Modeling of Dead Wood Potential Based on Tree Stand Data

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Modeling of Dead Wood Potential Based on Tree Stand Data © 2020 by the authors. Licensee MDPI, Basel, Switzerland. Published version Mikkonen, Ninni; Leikola, Niko; Halme, Panu; Heinaro, Einari; Lahtinen, Ari; Tanhuanpää, Topi Mikkonen, N., Leikola, N., Halme, P., Heinaro, E., Lahtinen, A., & Tanhuanpää, T. (2020). Modeling of Dead Wood Potential Based on Tree Stand Data. Forests, 11(9), Article 913. https://doi.org/10.3390/f11090913 2020 Article Modeling of Dead Wood Potential Based on Tree Stand Data Ninni Mikkonen 1,2,* , Niko Leikola 1, Panu Halme 3,4, Einari Heinaro 5, Ari Lahtinen 6and Topi Tanhuanpää 5,7 1 Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland; [email protected] 2Department of Geosciences and Geography, Faculty of Science, P.O. Box 64, University of Helsinki, FI-00014 Helsinki, Finland 3School of Resource Wisdom, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland; [email protected] 4Department of Biological and Environmental Science, University of Jyväskylä, P.O. Box 35, FI-40014 Jyväskylä, Finland 5Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00790 Helsinki, Finland; [email protected] (E.H.); [email protected] (T.T.) 6 Metsähallitus, Parks and Wildlife Finland, Talaskuja 3, FI-13200 Hämeenlinna, Finland; [email protected] 7Department of Geographical and Historical Studies, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland *Correspondence: [email protected]; Tel.: +358-504-418-980 Received: 3 July 2020; Accepted: 18 August 2020; Published: 20 August 2020   Abstract: Here we present a framework for identifying areas with high dead wood potential (DWP) for conservation planning needs. The amount and quality of dead wood and dying trees are some of the most important factors for biodiversity in forests. As they are easy to recognize on site, it is widely used as a surrogate marker for ecological quality of forests. However, wall-to-wall information on dead wood is rarely available on a large scale as field data collection is expensive and local dead wood conditions change rapidly. Our method is based on the forest growth models in the Motti forest simulator, taking into account 168 combinations of tree species, site types, and vegetation zones as well as recommendations on forest management. Simulated estimates of stand-level dead wood volume and mean diameter at breast height were converted into DWP functions. The accuracy of the method was validated on two sites in southern and northeastern Finland, both consisting of managed and conserved boreal forests. Altogether, 203 field plots were measured for living and dead trees. Data on living trees were inserted into corresponding DWP functions and the resulting DWPs were compared to the measured dead wood volumes. Our results show that DWP modeling is an operable tool, yet the accuracy differs between areas. The DWP performs best in near-pristine southern forests known for their exceptionally good quality areas. In northeastern areas with a history of softer management, the differences between near-pristine and managed forests is not as clear. While accurate wall-to-wall dead wood inventory is not available, we recommend using DWP method together with other spatial datasets when assessing biodiversity values of forests. Keywords: biodiversity; coarse woody debris; conservation planning; forests; forest simulation; forestry; land-use planning; spatial conservation prioritization 1. Introduction Land use, including forestry, is the main threat to biodiversity [ 1 , 2 ]. Forestry causes biodiversity loss as it fundamentally changes the ecosystem functioning and induces habitat loss and degradation. One of the most severe changes is the decline in the amount and quality of dead wood and dying trees, Forests 2020,11, 913; doi:10.3390/f11090913 www.mdpi.com/journal/forests Forests 2020,11, 913 2 of 21 which are a crucial part of the life cycle of forests and one of the most important factors for biodiversity in them [3–6]. Finland, situated in Fennoscandia in northern Europe, is one of the most forested countries in the world, with forests covering 75% (22.8 million hectares) of the land area [ 7 ]. Still, 76% of forest habitats and 9.8% of forest species are threatened. The main causes for this are the same for both groups: (1) reduction in the amount of dead wood, (2) reduction in old-growth forests and individual old trees, and (3) changes in tree species composition. These threats are interconnected as they usually occur simultaneously [ 8 – 10 ]. This decline in ecological condition results mainly from the intensive forest management during the last centuries, which has caused an alarming shortage of natural forests outside protected areas [11,12]. A significant difference between managed and natural forests is dead wood volume. The mean dead wood volume in Finnish forest areas is 5.8 m 3 per hectare, varying from managed forests with less than 2 m 3 per hectare [ 7 ] to forests with softer management practices, e.g., urban forests (median 10.1 m 3 per hectare [ 13 ]), and finally to natural forests that host a volumes between 40 and 170 m 3 per hectare [ 14 ]. What follows is that the dead wood continuum does not exist either [ 3 , 15 , 16 ]. Dead wood, in all forms, plays a significant role in the boreal forest ecosystems by producing a high resource supply and microhabitat diversity. Thereby, it causes, e.g., up to 75% higher species richness in saproxylic species in natural boreal forests compared to managed forests as 20–25% of forest species are dependent on dead wood [ 15 , 17 ]. Dead wood has been used as a surrogate marker for biodiversity as there are well-known dependencies between threatened forest biodiversity and the different size, stages, and composition of dead wood parcels especially in boreal forests [ 15 , 18 – 20 ]. Following this, dead wood is commonly monitored and, e.g., in Finland the National Forest Inventory (NFI) has compiled data on the amount of dead wood since the 1920s [ 21 ]. Based on collected data, the amount and quality of dead wood has been assessed and modeled in various ways (see [ 22 ]). In the beginning of the 21st century, the appearance of more biodiversity-oriented forestry has highlighted the importance of including dead wood in forest simulations (e.g., [ 23 ]), whereas during the last decades, its importance for climate change mitigation as carbon stock has emphasized it again (see, e.g., [ 24 , 25 ]). However, for the needs of conservation planning, this data is often insufficient. This is mostly because the big data sets, such as NFI, contain a minor amount of field plots with dead wood. The reason for this is that dead wood occurs randomly and the amounts are often very small. Following this, continuous dead wood information has not been achieved or the spatial accuracy has been insufficient. A challenge is also that data on dead wood becomes outdated quite fast as the wood decays [26]. In Finland, information on forest stands is collected regularly, producing several up-to-date national forest data sets for forest management or related purposes. These are nowadays based on remote sensing techniques and field inventoried sample plots. As field inventories have declined drastically, attempts to inventory biodiversity features based on remote sensed data have become necessary. Promising techniques for modeling the appearance of dead wood have been developed, but neither data nor techniques are yet operatively available for a wider audience [ 27 – 29 ]. A significant challenge for methodological development is the rarity of dead wood in forests [ 30 ], which causes a shortage of field plots containing dead wood. Here we report a framework for modeling dead wood potential (DWP) of forests based on forest stand data. The overall aim was to develop a method to assist in estimating the biodiversity values of forests on a large scale. These kinds of estimates are urgently needed in spatial land use planning concerning nature conservation, e.g., for the needs of The Forest Biodiversity Programme of Southern Finland, METSO [ 31 ]. Our DWP modeling is based on earlier developmental work on forest conservation values in Finland [32–36]. The method was validated with field data from two separate test areas in Finland. We were also interested in understanding how well the method works in areas that are known for their substantial dead wood volumes. Additionally, we wanted to know whether there are differences between different management histories. Forests 2020,11, 913 3 of 21 2. Materials and Methods 2.1. Dead Wood Potential Modeling Method The developed DWP is based on forest fertility classes, tree species richness, the mean diameter at breast height (DBH mean ), and the volume of trees on each site. The variables for DWP modeling were chosen for the following reasons: forest site types describe the capability of wood production on a site based on soil fertility (e.g., Cajander [ 37 ]). The higher the growth, the higher the potential to create resources such as tree biomass to support biodiversity on the site. Site types are also rather easy to determine. Tree species reflect, obviously, species richness per se, but also different living environments, biotopes, as every tree species maintains at least partly its unique set of biodiversity [ 38 ]. Age of the forest is one of the most important factors when considering its value for biodiversity (see, e.g., [ 10 , 19 , 39 ]). As sufficient data on forest age on a national scale are not available, the DBH mean was used as a surrogate. The total volume separates sparse and dense forests as well as low canopies from tall ones. On its own, this variable does not reveal much about biodiversity, but as a part of this framework, it provides additional information on the sites’ importance for biodiversity. DWP was calculated for each forest stand in three steps (Figure 1): simulations of forest growth (1), development of DWP functions (2), and conversion of forest stand data into DWP on each site (3). Steps one and two did not require spatial data. All the three steps are described in more detail below. Forests 2020, 11, x FOR PEER REVIEW 3 of 24 that are known for their substantial dead wood volumes. Additionally, we wanted to know whether there are differences between different management histories. 2. Materials and Methods 2.1. Dead Wood Potential Modeling Method The developed DWP is based on forest fertility classes, tree species richness, the mean diameter at breast height (DBH mean ), and the volume of trees on each site. The variables for DWP modeling were chosen for the following reasons: forest site types describe the capability of wood production on a site based on soil fertility (e.g., Cajander [37]). The higher the growth, the higher the potential to create resources such as tree biomass to support biodiversity on the site. Site types are also rather easy to determine. Tree species reflect, obviously, species richness per se, but also different living environments, biotopes, as every tree species maintains at least partly its unique set of biodiversity [38]. Age of the forest is one of the most important factors when considering its value for biodiversity (see, e.g., [10,19,39]). As sufficient data on forest age on a national scale are not available, the DBH mean was used as a surrogate. The total volume separates sparse and dense forests as well as low canopies from tall ones. On its own, this variable does not reveal much about biodiversity, but as a part of this framework, it provides additional information on the sites’ importance for biodiversity. DWP was calculated for each forest stand in three steps (Figure 1): simulations of forest growth (1), development of DWP functions (2), and conversion of forest stand data into DWP on each site (3). Steps one and two did not require spatial data. All the three steps are described in more detail below. Figure 1. Calculation of dead wood potential (DWP) was executed in three steps. First, simulations of forest growth produced the needed information of forest growth in 168 combinations of seven tree species, six forest site types, and four vegetation zones. Second, based on the previous information, altogether 168 different DWP functions were developed. Third, the spatial data of forest stands was Figure 1. Calculation of dead wood potential (DWP) was executed in three steps. First, simulations of forest growth produced the needed information of forest growth in 168 combinations of seven tree species, six forest site types, and four vegetation zones. Second, based on the previous information, altogether 168 different DWP functions were developed. Third, the spatial data of forest stands was converted into the DWP for each tree species with DWP functions. DW =dead wood, DBH =diameter at breast height, DBHmean =mean diameter at breast height. As a first step, the data required for DWP modeling were simulated with the freely available Motti forest stand simulator (version 3.3) [ 40 – 42 ], provided by the Natural Resources Institute Forests 2020,11, 913 4 of 21 Finland. The functionality of the simulator is built upon NFI. Simulations provided data on the amount of dead wood (m 3 per hectare) and diameter at breast height (DBH) of living trees (Figure 1, step 1). Simulations were made in 5-year intervals for 168 forest combinations of seven tree species (Alnus glutinosa ((L.) Gaertn.), Betula pendula (Roth), Betula pubescens (Ehrh.), Picea abies ((L.) H. Karst), Pinus sylvestris (L.), Populus tremula (L.), and otherbroadleaved tree species), sixforest sitetypes, andfour vegetation zones from hemiboreal to northern boreal zones [ 43 , 44 ] (see Figure 2and Appendix A for more details). Finnish forest management practice recommendations [ 45 ] set the guidelines for simulations. This information included, e.g., the number of seedlings per hectare and the timing of thinnings in a growing stand. No clear-cutting was executed in the simulations as stands were allowed to grow until the volume of the growing stock started to decline due to self-thinning effect. Simulations were executed on mineral soil only as simulations on peatland were not available in this version of Motti simulator. As the used version did not include information on the decomposition of dead wood, the volumes were larger than in reality. Based on expert opinion, the amounts of simulated dead wood were correct (considering the known deficiencies) and similar between tree species and forest site types. As the initial purpose for the modeling was to develop relative data on the probability of dead wood (differentiated from exact biological data) for spatial conservation prioritization needs, this was not seen as a problem. Forests 2020, 11, x FOR PEER REVIEW 5 of 24 Figure 2. The map of Finland, the vegetation zones used in DWP modeling, and locations of the validation sites. The vegetation zones (grey lines) are as follows: I Southern Finland (vegetation zones 1 (hemiboreal), and 2a and 2c (southern boreal)), II Middle Finland (2b (southern boreal)), III Ostrobothnia and Kainuu (3a–c (middle boreal)), and IV Northern Finland (4a–d (northern boreal)). Southern (Evo) and northeastern (Kuhmo) sites are marked with pink squares. The second step (Figure 1, Step 2) included the conversion of simulated estimates of dead wood and DBH into DWP functions. In total, 168 functions were formulated, one for each species–site type– vegetation zone combination (see Appendix A). This was done in four steps as described in Table 1. In Steps A and B, the amounts of dead wood (A) and DBH (B) were scaled between 0 and 1 in relation to the amount of them at the simulation maximum point, a point where the growing stock volume started to decline. In Step C, the scaled dead wood and the DBH were summed at each time step (minimum value 0 at time step 0, maximum value 2 at time step of 80 years in the example in Table 1). In Step D, the sum was rescaled from 0 to 1. These values were eventually used for fitting the DWP functions (Figure 3, Appendix A). Figure 2. The map of Finland, the vegetation zones used in DWP modeling, and locations of the validation sites. The vegetation zones (grey lines) are as follows: I Southern Finland (vegetation zones 1 (hemiboreal), and 2a and 2c (southern boreal)), II Middle Finland (2b (southern boreal)), III Ostrobothnia and Kainuu (3a–c (middle boreal)), and IV Northern Finland (4a–d (northern boreal)). Southern (Evo) and northeastern (Kuhmo) sites are marked with pink squares. Forests 2020,11, 913 5 of 21 The second step (Figure 1, Step 2) included the conversion of simulated estimates of dead wood and DBH into DWP functions. In total, 168 functions were formulated, one for each species–site type–vegetation zone combination (see Appendix A). This was done in four steps as described in Table 1. In Steps A and B, the amounts of dead wood (A) and DBH (B) were scaled between 0 and 1 in relation to the amount of them at the simulation maximum point, a point where the growing stock volume started to decline. In Step C, the scaled dead wood and the DBH were summed at each time step (minimum value 0 at time step 0, maximum value 2 at time step of 80 years in the example in Table 1). In Step D, the sum was rescaled from 0 to 1. These values were eventually used for fitting the DWP functions (Figure 3, Appendix A). Table 1. Example of construction of one dead wood potential (DWP) function. DWP function for other broadleaved trees in forest site type 1 (herb-rich forest) and in vegetation zone 2 (southern boreal 2b) was calculated as follows: Step A: the amount of dead wood was scaled between 0 and 1 in relation to the amount at the simulation maximum point (bolded). Step B: the DBH values were scaled similarly to the dead wood. Step C: the scaled dead wood and DBH were summed at each time step (min 0, max 2). Step D: this sum was rescaled from 0 to 1 forming DWP multiplier. Other Broadleaved Trees in Forest Site Type 1 and in Vegetation Zone 2 Simulated results Time steps, i.e., age of trees (years), starting from 0 60 65 70 75 80 85 Growing stock volume (m3per hectare) 155.2 170.7 183.1 190.6 190.8 181.9 DBH (cm) 15.4 16.0 16.5 17.0 17.5 18.0 Dead wood volume (m3per hectare) 4.7 7.9 13.6 23.5 39.4 63.3 Step A Rescaled dead wood in relation to the Motti simulation maximum value (max. vol. 39.41 m3per hectare) 0.12 0.20 0.35 0.60 1 Step B Rescaled DBH in relation to the Motti simulation maximum value (max. DBH 17.54 cm per hectare) 0.88 0.91 0.94 0.97 1 Step C Sum of rescaled values of dead wood and DBH at certain time step 0.99 1.11 1.29 1.57 2 Step D DWP multiplier (minimum 0, maximum 1) 0.50 0.55 0.64 0.78 1 Forests 2020, 11, x FOR PEER REVIEW 6 of 24 Table 1. Example of construction of one dead wood potential (DWP) function. DWP function for other broadleaved trees in forest site type 1 (herb-rich forest) and in vegetation zone 2 (southern boreal 2b) was calculated as follows: Step A: the amount of dead wood was scaled between 0 and 1 in relation to the amount at the simulation maximum point (bolded). Step B: the DBH values were scaled similarly to the dead wood. Step C: the scaled dead wood and DBH were summed at each time step (min 0, max 2). Step D: this sum was rescaled from 0 to 1 forming DWP multiplier. Other Broadleaved Trees in Forest Site Type 1 and in Vegetation Zone 2 Simulated results Time steps, i.e., age of trees (years), starting from 0 60 65 70 75 80 85 Growing stock volume (m3 per hectare) 155.2 170.7 183.1 190.6 190.8 181.9 DBH (cm) 15.4 16.0 16.5 17.0 17.5 18.0 Dead wood volume (m3 per hectare) 4.7 7.9 13.6 23.5 39.4 63.3 Step A Rescaled dead wood in relation to the Motti simulation maximum value (max. vol. 39.41 m3 per hectare) 0.12 0.20 0.35 0.60 1 Step B Rescaled DBH in relation to the Motti simulation maximum value (max. DBH 17.54 cm per hectare) 0.88 0.91 0.94 0.97 1 Step C Sum of rescaled values of dead wood and DBH at certain time step 0.99 1.11 1.29 1.57 2 Step D DWP multiplier (minimum 0, maximum 1) 0.50 0.55 0.64 0.78 1 Figure 3. Fitting the dead wood potential (DWP) functions and generating DWP multipliers. The figure describes one of the 168 functions, being the function for other broadleaved trees, growing in the most nutrient-rich soil (forest site type 1) in vegetation zone 2. DWP functions were formulated by fitting an 8-decimal function through the scaled DBH and dead wood values. Here, the X-axis is the diameter at breast height (DBH) and the Y-axis is the DWP multiplier for stratum volume. In this example the DWP multiplier for stratum volume for trees of, e.g., 10 cm mean diameter at breast height, is approximately 0.3. Step 3 (Figure 1), required spatial forest stand data (DBHmean and volume per hectare) as at this point the DWP functions were used to convert the stand variables into the DWP. The DWP multiplier was defined with the DWP function using the stand DBHmean (Figure 3, Appendix A). Due to the overestimates that resulted from the extrapolation of the DWP functions, some of the DWP multipliers were unrealistically high. This was because natural forests host larger trees than commercial forests, especially for class other broadleaved trees. As we wanted to preserve the value of the large trees in natural forests, and on the other hand limit the overestimation of the DWP multiplier, its maximum was set to 2. Finally, the volume was multiplied with the DWP multiplier resulting in the DWP value. If there were more than one stratum of the same tree species in a stand, their DWP values were summed. As an example, the stratum described in Figure 3 is Alnus incana ((L.) Moench) growing in herbrich forests in the southern boreal zone. The DWP multiplier for DBHmean 10 cm is approximately 0.3. When the volume is 30 m3/ha, the DWP for this is: 𝐷𝑊𝑃 = 𝐷𝑊𝑃 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 × 𝑣𝑜𝑙𝑢𝑚𝑒 (1) where the DWP multiplier stands for the DWP function value at DBHmean of 10 cm (here 0.3). This is multiplied with the volume (i.e., 30 m3/ha) to achieve the DWP value 0.3 × 30 = 9. 2.2. Validation of DWP Modeling Method Figure 3. Fitting the dead wood potential (DWP) functions and generating DWP multipliers. The figure describes one of the 168 functions, being the function for other broadleaved trees, growing in the most nutrient-rich soil (forest site type 1) in vegetation zone 2. DWP functions were formulated by fitting an 8-decimal function through the scaled DBH and dead wood values. Here, the X-axis is the diameter at breast height (DBH) and the Y-axis is the DWP multiplier for stratum volume. In this example the DWP multiplier for stratum volume for trees of, e.g., 10 cm mean diameter at breast height, is approximately 0.3. Step 3 (Figure 1), required spatial forest stand data (DBH mean and volume per hectare) as at this point the DWP functions were used to convert the stand variables into the DWP. The DWP multiplier was defined with the DWP function using the stand DBH mean (Figure 3, Appendix A). Due to the overestimates that resulted from the extrapolation of the DWP functions, some of the DWP multipliers were unrealistically high. This was because natural forests host larger trees than commercial forests, Forests 2020,11, 913 6 of 21 especially for class other broadleaved trees. As we wanted to preserve the value of the large trees in natural forests, and on the other hand limit the overestimation of the DWP multiplier, its maximum was set to 2. Finally, the volume was multiplied with the DWP multiplier resulting in the DWP value. If there were more than one stratum of the same tree species in a stand, their DWP values were summed. As an example, the stratum described in Figure 3is Alnus incana ((L.) Moench) growing in herb-rich forests in the southern boreal zone. The DWP multiplier for DBH mean 10 cm is approximately 0.3. When the volume is 30 m3/ha, the DWP for this is: DWP =DWP multiplier ×volume (1) where the DWP multiplier stands for the DWP function value at DBH mean of 10 cm (here 0.3). This is multiplied with the volume (i.e., 30 m3/ha) to achieve the DWP value 0.3 ×30 =9. 2.2. Validation of DWP Modeling Method The accuracy of the DWP modeling was validated on two separate sites in Finland, located in southern (Evo) and northeastern Finland (Kuhmo) (Figure 2). Both areas consist of managed and conserved boreal forests and are managed by Metsähallitus, the state-owned enterprise responsible for the management of state-owned areas. The most considerable differences between the areas, apart from vegetation zone, concern the management histories of the forests. Firstly, the Evo forest school, the oldest of the kind in Finland and still operational, has brought about systematic and intensive use and experimental research of forests for educational purposes in the Evo area. Secondly, throughout the history of the sites human population has been greater in southern Finland, inducing higher rates of forest utilization for self-sufficiency needs and later for private forestry [46]. The southern validation area was located in Hämeenlinna, in the Evo recreational forest area and in its surroundings (WGS84 lat: 63 ◦ 52 0 , lon: 29 ◦ 09 0 ). Altogether, 100 field plots were measured during the summer 2018 of which 82 were in managed and 18 in conserved forest areas (Table 2). Almost half of the field plots (40 plots) were in forest stands that have been signed as habitats of special importance in terms of biodiversity or alike. On these habitats, forestry is practiced with limitations or not at all, but exact information is not publicly available [ 47 – 49 ]. Mineral soil covered 90 and peatland 10 of the plots [50]. Table 2. Validation areas of Evo and Kuhmo in numbers. Area No. of Plots Strictly Conserved Some Degree of Conservation Management Not Restricted Plots on Mineral Soil/Peatland Evo 100 18 40 42 90/10 Kuhmo 103 59 10 34 66/37 In Evo, in total, 88 of the plots were allocated to different forest strata using inventory data provided by Metsähallitus. The area was divided into 100 theoretical strata according to the dominant tree species (pine, spruce, birch sp., aspen, and other deciduous), DBH class (0–10, 10–20, 20–30, 30–40, and 40–50 cm), and basal area class (0–15, 15–30, 30–45, and 45–60 m 2 /ha). In total, 51 of the theoretical strata were found from the site. The number of plots in each forest stratum was determined by the forest stands’ relative abundance in the study area. In addition, 12 field plots were positioned subjectively in areas where aspen (Populus tremula) was present. Plot-level DBH mean and mean volume were 24 cm and 260.4 m3/ha, and for dead trees 8 cm and 15.8 m3/ha, respectively (Table 3). Forests 2020,11, 913 7 of 21 Table 3. Plot-level characteristics for living and dead wood (DW) in the Evo validation area. DBH mean =mean diameter at breast height, H =mean height, Vol =volume, N =number of pieces, D=mean value of the dead wood diameters. Mean row indicates mean values for the whole area. DBHmean (cm) H (m) Vol (m3/ha) N_DW (N/ha) D_DW (cm) Vol_DW (m3/ha) Min 0 0 0 0 0 0 Max 49.6 31.6 955.4 432 37 320.2 Mean 24 20.7 260.4 51 8 15.8 The northeastern validation area was in Kuhmo, covering the Hiidenportti National Park and Teerisuo-Lososuo Mire Reserve, and the commercially managed forests between them (WGS84 lat: 61 ◦ 14 0 , lon: 25 ◦ 07 0 ). Here, 103 field plots were measured during the summer 2019 of which 44 were in managed and 59 in conserved forest areas (Table 2). Ten of the managed forest field plots were in forest stands with restricted utilization possibilities. Mineral soil covered 66 and peatland 37 of the plots. The plots were positioned using a systematic grid with 400 m distance between the neighboring plots in x and y directions. The plot-level DBH mean and mean volume were 18.5 cm and 117.6 m 3 /ha, and for dead trees 15.8 cm and 28.6 m3/ha, respectively (Table 4). Table 4. Plot-level characteristics for living and dead wood in the Kuhmo validation area. DBHmean (cm) H (m) Vol (m3/ha) N_DW (N/ha) D_DW (cm) Vol_DW (m3/ha) Min 0 0 0 0 0 0 Max 39.9 22.1 464.3 825 42.1 182.3 Mean 18.5 13.5 117.6 201 15.8 28.6 In both validation areas, the field sample was measured as circular plots with a fixed radius of 9 m (or 5.64 m for trees with DBH <4.5 cm). DBH and the tree species were determined for all living trees with a DBH of over 4.5 cm. For trees with a DBH of under 4.5 cm, only the height and number of the stems were recorded. Tree height was measured for 25% of trees in each forest stand stratum (i.e., for each tree species in both upper and lower canopy storey). The heights of individual trees were acquired by parametrizing Näslund’s height curve [ 51 ] using the measured sample trees. Tree volumes were calculated with taper curves [52]. Both standing and downed dead wood were measured in all plots. Standing and downed dead wood with a DBH of over 10 cm were measured for length and DBH (maximum diameter if breast height could not be defined). For fragments of dead wood, all pieces with a maximum diameter of 10 cm or more were recorded. For fallen trees, only the parts inside the plots were included in the inventory. For intact trunks, volumes were calculated with taper curves [ 52 ], whereas for snags and coarse woody debris, volume was derived using the formula of truncated cone. The species was determined for all dead wood whenever possible. Plot-level attributes for living and dead wood were calculated for each stratum. Mean diameter was weighted with the basal area (Equation (2)), and for height value, Lorey’s mean height (Equation (3)) was used. DBHmean = PBA ×DBH PBA (2) hl= PBA ×h PBA (3) BA stands for tree-level basal area, DBH for diameter at breast height (i.e., 1.3 m), and h for tree height. Information on the forest site type was taken from the Metsähallitus database [53,54]. Forests 2020,11, 913 8 of 21 The DWP conversion was carried out for Evo with functions from vegetation zone 1 and for Kuhmo with zone 3. Strata from the same tree species in the same forest stand were summed. For dead wood comparison, all DWPs in the same field plot were summed. This information was compared with the field measured amount of dead wood. 3. Results The modeling method was validated on two separate areas in Finland (Figure 2) by comparing the reference volumes of dead wood to the modeled index values, i.e., DWP (Figure 4). The modeled DWP values were calculated based on field plot data that had been collected simultaneously with the dead wood data. Dead wood and DWP volumes of different tree species occurring in the same field plot were summed for the comparison. In Evo, the DWP values varied between 0 and 1623 (mean 132) and volume of dead wood from 0 to 320.2 m 3 per hectare (mean 15.8). In Kuhmo, the DWP values ranged between 0 and 301 (mean 54) and dead wood volumes from 0 to 182.3 m3per hectare (mean 28.6). Forests 2020,11, 0 8 of 21 The DWP conversion was carried out for Evo with functions from vegetation zone 1 and for Kuhmo with zone 3. Strata from the same tree species in the same forest stand were summed. For dead wood comparison, all DWPs in the same field plot were summed. This information was compared with the field measured amount of dead wood. 3. Results The modeling method was validated on two separate areas in Finland (Figure 2) by comparing the reference volumes of dead wood to the modeled index values, i.e., DWP (Figure 4). The modeled DWP values were calculated based on field plot data that had been collected simultaneously with the dead wood data. Dead wood and DWP volumes of different tree species occurring in the same field plot were summed for the comparison. In Evo, the DWP values varied between 0 and 1623 (mean 132) and volume of dead wood from 0 to 320.2 m 3 per hectare (mean 15.8). In Kuhmo, the DWP values ranged between 0 and 301 (mean 54) and dead wood volumes from 0 to 182.3 m3per hectare (mean 28.6). Forests 2020, 11, x FOR PEER REVIEW 9 of 24 Figure 4. Correlation between the dead wood potential (DWP) and dead wood volume in Evo and Kuhmo. The x-axis describes the plot level dead wood volume (m3 per hectare) and y-axis the DWP value for each plot. Both values are sums of different tree species in each plot. Plots on managed and conserved areas are marked with black triangles and gray circles, respectively. Coefficients of determinations are shown with dashed lines for managed and with dotted line for conserved field plots. Results show that correlations between DWP and reference dead wood volumes (see Table 5) are more constant in Kuhmo than in Evo. When validation areas are observed separately, the correlation is stronger in Evo (R 2 = 0.54) than in Kuhmo (R 2 = 0.29). Correlation in conserved areas (strictly or with some degree of conservation) is stronger in Evo (R 2 = 0.62) than in Kuhmo (R 2 = 0.24). The results were investigated also based on soil type as the Motti simulations were executed only with mineral soil variables. Based on the results from Kuhmo, where 36% of field plots were on peatland, there were no significant differences between the correlations on different soil types (mineral soil and peatland both R 2 = 0.24). As for Evo, where only 10% of field plots were on peatland, the correlation for mineral soil DWP was higher (R 2 = 0.57) than for peatland (R 2 = 0.30). Table 5. Coefficients of determinations for two validation sites. Total = all field plots on area, conserved = field plots in permanently protected areas and areas with restricted forest management, managed forests = field plots in non-conserved areas, mineral soil = field plots situated in mineral soil, and peatland = field plots in peatland. Area R 2 Total R 2 Conserved R 2 Managed R 2 Mineral Soil R 2 Peatland Evo 0.54 0.62 0.11 0.57 0.3 Kuhmo 0.29 0.24 0.23 0.24 0.24 In addition to plot-level validation, the DWP method was also examined on a larger scale using stand data for all forest stands in the Evo area managed by Metsähallitus [53–55]. The DWPs of different tree species were summed. The locations of areas known for their high biodiversity value and substantial dead wood volumes were revealed as shown in Figure 5. Figure 4. Correlation between the dead wood potential (DWP) and dead wood volume in Evo and Kuhmo. The x-axis describes the plot level dead wood volume (m 3 per hectare) and y-axis the DWP value for each plot. Both values are sums of different tree species in each plot. Plots on managed and conserved areas are marked with black triangles and gray circles, respectively. Coefficients of determinations are shown with dashed lines for managed and with dotted line for conserved field plots. Results show that correlations between DWP and reference dead wood volumes (see Table 5) are more constant in Kuhmo than in Evo. When validation areas are observed separately, the correlation is stronger in Evo (R 2 =0.54) than in Kuhmo (R 2 =0.29). Correlation in conserved areas (strictly or with some degree of conservation) is stronger in Evo (R 2 =0.62) than in Kuhmo (R 2 =0.24). The results were investigated also based on soil type as the Motti simulations were executed only with mineral soil variables. Based on the results from Kuhmo, where 36% of field plots were on peatland, there were no significant differences between the correlations on different soil types (mineral soil and peatland both R 2 =0.24). As for Evo, where only 10% of field plots were on peatland, the correlation for mineral soil DWP was higher (R2=0.57) than for peatland (R2=0.30). Table 5. Coefficients of determinations for two validation sites. Total =all field plots on area, conserved =field plots in permanently protected areas and areas with restricted forest management, managed forests =field plots in non-conserved areas, mineral soil =field plots situated in mineral soil, and peatland =field plots in peatland. Area R2Total R2Conserved R2Managed R2Mineral Soil R2Peatland Evo 0.54 0.62 0.11 0.57 0.3 Kuhmo 0.29 0.24 0.23 0.24 0.24 Figure 4. Correlation between the dead wood potential (DWP) and dead wood volume in Evo and Kuhmo. The x-axis describes the plot level dead wood volume (m 3 per hectare) and y-axis the DWP value for each plot. Both values are sums of different tree species in each plot. Plots on managed and conserved areas are marked with black triangles and gray circles, respectively. Coefficients of determinations are shown with dashed lines for managed and with dotted line for conserved field plots. Results show that correlations between DWP and reference dead wood volumes (see Table 5) are more constant in Kuhmo than in Evo. When validation areas are observed separately, the correlation is stronger in Evo (R 2 =0.54) than in Kuhmo (R 2 =0.29). Correlation in conserved areas (strictly or with some degree of conservation) is stronger in Evo (R 2 =0.62) than in Kuhmo (R 2 =0.24). The results were investigated also based on soil type as the Motti simulations were executed only with mineral soil variables. Based on the results from Kuhmo, where 36% of field plots were on peatland, there were no significant differences between the correlations on different soil types (mineral soil and peatland both R 2 =0.24). As for Evo, where only 10% of field plots were on peatland, the correlation for mineral soil DWP was higher (R2=0.57) than for peatland (R2=0.30). Table 5. Coefficients of determinations for two validation sites. Total =all field plots on area, conserved =field plots in permanently protected areas and areas with restricted forest management, managed forests =field plots in non-conserved areas, mineral soil =field plots situated in mineral soil, and peatland =field plots in peatland. Area R2Total R2Conserved R2Managed R2Mineral Soil R2Peatland Evo 0.54 0.62 0.11 0.57 0.3 Kuhmo 0.29 0.24 0.23 0.24 0.24 Forests 2020,11, 913 15 of 21 79 Betula pubescens 2 Herb-rich forest y=0.0000005078x5−0.0000396260x4+0.0010777939x3 −0.0118120982x2+0.0575682212x −0.0096812959 34.9 80 Betula pubescens 2 Herb-rich heath forest y=0.0000007892x5−0.0000545302x4+0.0013190001x3 −0.0129279489x2+0.0586627985x −0.0068225071 31.1 81 Betula pubescens 2Mesic heath forest y=0.0000009307x5−0.0000626945x4+0.0014860637x3 −0.0143806824x2+0.0637802760x −0.0046716255 30.1 82 Betula pubescens 2Sub-xeric heath forest y=0.0000013047x5−0.0000748816x4+0.0015031182x3 −0.0121962798x2+0.0522196363x −0.0092041945 26.8 83 Betula pubescens 2Xeric heath forest y=0.0000199097x5−0.0006442758x4+0.0074117015x3 −0.0353237563x2+0.0913360115x −0.0032317430 15.1 84 Betula pubescens 2Barren heath forest y=0.0000434301x5−0.0012348638x4+0.0125008008x3 −0.0525099819x2+0.1142383729x −0.0022700043 13.1 85 Betula pubescens 3 Herb-rich forest y=0.0000008926x5−0.0000586134x4+0.0013438732x3 −0.0124500644x2+0.0553706773x −0.0088025839 30.1 86 Betula pubescens 3 Herb-rich heath forest y=0.0000011983x5−0.0000734126x4+0.0015788582x3 −0.0138170861x2+0.0589918348x −0.0079444343 27.9 87 Betula pubescens 3Mesic heath forest y=0.0000024729x5−0.0001290246x4+0.0023699864x3 −0.0177546791x2+0.0658269113x −0.0055008469 23.9 88 Betula pubescens 3Sub-xeric heath forest y=0.0003131284x 3− 0.0068869653x 2 +0.0632760365x − 0.0340467609 20 89 Betula pubescens 3Xeric heath forest y=0.0000469941x5−0.0013264440x4+0.0132503785x3 −0.0542358803x2+0.1129146343x −0.0040491170 13 90 Betula pubescens 3Barren heath forest y=0.0000973178x5−0.0024765182x4+0.0223414139x3 −0.0827234057x2+0.1457634078x −0.0033752688 11.5 91 Betula pubescens 4 Herb-rich forest y=0.0001056533x3−0.0018595467x2+0.0276357197x + 0.0014319387 23.7 92 Betula pubescens 4 Herb-rich heath forest y=0.0000135491x 4− 0.0002887099x 3 +0.0014516350x 2 + 0.0247671329x −0.0038273969 20.5 93 Betula pubescens 4Mesic heath forest y=0.0000520241x4−0.0015190382x3+0.0140511857x2 −0.0150064164x +0.0165457552 18.4 94 Betula pubescens 4Sub-xeric heath forest y=0.0000946763x4−0.0025073073x3+0.0211413109x2 −0.0274326999x +0.0237633916 16.1 95 Betula pubescens 4Xeric heath forest y=0.0001112251x5−0.0027012577x4+0.0231219019x3 −0.0808900733x2+0.1407658016x −0.0085794462 11.1 96 Betula pubescens 4Barren heath forest y=0.0002010279x5−0.0045434558x4+0.0360747028x3 −0.1164661501x2+0.1750207637x −0.0087745744 10.2 97 Populus tremula (L.) 1 Herb-rich forest y=0.0000004911x5−0.0000364224x4+0.0009463543x3 −0.0099537068x2+0.0497723271x −0.0052573545 33.7 98 Populus tremula 1 Herb-rich heath forest y=0.0000006389x5−0.0000448857x4+0.0011066815x3 −0.0110807294x2+0.0529277624x −0.0053017914 31.9 99 Populus tremula 1Mesic heath forest y=0.0000079795x4−0.0004296518x3+0.0072278336x2 −0.0222549155x +0.0216449596 31.4 100 Populus tremula 1Sub-xeric heath forest y=0.0000025340x5−0.0001252167x4+0.0021839468x3 −0.0155782893x2+0.0596280231x −0.0065490851 23 101 Populus tremula 1Xeric heath forest y=0.0000113631x5−0.0004077298x4+0.0051975367x3 −0.0273533659x2+0.0790810564x −0.0031555977 16.8 102 Populus tremula 1Barren heath forest y=0.0000233258x5−0.0007620291x4+0.0088408222x3 −0.0423768934x2+0.1028515013x −0.0028496552 15 103 Populus tremula 2 Herb-rich forest y=0.0000004952x5−0.0000373045x4+0.0009829351x3 −0.0104664729x2+0.0520280776x −0.0067635479 34 104 Populus tremula 2 Herb-rich heath forest y=0.0000005734x5−0.0000429847x4+0.0011286691x3 −0.0120179552x2+0.0581328449x −0.0081317520 33.5 105 Populus tremula 2Mesic heath forest y=0.0000084733x4−0.0004459214x3+0.0073424690x2 −0.0218166161x +0.0214580688 30.8 106 Populus tremula 2Sub-xeric heath forest y=0.0000026447x5−0.0001281296x4+0.0021927625x3 −0.0153685162x2+0.0588012658x −0.0058806919 22.7 Forests 2020,11, 913 16 of 21 107 Populus tremula 2Xeric heath forest y=0.0000108433x5−0.0003926531x4+0.0050555795x3 −0.0269215264x2+0.0788169919x −0.0034048924 17 108 Populus tremula 2Barren heath forest y=0.0000245697x5−0.0007935809x4+0.0091121621x3 −0.0432970898x2+0.1043628446x −0.0028581742 14.8 109 Populus tremula 3 Herb-rich forest y=0.0000005817x5−0.0000388786x4+0.0009104427x3 −0.0086232657x2+0.0433743113x −0.0060358705 31.2 110 Populus tremula 3 Herb-rich heath forest y=0.0000008455x5−0.0000541241x4+0.0012171283x3 −0.0111218403x2+0.0512982223x −0.0068051191 29.4 111 Populus tremula 3Mesic heath forest y=0.0001562745x 3− 0.0046067531x 2 +0.0537072162x − 0.0412866515 26 112 Populus tremula 3Sub-xeric heath forest y=0.0003543187x 3− 0.0078771000x 2 +0.0700779711x − 0.0376300965 19.6 113 Populus tremula 3Xeric heath forest y=0.0000206683x5−0.0006761866x4+0.0078290083x3 −0.0371976946x2+0.0928146663x −0.0049042456 15.2 114 Populus tremula 3Barren heath forest y=0.0000439513x5−0.0012918402x4+0.0134394830x3 −0.0573061683x2+0.1192271055x −0.0040964012 13.4 115 Populus tremula 4 Herb-rich forest y=0.0000048016x 4− 0.0001117991x 3 +0.0003543209x 2 + 0.0226836786x −0.0063111968 25.8 116 Populus tremula 4 Herb-rich heath forest y=0.0000919159x3−0.0013406533x2+0.0229964151x + 0.0086222734 23.7 117 Populus tremula 4Mesic heath forest y=0.0001862663x 3− 0.0038237204x 2 +0.0422245854x − 0.0127054555 21.8 118 Populus tremula 4Sub-xeric heath forest y=0.0000116611x5−0.0003967235x4+0.0047990655x3 −0.0240006334x2+0.0726713154x −0.0077883137 16.3 119 Populus tremula 4Xeric heath forest y=0.0000449284x5−0.0013038827x4+0.0133998791x3 −0.0566353526x2+0.1195635316x −0.0100572770 13.3 120 Populus tremula 4Barren heath forest y=0.0000843400x5−0.0022248614x4+0.0207460282x3 −0.0793170088x2+0.1446013272x −0.0096399207 11.9 121 Alnus glutinosa ((L.) Gaertn.) 1 Herb-rich forest y=0.0000074496x5−0.0003525090x4+0.0059404760x3 −0.0415701978x2+0.1244785725x −0.0023168716 20.5 122 Alnus glutinosa 1 Herb-rich heath forest y=0.0000128704x5−0.0005507459x4+0.0084512065x3 −0.0543659749x2+0.1493117306x −0.0015781598 18.3 123 Alnus glutinosa 1Mesic heath forest y=0.0000120440x5−0.0004985693x4+0.0073668242x3 −0.0453107979x2+0.1239013027x −0.0021327974 18.1 124 Alnus glutinosa 1Sub-xeric heath forest y=0.0000988573x5−0.0026154349x4+0.0245845858x3 −0.0956244366x2+0.1696714860x −0.0020026933 11.8 125 Alnus glutinosa 1Xeric heath forest y=0.0004043769x5−0.0089613217x4+0.0712133327x3 −0.2367022954x2+0.3241998142x −0.0006634528 9.37 126 Alnus glutinosa 1Barren heath forest y=0.0053924930x 3− 0.0590984236x 2 +0.2201961376x − 0.0256368371 8.4 127 Alnus glutinosa 2 Herb-rich forest y=0.0000075081x5−0.0003506327x4+0.0058294432x3 −0.0402069148x2+0.1199446382x −0.0026136340 20.3 128 Alnus glutinosa 2 Herb-rich heath forest y=0.0000145047x5−0.0006014285x4+0.0089383388x3 −0.0556707462x2+0.1490098749x −0.0016618983 17.8 129 Alnus glutinosa 2Mesic heath forest y=0.0000132149x5−0.0005805631x4+0.0091730318x3 −0.0610986544x2+0.1701848768x −0.0011818426 18.5 130 Alnus glutinosa 2Sub-xeric heath forest y=0.0019794703x 3− 0.0307260916x 2 +0.1574728842x − 0.0118975512 12 131 Alnus glutinosa 2Xeric heath forest y=0.0004318896x5−0.0094351732x4+0.0739824124x3 −0.2429702683x2+0.3295543992x −0.0006597002 9.23 132 Alnus glutinosa 2Barren heath forest y=0.0057173199x 3− 0.0617247899x 2 +0.2259910364x − 0.0255182941 8.26 133 Alnus glutinosa 3 Herb-rich forest y=0.0000082551x5−0.0003559860x4+0.0054499415x3 −0.0344546810x2+0.1004627207x −0.0032690688 19.2 134 Alnus glutinosa 3 Herb-rich heath forest y=0.0000141998x5−0.0005663320x4+0.0080472709x3 −0.0475170982x2+0.1254229615x −0.0030199959 17.5 Forests 2020,11, 913 17 of 21 135 Alnus glutinosa 3Mesic heath forest y=0.0000162769x5−0.0005859380x4+0.0075014554x3 −0.0397237899x2+0.1025931440x −0.0025992132 16.3 136 Alnus glutinosa 3Sub-xeric heath forest y=0.0001040101x5−0.0027123121x4+0.0250222206x3 −0.0946173574x2+0.1625612077x −0.0038541966 11.6 137 Alnus glutinosa 3Xeric heath forest y=0.0003465269x5−0.0075489957x4+0.0583114029x3 −0.1852030100x2+0.2496181502x −0.0024301401 9.47 138 Alnus glutinosa 3Barren heath forest y=0.0006307148x5−0.0127831695x4+0.0918602416x3 −0.2716487035x2+0.3275330978x −0.0017410250 8.66 139 Alnus glutinosa 4 Herb-rich forest y=0.0000109015x5−0.0004002291x4+0.0052118785x3 −0.0279047711x2+0.0799147601x −0.0042342366 17.1 140 Alnus glutinosa 4 Herb-rich heath forest y=0.0000232563x5−0.0007786065x4+0.0092497879x3 −0.0451945856x2+0.1074552414x −0.0040762029 15.2 141 Alnus glutinosa 4Mesic heath forest y=0.0000222440x5−0.0007248221x4+0.0083510986x3 −0.0394504590x2+0.0966699136x −0.0079985370 15 142 Alnus glutinosa 4Sub-xeric heath forest y=0.0001195714x5−0.0029180046x4+0.0250762833x3 −0.0880100258x2+0.1496041466x −0.0094044374 11.1 143 Alnus glutinosa 4Xeric heath forest y=0.0003526446x5−0.0074890484x4+0.0557315217x3 −0.1679583885x2+0.2211855538x −0.0091879247 9.41 144 Alnus glutinosa 4Barren heath forest y=0.0049165839x 3− 0.0569047042x 2 +0.2187235843x − 0.0565417242 8.77 145 Other broadleaved 1 Herb-rich forest y=0.0000159331x5−0.0006551323x4+0.0096463222x3 −0.0594495816x2+0.1557082599x −0.0009990372 17.6 146 Other broadleaved 1 Herb-rich heath forest y=0.0000205100x5−0.0007254342x4+0.0093421540x3 −0.0512173513x2+0.1319081391x −0.0004774367 15.4 147 Other broadleaved 1Mesic heath forest y=0.0000401044x5−0.0013620237x4+0.0166251208x3 −0.0854673359x2+0.1885573057x −0.0007201616 14.6 148 Other broadleaved 1Sub-xeric heath forest y=0.0002650248x5−0.0060471431x4+0.0492898767x3 −0.1672909525x2+0.2451454027x −0.0006349294 9.91 149 Other broadleaved 1Xeric heath forest y=0.0026390485x4−0.0378261877x3+0.1704826425x2 −0.1726942108x +0.0023550176 7.95 150 Other broadleaved 1Barren heath forest y=0.0093497583x 3− 0.0837830610x 2 +0.2517833753x − 0.0135120058 7.01 151 Other broadleaved 2 Herb-rich forest y=0.0000154781x5−0.0006291426x4+0.0091523543x3 −0.0556536543x2+0.1459033017x −0.0011735548 17.5 152 Other broadleaved 2 Herb-rich heath forest y=0.0010104419x 3− 0.0177529097x 2 +0.1097035692x − 0.0173601891 14.3 153 Other broadleaved 2Mesic heath forest y=0.0009902020x 3− 0.0180949531x 2 +0.1133238427x − 0.0136136818 14.7 154 Other broadleaved 2Sub-xeric heath forest y=0.0002614646x5−0.0059727446x4+0.0487995206x3 −0.1663751121x2+0.2456566987x −0.0006193862 9.92 155 Other broadleaved 2Xeric heath forest y=0.0070031761x 3− 0.0686189565x 2 +0.2293861734x − 0.0180324391 7.67 156 Other broadleaved 2Barren heath forest y=0.0096623996x 3− 0.0864707998x 2 +0.2587742488x − 0.0141651976 6.95 157 Other broadleaved 3 Herb-rich forest y=0.0000191741x5−0.0007210959x4+0.0096521416x3 −0.0536080315x2+0.1325356110x −0.0018781810 16.5 158 Other broadleaved 3 Herb-rich heath forest y=0.0000341563x5−0.0011529939x4+0.0139572824x3 −0.0707435928x2+0.1588258703x −0.0012299346 14.7 159 Other broadleaved 3Mesic heath forest y=0.0000440456x5−0.0013787066x4+0.0153463234x3 −0.0707112495x2+0.1470997311x −0.0013175106 13.9 160 Other broadleaved 3Sub-xeric heath forest y=0.0002722786x5−0.0060045435x4+0.0469498663x3 −0.1510238501x2+0.2142271115x −0.0017976770 9.74 161 Other broadleaved 3Xeric heath forest y=0.0009667794x5−0.0183945971x4+0.1240917529x3 −0.3448562969x2+0.3852069122x −0.0009318150 8.09 162 Other broadleaved 3Barren heath forest y=0.0076237774x 3− 0.0730281879x 2 +0.2298242375x − 0.0247438319 7.54 Forests 2020,11, 913 18 of 21 163 Other broadleaved 4 Herb-rich forest y=0.0000323070x5−0.0010219432x4+0.0114310178x3 −0.0524164810x2+0.1155566015x −0.0031287472 14.4 164 Other broadleaved 4 Herb-rich heath forest y=0.0000634743x5−0.0018274664x4+0.0186526646x3 −0.0783238736x2+0.1495900203x −0.0025211789 12.8 165 Other broadleaved 4Mesic heath forest y=0.0000571733x5−0.0015683402x4+0.0151450449x3 −0.0595812600x2+0.1177980604x −0.0055481454 12.7 166 Other broadleaved 4Sub-xeric heath forest y=0.0003133823x5−0.0064099859x4+0.0458978425x3 −0.1330159652x2+0.1814353937x −0.0061431270 9.31 167 Other broadleaved 4Xeric heath forest y=0.0007835618x5−0.0145607268x4+0.0942725426x3 −0.2456538957x2+0.2703820828x −0.0062743999 8.24 168 Other broadleaved 4Barren heath forest y=0.0011059106x5−0.0200709884x4+0.1267110787x3 −0.3214094244x2+0.3292721334x −0.0068760501 7.94 References 1. 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