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Article

Simulating Sustainable Forest Management Practices Using Crown Attributes: Insights for Araucaria angustifolia Trees in Southern Brazil

by
César Augusto Guimarães Finger
1,
Emanuel Arnoni Costa
1,2,
André Felipe Hess
2,
Veraldo Liesenberg
2 and
Polyanna da Conceição Bispo
3,*
1
Graduate Program in Forest Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
2
Department of Forest Engineering, Santa Catarina State University (UDESC), Lages 88520-000, Brazil
3
Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK
*
Author to whom correspondence should be addressed.
Forests 2023, 14(7), 1285; https://doi.org/10.3390/f14071285
Submission received: 18 May 2023 / Revised: 17 June 2023 / Accepted: 19 June 2023 / Published: 21 June 2023
(This article belongs to the Special Issue Advances in Forest Growth and Biomass Estimation)

Abstract

:
Araucaria angustifolia (Bertol.) Kuntze, commonly known as Brazilian pine, is a significant tree species in the Brazilian flora that once covered an area of 200,000 km2 in the Southern region. During the 1970s, high-quality timber logs from this conifer became the primary export product of the country. However, the species is endangered due to uncontrolled exploitation and is subject to a harvesting ban. It is crucial, therefore, to explore sustainable cultivation methods for this species, which necessitates urgent research and scientific insights. In this study, we present a simulation of a management strategy for in situ conservation by manipulating growth space and crown size dynamics. Forest inventory data and mixed forest regression equations were employed to describe the horizontal dimensions of average and maximum potential crown growth, resulting in two management scenarios. The results presented in management diagrams show that both approaches required logging numerous trees to ensure adequate space for healthy tree growth and provide soil coverage and forest protection. Therefore, the absence of effective forest management initiatives for Araucaria forests may have further implications for the structure, production, conservation, and overall development. To address these challenges, we propose two hypotheses: firstly, that tree diameter depends on crown dimensions, which are in turn influenced by tree growth space, and, secondly, that crown dimensions serve as a reliable indicator of existing competition and can be utilized to simulate forest management practices. We urge that implementing sustainable forest management initiatives for Araucaria angustifolia at selected locations can contribute to expanding natural forest areas, mitigate deterioration caused by high competition, discourage illegal logging, and prevent overexploitation of their edible seeds, which hinders regeneration. Our results underscore the significant implications of the lack of forest management initiatives in rural properties, potentially resulting in irreversible deterioration. The exact consequences of this deterioration remain unclear, emphasizing the need for further studies to understand its eventual effects on the growth reaction of trees of different diameters, ages, and crown conditions after the liberation of their crowns.

1. Introduction

Araucaria angustifolia (Bertol.) Kuntze (Brazilian pine) trees were important in the Brazilian economic matrix during the 1970s. They occupied, at that time, about 200,000 km2, mainly in the three southern states of Brazil (Paraná, Santa Catarina, and Rio Grande do Sul). Prior to logging, this species was the dominant species in forests over much of its range. It is well-known for the high-quality wood density of its logs and the production of edible seeds by female trees. As a consequence of overexploitation and subsequent drastic loss of native forest areas, legal barriers to logging were imposed, which culminated in the total disinterest of landowners and local farmers in considering planting Araucaria, and instead, they opted for exotic tree species, especially Pinus taeda and P. elliottii [1,2]. As a result, this tree species is restricted to forest remnants and usually confined to legally protected reserves as a requirement of the Brazilian Forest Act [3]. However, as noticed in several rural properties, legal reserves do not assure integral protection, being still susceptible to illegal logging and overexploiting of edible seeds [4,5], which is one of the important causes of the reduced natural regeneration capacity of these forest remnants.
Araucaria forest’s actual situation, which, in a few decades, passed from being an abundant and valuable forest to restricted areas remnants, corroborates the lack of an efficient forest policy based on the sustainable principle, first described by von Carlowitz in 1713. Under this principle, economic, environmental, and social sustainability must be considered together; that is, forests must produce material and immaterial benefits for the actual and future population in an equal quantity and quality as today. A condition for this is the maintenance of large forest areas.
Land use changes are not only a Brazilian reality but are common in people’s history and continue to occur worldwide. The solution is not prohibiting forest use, but, rather, conservation through scientific and technical knowledge. Forests without management will be overstocked until a natural disaster such as wind, fire, herbivory, or diseases favors gaps opening, reducing the density and tree competition.
Unmanaged Araucaria forest presents high-density trees, with intra-specific and interspecific competition. Trees have crown size reduction for any stage of development, from young to old trees, which results in a reduction of effective crown coverage and leaf area, photosynthesis, growth, seed production, and early death of the plant, conditions that are easily verified in various forest dynamics studies [6,7,8]. Trees require differentiated growth space and nutrition conditions with distinct dynamics as they develop, as shown by the studies of morphometric relationships of free-growing trees and trees under competition [9,10,11,12], as well as in the studies of size, competition, and growth relationships [13,14], among others. Therefore, such studies allow quantifying the growth space needed for young and adult trees in pure and mixed forests [15].
Plant growth rates are driven by factors influencing the number of resources captured and resource use efficiency. In trees, the amount of light caught and the efficiency of light use strongly depend on crown characteristics and leaf traits [16]. For example, Araucaria trees have horizontal branches but direct them to the top of the canopy in search of light when under competition [12,13,17,18].
As competition increases, death and the fall of branches occur, resulting in short and irregularly shaped crowns. In contrast, trees with adequate growth space have horizontally distributed branches and long circular-shaped crowns. Trees that grow without competition have canopies with maximum dimensions for the environment providing the highest growth rates [19] and are a reference for the species’ potential [20].
Crown diameter and diameter at the breast height (dbh) ratios efficiently determine growth space, finding viable applications in pure and mixed forests [21,22,23], such as those remnants with Araucaria. Moreover, such studies allow for determining the effect of the intraspecific and interspecific competitors.
Other descriptors such as slenderness, stand density, and proportion of the basal area of the species can also be used as independent variables to develop allometric models with mixed effects to estimate the crown diameter [24]. In summary, the growth space and logging can be modeled and indicate an appropriate time for re-establishing trees with high vitality under a proper growth space.
Thus, due to the lack of management initiatives for the Araucaria forests, consequences in terms of structure, production, conservation, and development need to be considered, which also supports the proposed hypotheses: (i) the tree diameter depends on the crown dimension and this on trees’ growing space, and (ii) the crown dimension is a good reference element for the actual competition and to simulate management regimes.
This research aims to establish practical management guidelines that describe the growth space for forming trees with regular vital crowns under two forest management scenarios. The first one considers the size of the mean potential crown, while the second one the dimension of the maximum potential crown. In both cases, the space and number of trees per unit area that maintains productive, stable, and healthy forest growth was also considered.

2. Materials and Methods

2.1. Study Area Description

The data for describing growth space originated from the Long Term Ecological Project—PELD/CNPq “Conservation and Sustainable Management of Forest Ecosystems—Araucaria Biome and its Transitions” installed in a natural unmanaged forest area at the National Forest of São Francisco de Paula (29°24′ and 29°22′ S and 50°22′ and 50°25′ W, 900 m altitude), in the municipality of São Francisco de Paula, Rio Grande do Sul, Brazil. The vegetation is classified as Mixed Ombrophylous Forest (MOF) according to the phytogeographic classification [25]. The region’s climate is Cfb by Köppen classification, indicating a mesothermic and super-humid climate characterized by mild summers and cold winters [26].

2.2. Field Inventory

In this study, we used data measured in a sample plot of 0.5 ha, installed to monitor the forest dynamics. The sample plot was refined considering 50 subunits of 10 × 10 m and comprised all living trees numbered and located with UTM co-ordinates (east and north). The sampling unit was located within a forest remnant, with no border effect. However, to quantify the number of trees in the sample unit, trees whose crown projection exceeded the limit of the sample unit were systematically either included or excluded from the chosen sample unit.
To demonstrate the method, we considered only the competition established by the crown in selecting trees. Therefore, sanity and defects criteria were not considered, as they do not influence the method, although, in practical situations, they must be observed as a selection criterion.
All trees were identified at the family, gender, and species level, and their circumference at breast height (c) and total height (h) were also measured. The regeneration was also characterized by identifying tree species seedlings inside the sample plot. Finally, the c was converted to the diameter at the breast height (dbh).
In this research, all tree species other than Araucaria were disregarded since they occupied the great majority of the canopy’s lower strata. Instead, the tree species found in the lower strata of the forest were considered in other studies [14]. Thus, the adjustments and simulation of management models based on the canopy diameter and basal area were for Araucaria, since its canopy dominance, economic value by having good shape logs, and release with cuts will allow light entry, temperature increase, and better growth conditions for species that grow below the canopy [27,28,29,30].
Here, conifers were prioritized, assuming that releasing Araucaria crowns at a larger size and better quality allows a better growth condition for the remaining conifer and latifoliate that usually grows below the canopy.

2.3. Crown Diameter Estimation

We simulate crown diameter (Cd) using a regression model in which the dbh is the only input data (Equation (1)). The model was proposed by Costa et al. [31] and described the crown diameter of Araucaria trees without competition and dominant trees growing within the forest. This model used several field inventory data, including data from the same study area (Appendix A). The coefficient of determination (R2) was 0.84, and the root mean square error (RMSE%) percentage was 14.5.
Cd = 1.3886 + 0.2038 × dbh
The authors demonstrated the statistical coincidence of inclination and level of independent equations calculated for the two growth situations that were motivated by similar linear relationships between these variables described by Volkart [32], Longhi [33], Seitz [9], Wachtel [34], Chassot et al. [35], and Zanon [36]. These studies encompassed almost all the original occurrences of Araucaria in Southern Brazil.
For the determination of the maximum potential crown diameter (Cd pot. max.) the Equation (2) had the intersection coefficient recalculated to describe the values of maximum crown diameter found in the sampled set composed of 694 trees growing with some competition (9.9 > dbh > 97.0) and 175 trees growing without competition (14.0 > dbh > 59.0). Similar procedures as the mentioned above were successfully adopted by Grote [20], Pretzsch [37], Pretzsch et al. [38], and Costa et al. [31].
Cd pot. max. = 4.8601 + 0.2038 × dbh
The two equations developed at the individual tree level that express the crown’s dimensional relationships have an application in even age, uneven age, multi-species, and multi-layered forests [20,21,23,39,40].
Additionally, to reinforce the usage of Equations (1) and (2) in this study and to gather the main crown diameter equations for Araucaria for forest management purposes, an artificial neural network (ANN) was developed containing 27 valid regression equations coming from studies developed in different geographic regions of occurrence of the species (Appendix B). The study areas were from Misiones, Argentina, to different locations in the Brazilian states of Rio Grande do Sul (RS), Santa Catarina (SC), and Paraná (PR) (Appendix A and Appendix B).

2.4. Evaluated Scenarios

The spatial distribution of all trees was determined using ArcGIS 10.3 software. Then, based on the polar co-ordinates of the plot vertex (x, y), the position of each single Araucaria tree was obtained in relation to the origin with the polar co-ordinate (0, 0).
The distance between every single tree and the nearby trees was determined by comparing the crown radii plus the radius of the crown. When the sum of the crown radii was greater than the distance between the trees [(rcrown i + rcrown target) > dist. i, target], the tree was defined as a competitor and received coding for potential logging. The next non-concurrent tree was then selected as the target tree and the process restarted from the beginning.
The procedure was performed by establishing two density scenarios: (i) the average crown diameter [using Equation (1)] and (ii) the maximum potential crown diameter [using Equation (2)]. The number of remaining trees (N) and their respective basal areas (G) were then determined for each scenario.
Thus, the current dbh of a tree is the result of a growth stage with a compatible crown size. With increasing age or competition, successively smaller increments and canopy size reduction occur, keeping the tree alive with insignificant increments due to lower photosynthetic capacity (Figure 1).
Finally, the potential size of the crown, density, and basal area was used to build a forest management dendrogram [41,42]. The forest management dendrogram further identifies thinning intensity scenarios over the chosen study area, suggesting insights for forest managers. It is important to mention that trees with dbh ≥ 40 cm, broken crowns, and deformities were removed from the sample because, under these conditions, the current dbh cannot be explained.
According to Ishii et al. [41], as trees grow larger, the expansion of their crowns may not be accurately reflected in dbh measurements. This discrepancy contributes to the inaccuracy of estimating forest productivity. However, there is a feedback loop between the structure, environment, and tree growth, as Pretzsch [37,38] described. In mixed forests, the canopy structure is both influenced by and influences interspecific environmental conditions. Therefore, understanding the mechanisms behind diversity–productivity relationships requires considering both the crown plasticity promoted by the mixture and the variations in crown structural traits among co-existing species [42,43,44,45,46].

3. Results

3.1. Forest Inventory Results

The forest inventory showed 49 broadleaved species with a density of 912 trees·ha−1 and presented an average dbh of 15.8 cm with a coefficient of variation (CV) of 151.3%. The mean h achieved 12.8 m with a CV of 35.2%. On the other hand, Araucaria showed a density of 288 trees·ha−1, a dbh of 39.6 cm, and a CV of 148.5%. The mean h was 20.2 m, with a CV of 17.3%. The basal area reached 60.0 m2·ha−1, of which 35.6 m2·ha−1 (59.3%) belonged to Araucaria, a dominant tree species, confirming the high competition in the chosen uneven-aged, mixed forest remnant.
It is also important to mention that natural regeneration was not noticed in the study area due to the low light intensity near the soil. Most of the plants that establish themselves do not develop due to low luminosity, this being a factor in forest aging.

3.2. Forest Management Scenarios

In this research, 72 trees·ha−1 were maintained with a tree density adjusted with the mean crown equation (Scenario 1) and 50 trees·ha−1 with the maximum potential crown size equation (Scenario 2), respectively, as shown in Table 1.
In both management scenarios, the number of remaining trees ensures the preservation of protective effects on the environment, forest stock containing healthy trees, and conditions to establish and grow natural regeneration (Figure 2), a condition not usually found in unmanaged Araucaria forests, due to the legal barrier to using it.
In the simulation developed with the average crown (Equation (1)), the dbh of the remnant mean basal area reached 27.3 cm and 31.9 cm with the maximum potential crown (Equation (2)). The remaining trees in the forest do not show a regular distribution, which is why it is less than ideal, i.e., 150 to 180 trees, for the target dbh of 40 cm [9]. For this dbh, the average crown equation of this work resulted in 140 trees, proving the adequacy of the proposed management guidelines.
In contrast, after both simulations, the low number of tree remnants resulted from the high tree density and their irregular distribution, which brought many competitors that were suggested for logging. The distribution and dimensions of trees before simulation result from the original irregular location of trees, a common characteristic of uneven-aged mixed forests, and clarifies the absence of trees at some points of the simulated area (Figure 2). However, even in this condition, applying silvicultural techniques at an appropriate time, before the high competition was established, could provide a better distribution of target trees and, consequently, reduce the loss of forest increment due to the non-regular occupation of the soil. As a result, more trees would be well-distributed, with more significant individual increments, resulting in higher forest production.
As a comparison, if we consider a tree with an average basal area of 44.0 cm found in the forest prior to the simulation, theoretically, it would be possible to maintain a density of 114 trees per hectare with this diameter at breast height (dbh) if they were evenly distributed. This value is higher than the 72 trees per hectare reported in Table 1, which resulted from the management approach based on Equation (1).
The chosen simulation may generate heterogeneous openings in the canopy, known as gaps, due to irregular tree distribution and intense competition. Consequently, the number of trees that remain standing after logging is smaller compared to the potential number if the trees were evenly distributed across the terrain. This outcome is likely to facilitate the establishment and persistence of broadleaf species, which are currently not competitors of Araucaria. Interestingly, this condition may persist until the next revision of the forest management plan.
However, in addition to the irregular distribution of the trees in the study area, some remaining trees had larger dbh, reaching up to 97 cm, which requires a larger area for crown expansion and the consequent reduction in remaining standing trees. In this scenario, if the remaining trees in one hectare had grown without competition, the average canopy area would cover 5343.64 m2 of the soil surface, leaving 4656.36 m2·ha−1, which would be occupied by trees of natural regeneration or planted trees, when these were not satisfactorily established.
According to the adopted criteria, the forest manager can prioritize the release of trees based on their crowns and vitality and cut trees above a specific dbh or those with defective or diseased stems, among other factors. However, depending on the decision, it also implies the final spatial distribution of the remaining trees, dbh range, growth stock, and the stock removed from the forest. Therefore, establishing only a dbh criteria may not assure a more significant number of standing trees besides reducing the mean dbh of the remaining trees.
Thus, selecting one of the forest management scenarios should be preceded by a careful analysis of the general conditions of the forest, such as tree size, crown vitality, and the interest in reducing or increasing the period between successive logging interventions. In any case, forest management yields better results if it favors the growth of young trees with growth potential that does not require large gaps in the canopy, assuring a higher incidence of light onto the lower canopy layer, keeping more trees growing in the forest. Likewise, regular management actions at reduced intervals allow the formation of healthy trees that are well-distributed on the dimension classes [47].

3.3. Forest Management Dendrogram

Figure 3 presents the density of two (dashed line) and three (solid grey line) times the number of trees calculated for the average condition, i.e., conditions found in competing forests that indicate the potential for selective logging. By using the maximum potential crown size as a reference, the interval between logging interventions can be extended. This results in increased free space between the remaining trees, fostering optimal conditions for establishing and growing natural regeneration. Notably, the study area exhibited a complete absence of natural regeneration.
On the other hand, the crown of high vitality may indicate a more conservative scenario, maintaining a greater number of trees growing in the forest and reducing the period between logging interventions, as shown in Figure 3. The scenario that predicts the horizontal projection area of the maximum potential of the crown shows, respectively, between dbh 15 to 80 cm, crowns with 7.9 to 21.2 m, where the potential number of trees per hectare is between 204 and 28 trees, respectively.
In the same range of the dbh mentioned above, using the mean crown ratio, crowns would reach diameters from 4.4 m to 17.7 m, allowing the establishment of 657 trees and 41 trees, respectively. Interestingly, the region between the two proposed management densities in Figure 3 is considered to have the optimal density. In such conditions, the crowns show a more significant increment, and soil coverage and a greater volume of growing timber in the forest remain.
The density lines—2× (dashed line) and 3× (solid grey line)—in Figure 3, represent, respectively, the density of two and three times the number of trees calculated for the average condition found in competing forests that indicate the need for the application of release cut-offs.

4. Discussion

4.1. Forest Management Simulations

The Araucaria crown is composed of structural organs (i.e., branches) and photosynthetic organs (i.e., needles) that are vital constituents in determining the magnitude of light captured [48] and the ability to shade neighboring trees [49,50]. Therefore, crown attributes (i.e., size and architecture) are commonly used in modeling individual tree growth [51,52].
The reported results confirm the inferred hypothesis that the crown architecture and structure (shape and dimension) play an essential role in photosynthetic capacity and growth rate. Its development is influenced by the density, competition, and availability of space and resources, serving as an indicative element for planned interventions in forest management. For example, crown shape (defined as the ratio of crown length to crown diameter) results from the spatial organization of branches and foliage, which may be related to the efficiency of light utilization [16].
Thus, the results of the work (Figure 3) indicate the need for intervention in the forest, favoring the forest ecosystem, structure, stability, diversity, and production, since leaves (needles in the case of Araucaria) are of significant importance for tree productivity due to their fundamental role in carbon (C) assimilation through photosynthesis and transpiration [16,53].
Araucaria, under competition, promotes an initial loss of needles and branches in the lower part of the crown and then the lateral canopy (Figure 1), making it asymmetric and, consequently, reducing the growth capacity of the tree. In addition, the change in the competitive environment may lead to the growth of new branches near the trunk and end of the branches, a fact commonly observed in the Araucariaceae family [54], without ensuring effective plant growth, at least in a short period.
The natural and unmanaged forest sampled here has among the highest values mentioned in ten other studies where densities between 25.7 m2·ha−1 to 46 m2·ha−1 were reported [46]. In these high-density conditions, Araucaria would register a growth space of 6.6 m, which would allow reaching a maximum dbh of 25.0 cm. This competition promoted a substantial reduction in crown size, developing an irregular crown shape and losing needles and vitality (Figure 1), with a subsequent decrease in dbh increment rates. The estimation of crown size here did not seek to describe the actual crown size and shape, but the dimension that a tree of equal dbh would have while growing without competition.
On the one hand, the current diameter at breast height (dbh) of a tree is influenced by its available space and crown size before any competition occurs. Therefore, in order to determine the required growing space for its full development, it is essential to implement thinning regimes at specific stages of tree growth. On the other hand, the absence of such interventions may lead to crown size reduction and deformation, as observed in the selected study area (Figure 1).
The proposed management was carried out at the level of the trees, which were dispersed over the terrain occupying different sites, had different dimensions, and were found at variable distances from each other, as in uneven forests or thinned plantations. In this scenario, the plan was to harvest selected trees and not the forest, which provides, in each period, the necessary growth space for the target trees. Understocked areas and gaps can occur temporarily due to competitors’ harvesting or even the absence of a tree on the site. Consequently, the volume of the area is reduced, although the dbh of the remaining tree increases.
Interestingly, the relationships between growth space, dbh, number of trees, volumetric production, wood quality, health, stability of the trees, and natural regeneration are dynamic processes that change periodically, directly affected by the rate and intervals between cuts. Therefore, light and frequent interventions make it possible to reduce risks, maintain the stability and health of the trees, and reduce empty spaces. In this work, no considerations were made in this sense, which would make it theoretical and lengthy, since the heterogeneity and complexity of the forest, in its changing environmental aspects, require focus on the objectives established for the forest and require that a forester be aware of environmental, growth, and economic relationships. Thus, the described strategies must be considered as management guidelines, adaptable to the environment and the production objectives established for the location, observing the site’s capacity.
The greater growth space provided by cutting according to the maximum canopy size guideline reduces the number of trees remaining after cutting, with a consequent gain in tree size. Still, the lower density of trees will lead to a greater reduction in the total volume of wood.
Finally, the number of remaining trees will be greater if the average dimensions of the crowns are followed, generating fewer empty spaces on the land and a greater volume of wood produced. Thus, these guidelines do not oblige but indicate intervention possibilities that need to be evaluated for each tree, with considerations that transcend the available space, size, stability, and health of the tree. The selection of a specific guideline curve from Figure 3 and determining the appropriate time for its application are technical decisions that should be tailored to each forest environment, taking into account the established objectives. It is reasonable to expect that the optimal cutting intensity lies between the two guideline curves presented.

4.2. Thinning Intensity Issues

The resulting number of trees in both simulations, average and maximum, reported in Table 1, will undoubtedly cause heterogeneous openness in the canopy due to the irregular distribution of trees and the intense intra- and interspecific competition as expected for uneven-aged mixed forests. Thus, the number of trees that remained standing was smaller than the potential number of trees in the forest if the trees were regularly distributed over the terrain or the tree release process started earlier. As mentioned before, it also allowed the maintenance of broad-leaved species that did not compete with Araucaria, with permanence until the next revision of the forest management plan, as well as facilitating natural regeneration, leaving the forester to regulate the desired species.
Interestingly, our field observations, conducted over several years in the study area and Southern Brazil region, have demonstrated the ability of certain adult trees to exhibit restored height growth. This was observed, for instance, in a thinned population interplanted with Pinus taeda L., and as well as trees growing without competition (Figure 4).
It is easy to notice the crown limit, in a flat form that has grown until height stagnation (old crown), and the parable crown overlapping the previous (Figure 4, left). The image on the right shows the inner crown with a superposition of pseudo-whorls produced during stagnation. Stem elongations follow in increasing dimensions and also increase the distance of the pseudo-whorls in the young part of the tree.
Therefore, logging interventions would promote an increase in the growth space and allow the maintenance of regular growth rates. However, it is important to highlight that those silvicultural interventions, when established at the right time, avoid crown deformation and the reduction of dbh tree increment, which may sometimes be irreversible or might need a longer time to recover the crown shape and size and use the great free space among the remaining trees.
According to the current dbh, the management strategies described here provide the necessary growth space. Since the crown is responsible for photosynthesis and plant growth, it is accepted that trees with isolated growth present the highest growth efficiency for the site, which can be described by the increment ratio in volume and dbh, among others, in relation to the space occupied on the ground, expressed by the horizontal projection of the crown. In the same way, as highlighted by Costa et al. [40], the reduction of the growth space leads to a reduction in the dimension of the crown, deformation of the structure, reduction of the leaf area, and growth efficiency. Greater absorption of photosynthetically active radiation (APAR) is often proposed as a reason for greater productivity in mixed-species forests than in monocultures [37,55,56,57].
Although the crown dimension is reported to be a good reference element for the actual competition and can be used to simulate forest management regimes, it is important to consider that neglecting any silvicultural interventions is a clear sign that the crown growth could be affected in an irreversible way. In the near future, this is expected to lead to high levels of thinning intensity, which could have potential implications for the remaining trees’ wind resistance and timber properties. Therefore, these factors need to be carefully considered.
In addition, the scenarios were compatible with the space required for the growth of Araucaria trees with large dbh and crowns. These effects may improve resource acquisition or support higher resource-use efficiency [58].
Contrasting tree allometric relationships in mixtures compared with monocultures can also influence APAR. For a given tree diameter, the crown sizes (width, length, surface area, and leaf area), shapes, or heights of a given species can differ in mixtures compared with monocultures [37,59,60,61,62]. These allometric differences can add to the effects of the vertical foliage distribution when it allows crowns to expand sideways at different levels in the canopy or upwards or downwards away from other species [59].
Although Araucaria is able to withstand shading, it suffers a substantial increment reduction [63]. Young Araucaria trees that are established below the upper canopy strata of dominant trees, growing without lateral competitors, require between two and seven years to effectively grow (in diameter, height, and crown aspects) once the trees occupying the upper strata are logged.

4.3. Insights of Forest Management Initiatives

The selected study area shows some competition due to the deformed crowns of some Araucaria trees (Figure 1 and Figure 4). In these conditions, the positive effect of crown liberation and growth recovery must be considered individually. However, the genus Araucaria is genetically capable of restoring growth with the apex reiteration of branches and lateral twigs [54]. Seitz also reported such evidence, observing the cyclic growth in the height of adult trees for up to seven years and the establishment of natural regeneration after logging some adult trees [9].
The panorama vitality loss of Araucaria crowns and the reduction of growth capacity, coupled with the lack of management protocols currently imposed by the legislation, induces illegal logging. Previous studies reported illegal logging as one of the significant environmental threats besides deforestation and land conversion [47].
Other threats include the overexploitation of edible seeds, which also impacts regeneration. On the other hand, the proposed forest management promotes the formation and management of trees of great vitality and desired dimension, besides maintaining environmental benefits, adding value to forest products, and economically making the maintenance and increasing of forest remnants viable in rural properties. Furthermore, this approach could help alleviate the antagonism between landowners and the government, fostering a greater motivation to convert land currently used for cattle ranching, such as pastures, back into Araucaria forests. This would offer an intriguing opportunity for land use transformation and contribute to the restoration of forested areas.
Therefore, as Kjučukov et al. [64] mentioned, integrative forest management protocols are necessary to support biodiversity in forest remnants of rural properties that would be viable under active forest management initiatives. In their research conducted in the Czech Republic, the authors also highlighted the need to maintain some undisturbed areas, mainly within protected reserves, to ensure the presence of old-growth trees. Hence, when carried out with caution and consistent spatial and temporal practices, sustainable forest management yields benefits for the forest ecosystem in terms of environmental, social, and economic aspects. Therefore, the significance of this study can be justified by its potential to enhance the growth of Araucaria, ensure the continuity of natural regeneration, provide resources and space for growth, and indirectly contribute to carbon sequestration, plant diversity, soil protection, and climate regulation [65].
In the proposed method, the selection of Araucaria trees that remain after logging (target trees) should consider the distance and crown length, prioritizing long regular crown trees with high vitality and the qualitative characteristics of the stem. For example, larger crowns have a higher number of needles and a higher leaf area index than smaller crowns. Therefore, they are more efficient, as they have a greater photosynthetic area per m2 of soil cover, providing a more significant production [65], and can be used as a competition descriptor [4,23]. Furthermore, it is important to avoid maintaining poor-quality trees in the forest. Not only do they have lower commercial value, but they are also more susceptible to diseases, which can hinder efficient forest renewal and the dispersal of seeds [66].
Interestingly, the growth efficiency (stem wood volume growth per unit leaf area) is considered a measure of tree vigor, where leaf area is the measure of occupied growing space [67,68]. It has been assumed that the growth of stem wood volume is the best measure of the efficiency with which growing space is occupied because allocation to stem wood generally has a lower priority than allocation to shoots, roots, biochemical defensive, and storage compounds [69,70]. Furthermore, differences in growth and the crown should be explainable within a species regarding individual trees’ capture and utilization of growth-limiting resources and overall stand dynamics [71].
Therefore, the growth of a forest can be influenced by high-density cuts, which can improve space and resource conditions with minimal intervention rates. This approach aims to maintain species conservation and provide ecosystem services. The crown structure is a crucial variable influencing stand productivity, although its response to different stand factors has been reported to vary [71].
Shape and dimension are influenced by factors such as density, and resource and space availability. In mixed stands, using the crown dimension as a variable in the management modeling may better predict the changes in the ecosystem than just the dbh [72].
Araucaria’s crown shape and length depend highly on density intra- and interspecific competition. However, this study confirmed the proposed hypotheses indicating that moderate interventions may contribute to increasing diversity and maintaining growth rates, structure, stability, natural regeneration, and production. Closed canopy forests limit the growth of codominant and dominated trees of old-growth species. In addition, the absence of light in the sub-canopy decreases the temperature and does not break the seeds’ dormancy.

4.4. Future Research Perspectives

Adding variables such as crown attributes and dbh showed some perspectives on current and forthcoming forest management initiatives with Araucaria at selected rural properties where the abundance of Araucaria trees is representative. This also raises additional concerns for environmental agencies, as they have to review forest management plans and ensure the accurate monitoring of the number of trees being logged. Interestingly, previous studies showed how remotely sensed systems can be used to detect individuals or groups of trees [73,74,75,76] using high spatial resolution images derived from remotely piloted aircraft (RPAs). Such approaches can retrieve these attributes to support ground measurements and policy monitoring initiatives. This aspect becomes even more critical when we consider that management practices in MOF have not been adopted for more than four decades, and this non-intervention, as reported in this study, has apparently not improved the conservation status of the tree species.
Finally, many challenges remain to be faced when it comes to the forest management of Araucaria. For this reason, advancements in managing mixed forests are welcome and require additional studies to make sustainable management viable in selected rural properties of Southern Brazil, where an abundance of Araucaria trees is evident. The fall of legal barriers to the use of mixed forests can return the interests of rural owners to Araucaria, as a product with high value that can be continuously offered when the sustainability concept is used in forest management.
Aspects related to the context of mixed forest management, especially those related to the ecology of systems, are beyond the scope of this research. However, they should be included in future studies to ensure the effective, sustainable forest management implementation of this tree species.

5. Conclusions

The study area is very characteristic of this forest typology and represents the reality in several legal reserves existing in rural properties in Southern Brazil. In summary, the occurrence of trees with reduced crown size, irregular distribution, or partially absent crowns, despite having a relatively large diameter at breast height (dbh), confirms the presence of high local competition. It suggests that the actual tree diameter results from competition conditions established before the current time. It further supports the notion that crown dimensions influence dbh, which, in turn, depends on the available growing space for the trees. The results reported in this research evidence that there is a need to regulate the crown structure by logging some trees under competition and to promote the gap opening for better conditions for the remaining trees to grow and promote natural regeneration.
The crown size is demonstrated to be a valuable source of information for accurately simulating Araucaria’s forest management without affecting the forest ecosystem’s conditions. Such interventions may ensure greater vitality in the remaining trees and improve their structure and productivity. Therefore, we argue that the crown dimension is a good reference element for the actual competition and can be used to simulate forest management regimes at selected rural properties. However, neglecting silvicultural interventions might affect crown growth in an irreversible way, which could cause high levels of thinning intensity in the near future, whose implications for wind resistance and timber properties of the remaining trees remain unknown.
Furthermore, the proposed method promotes and encourages additional studies and simulations. It is also strongly suggested that one considers the species’ auto-ecology, wildlife interaction, and some additional peculiarities of the study area not investigated here. However, such factors should be addressed in the future to ensure the proper implementation of the forest management of Araucaria in the southern region of Brazil.

Author Contributions

Conceptualization, methodology, and formal analysis, C.A.G.F. and E.A.C.; software and validation, C.A.G.F. and E.A.C.; investigation, C.A.G.F. and E.A.C.; resources and data curation, C.A.G.F.; writing—original draft preparation, C.A.G.F. and E.A.C.; writing—review and editing, C.A.G.F., E.A.C., A.F.H. and V.L.; visualization, C.A.G.F., E.A.C., A.F.H., V.L. and P.d.C.B.; project administration, C.A.G.F.; funding acquisition C.A.G.F., V.L. and P.d.C.B. All authors have read and agreed to the published version of the manuscript.

Funding

Financial support was provided by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Finance Code 001), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico; 313887/2018-7, 317538/2021-7), and FAPESC (Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina; 2017TR1762, 2017TR639, 2019TR816, 2019TR828).

Data Availability Statement

The datasets can be provided upon request to the authors.

Acknowledgments

We want to thank the owners of the rural properties assessed in this research and the managers in the national forest of São Francisco de Paula for allowing us to realize this research and for their availability and generosity. We also thank both the graduate program of Forest Engineering of the Federal University of Santa Maria (UFSM) and the Santa Catarina State University (UDESC).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Araucaria crown diameter equations published for different occurrence regions.
Table A1. Araucaria crown diameter equations published for different occurrence regions.
Id.AuthorsTypeLocaldbh (cm)Shape of the CrownStratumDefinitionModelsβ0β1β2adj.RMSERMSE%
1.
1.(1)Costa et al. [13]1. Open Grown TreesLGS (SC)25–63(1) Conical cd = β0dbhβ10.34170.8762 0.601.5
1.(2)25–64(2) Hemispheric0.41290.8442 0.820.9
1.(3)33–68(3) Umbel1.53560.5102 0.391.5
1.(4).I25–68(4) WI0.48650.7936 0.621.4
1.(4).IICosta et al. [12]18–59cd = β0 + β1 × dbh1.24870.1993 0.72 12.8
1.(4).IIICNS (SC)14–591.15020.2030 0.78 15.3
1.(4).IVLGS (SC) + CNS (SC)14–591.20860.2007 0.76 13.6
1.(4).VVolkart [32]EL (PM − Arg.)≈5–700.65900.1970 0.98
1.(4).VIMB (PM − Arg.)≈5–951.81600.1482 0.89
1.(4).VIIEL (PM − Arg.) + MB (PM − Arg.)≈5–951.42600.1636 0.92
2.
2.ICurto [77]2. PlantedCL (PR)≈10–69 cd = β0 + β1 × dbh0.58480.2010 0.76 13.5
2.IIZanon et al. [36]SFP (RS)14–690.43100.1786 0.70 16.4
3.
3.[1]Costa [78]3. Natural Forest LGS (SC)14–86 [1] PS1 cd = β0 + β1 × dbh-0.2285 0.982.5
3.[2]10–59[2] PS2 -0.2196 0.972.3
3.[3]10–50[3] PS3 -0.2496 0.962.4
3.[4].I14–86[4] WI 0.76240.2138 0.842.5
3.[4].IICosta et al. [12]10–861.31490.2112 0.84 13.7
3.[4].IIISFP (RS)12–970.89470.2032 0.85 15.4
3.[4].IVLGS (SC) + SFP (RS)10–971.49590.2030 0.84 14.5
3.[4].VLonghi [33]SJT (PR)≈10–800.12760.2326 0.85
3.[4].VISeitz [9]SJT (PR)≈10–60−0.70600.2420 0.95
3.[4].VIIWachtel [34]CBS (SC) 10–70 +0.92390.1372
3.[4].VIIIFigueiredo-Filho et al. [79]FP (PR) + TS (PR)25–752.11940.1778 0.930.97.3
4.
4.INutto [80]4. Open Grown Trees + Planted + Natural ForestSFP (RS) + CP (SC) + QI (PR)5–128 cd0.5 = β0 + β1 × dbh + β2 × dbh²0.93890.0473−0.000150.93
5.
5.{1}Costa et al. [12] #5. Open Grown Trees + Natural Forest LGS (SC) + CNS (SC) + SFP (RS)10–97 {1} Minimumcd = β0 + β1 × dbh−2.08290.2038 ----
5.{2}{2} Mean1.38860.2038 0.83 14.5
5.{3}{3} Maximum4.86010.2038 ----
Mean and SD of parameters:0.96610.2005 0.82 ± 0.141.8 ± 0.713.7 ± 2.5
Where: Id.—identification; cd—crown diameter, in m; dbh—diameter at breast height, in cm; β0, β1 and β2—estimated regression coefficients; WI—without identification; SP1—sociological position dominant; SP2—sociological position codominant; SP3—sociological position dominated; LGS—Lages (SC); CNS—Canoinhas (SC); CL—Campo Largo (PR); SFP—São Francisco de Paula (RS); EL = Eldorado, Província de Misiones (M), Argentina (Arg.); MB = Manuel Belgrano, Misiones (M), Argentina (Arg.); SJT—São João do Triunfo (PR); CBS—Campo Belo do Sul (SC); FP—Fernandes Pinheiro (PR); TS—Teixeira Soares (PR); CP—Correia Pinto (SC); QI—Quedas do Iguaçu (PR). + Stipulated values; # Models used for the present study.

Appendix B

Appendix B.1. Objectives

(i)
Create an artificial neural network (ANN) to group the 27 main crown diameter regressions (cd) (Table A1 in Appendix A), developed over a wide area of A. angustifolia natural distribution;
(ii)
Verify the accuracy of the management guidelines presented compared to specific regional equations.

Appendix B.2. Methods

They represent the published models for the crown diameter, where 1 RNA of type Multilayer Perceptron (MLP) was trained with the 47-11-1 configuration, with the following: input of 47 [27 models; range of diameters (dbh); five categories for Type; five categories for Shape of the crown (SCr); five categories for Stratum; and four categories for Definition]; and 11 neurons in the hidden layer and one at the output (cd).
Models were subdivided according to: Type: 1. Open Grown Trees; 2. Planted; 3. Natural Forest; 4. Open Grown Trees + Planted + Natural Forest; 5. Open Grown Trees + Natural Forest. Shape of the Crown: (1) Conical; (2) Hemispheric; (3) Umbel and (4) WI. Stratum: [1] PS1; [2] PS2; [3] PS3 and [4] WI. Definition: {1} Minimum; {2} Mean and {3} Maximum (Table A2).
Table A2. Qualitative and quantitative variables used for artificial neural network training.
Table A2. Qualitative and quantitative variables used for artificial neural network training.
IdentificationAuthorsdbhTypeSCrStratumDefinition
1.(1)1[25–63]1100
1.(2)2[25–64]1200
1.(3)3[33–68]1300
1.(4).I4[25–68]1400
1.(4).II5[18–59]1400
1.(4).III6[14–59]1400
1.(4).IV7[14–59]1400
1.(4).V8[5–70]1400
1.(4).VI9[5–95]1400
1.(4).VII10[5–95]1400
2.I11[10–69]2000
2.II12[14–69]2000
3.[1]13[14–86]3010
3.[2]14[10–59]3020
3.[3]15[10–50]3030
3.[4].I16[14–86]3040
3.[4].II17[10–86]3040
3.[4].III18[12–97]3040
3.[4].IV19[10–97]3040
3.[4].V20[10–80]3040
3.[4].VI21[10–60]3040
3.[4].VII22[10–70]3040
3.[4].VIII23[25–75]3040
4.I24[5–128]4000
5.{1}25[10–97]5001
5.{2}26[10–97]5002
5.{3}27[10–97]5003
The RNA with the highest correlation value ϴ was selected to group all models in a single network with accuracy.

Appendix B.3. Results

The representation of the 27 regression equations used in the development of the ANN shows overlapping regression lines in the amplitude described by the maximum (cd max.) and minimum (cd min.) crown equations, with a higher concentration of lines over the average regression line [cd = 1.3886 + 0.2038 × dbh] in detail: all; 1. Open Grown Trees; 2. Planted; 3. Natural Forest; 4. Open Grown Trees + Planted + Natural Forest; and 5. Open Grown Trees + Natural Forest (maximum, middle, and minimum crown diameter) (Figure A1).
Figure A1. Crown diameter of A. angustifolia derived from 27 regressions equations used in the natural geographical distribution area.
Figure A1. Crown diameter of A. angustifolia derived from 27 regressions equations used in the natural geographical distribution area.
Forests 14 01285 g0a1
The training of the neural network (ANN) obtained a correlation value (ϴ = 0.9999), with the activation function in hidden layer (Tanh) and output layer (Identity) selected. The largest residual difference found between the model estimated by the authors and those obtained with RNA was 0.0409 m. This result confirms the good generalization capacity of ANN to estimate throughout the natural distribution region of the species (Figure A1), covered by specific equations (Appendix A) describing the crown diameter of A. angustifolia. The value of ANN weights are in Table A3.
This result also confirms the ability and appropriateness of using equations that describe the average crown diameter and maximum canopy diameter to determine the growth space of A. angustifolia, defining the distance between trees of different dimensions.
Table A3. Artificial neural networks weight values.
Table A3. Artificial neural networks weight values.
Weight Id.Connections MLP 47-11-1Weight Values
1d → hidden neuron 10.51132
2Authors(1) → hidden neuron 10.01277
3Authors(10) → hidden neuron 1−0.12741
4Authors(11) → hidden neuron 10.01719
5Authors(12) → hidden neuron 1−0.11927
6Authors(13) → hidden neuron 10.16582
7Authors(14) → hidden neuron 1−0.03469
8Authors(15) → hidden neuron 10.01865
9Authors(16) → hidden neuron 10.04022
10Authors(17) → hidden neuron 10.05523
11Authors(18) → hidden neuron 1−0.02059
12Authors(19) → hidden neuron 10.03728
13Authors(2) → hidden neuron 10.03469
14Authors(20) → hidden neuron 10.18168
15Authors(21) → hidden neuron 10.14936
16Authors(22) → hidden neuron 1−0.17653
17Authors(23) → hidden neuron 1−0.22716
18Authors(24) → hidden neuron 1−0.61896
19Authors(25) → hidden neuron 10.07079
20Authors(26) → hidden neuron 10.06884
21Authors(27) → hidden neuron 10.14846
22Authors(3) → hidden neuron 1−0.04745
23Authors(4) → hidden neuron 10.01025
24Authors(5) → hidden neuron 10.04912
25Authors(6) → hidden neuron 10.05043
26Authors(7) → hidden neuron 10.01111
27Authors(8) → hidden neuron 10.00934
28Authors(9) → hidden neuron 1−0.10374
29Type(1) → hidden neuron 1−0.08875
30Type(2) → hidden neuron 1−0.11677
31Type(3) → hidden neuron 10.14817
32Type(4) → hidden neuron 1−0.62928
33Type(5) → hidden neuron 10.26518
34SCr(0) → hidden neuron 1−0.32616
35SCr(1) → hidden neuron 10.01902
36SCr(2) → hidden neuron 10.03382
37SCr(3) → hidden neuron 1−0.05895
38SCr(4) → hidden neuron 1−0.10209
39Stratum(0) → hidden neuron 1−0.57002
40Stratum(1) → hidden neuron 10.17707
41Stratum(2) → hidden neuron 1−0.01625
42Stratum(3) → hidden neuron 1−0.00509
43Stratum(4) → hidden neuron 10.00013
44Definition(0) → hidden neuron 1−0.66388
45Definition(1) → hidden neuron 10.04358
46Definition(2) → hidden neuron 10.07140
47Definition(3) → hidden neuron 10.13997
48d → hidden neuron 2−1.16567
49Authors(1) → hidden neuron 20.03610
50Authors(10) → hidden neuron 2−0.10306
51Authors(11) → hidden neuron 20.12023
52Authors(12) → hidden neuron 20.04034
53Authors(13) → hidden neuron 20.08576
54Authors(14) → hidden neuron 20.03974
55Authors(15) → hidden neuron 20.04726
56Authors(16) → hidden neuron 20.03110
57Authors(17) → hidden neuron 2−0.00168
58Authors(18) → hidden neuron 2−0.03010
59Authors(19) → hidden neuron 2−0.04065
60Authors(2) → hidden neuron 20.07471
61Authors(20) → hidden neuron 20.19290
62Authors(21) → hidden neuron 20.18703
63Authors(22) → hidden neuron 2−0.25647
64Authors(23) → hidden neuron 2−0.23177
65Authors(24) → hidden neuron 2−0.90357
66Authors(25) → hidden neuron 20.21104
67Authors(26) → hidden neuron 20.14258
68Authors(27) → hidden neuron 20.07367
69Authors(3) → hidden neuron 20.02704
70Authors(4) → hidden neuron 20.08902
71Authors(5) → hidden neuron 20.05286
72Authors(6) → hidden neuron 20.06808
73Authors(7) → hidden neuron 20.07161
74Authors(8) → hidden neuron 20.06824
75Authors(9) → hidden neuron 2−0.16606
76Type(1) → hidden neuron 20.13760
77Type(2) → hidden neuron 20.15477
78Type(3) → hidden neuron 20.04898
79Type(4) → hidden neuron 2−0.91892
80Type(5) → hidden neuron 20.42616
81SCr(0) → hidden neuron 2−0.26309
82SCr(1) → hidden neuron 2−0.00551
83SCr(2) → hidden neuron 20.02705
84SCr(3) → hidden neuron 20.02861
85SCr(4) → hidden neuron 20.03426
86Stratum(0) → hidden neuron 2−0.20187
87Stratum(1) → hidden neuron 20.06957
88Stratum(2) → hidden neuron 20.02038
89Stratum(3) → hidden neuron 20.05849
90Stratum(4) → hidden neuron 2−0.11968
91Definition(0) → hidden neuron 2−0.55272
92Definition(1) → hidden neuron 20.23688
93Definition(2) → hidden neuron 20.15953
94Definition(3) → hidden neuron 20.05663
95d → hidden neuron 30.40999
96Authors(1) → hidden neuron 3−0.01943
97Authors(10) → hidden neuron 30.04701
98Authors(11) → hidden neuron 30.08262
99Authors(12) → hidden neuron 3−0.01599
100Authors(13) → hidden neuron 3−0.15038
101Authors(14) → hidden neuron 30.00192
102Authors(15) → hidden neuron 30.03839
103Authors(16) → hidden neuron 30.00863
104Authors(17) → hidden neuron 30.03155
105Authors(18) → hidden neuron 30.04634
106Authors(19) → hidden neuron 30.04478
107Authors(2) → hidden neuron 3−0.01075
108Authors(20) → hidden neuron 3−0.06310
109Authors(21) → hidden neuron 30.05509
110Authors(22) → hidden neuron 3−0.02231
111Authors(23) → hidden neuron 30.02388
112Authors(24) → hidden neuron 30.16507
113Authors(25) → hidden neuron 30.00132
114Authors(26) → hidden neuron 3−0.01221
115Authors(27) → hidden neuron 3−0.07746
116Authors(3) → hidden neuron 3−0.08534
117Authors(4) → hidden neuron 3−0.03845
118Authors(5) → hidden neuron 3−0.01357
119Authors(6) → hidden neuron 3−0.06015
120Authors(7) → hidden neuron 3−0.04377
121Authors(8) → hidden neuron 30.00243
122Authors(9) → hidden neuron 30.05136
123Type(1) → hidden neuron 3−0.17565
124Type(2) → hidden neuron 30.03641
125Type(3) → hidden neuron 3−0.06387
126Type(4) → hidden neuron 30.18114
127Type(5) → hidden neuron 3−0.09713
128SCr(0) → hidden neuron 30.11670
129SCr(1) → hidden neuron 30.00680
130SCr(2) → hidden neuron 30.02217
131SCr(3) → hidden neuron 3−0.06807
132SCr(4) → hidden neuron 3−0.09239
133Stratum(0) → hidden neuron 3−0.06087
134Stratum(1) → hidden neuron 3−0.17018
135Stratum(2) → hidden neuron 30.00155
136Stratum(3) → hidden neuron 30.04873
137Stratum(4) → hidden neuron 30.07337
138Definition(0) → hidden neuron 3−0.00625
139Definition(1) → hidden neuron 30.01492
140Definition(2) → hidden neuron 3−0.03615
141Definition(3) → hidden neuron 3−0.09094
142d → hidden neuron 4−1.75690
143Authors(1) → hidden neuron 4−0.00841
144Authors(10) → hidden neuron 40.07753
145Authors(11) → hidden neuron 4−0.09759
146Authors(12) → hidden neuron 4−0.08361
147Authors(13) → hidden neuron 40.05468
148Authors(14) → hidden neuron 40.01995
149Authors(15) → hidden neuron 4−0.02732
150Authors(16) → hidden neuron 4−0.03177
151Authors(17) → hidden neuron 4−0.06456
152Authors(18) → hidden neuron 4−0.03011
153Authors(19) → hidden neuron 4−0.04243
154Authors(2) → hidden neuron 4−0.08937
155Authors(20) → hidden neuron 40.08377
156Authors(21) → hidden neuron 40.01946
157Authors(22) → hidden neuron 40.08409
158Authors(23) → hidden neuron 4−0.02335
159Authors(24) → hidden neuron 4−0.05298
160Authors(25) → hidden neuron 40.29306
161Authors(26) → hidden neuron 40.12181
162Authors(27) → hidden neuron 40.07515
163Authors(3) → hidden neuron 4−0.24504
164Authors(4) → hidden neuron 4−0.18935
165Authors(5) → hidden neuron 40.08906
166Authors(6) → hidden neuron 40.06356
167Authors(7) → hidden neuron 40.08162
168Authors(8) → hidden neuron 40.11728
169Authors(9) → hidden neuron 40.08503
170Type(1) → hidden neuron 4−0.02893
171Type(2) → hidden neuron 4−0.18017
172Type(3) → hidden neuron 4−0.03396
173Type(4) → hidden neuron 4−0.08216
174Type(5) → hidden neuron 40.51131
175SCr(0) → hidden neuron 40.25251
176SCr(1) → hidden neuron 40.00925
177SCr(2) → hidden neuron 4−0.07145
178SCr(3) → hidden neuron 4−0.24344
179SCr(4) → hidden neuron 40.36023
180Stratum(0) → hidden neuron 40.28447
181Stratum(1) → hidden neuron 40.03795
182Stratum(2) → hidden neuron 40.00713
183Stratum(3) → hidden neuron 4−0.01108
184Stratum(4) → hidden neuron 4−0.07327
185Definition(0) → hidden neuron 4−0.27326
186Definition(1) → hidden neuron 40.31440
187Definition(2) → hidden neuron 40.13080
188Definition(3) → hidden neuron 40.09735
189d → hidden neuron 5−0.72094
190Authors(1) → hidden neuron 5−0.02680
191Authors(10) → hidden neuron 50.02307
192Authors(11) → hidden neuron 50.27604
193Authors(12) → hidden neuron 5−0.01611
194Authors(13) → hidden neuron 5−0.19424
195Authors(14) → hidden neuron 50.20841
196Authors(15) → hidden neuron 50.12261
197Authors(16) → hidden neuron 5−0.04241
198Authors(17) → hidden neuron 5−0.08402
199Authors(18) → hidden neuron 5−0.04908
200Authors(19) → hidden neuron 5−0.11600
201Authors(2) → hidden neuron 5−0.05345
202Authors(20) → hidden neuron 50.09312
203Authors(21) → hidden neuron 50.23919
204Authors(22) → hidden neuron 50.11016
205Authors(23) → hidden neuron 5−0.11443
206Authors(24) → hidden neuron 5−0.12138
207Authors(25) → hidden neuron 5−0.03287
208Authors(26) → hidden neuron 5−0.08213
209Authors(27) → hidden neuron 5−0.27911
210Authors(3) → hidden neuron 5−0.09518
211Authors(4) → hidden neuron 5−0.02749
212Authors(5) → hidden neuron 50.12003
213Authors(6) → hidden neuron 50.11790
214Authors(7) → hidden neuron 50.10588
215Authors(8) → hidden neuron 50.19866
216Authors(9) → hidden neuron 50.05432
217Type(1) → hidden neuron 50.37638
218Type(2) → hidden neuron 50.29383
219Type(3) → hidden neuron 50.16284
220Type(4) → hidden neuron 5−0.12789
221Type(5) → hidden neuron 5−0.38163
222SCr(0) → hidden neuron 5−0.07829
223SCr(1) → hidden neuron 5−0.00391
224SCr(2) → hidden neuron 5−0.02495
225SCr(3) → hidden neuron 5−0.07982
226SCr(4) → hidden neuron 50.54052
227Stratum(0) → hidden neuron 50.16422
228Stratum(1) → hidden neuron 5−0.21503
229Stratum(2) → hidden neuron 50.17085
230Stratum(3) → hidden neuron 50.15421
231Stratum(4) → hidden neuron 50.00827
232Definition(0) → hidden neuron 50.70307
233Definition(1) → hidden neuron 5−0.02521
234Definition(2) → hidden neuron 5−0.06231
235Definition(3) → hidden neuron 5−0.27576
236d → hidden neuron 61.17965
237Authors(1) → hidden neuron 6−0.02307
238Authors(10) → hidden neuron 6−0.06951
239Authors(11) → hidden neuron 6−0.08265
240Authors(12) → hidden neuron 6−0.10153
241Authors(13) → hidden neuron 6−0.07026
242Authors(14) → hidden neuron 6−0.07741
243Authors(15) → hidden neuron 60.01514
244Authors(16) → hidden neuron 60.06756
245Authors(17) → hidden neuron 60.08993
246Authors(18) → hidden neuron 60.03712
247Authors(19) → hidden neuron 60.04384
248Authors(2) → hidden neuron 60.00508
249Authors(20) → hidden neuron 6−0.03437
250Authors(21) → hidden neuron 60.02614
251Authors(22) → hidden neuron 6−0.32616
252Authors(23) → hidden neuron 60.01901
253Authors(24) → hidden neuron 60.00303
254Authors(25) → hidden neuron 60.08092
255Authors(26) → hidden neuron 60.17462
256Authors(27) → hidden neuron 60.33237
257Authors(3) → hidden neuron 6−0.15870
258Authors(4) → hidden neuron 6−0.15137
259Authors(5) → hidden neuron 60.04295
260Authors(6) → hidden neuron 60.06572
261Authors(7) → hidden neuron 60.05104
262Authors(8) → hidden neuron 60.00749
263Authors(9) → hidden neuron 6−0.17158
264Type(1) → hidden neuron 6−0.44336
265Type(2) → hidden neuron 6−0.22912
266Type(3) → hidden neuron 6−0.17982
267Type(4) → hidden neuron 60.01925
268Type(5) → hidden neuron 60.58439
269SCr(0) → hidden neuron 60.19628
270SCr(1) → hidden neuron 6−0.02804
271SCr(2) → hidden neuron 60.00267
272SCr(3) → hidden neuron 6−0.15375
273SCr(4) → hidden neuron 6−0.22055
274Stratum(0) → hidden neuron 60.01216
275Stratum(1) → hidden neuron 6−0.03498
276Stratum(2) → hidden neuron 6−0.07766
277Stratum(3) → hidden neuron 60.00120
278Stratum(4) → hidden neuron 6−0.07375
279Definition(0) → hidden neuron 6−0.79776
280Definition(1) → hidden neuron 60.07465
281Definition(2) → hidden neuron 60.17080
282Definition(3) → hidden neuron 60.31619
283d → hidden neuron 7−1.21283
284Authors(1) → hidden neuron 7−0.02012
285Authors(10) → hidden neuron 70.10521
286Authors(11) → hidden neuron 7−0.02208
287Authors(12) → hidden neuron 70.16743
288Authors(13) → hidden neuron 7−0.03954
289Authors(14) → hidden neuron 70.09455
290Authors(15) → hidden neuron 70.00817
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316SCr(0) → hidden neuron 7−0.03241
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318SCr(2) → hidden neuron 70.03362
319SCr(3) → hidden neuron 70.04891
320SCr(4) → hidden neuron 70.52730
321Stratum(0) → hidden neuron 70.56065
322Stratum(1) → hidden neuron 7−0.05629
323Stratum(2) → hidden neuron 70.08887
324Stratum(3) → hidden neuron 70.00511
325Stratum(4) → hidden neuron 7−0.06902
326Definition(0) → hidden neuron 70.28404
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365SCr(2) → hidden neuron 8−0.00053
366SCr(3) → hidden neuron 80.02087
367SCr(4) → hidden neuron 80.22134
368Stratum(0) → hidden neuron 8−0.44896
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370Stratum(2) → hidden neuron 8−0.04615
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372Stratum(4) → hidden neuron 80.16572
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389Authors(2) → hidden neuron 9−0.00143
390Authors(20) → hidden neuron 9−0.04288
391Authors(21) → hidden neuron 90.04907
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394Authors(24) → hidden neuron 9−0.38450
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396Authors(26) → hidden neuron 9−0.13627
397Authors(27) → hidden neuron 9−0.34136
398Authors(3) → hidden neuron 90.10893
399Authors(4) → hidden neuron 9−0.16294
400Authors(5) → hidden neuron 90.07913
401Authors(6) → hidden neuron 90.04250
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403Authors(8) → hidden neuron 90.12151
404Authors(9) → hidden neuron 9−0.01166
405Type(1) → hidden neuron 90.26982
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408Type(4) → hidden neuron 9−0.39181
409Type(5) → hidden neuron 9−0.34973
410SCr(0) → hidden neuron 9−0.16429
411SCr(1) → hidden neuron 90.01025
412SCr(2) → hidden neuron 9−0.00603
413SCr(3) → hidden neuron 90.09224
414SCr(4) → hidden neuron 90.15885
415Stratum(0) → hidden neuron 9−0.18260
416Stratum(1) → hidden neuron 9−0.03402
417Stratum(2) → hidden neuron 90.11045
418Stratum(3) → hidden neuron 90.09343
419Stratum(4) → hidden neuron 90.08856
420Definition(0) → hidden neuron 90.45866
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422Definition(2) → hidden neuron 9−0.12189
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424d → hidden neuron 100.51091
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427Authors(11) → hidden neuron 10−0.06044
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429Authors(13) → hidden neuron 10−0.03744
430Authors(14) → hidden neuron 10−0.01596
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437Authors(20) → hidden neuron 10−0.02280
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439Authors(22) → hidden neuron 10−0.07463
440Authors(23) → hidden neuron 10−0.01303
441Authors(24) → hidden neuron 10−0.05313
442Authors(25) → hidden neuron 100.05221
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444Authors(27) → hidden neuron 10−0.01032
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448Authors(6) → hidden neuron 10−0.04940
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451Authors(9) → hidden neuron 100.02095
452Type(1) → hidden neuron 100.06057
453Type(2) → hidden neuron 10−0.04110
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455Type(4) → hidden neuron 10−0.07256
456Type(5) → hidden neuron 100.06368
457SCr(0) → hidden neuron 10−0.03788
458SCr(1) → hidden neuron 100.03294
459SCr(2) → hidden neuron 100.06816
460SCr(3) → hidden neuron 10−0.01826
461SCr(4) → hidden neuron 10−0.08968
462Stratum(0) → hidden neuron 100.00374
463Stratum(1) → hidden neuron 10−0.03919
464Stratum(2) → hidden neuron 10−0.03119
465Stratum(3) → hidden neuron 10−0.00038
466Stratum(4) → hidden neuron 100.09242
467Definition(0) → hidden neuron 10−0.05324
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469Definition(2) → hidden neuron 100.03944
470Definition(3) → hidden neuron 10−0.02195
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472Authors(1) → hidden neuron 11−0.07502
473Authors(10) → hidden neuron 110.06993
474Authors(11) → hidden neuron 11−0.00950
475Authors(12) → hidden neuron 11−0.09304
476Authors(13) → hidden neuron 110.03657
477Authors(14) → hidden neuron 11−0.04690
478Authors(15) → hidden neuron 110.00824
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482Authors(19) → hidden neuron 11−0.00072
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488Authors(24) → hidden neuron 110.19201
489Authors(25) → hidden neuron 11−0.08240
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491Authors(27) → hidden neuron 11−0.03530
492Authors(3) → hidden neuron 11−0.07228
493Authors(4) → hidden neuron 11−0.06232
494Authors(5) → hidden neuron 11−0.10172
495Authors(6) → hidden neuron 11−0.06359
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498Authors(9) → hidden neuron 110.08107
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500Type(2) → hidden neuron 11−0.13923
501Type(3) → hidden neuron 110.11382
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503Type(5) → hidden neuron 11−0.09722
504SCr(0) → hidden neuron 110.08966
505SCr(1) → hidden neuron 11−0.10438
506SCr(2) → hidden neuron 11−0.10693
507SCr(3) → hidden neuron 11−0.08289
508SCr(4) → hidden neuron 11−0.17880
509Stratum(0) → hidden neuron 11−0.50554
510Stratum(1) → hidden neuron 110.03159
511Stratum(2) → hidden neuron 11−0.04112
512Stratum(3) → hidden neuron 110.00725
513Stratum(4) → hidden neuron 110.09582
514Definition(0) → hidden neuron 11−0.30385
515Definition(1) → hidden neuron 11−0.06813
516Definition(2) → hidden neuron 11−0.03404
517Definition(3) → hidden neuron 11−0.00816
518input bias → hidden neuron 1−0.39243
519input bias → hidden neuron 2−0.12203
520input bias → hidden neuron 3−0.08975
521input bias → hidden neuron 40.24613
522input bias → hidden neuron 50.34593
523input bias → hidden neuron 6−0.19805
524input bias → hidden neuron 70.56858
525input bias → hidden neuron 8−0.26715
526input bias → hidden neuron 90.09001
527input bias → hidden neuron 100.00154
528input bias → hidden neuron 11−0.39815
529hidden neuron 1 → cd0.88693
530hidden neuron 2 → cd−0.79060
531hidden neuron 3 → cd−0.11064
532hidden neuron 4 → cd−0.25423
533hidden neuron 5 → cd0.52482
534hidden neuron 6 → cd0.93412
535hidden neuron 7 → cd−0.15989
536hidden neuron 8 → cd0.28240
537hidden neuron 9 → cd0.10351
538hidden neuron 10 → cd−0.23069
539hidden neuron 11 → cd−0.60563
540hidden bias → cd0.46311

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Figure 1. Aspects of crown asymmetry resulting from intraspecific competition. From (Left) to (right): reduction of crown length and loss of lower branches; one-sided crown, trunk with curvature. Below left: crown of reduced length, with reduced number of branches on one side. All previous images present a reduction in the number of needles. On the right: a circular canopy on dominant trees. The pictures were taken inside the study area (photo by César A. G. Finger).
Figure 1. Aspects of crown asymmetry resulting from intraspecific competition. From (Left) to (right): reduction of crown length and loss of lower branches; one-sided crown, trunk with curvature. Below left: crown of reduced length, with reduced number of branches on one side. All previous images present a reduction in the number of needles. On the right: a circular canopy on dominant trees. The pictures were taken inside the study area (photo by César A. G. Finger).
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Figure 2. Distribution of the trees of Araucaria angustifolia in a sample unit of 0.5 ha, with emphasis on objective trees (black thick line) and competition trees (clear-cut—fine blue lines) calculated with the mean (Equation (1); (a)) and maximum crown diameter (Equation (2); (b)). The circles represent the calculated dimension for the crown and not the actual dimension, allowing us to see the competition to which trees are subjected. The horizontal and vertical co-ordinates are shown in SIRGAS 2000/UTM zone 22S.
Figure 2. Distribution of the trees of Araucaria angustifolia in a sample unit of 0.5 ha, with emphasis on objective trees (black thick line) and competition trees (clear-cut—fine blue lines) calculated with the mean (Equation (1); (a)) and maximum crown diameter (Equation (2); (b)). The circles represent the calculated dimension for the crown and not the actual dimension, allowing us to see the competition to which trees are subjected. The horizontal and vertical co-ordinates are shown in SIRGAS 2000/UTM zone 22S.
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Figure 3. Number of remaining trees according to the crown diameter obtained by mean (Equation (1)) and potential (Equation (2)) crown size.
Figure 3. Number of remaining trees according to the crown diameter obtained by mean (Equation (1)) and potential (Equation (2)) crown size.
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Figure 4. Crown after recovery of height growth. (Left) parable crown shape (new crown) grown after height recovery. (Right) arrangement of pseudo-whorls in two periods: before and after recovery of height growth. The pictures were taken nearby the study area (photo by César A. G. Finger).
Figure 4. Crown after recovery of height growth. (Left) parable crown shape (new crown) grown after height recovery. (Right) arrangement of pseudo-whorls in two periods: before and after recovery of height growth. The pictures were taken nearby the study area (photo by César A. G. Finger).
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Table 1. Biophysical characteristics and number of trees to be logged and maintained according to the average and maximum crown diameter as a function of the current measured diameter breast height.
Table 1. Biophysical characteristics and number of trees to be logged and maintained according to the average and maximum crown diameter as a function of the current measured diameter breast height.
TypeTreesNGCPADist.
Total 28835.6
Mean crown diameter
(Scenario I)
Remnants 723.038.513.3
Selective logging21632.6
Maximum crown diameter
(Scenario II)
Remnants504.0102.116.0
Selective logging23831.6
Where: N = number of trees·ha−1; G = basal area, m2·ha−1; CPA = horizontal crown projection area, m2; Dist. = average distance between target trees, m.
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MDPI and ACS Style

Finger, C.A.G.; Costa, E.A.; Hess, A.F.; Liesenberg, V.; Bispo, P.d.C. Simulating Sustainable Forest Management Practices Using Crown Attributes: Insights for Araucaria angustifolia Trees in Southern Brazil. Forests 2023, 14, 1285. https://doi.org/10.3390/f14071285

AMA Style

Finger CAG, Costa EA, Hess AF, Liesenberg V, Bispo PdC. Simulating Sustainable Forest Management Practices Using Crown Attributes: Insights for Araucaria angustifolia Trees in Southern Brazil. Forests. 2023; 14(7):1285. https://doi.org/10.3390/f14071285

Chicago/Turabian Style

Finger, César Augusto Guimarães, Emanuel Arnoni Costa, André Felipe Hess, Veraldo Liesenberg, and Polyanna da Conceição Bispo. 2023. "Simulating Sustainable Forest Management Practices Using Crown Attributes: Insights for Araucaria angustifolia Trees in Southern Brazil" Forests 14, no. 7: 1285. https://doi.org/10.3390/f14071285

APA Style

Finger, C. A. G., Costa, E. A., Hess, A. F., Liesenberg, V., & Bispo, P. d. C. (2023). Simulating Sustainable Forest Management Practices Using Crown Attributes: Insights for Araucaria angustifolia Trees in Southern Brazil. Forests, 14(7), 1285. https://doi.org/10.3390/f14071285

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