions, respectively. We found that the threshold ranges on the upper constraint lines were similar to those on the lower constraint lines for each species, which was our main focus. We did not focus on the mean lines and related thresholds because the normal environmental conditions for the understory vegetation were difficult to identify (Cade and Noon, 2003 ). Overall, the canopy density thresholds of Chinese fir, cypress, and Masson pine were approximately 50%, 30%~40%, and 50%~60%, respectively. A planner may design the density of a planted forest based on the results to ensure the growth of understory vegetation. Specifically, planners can choose a corresponding planting density according to the indicators of interest, such as the height and coverage of the understory vegetation as well as the features of shrubs and herbs. Understory vegetation can effectively maintain the entire ecosystem stability of planted forests (Stevens et al., 2015 ; Thrippleton et al., 2017 ). However, some studies have shown that the ecological function of simple shrubs or herbs is far inferior to that of the combination of shrubs and herbs (Benes and Carpenter, 2016 ). Single understory vegetation species and low biodiversity may cause many ecological problems (Spicer et al., 2020 ). Therefore, we suggest that planners comprehensively consider multiple indicators about understory vegetation when planting forests. In addition to the ecological benefits, the planted forests could provide great economic value, such as timber supply, medicinal values, and forest tourism (Zhao et al., 2016 ). For example, Chinese fir grows fast and it is an important raw material for the construction and wood fiber industries. Mixed forests can make full use of space and nutrient areas as well as enhance the ability to resist natural disasters. Thus, the site conditions would be improved to increase the quantity and quantity of forest products. In future research, we will focus on the relationship of the mixed forest and their species composition with the understory vegetation. Sometimes, in order to pursue economic benefits, over-harvesting of forests occurs from time to time. According to the results of the Eighth China Forest Resources Inventory, 64% of China's forest harvesting and depletion stock comes from young and middle-aged forests, and the proportion of the area of young and middle-aged forests subjected to over-intensive logging operations is 45%. These irrational logging practices have resulted in lower forest productivity and less obvious ecological benefits. The threshold range of forest canopy density obtained in this paper could be used as a reference for harvesting and replanting in the planted forests in the southern China. We could obtain the optimal amount of forest supply while ensuring the healthy growth of understory vegetation, which in turn promotes the development of forest tourism, thus creating more economic and ecological values. 4.3 Limitation of the study In this study, the relationship between the canopy density and the understory vegetation was conducted for the three planted forest species commonly found in the hilly areas of southern China. Among them, there were only 26 sample points for Cypress, but the analysis was still carried out using the constraint line method, which was slightly inconsistent with the constraint line requirement of a large number of data samples. As a result, the constraint line fitted sample data of Cypress were not very good. In the future, when using the constraint line method, we would collect more samples for analysis. In this paper, only height and coverage of understory vegetation were observed. However, biodiversity may be an important aspect of the effect of canopy density on the understory vegetation. Understory vegetation diversity is also important for the health of the forest system and the supply of forest ecosystem services. The effects of canopy density on understory vegetation diversity would be considered in future studies. In addition, the purpose to study the relationship between the canopy density and the understory vegetation in the planted forests is to provide scientific advice for future planting and management of the planted forest to ensure the health of forest ecosystems and the sustainable supply of forest ecosystem services. However, the real situation of ecological problems with understory vegetation has not been observed and assessed, such as soil erosion, surface runoff loss, and air freshness, etc. If these ecosystem service indicators are combined to determine the optimal canopy density, the health of forest ecosystems can be more comprehensively characterized. 5. Conclusions A lack of understory vegetation may result in serious ecological problems. Favorable canopy density in the planted forest could promote the growth of understory vegetation, including shrubs and herbs. In this study, we explored the relationship between the canopy density of Chinese fir, Cypress, and Masson pine and the indicators of understory vegetation, including the height and coverage of shrubs and herbs, from the aspects of the upper constraint lines, lower constraint lines, and mean lines. We found that as opposed to Chinese fir and Cypress, the lower constraint lines of Masson pine were inverse hump-shaped. However, almost all the relationships between the canopy density and the indicators of understory vegetation on the upper constraint lines were nonmonotonic with thresholds. Regardless of how good the environmental conditions were, the canopy density thresholds of Chinese fir, Cypress, and Masson pine were approximately 50%, 30%~40%, and 50%~60%, respectively. When the canopy density of different coniferous species was greater than the corresponding thresholds, the height and coverage of shrubs and herbs decreased with the increasing canopy density. Therefore, the thresholds of the canopy density may help guide forest management and planning. Declarations Conflict of interest statement All the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Ruifang Hao conceived the ideas, designed and analyzed the data, and led the writing of the manuscript. Mingchang Shi collected the data. Bing Wang provided the photos of the coniferous forest and helped to explain the results. Yun Sun polished the original draft. Jianmin Qiao edited the figures. Acknowledgments This research was supported by the Programs of National Natural Science Foundation of China [grant number 42001260], the National Key R&D Program of China [Green Watershed Evaluation Index System and Method and the grant number of 2023YFC3205600]. References Bell, F.W., Lamb, E.G., Sharma, H., Anand, M., Dacosta, J., Newmaster, S.G., 2016. Relative influence of climate, soils, and disturbance on plant species richness in northern temperate and boreal forests. FOREST ECOL MANAG 381, 93-105. Benes, K.M., Carpenter, R.C., 2016. Kelp canopy facilitates understory algal assemblage via competitive release during early stages of secondary succession. Ecology 96, 241-251. Bohlman, S.A., 2015. 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Doi: 10.13870/j.cnki.stbcxb.2020.01.004 Ligot, G., Balandier, P., Courbaud, B.T., Jonard, M., Kneeshaw, D., Claessens, H., 2014. Managing understory light to maintain a mixture of species with different shade tolerance. Forest Ecology & Management 327, 189-200. Medinski, T.V., Mills, A.J., Esler, K.J., Schmiedel, U., Jürgens, N., 2010. Do soil properties constrain species richness? Insights from boundary line analysis across several biomes in south western Africa. Journal of Arid Environments 74, 1052-1060. Mills, A., Fey, M., Donaldson, J., Todd, S., Theron, L., 2009. Soil infiltrability as a driver of plant cover and species richness in the semi-arid Karoo, South Africa. Plant and Soil 320, 321-332. Niko, K., Leena, P., Lasse, H.M., De Grandpré, L., Timo, K., Tuomas, A., 2018. At What Scales and Why Does Forest Structure Vary in Naturally Dynamic Boreal Forests? An Analysis of Forest Landscapes on Two Continents. Ecosystems 22, 709–724. Paletto, A., Tosi, V., 2009. Forest canopy density and canopy closure: comparison of assessment techniques. European Journal of Forest Research 128, 265-272. Rollinson, C.R., Alexander, M.R., Dye, A.W., Moore, D.J.P., Pederson, N., Trouet, V., 2020. Climate sensitivity of understory trees differs from overstory trees in temperate mesic forests. Ecology 102, e03264. Doi: 10.1002/ecy.3264 Schnug, E., Heym, J., Achwan, F., 2008. Establishing critical values for soil and plant analysis by means of the boundary line development system (bolides). Communications in Soil Science and Plant Analysis 27, 2739-2748. Scott, N.M., David, D.B., Clifton, W.M., 2000. Spatial distributions of understory light along the grassland/forest continuum: effects of cover, height, and spatial pattern of tree canopies. Ecological Modelling 126, 79-93. Spicer, M.E., Mellor, H., Carson, W.P., 2020. Seeing beyond the trees: a comparison of tropical and temperate plant growth‐forms and their vertical distribution. Ecology 101, e02974. Doi: 10.1002/ecy.2974 Stevens, J.T., Safford, H.D., Harrison, S., Latimer, A.M., 2015. Forest disturbance accelerates thermophilization of understory plant communities. Journal of Ecology 103, 1253-1263. Szwaluk, K.S., Strong, W.L., 2003. Near-surface soil characteristics and understory plants as predictors of Pinus contorta site index in southwestern Alberta, Canada. Forest Ecology & Management 176, 13-24. Tan, Y., He, Q., Zheng, W., Peng, Y., Hou, Y., He, F., Shen, W., 2016. Effects of canopy structure on understory vegetation in shelterbelt forests along the middle and upper reaches of Pearl River. Chinese Journal of Ecology 35, 3148-3156. Doi: 10.13292/j.1000-4890.201612020 Thomson, J.D., Weiblen, G., Thomson, B.A., Alfaro, S., Legendre, P., 1996. Untangling Multiple Factors in Spatial Distributions: Lilies, Gophers, and Rocks. Ecology 77, 1698-1715. Thrippleton, T., Bugmann, H., Folini, M., Snell, R.S., 2017. Overstorey–Understorey Interactions Intensify After Drought-Induced Forest Die-Off: Long-Term Effects for Forest Structure and Composition. Ecosystems 27, 723-739. Doi: 10.1007/s10021-017-0181-5 Tomita, Seiwa, 2004. Influence of canopy tree phenology on understorey populations of Fagus crenata. J VEG 2004,15(3), 379-388. Zhao, Y., Zhang, D., Zhang, J., Zhou, H., Wei, D., Zhang, J., Yuan, Y., 2016. Understory vegetation diversity of Pinus massoniana plantations with various canopy density. Chin J Appl Environ Biol 22, 1048-1054. Doi: 10.3724/SP.J.1145.2016.04052 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2025 Read the published version in New Forests → Version 1 posted Editorial decision: Revision requested 24 Feb, 2025 Reviews received at journal 11 Feb, 2025 Reviewers agreed at journal 10 Feb, 2025 Reviewers agreed at journal 06 Sep, 2024 Reviewers invited by journal 04 Sep, 2024 Submission checks completed at journal 27 May, 2024 Editor assigned by journal 27 May, 2024 First submitted to journal 24 May, 2024 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. 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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-4469916","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310351991,"identity":"73d89fec-ab21-4bac-84c3-aec8a6f9e757","order_by":0,"name":"Ruifang Hao","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Ruifang","middleName":"","lastName":"Hao","suffix":""},{"id":310351992,"identity":"d4f3bd95-70fe-494f-b907-54562c8d7018","order_by":1,"name":"Mingchang Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCcYGBoYKZgYGZtK0nGGWIEULEDO2MUsQ7y752c2tG37Os66Td2d/wPCjYhthLQZ3Drbd7N2WLmF4mMeAsefMbSK0SCS23WbcdljCsJmHgZmxjQgt8jNAWuaAtLA/IE4Lww2QlobDEvLMDAbEaTEAarnZcyxdcgMzj8FBovwiPyP92Y0fNdb88v3HHz74UUGMw+DWHWBgOECCepB1DaSpHwWjYBSMghEEADl9OyLXKzYzAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Mingchang","middleName":"","lastName":"Shi","suffix":""},{"id":310351993,"identity":"034aa502-bfdc-458c-968c-921bdde04324","order_by":2,"name":"Bin Wang","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Wang","suffix":""},{"id":310351994,"identity":"e5acb214-ae8b-4919-b0ce-e0b30629be78","order_by":3,"name":"Yun Sun","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Sun","suffix":""},{"id":310351995,"identity":"14a30d4b-daef-4371-9e6a-4f48dfdf9cc0","order_by":4,"name":"Jianmin Qiao","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jianmin","middleName":"","lastName":"Qiao","suffix":""}],"badges":[],"createdAt":"2024-05-24 04:23:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4469916/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4469916/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11056-025-10102-z","type":"published","date":"2025-04-21T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57905419,"identity":"b04210f4-11d6-43f0-ac26-ff5c8629a667","added_by":"auto","created_at":"2024-06-07 09:45:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6963601,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlanted forest and understory vegetation in Jiuhua Mountain, Hubei Province, China\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/147bcebf6bdbe0bfcb043f0d.jpg"},{"id":57906263,"identity":"ed3f2d5c-2776-4021-bd15-87ffc41b8ad6","added_by":"auto","created_at":"2024-06-07 09:53:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4326366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of Hubei Province and sample spatial distribution (DEM: Digital Elevation Model)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/f445abcae9d4da06eac69956.jpg"},{"id":57906264,"identity":"30117ebf-6631-4331-b530-2288c6f3fb2f","added_by":"auto","created_at":"2024-06-07 09:53:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4734749,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLand use/cover of Hubei Province in 2015\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/937a3064a43e9f15f091f994.jpg"},{"id":57905420,"identity":"49d4f9dc-080e-4baa-acaf-34207feac274","added_by":"auto","created_at":"2024-06-07 09:45:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1460245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtraction of upper constraint lines, lower constraint lines, and mean lines\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/ccdbee69a83c5a70df56aaa6.jpg"},{"id":57906766,"identity":"6c75cd9b-5385-48bc-a602-26f188834e7c","added_by":"auto","created_at":"2024-06-07 10:01:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4170997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots, mean line, upper constraint lines, and lower constraint lines of all samples, indicatingthe relationships between canopy density and (a) understory shrub height, (b) understory herb height, (c) understory shrub coverage, (d) understory herb coverage, and (e) understory vegetation coverage.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/7d15ac6ffbf7fe87e12e1882.jpg"},{"id":57906764,"identity":"4f20c40c-c661-4cc6-8a97-7f06a03dd6c0","added_by":"auto","created_at":"2024-06-07 10:01:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3914773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots, mean line, upper constraint lines, and lower constraint lines of Chinese fir, indicating the relationship between canopy density and (a) understory shrub height, (b) understory herb height, (c) understory shrub coverage, (d) understory herb coverage, and (e) understory vegetation coverage.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/46ac0e6ae5d1bb8e5b5302d1.jpg"},{"id":57905424,"identity":"8e0e43bf-c12c-437d-a152-734cc4d798eb","added_by":"auto","created_at":"2024-06-07 09:45:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3427185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots, mean line, upper constraint lines, and lower constraint lines of Cypress, indicating the relationship between canopy density and (a) understory shrub height, (b) understory herb height, (c) understory shrub coverage, (d) understory herb coverage, and (e) understory vegetation coverage.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/49094a2171ac63770880b3a3.jpg"},{"id":57905422,"identity":"14e103f2-9bf6-49a0-847c-2de6dc3cf81d","added_by":"auto","created_at":"2024-06-07 09:45:16","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4160335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots, mean line, upper constraint lines, and lower constraint lines of Masson pine, indicating the relationship between canopy density and (a) understory shrub height, (b) understory herb height, (c) understory shrub coverage, (d) understory herb coverage, and (e) understory vegetation coverage.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/38c61595ef346b005a34e72b.jpg"},{"id":57905427,"identity":"0128800d-04b7-4afe-9e07-9874a09f254d","added_by":"auto","created_at":"2024-06-07 09:45:17","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3006522,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThresholds of canopy density for different species on the mean lines, upper constraint lines, and lower constraint lines.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/5aa7d8e0909fae87e97c7b6c.jpg"},{"id":57905429,"identity":"6c222b7d-fe5c-44b0-9b36-22bbf29102bd","added_by":"auto","created_at":"2024-06-07 09:45:17","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3225261,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot and lower constraint lines of humus thickness and understory vegetation coverage with (a) all samples, (b) Chinese fir samples, (c) cypress samples, and (d) Masson pine samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/99b275d3bcf5b607aa9055a7.jpg"},{"id":81569540,"identity":"c0edba92-eef8-4e35-a786-c11d31bea6ee","added_by":"auto","created_at":"2025-04-28 16:06:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":40926591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4469916/v1/98f61d4d-011d-459f-8bf5-b27ed224203b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the relationship between canopy density and understory vegetation in planted forest by using constraint line methodology","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePlanted forests are forests formed by planting and/or intentional seeding of native or introduced tree species (FAO \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Planted forests have become an important part of terrestrial ecosystems, and bring considerable economic and ecological benefits (Stevens et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Chandler and McGraw \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Spicer et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Meanwhile, planted forests have also been recognized as an effective way to mitigate climate change (Cheng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). China has been implementing afforestation and reforestation projects since the 1970s, establishing more than a quarter of the world's planted forest area (FAO \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The results of China's eighth consecutive national forest resources inventory (2009\u0026ndash;2013) show that the planted forests cover an area of 69.33\u0026nbsp;million hectares, accounting for 36% of the country's forested land. Compared with natural forests, the planted forests tend to have fewer species, younger stands, and a higher forest management intensity (Rollinson et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Cheng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, the forest ecosystems dominated by the planted forests are prone to a series of ecological problems, especially affecting the healthy growth of understory vegetation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Understory vegetation is an important part of forest ecosystems (Scott et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Chandler and McGraw \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although understory vegetation accounts for a small proportion of the total biomass of the forest ecosystem, it plays an important role in maintaining forest diversity and conserving water and soil (Bohlman \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Bell 2016, Benes and Carpenter \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Increased shrub and herb coverage or diversity may benefit various species groups, such as birds and invertebrates (D\u0026iacute;az \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, the growth of understory vegetation is of great significance to the sustainable management of the ecosystems in the planted forest.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSome studies have suggested that understory vegetation does not grow due to allelopathy, that is, coniferous forests secrete tannins that inhibit the growth of understory vegetation (e.g. Benes and Carpenter \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, other studies have reported that the excessive canopy density of artificial coniferous forests hinders the growth of understory vegetation (Scott et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Ligot et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Benes and Carpenter \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Chandler and McGraw \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Canopy density is a key factor that affects the microclimatic like light transmission and forest precipitation, and it has an important impact on the development of understory vegetation (e.g. Bell et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the relationship between the canopy density and the understory vegetation is very variable and depending in climate conditions, and the thresholds of the relationship have not been clearly determined. Previous studies have been performed using different thinning intensities or retention densities to analyze the relationship between the canopy density and the understory vegetation (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Chandler and McGraw \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is difficult to ensure the continuous change of the control variables in the quantitative control experiments, which leads to different results in different studies, thus making it difficult to obtain an accurate threshold of canopy density (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Stevens et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Cao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, understory vegetation is possibly affected not only by the canopy density but also by forest age, topography, and soil fertility (Ligot et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Stevens et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Spicer et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Researchers have used correlation analysis and regression analysis to study the relationship between canopy density and understory vegetation, for example, Zhao et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that the diversity of understory plants increase with the decreasing canopy density by the regression analysis. These statistical methods obtain only the average relationship between the variables; however, factors other than canopy density could influence understory, and the statistical results mixed the effect of other factors may elicit the wrong result (Laiho et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Chandler and McGraw \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, the result of correlation analysis represents a monotonic relationship, but the relationships between ecological variables are nonlinear and have thresholds (Bohlman \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For example, canopy density of Masson pine (\u003cem\u003ePinus massoniana\u003c/em\u003e) that is too low (\u0026lt;\u0026thinsp;0.2) or too high (\u0026gt;\u0026thinsp;0.8) is not conducive to soil conservation, and a canopy density of 0.6 has the best effect in terms of preventing soil erosion (Zhao et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Cao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe constraint line provides a useful method to analyze the complex nonlinear relationships between ecological variables based on massive data (Hao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The concept of the constraint line was first proposed and used to represent the best state of population growth (Holling \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, Guo and Rundel \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, Hao et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Due to the interaction of multiple factors in a complex ecological process, the two variables studied in the real situation commonly show scattered point clouds, but the ecological processes represented by different parts of the cloud-shaped data are not the same and contain rich ecological information (Thomson et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Cade and Noon \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). When the supply of all factors, except the measurement factor, is at a free level, it is considered an ideal situation. In this case, only the measurement factor has a restrictive effect on the related relationship, which could be represented by the upper constraint lines (Evanylo and Sumner \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, Schnug et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, the upper constraint line represents the boundary at which the response variable is primarily limited by the constraint variable, rather than by other factors (Guo and Rundel \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In contrast, the lower constraint line represents the change trend of the response factor when the other impacting factors are at their worst levels (Cade and Noon \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The mean line represents the general relationship between the measurement factor and the response factor in the real situation.\u003c/p\u003e \u003cp\u003eThe selection of planted forest species varies according to the region, climate and other conditions (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In the hilly areas of southern China, planted forest species are selected from Chinese fir (\u003cem\u003eCunninghamia lanceol\u003c/em\u003eata), cypress (\u003cem\u003eCupressus funebris\u003c/em\u003e), Masson pine (\u003cem\u003ePinus massoniana\u003c/em\u003e), and eucalyptus (\u003cem\u003eEucalyptus sp\u003c/em\u003e), etc.; in the plains of northern China, poplar (\u003cem\u003ePopulus\u003c/em\u003e) and paulownia (\u003cem\u003ePaulownia fortunei\u003c/em\u003e) are the main species; in the mountains of northeastern China, larch (\u003cem\u003eLarix gmelinii\u003c/em\u003e) is the main species (Cao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the early 1990s, to speed up the progress of wasteland eradication, aerial seeding and high-density artificial afforestation were adopted (Tan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Several dense forests have been formed with an average density of 3000 trees per hectare (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Cao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Most of the planted forests are pure forests with sparse understory vegetation and single biodiversity metrics, resulting in serious understory ecological problems (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), such as severe soil erosion and nonpoint source pollution (Paletto and Tosi \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Cao et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Li et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Especially, under the planted forests of pine and cypress, because pine needles are hard and unpalatable, shrubs and herbs do not grow well in the forest understory, resulting in poor stability of the entire ecosystem, which is not conducive to the sustainability of the regional ecosystem (Li et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, Hubei Province, located in the southern China, was chosen as the study area. The three planted tree species (Chinese fir, cypress, and Masson pine), commonly found in the hilly areas of southern China, were selected to analyze the effects of canopy density on the understory vegetation, meanwhile, the thresholds of canopy density were explored. The analysis results of this paper would provide suggestions for the plantation density and management of the planted forest in the hilly areas of southern China. The research methods and ideas in this paper could also be used in the decision analysis of optimal canopy density for the planted forests in China and around the world. Specifically, this paper has the following two objectives: 1) explore the relationships between the canopy density and the indicators of understory vegetation by using the mean lines, upper constraint lines, and lower constraint lines; 2) reveal the optimal canopy density values of the three planted tree species that are conducive to the growth of understory shrubs and herbs.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eHubei Province is located between 29\u0026deg;05'\u0026ndash;33\u0026deg;20'N and 108\u0026deg;21'\u0026ndash;116\u0026deg;07'E, comprising a total area of 186,000 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During 2010\u0026ndash;2020, nearly 14,000 km\u003csup\u003e2\u003c/sup\u003e of planted forests had been completed, making it one of the largest provinces in China in terms of planted forests. In 2015, the forest accounted for 50% of the entire area of Hubei Province and became the largest land use type in terms of area covered (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The forest is mainly distributed in the eastern and western Hubei Province where the elevation is high. The terrain of Hubei Province is rough and surrounded by mountains in the east, west, and north with a low elevation. In particular, the areas of elevation above 600 m account for 30% of the entire area, and they are mainly in the western area. Hubei Province is in the subtropical zone in a typical monsoon zone. Except for the high mountainous areas, most areas have a humid subtropical monsoon climate with sufficient light energy, abundant heat, a long frost-free period, and abundant precipitation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling\u003c/h2\u003e \u003cp\u003eWe established 415 plots on the woodland land use type for the survey, and the sample size is 20m\u0026times;20m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In each sample, species covered more than 80% of the area were considered to be the dominant species. Three species of Chinese fir, cypress, and Masson pine were included in the plots, and the sample numbers were 95, 26, and 294, respectively. In each plot, five small squares of 1m\u0026times;1m were set up along the diagonal. The heights of shrubs and herbs in the five samples were determined by using a ruler, and the average heights were taken as the corresponding indicators in that plot. The photos obtained by camera in the five small samples were used to calculate the vegetation cover and the canopy density, and the average values were also as the corresponding indicators in that plot. The vegetation coverage includes the overall vegetation cover, herb vegetation cover, and shrub vegetation cover. In the study, the understory vegetation coverage is a combination of shrub and herb coverage. The survey time was between June and August in 2014. The samples of Masson pine were distributed in the western, eastern, and northern parts of Hubei Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The samples of Chinese fir were mainly distributed in the western and southeastern parts of Hubei Province. The samples of cypress were distributed in the western region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Extraction of the constraint lines from scatter plots\u003c/h2\u003e \u003cp\u003eWe found that the sample data was in the form of scatter clouds and showed clear boundary lines. Therefore, we used the constraint line method to analyze the relationships between the canopy density and the indicators representing understory vegetation. In this study, segmented quantile regression (Mills et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Medinski et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was employed to obtain the constraint lines, which are characterized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The value range of the X variable was equally divided into 100 parts in the columns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We calculated the maximum, mean, and minimum in each column as the upper boundary points, mean points, and lower boundary points, respectively. Then, the upper boundary points, mean points, and lower boundary points in all columns were fitted to obtain the upper constraint lines, mean lines, and lower constraint lines, respectively (Hao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The line types were evaluated based on the shapes of the scatter cloud and the goodness-of-fit values (R\u003csup\u003e2\u003c/sup\u003e). We analyzed the upper constraint lines, mean lines, and lower constraint lines of canopy density and the understory shrub height, understory herb height, understory shrub coverage, understory shrub coverage, and understory vegetation coverage with all samples and with samples of Chinese fir, cypress, and Masson pine separately. Finally, the thresholds for the mean lines, upper constraint lines, and lower constraint lines were extracted by solving their derivative function (Hao \u003cem\u003eet al.\u003c/em\u003e, 2022; Li \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Constraint effect of canopy density on the indicators of understory vegetation by all samples\u003c/h2\u003e \u003cp\u003eOverall, the fitting equations significantly characterized the relationship between canopy density and the indicators of understory vegetation with very high goodness-of-fit on the mean lines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The means of understory shrub height, understory herb height, and understory shrub coverage first increased and then decreased with the increasing canopy density (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Except for the understory vegetation coverage, the relationship between the canopy density and other indicators of understory vegetation had an obvious upper constraint line (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, only the understory herb coverage and understory vegetation coverage had obviously lower constraint lines with canopy density (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For all significant upper and lower constraint relationships between the canopy density and the indicators of understory vegetation, the corresponding lines were hump-shaped or inverse hump-shaped with a threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\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\u003eThe goodness-of-fit (R\u003csup\u003e2\u003c/sup\u003e) of mean lines, upper constraint lines, and lower constraint lines between canopy density and indicators of understory vegetation\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLine types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eThe indicators whose relationship with canopy density\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eunderstory shrub height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eunderstory herb height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eunderstory shrub coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eunderstory herb coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eunderstory vegetation coverage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMean line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.60**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese fir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCypress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMasson pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUpper constraint line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese fir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.81**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.95**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCypress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMasson pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLower constraint line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.73**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese fir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCypress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMasson pine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.63**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ** and * indicate that the relationship is significant at the levels of 0.01 and 0.05, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Constraint effect of canopy density on the indicators of understory vegetation by different species\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Samples of Chinese fir\u003c/h2\u003e \u003cp\u003eAll the lines between the canopy density of Chinese fir and the indicators of understory vegetation, including the mean lines, upper constraint lines, and lower constraint lines, were significant (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They were nonmonotonic and generally presented a trend of first increasing and then decreasing (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Compared with the mean lines and lower constraint lines, the corresponding curvatures of the upper constraint lines were greater (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Even though the understory vegetation coverage of all the scatter clusters was between 50% and 100%, the upper constraint line and the lower constraint line described the relationship well (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Samples of Cypress\u003c/h2\u003e \u003cp\u003eOverall, the upper and lower constraint lines of the Cypress samples were fitted better with higher R\u003csup\u003e2\u003c/sup\u003e values than the mean lines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the cypress species, the shapes and trends of the upper constraint lines and lower constraint lines were almost consistent. For example, on the upper constraint line and lower constraint line, the maximum and minimum understory shrub coverage sharply decreased with increasing cypress canopy density (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). However, the trend of the mean lines was diverse and varied with the understory vegetation indicators, and most of them were different with the upper and lower constraint lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For example, the mean line of canopy density and understory herb coverage only increased, but the mean line of canopy density and understory shrub coverage first decreased and then increased (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Samples of Masson pine\u003c/h2\u003e \u003cp\u003eFor the Masson pine species, all of the mean lines and upper constraint lines characterized the relationships between the canopy density and the understory indicators with statistically significant fitting equations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The trends of the lower constraint lines of canopy density and understory shrub height, herb height, and understory shrub coverage were not obvious, but the upper constraint lines between the Masson pine canopy density and the understory vegetation indicators were all hump-shaped with an obvious threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Except for the mean line between the canopy density and the understory herb coverage, which had a decreasing trend, the other mean lines were nonmonotonic (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Thresholds of the relationship between canopy density and the indicators of understory vegetation\u003c/h2\u003e \u003cp\u003eOverall, the threshold ranges of the canopy density on the lower constraint lines were wider than those on the mean lines and the upper constraint lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The thresholds ranged from 32\u0026ndash;61% on the mean lines, from 31\u0026ndash;66% on the upper constraint lines, and from 28\u0026ndash;89% on the lower constraint lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Except for the thresholds on the mean lines of Cypress, other thresholds indicated that when the canopy density was greater than the thresholds, the indicators of understory vegetation decreased with increasing canopy density (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). On the upper constraint lines, the thresholds for Cypress were small and clustered between 32% and 38%, while those for Masson pine and Chinese fir were larger, being clustered between approximately 49\u0026ndash;61% and 46\u0026ndash;58%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). On the lower constraint lines, the thresholds for Cypress were smaller and clustered at approximately 30%, while the thresholds for Chinese fir were larger and evenly distributed between 44% and 65% (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn Hubei Province, since the 1970s, the density of plantations has gradually increased, and the proportion of natural forests in some forest areas has been significantly reduced. Coniferous forest is the main woodland type in Hubei Province, and the main plantation tree species are Chinese fir and Masson pine (Tan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Reasonable planting and management of existing planted forests are key issues to ensure the healthy development of the forest ecosystem in Hubei Province.\u003c/p\u003e \u003cp\u003eResearchers commonly use the means of samples to analyze the relationships between variables (Cade and Noon, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, in this study, the results of the constraint lines showed that the relationships of mean values may interfere with complicated ecological processes, and it was difficult to clearly identify only the relationship between the two variables studied (Mills et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The results regarding the constraint effect between the canopy density and the indicators of understory vegetation could guide rational management of planted forests.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1 Interpretation of the constraint effect between the canopy density of planted forests and the understory vegetation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe canopy density is thought to affect the understory light environment, air permeability, rain, and temperature, which jointly influence the growth of understory vegetation (Tomita and Seiwa, 2004; Ligot et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rollinson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). When the canopy density is low and the temperature and light are high, the conditions enhance the decomposition of the thin humus under forest (Tomita and Seiwa, 2004; Ligot et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). We analyzed the lower constraint lines between humus thicknesses and the understory vegetation coverage, and found that all the lines are with high goodness-of-fit values (R\u003csup\u003e2\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The four lower constraint lines with upward trends indicated that even in an environment with poor growth in Hubei Province, the understory vegetation coverage increased with increasing humus thickness. Cao et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that soil thickness and soil nutrients are important factors affecting the growth of understory vegetation. Humus is rich in nutrients, generally accounting for 85\u0026ndash;90% of the total soil organic matter, and humus provides abundant energy for plant growth (Szwaluk and Strong, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA higher canopy density reduces rainfall erosion of understory vegetation (Spicer et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, a suitable canopy density may create a good growth environment for understory vegetation. However, when the canopy density is higher than the thresholds, a high canopy density reduces the light transmittance and air permeability under the coniferous forest, which may be the main reasons for the reduced growth of understory vegetation (Thrippleton et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The thresholds of canopy density for shrubs were larger than those for herbs, which was similar to the result of Tan et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The understory light was most influenced by canopy density and could be modified by forest height and stand structure (Scott et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Laiho et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Chinese fir is a large evergreen tree with an average height of 30 to 40 meters and a diameter at breast height greater than 3 meters, and Cypress is generally up to 20 meters high. It is that taller, wider trees such as Chinese fir would block out more light and thus inhibit understory growth in comparison to the smaller cypress. Therefore, this may be one of the main reasons why Chinese fir had a nonmonotonic relationship between the canopy density and the indicators of understory vegetation, while the increase in cypress canopy density monotonically inhibited the growth of the understory shrubs and herbs. Additionally, Cypress is densely branched with many thin and weak branches as well as dense leaves and the crown is completely surrounded by branches and leaves (Tan et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), therefore, the light transmission and air permeability under such dense foliage are poor. The reason of diverse relationships for Masson pine as the mean lines and the lower constraint lines showed were with various environmental conditions. Masson pine is widely distributed in Hubei Province, and it can reach 45 meters in height and 1.5 meters in diameter at breast height. Possible reasons for the lack of hump-shaped relationship for lower constraint lines of Masson pine is intolerant to shade and prefers high light and temperature (Zhao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, the soil requirement of Masson pine is not strict, and it can grow in gravelly soil, sandy soil, clay, and on steep rocky mountains (Zhao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, the constraint lines of Masson pine performed hump-shaped in the upper constraint lines and inverse hump-shaped in the lower constraint lines.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Management implications of the thresholds of canopy density\u003c/h2\u003e \u003cp\u003eThe canopy density thresholds can guide planners to arrange the planting density of artificial coniferous forests. The upper constraint lines, lower constraint lines, and mean lines represent the relationships between the canopy density and the indicators of understory vegetation under the best, worst, and general environmental conditions, respectively. We found that the threshold ranges on the upper constraint lines were similar to those on the lower constraint lines for each species, which was our main focus. We did not focus on the mean lines and related thresholds because the normal environmental conditions for the understory vegetation were difficult to identify (Cade and Noon, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Overall, the canopy density thresholds of Chinese fir, cypress, and Masson pine were approximately 50%, 30%~40%, and 50%~60%, respectively. A planner may design the density of a planted forest based on the results to ensure the growth of understory vegetation. Specifically, planners can choose a corresponding planting density according to the indicators of interest, such as the height and coverage of the understory vegetation as well as the features of shrubs and herbs.\u003c/p\u003e \u003cp\u003eUnderstory vegetation can effectively maintain the entire ecosystem stability of planted forests (Stevens et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thrippleton et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, some studies have shown that the ecological function of simple shrubs or herbs is far inferior to that of the combination of shrubs and herbs (Benes and Carpenter, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Single understory vegetation species and low biodiversity may cause many ecological problems (Spicer et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, we suggest that planners comprehensively consider multiple indicators about understory vegetation when planting forests.\u003c/p\u003e \u003cp\u003eIn addition to the ecological benefits, the planted forests could provide great economic value, such as timber supply, medicinal values, and forest tourism (Zhao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For example, Chinese fir grows fast and it is an important raw material for the construction and wood fiber industries. Mixed forests can make full use of space and nutrient areas as well as enhance the ability to resist natural disasters. Thus, the site conditions would be improved to increase the quantity and quantity of forest products. In future research, we will focus on the relationship of the mixed forest and their species composition with the understory vegetation. Sometimes, in order to pursue economic benefits, over-harvesting of forests occurs from time to time. According to the results of the Eighth China Forest Resources Inventory, 64% of China's forest harvesting and depletion stock comes from young and middle-aged forests, and the proportion of the area of young and middle-aged forests subjected to over-intensive logging operations is 45%. These irrational logging practices have resulted in lower forest productivity and less obvious ecological benefits. The threshold range of forest canopy density obtained in this paper could be used as a reference for harvesting and replanting in the planted forests in the southern China. We could obtain the optimal amount of forest supply while ensuring the healthy growth of understory vegetation, which in turn promotes the development of forest tourism, thus creating more economic and ecological values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitation of the study\u003c/h2\u003e \u003cp\u003eIn this study, the relationship between the canopy density and the understory vegetation was conducted for the three planted forest species commonly found in the hilly areas of southern China. Among them, there were only 26 sample points for Cypress, but the analysis was still carried out using the constraint line method, which was slightly inconsistent with the constraint line requirement of a large number of data samples. As a result, the constraint line fitted sample data of Cypress were not very good. In the future, when using the constraint line method, we would collect more samples for analysis. In this paper, only height and coverage of understory vegetation were observed. However, biodiversity may be an important aspect of the effect of canopy density on the understory vegetation. Understory vegetation diversity is also important for the health of the forest system and the supply of forest ecosystem services. The effects of canopy density on understory vegetation diversity would be considered in future studies.\u003c/p\u003e \u003cp\u003eIn addition, the purpose to study the relationship between the canopy density and the understory vegetation in the planted forests is to provide scientific advice for future planting and management of the planted forest to ensure the health of forest ecosystems and the sustainable supply of forest ecosystem services. However, the real situation of ecological problems with understory vegetation has not been observed and assessed, such as soil erosion, surface runoff loss, and air freshness, etc. If these ecosystem service indicators are combined to determine the optimal canopy density, the health of forest ecosystems can be more comprehensively characterized.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eA lack of understory vegetation may result in serious ecological problems. Favorable canopy density in the planted forest could promote the growth of understory vegetation, including shrubs and herbs. In this study, we explored the relationship between the canopy density of Chinese fir, Cypress, and Masson pine and the indicators of understory vegetation, including the height and coverage of shrubs and herbs, from the aspects of the upper constraint lines, lower constraint lines, and mean lines. We found that as opposed to Chinese fir and Cypress, the lower constraint lines of Masson pine were inverse hump-shaped. However, almost all the relationships between the canopy density and the indicators of understory vegetation on the upper constraint lines were nonmonotonic with thresholds. Regardless of how good the environmental conditions were, the canopy density thresholds of Chinese fir, Cypress, and Masson pine were approximately 50%, 30%~40%, and 50%~60%, respectively. When the canopy density of different coniferous species was greater than the corresponding thresholds, the height and coverage of shrubs and herbs decreased with the increasing canopy density. Therefore, the thresholds of the canopy density may help guide forest management and planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statement\u003c/h2\u003e \u003cp\u003eAll the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRuifang Hao conceived the ideas, designed and analyzed the data, and led the writing of the manuscript. Mingchang Shi collected the data. Bing Wang provided the photos of the coniferous forest and helped to explain the results. Yun Sun polished the original draft. Jianmin Qiao edited the figures.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research was supported by the Programs of National Natural Science Foundation of China [grant number 42001260], the National Key R\u0026amp;D Program of China [Green Watershed Evaluation Index System and Method and the grant number of 2023YFC3205600].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBell, F.W., Lamb, E.G., Sharma, H., Anand, M., Dacosta, J., Newmaster, S.G., 2016. Relative influence of climate, soils, and disturbance on plant species richness in northern temperate and boreal forests. FOREST ECOL MANAG 381, 93-105.\u003c/li\u003e\n\u003cli\u003eBenes, K.M., Carpenter, R.C., 2016. Kelp canopy facilitates understory algal assemblage via competitive release during early stages of secondary succession. Ecology 96, 241-251.\u003c/li\u003e\n\u003cli\u003eBohlman, S.A., 2015. 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Doi: 10.3724/SP.J.1145.2016.04052\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"planted forest, constraint line, understory herb, understory shrub, humus thickness","lastPublishedDoi":"10.21203/rs.3.rs-4469916/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4469916/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGreater shrub and herb abundance and diversity under the forest would potentially benefit biodiversity and improve forest ecosystem stability. Therefore, determining whether a high canopy density results in reduced growth of understory vegetation and associated thresholds may guide the reasonable planting density of planted forest. In this study, we selected an artificial coniferous forest planting area in Hubei Province, China as the research area, and adopted a constraint line to explore the relationship between the canopy density of different species, including Chinese fir (\u003cem\u003eCunninghamia lanceolata\u003c/em\u003e), cypress (\u003cem\u003eCupressus funebris\u003c/em\u003e), and Masson pine (\u003cem\u003ePinus massoniana\u003c/em\u003e), and the height and coverage of shrubs and herbs in the forests. The upper constraint lines, lower constraint lines, and mean lines were extracted by segmented quantile regression derived from the maximums, means, and minimums in the scatter clounds, which represented the relationships under the best, worst, and general environmental conditions, respectively. The results showed that for different species, the upper constraint lines were almost hump-shaped with thresholds, indicating that regardless of how good the environmental conditions were, the indicators of understory shrubs and herbs first increased and then decreased with an increase in canopy density. The canopy density thresholds for Chinese fir, cypress, and Masson pine were approximately 50%, 30%~40%, and 50%~60%, respectively. Overall, the thresholds of canopy density for herb indicators were greater than those of shrubs. Planning reasonable canopy density may enhance the growth of understory vegetation.\u003c/p\u003e","manuscriptTitle":"Assessing the relationship between canopy density and understory vegetation in planted forest by using constraint line methodology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 09:45:11","doi":"10.21203/rs.3.rs-4469916/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-24T15:54:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-11T12:27:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115735826912789291581313141512595965596","date":"2025-02-10T15:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315802341022453384417062960336615472960","date":"2024-09-06T18:08:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-04T17:42:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-27T08:21:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-27T08:21:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"New Forests","date":"2024-05-24T04:22:31+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":"0386fab8-8d9d-4cf1-8c04-d8d49a002976","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T15:59:27+00:00","versionOfRecord":{"articleIdentity":"rs-4469916","link":"https://doi.org/10.1007/s11056-025-10102-z","journal":{"identity":"new-forests","isVorOnly":false,"title":"New Forests"},"publishedOn":"2025-04-21 15:57:04","publishedOnDateReadable":"April 21st, 2025"},"versionCreatedAt":"2024-06-07 09:45:11","video":"","vorDoi":"10.1007/s11056-025-10102-z","vorDoiUrl":"https://doi.org/10.1007/s11056-025-10102-z","workflowStages":[]},"version":"v1","identity":"rs-4469916","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4469916","identity":"rs-4469916","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}