Differences in tree interactions between dominant species in pure and mixed forests in northern Hebei, China

Schlüsselbegriffe: Koexistenz von Arten, Interaktionen zwischen Bäumen, Lebens­stadien, Waldökosysteme

Available at https://doi.org/10.53203/fs.2301.2

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Abstract

Tree interactions have long been recognised as key biotic interactions in forest ecosystems and their spatial patterns can improve our understanding of forest structure and dynamics. In this study, we explored the intraspecific and interspecific interactions of Betula platyphylla Suk. (birch) and Larix principis-rupprechtii Mayr. (larch) in mountainous forests of northern Hebei, China. The mark correlation function based on tree height was employed in this analysis. Our results showed that the intraspecific interactions in pure birch and larch stands were mainly competition, while in the mixed birch–larch stand, they were mainly facilitation. The intraspecific interaction between saplings and adults changed from competition in the pure birch stand to facilitation in the mixed stand (4.5 < r < 6 m) and an opposite variation was observed for the interaction between birch juveniles and adults (2 < r < 3.5 m). The interspecific interaction between birch and larch mainly appeared as competition, while facilitation between birch saplings/adults and larch juveniles was significant. In contrast to previous studies, our results revealed that the coexistence of birch and larch in mixed forests significantly regulated tree interactions, including competition and facilitation. The findings of this study will help understand the dynamics of similar forest ecosystems and may provide new perspectives for forest management.

Zusammenfassung

Die Interaktionen zwischen Bäumen sind seit langem als wichtige biotische Interaktionen in Waldökosystemen bekannt und ihre räumlichen Muster können unser Verständnis der Waldstruktur und Walddynamik verbessern. In dieser Studie untersuchten wir die intraspezifischen und interspezifischen Interaktionen von Betula platyphylla Suk. (Birke) und Larix principis-rupprechtii Mayr. (Lärche) in Bergwäldern im Norden von Hebei, China. Es wurde die auf Baumhöhe basierende Markkorrelationsfunktion verwendet. Unsere Ergebnisse zeigten, dass die intraspezifischen Interaktionen in reinen Birken- und Lärchen-Beständen hauptsächlich auf Konkurrenz basieren, während in gemischten Birken-Lärchen-Beständen hauptsächlich eine Förderung beobachtet wurde. Darüber hinaus veränderte sich die intraspezifische Interaktion zwischen Jungpflanzen und ausgewachsenen Bäumen von Konkurrenz im reinen Birkenbestand zur Förderung im gemischten Bestand (4,5 < r < 6 m) und eine entgegengesetzte Variation wurde für die Interaktion zwischen jungen und ausgewachsenen Birken beobachtet (2 < r < 3,5 m). Die interspezifische Interaktion zwischen Birke und Lärche trat hauptsächlich als Konkurrenz auf, während die Förderung zwischen jungen/erwachsenen Birken und jungen Lärchen signifikant war. Im Vergleich zu früheren Studien haben unsere Ergebnisse gezeigt, dass die Koexistenz von Birke und Lärche in Mischwäldern durch die Interaktionen zwischen den Bäumen, wie Konkurrenz und Förderung, reguliert wird. Die Ergebnisse dieser Studie können zu einem besseren Verständnis der Entwicklung von Waldökosystemen und deren Dynamik beitragen und neue Perspektiven für die Waldbewirtschaftung eröffnen.

1 Introduction

Plant interactions are key drivers of forest structure and function. Trees are core components of forests, since they link belowground and aboveground communities and regulate stand structures (Jakobsson et al. 2019). Tree interactions are essential ecological forces that shape forest dynamics in time and space, and govern growth, mortality and regeneration processes in plant communities (Seifan & Seifan 2015; Pommerening & Sánchez Meador 2018). The combined effects of tree interactions and environmental conditions determine the spatial distributions of species in forests (Zhang et al. 2010; Sigdel et al. 2020), and also provide insight into the mechanisms of plant coexistence (Zillio & He 2010; Ehlers et al. 2016). Therefore, a clear understanding of species coexistence and tree interactions has both theoretical and practical implications for forest management (Qi et al. 2016). However, it is still a challenge to recognize the relationship between plant species coexistence patterns and complex tree interactions, such as competition and facilitation (Zhou et al. 2019).

In plant communities, previous studies about plant interaction mostly focused on competition (Michalet & Pugnaire 2016). In fact, competition and facilitation operate simultaneously, and thereby affect the outcome of tree interactions (Choler et al. 2001; Bonanomi et al. 2016). Complex combinations of them often occur between plant species and even among the same individuals (Callaway & Walker 1997). Moreover, both competition among reciprocal species and facilitation among competitors could stabilize plant communities (Losapio et al. 2021). One species or individual may benefit others even when they compete for the same limiting resources with it (Wang & Wu 2017). If the balance between benefits and costs is positive, a facilitative interaction is possible to appear and lead to the coexistence of both species (Valiente-Banuet & Verdú 2008; Soliveres et al. 2015). Meanwhile, large numbers of competing species can coexist in ecosystems because of resource limitation and spatial heterogeneity (Harpole & Tilman 2007; Douda et al. 2021).

Plant interactions are usually measured by performance indicators of single plants or plant communities, without consideration for neighbour plants (Pommerening & Sánchez Meador 2018), and these indicators are hard to measure in forests. Increasing methods have been applied to study tree interactions among individuals based on the distance and size of neighbor trees, such as Hegyi's competition index (Hegyi 1974), mingling index (Gadow & Füldner 1993), uniform angle index (Hui et al. 1998), and dominance index (Gadow et al. 2012). Moreover, by indicating the positions of trees, many distribution patterns have been explored and used to analyse tree interactions (Stoyan & Penttinen 2000; Koukoulas & Blackburn 2005; Carrer et al. 2018; Bianchi et al. 2021). The development of spatial point pattern analysis has largely promoted studies on the spatial distribution in natural forests (Ghalandarayeshi et al. 2017; He et al. 2018) and plantations (Zhang et al. 2019). In the spatial point pattern analysis, the mark correlation function k_mm(r) was an effective tool and capable of identifying interactive effects in forests (Yilmaz et al. 2019; Yuan et al. 2020). The function was frequently used to analyze spatial interactions of a mark (Illian et al. 2008), which could be a tree size such as diameter or height (Ledo et al. 2011; Erfanifard & Stereńczak 2017). In such analysis, the employment of mark correlation function, which considered the species of trees and their sizes simultaneously, can reveal the specific performance in tree interactions (Wiegand & Moloney 2004). In addition, studying tree interactions by these nonclassical methods can improve the understanding of stand spatial structure and provide more information to guide forest management (Yilmaz et al. 2019).

Mixed-species forests and plantations sometimes have more forest productivity than pure stands, depending on the influences of different plant interactions (Forrester 2014). In mixed stands, plant competition and facilitation are essential to the performance of stands, and can generate greater effects than pure stands (del Río et al. 2014b). Some studies have found that interspecific interactions can improve tree growth in mixed stands under conditions of reduced competition or existing facilitation (Aussenac et al. 2019), and the effects of neighbourhood may vary with growth conditions, species composition and structure (del Rio & Sterba 2009; del Río et al. 2014a). For instance, an experiment in the multispecies forest showed that variations in the competition intensity among trees along soil fertility depend on the context and species (Coates et al. 2013). In addition, the intensity and direction of plant interactions are regulated by life stages (Keammerer & Hacker 2013; Ledo 2015), and competition may intensify from early to late life stages (Schiffers & Tielborger 2006). Despite over a long period of research, however, the differences in tree interactions between mixed and pure stands, and the roles of life stages in this process, are still obscure.

In this study, we hypothesize that tree interactions among life stages will show different patterns when two species coexist in a mixed forest. The mark correlation function was applied to reveal the specific performance in tree interactions by considering the species of trees and their sizes simultaneously (Wiegand & Moloney 2004; Pommerening & Särkkä 2013; Ballani et al. 2019). Tree height was selected as a mark, since it is obviously related to the ability of trees to compete for above-canopy light (Noyer et al. 2019). Using as a case study the tree interactions and coexistence patterns of Betula platyphylla Suk (birch) and Larix principis-rupprechtii Mayr. (larch) in mountainous forests of northern Hebei, China, we aim to 

(1) explore the intra- and interspecific interactions among life stages, and 

(2) reveal the differences in tree interactions of birch and larch between pure and mixed stands.

2 Material and methods

2.1 Study area

The research was conducted in the mountainous forests of northern Hebei, China. Due to harsh climate conditions and high soil erosion potential in this region, the ecosystem functioning of mountainous forests became threatened (Wang 2001). Human disturbance, such as deforestation, has caused further extensive forest degradation (Liu et al. 2018).

The study area is located in the southeastern Chongli District (40°47’N – 41°17’N, 114°17’E – 115°34’E), the northwest of Hebei province, China (Figure 1). A long snowfall period is prevalent lasting about 150 days. The annual average temperature is 3.7 °C, and the mean annual precipitation is 483.3 mm (Zhang et al. 2021). Mountainous regions with elevation ranging between 1700 m to 2100 m account for 80% of the Chongli District with a forest cover of 71.53% in 2022 (Beijing Daily, http://www.bjd.com.cn). The forest ecosystems are dominated by deciduous broad-leaved and coniferous species, with Betula platyphylla Suk (birch) and Larix principis-rupprechtii Mayr. (larch) being the main tree species (Song et al. 2018). These two tree species are also widespread in northern China and are considered to provide timber with high quality (Guo et al. 2017; Sun et al. 2017).

2.2 Field measurements and data processing

Three 1 hectar (100 m × 100 m) sample plots were established in the study area (Figure 1): PB (pure birch stand) is a secondary forest, PL (pure larch stand) is a plantation and BL (mixed birch–larch stand) is a mixed forest from early planting of larch trees in birch secondary forests. These selected forests are far away from the influence of human activities and have a healthy appearance with little or no dead trees present (Figure A1). We have fenced the perimeter of the sample plots to prevent the damage by humans and/or large animals. The three plots had in general similar ecological conditions, such as aspect, slope, and soil type, to exclude the interference of environmental factors (Table 1). According to the Chinese forest inventory data (National Forestry and Grassland Science Data Center), all three plots were middle-aged forests, with the pure birch stand being somewhat older (Table 1). During the summer of 2020, DBH (diameter at breast height) was measured by a Vernier caliper (precision: 0.01 cm), tree height (H) was measured by an altimeter (precision: 0.1 m), and position was measured by a GPS locator (CHCNAV i70, precision: 1 cm). All trees in three plots with DBH ≥ 1 cm were tagged. Based on the DBH classes, trees were divided into three life stages: (a) saplings, 1 cm ≤ DBH < 5 cm; (b) juveniles, 5 cm ≤ DBH < 15 cm; and (c) adults, DBH ≥ 15 cm (Liu et al. 2020). Life stages less than 50 individuals were excluded (Zhou et al. 2019). Seedlings represent individuals with DBH < 1 cm or tree height < 1.3 m, which were not put into our analyses.

All data were used in ArcGIS 10.2 (Environmental Systems Research Institute, Redlands, State of California, USA), planar coordinates were obtained by a projection transformation tool, and then the latitude and longitude were translated to coordinates x and y within the range of 0–100 m. Then, the distribution of tree positions and heatmaps according to tree height were created using Origin 2021 software (OriginLab, Northampton, Massachusetts, USA). Skewness and kurtosis were calculated in SPSS 24 (International Business Machines Corporation, Armonk, New York, USA) to describe the asymmetry and peak of size (DBH and tree height) distribution, and the normality test was applied by a z-test as shown in Equation (1) (Kim 2013).

where Z_S and Z_k are z scores for skewness and kurtosis, S and K are values of skewness and kurtosis, SE_S and SE_K are standard errors of skewness and kurtosis.

For sample size n ≤ 50, if |Z_S| or |Z_k| > 1.96, the distribution is nonnormal (α = 0.05); for 50 < n ≤ 300, if |Z_S| or |Z_k| > 3.29, the distribution is nonnormal (α = 0.05); for n > 300, if |S| > 2 or |K| > 7, the distribution is nonnormal.

 

2.3 Spatial point pattern analysis

In our study, tree height was applied as a mark in the mark correlation function k_mm(r), which was seldom used because of its difficulty in measurement. This research used univariate mark correlation functions to analyze intraspecific interactions and bivariate mark correlation functions to analyze both intra- and interspecific interactions between life stages. All functions were computed in Programita software (Weigand & Moloney 2014). The null model of our study was random labeling, and the moving window of the radius in the software was set to 30 m (Wiegand & Moloney 2004). To test the significance level of statistical analysis, 99% confidence envelopes were obtained based on the fifth highest and lowest of 999 Monte Carlo simulations, using a GoF (goodness-of-fit) test with α = 0.01 (Ghalandarayeshi et al. 2017). After exporting the data, line graphs were plotted in Origin 2021. If the observed k_mm(r) was above the envelope, the tree heights of two trees were positively correlated; if the observed k_mm(r) was inside the envelope, the tree heights were independent; otherwise, the tree heights were negatively correlated. A positive correlation indicated that two trees were facilitative, while a negative correlation indicated that two trees were competitive (Weigand & Moloney 2014).

 

2.4 Interaction intensity index

In plant communities, the strength of interaction was usually measured as the ratio of some performance variables, and the interaction index should be relative (Armas et al. 2004). The deviation of the k_mm(r) curve from the envelope frequently indicated competition or facilitation (Weigand & Moloney 2014). To show the strength of competition and facilitation, the ratio of the area of the k_mm(r) curve deviating from the confidence envelope line to the area of the confidence envelope was used to represent the interaction intensity (I). If I > 0, the interaction was mainly facilitative; if I = 0, the interaction was mainly independent; and if I < 0, the interaction was mainly competitive. In total, three scenarios were presented as follows:

(1) Scenario I: The k_mm(r) curve is above the envelope.

where r_i and r_j denote horizontal coordinates of intersections between the k_mm(r) curve and the upper envelope line, U(r) denotes the upper envelope line, and R denotes the moving window of radius.

(2) Scenario II: The k_mm(r) curve is completely inside the envelope. I = 0, and the interaction is independent.

(3) Scenario III: The k_mm(r) curve is below the envelope.

where r_i and r_j  denote horizontal coordinates of intersections between the k_mm(r) curve and the upper envelope line, L(r) denotes the lower envelope line, and R denotes the moving window of radius.

3.1 Stand structure

In three 1-ha plots, a total of 4033 trees were measured, and more birch trees were observed than larch. Birch had a higher basal area than larch in pure stands, while the opposite result was observed in mixed stands (Table 1). Across three plots, we observed more juveniles, then adults, and a few saplings (Table 2). Heatmaps showed the spatial distribution of tree height created from field data. In general, tree height was displayed in a patchy distribution across all plots (Figure 2), and values at coordinate x of 0–50 m were significantly higher than at 50–100 m in PB (F = 37.858, p < 0.001). The tree height in three plots increased with DBH size class (Figure A2, p < 0.001), and larch trees were taller than birch trees in general (Table A1).

As shown in Figure 2, the distributions of DBH and tree height were all normal (|S| < 2 and |K| < 7). In PB, the DBH size structure of birch showed significantly positive skewness (p < 0.05), and there were more juveniles than saplings and adults (Figure 2a). The range of DBH size was wide, up to 45 cm, while the tree height structure (R² = 0.9476, skewness = -0.308, kurtosis = 0.176) tended to be more normally distributed than the DBH (R² = 0.9003, skewness = 0.887, kurtosis = 0.710). Within the DBH size class, the tree height structure conformed to the total structure, except that the tree height of individuals with DBH at 15-30 cm showed a non-normal distribution (Table A1). In PL, the DBH structure was also well described by normal distribution (R² = 0.9993), and most larch trees were approximately 15 m tall (Figure 2b). The tree height structure was similar to the DBH structure, while the skewness was negative. Moreover, normal distributions were observed within all DBH classes (Table A1).

For birch trees in BL, the skewness of DBH structure was similar to that in PB, and the distribution was normal (Figure 2c). In contrast, the tree height structure varied in BL, showing a J shape and poor normality (R² = 0.7694). Within the DBH size class, the tree height distribution of birch saplings was non-normal (|Z_S| > 3.29). For larch trees in BL, a non-normal distribution was also observed (Table A1). However, the DBH distribution of larch trees was different from that of birch. The negative skewness of the DBH structure indicated a predominance of large larch trees (Figure 2d).

 

3.2 Intraspecific competition and facilitation between trees

In all the three plots, both birch and larch trees showed a significant aggregated distribution (Figure A3). For birch trees in PB, a significant spatial correlation (p < 0.01) was found among the tree height of individuals at all scales (Figure 3a), which can also be observed between saplings and adults (Figure 3c). Conversely, birch saplings and juveniles showed positive correlations at most scales, so did juveniles and adults at all scales (Figure 3b, c).

In PL, no significant interactions (p > 0.01) were found for larch trees (Figure 4). The k_mm(r) curves were within the simulation envelopes at most scales, indicating that the tree height of two nearby larch trees tends to be independent, although negative interactions appeared at 15–16 m for all trees (Figure 4a). The interaction intensities (I) in the three plots were calculated using the integral area for the three scenarios, and the results indicated that the strongest competitive (all birch trees) and facilitative (juveniles vs adults) effects occurred in PB. However, the weakest competition (all larch trees in PL) and facilitation (larch juveniles in BL) were observed among larch trees.

For birch trees in BL, a significant positive correlation was found among the tree height of all trees (Figure 5a). Similarly, saplings were attractive to juveniles at all scales (Figure 5b). However, independent associations were observed at most scales between saplings and adults, as well as juveniles and adults (Figure 5c, d). There was a weak competitive effect among saplings (I = -0.0044), while both juveniles and adults have a significant positive spatial correlation with the tree height of saplings (Figure 5b, c). For larch trees in BL, the k_mm(r) curves exhibited no significant interactions (p > 0.01) among individuals (Figure 6). Conversely, facilitation was found among all trees at 2–5 m, 6–7 m, and 13–16 m (Figure 6a), while juveniles and adults were independent at all scales (Figure 6b). In terms of interactions within the same life stage, strong facilitation was observed among birch juveniles, while weak facilitation was observed for larch juveniles and adults. Moreover, the strength of facilitation among birch trees was stronger than that among larch trees. Generally, the above intraspecific interactions were largely driven by birch juveniles and larch adults (Figure A4).

When comparing pure and mixed stands, the competition among birch trees in PB was largely stronger than the facilitation in BL (Table 3). Meanwhile, interactions within birch saplings and birch juveniles in PB were opposite to birch trees in BL (Figure A4), and similar conditions occurred between different life stages. Conversely, the competition among larch trees in PL was smaller than the facilitation in BL, and stronger interactions among different life stages were observed for larch trees in BL than in PL (Figure A4).

3.3 Interspecific competition and facilitation between trees

The interspecific interactions between birch and larch trees are shown in Figure 7. It can be seen that a significant negative (p = 0.0010) correlation arised between the tree height of juveniles at 0–3 m (Figure 7c), indicating that birch and larch juveniles mutually hindered the growth of tree height. The competitive effect between larch juveniles and birch juveniles (|I| = 0.0418) was stronger than the facilitative effect within larch juveniles (|I| = 0.0001), but birch juveniles and larch adults had stronger competition than facilitation within larch adults. However, birch and larch adults were independent at all scales (Figure 7f), and there was no significant departure (p > 0.01) from envelopes, revealing that adults of the two dominant species had no spatial interaction in terms of tree height.

Turning now to species pairs between different life stages, both positive and negative associations were observed. Birch saplings and adults were attractive to larch juveniles at some scales, while the facilitation between birch saplings and larch juveniles was stronger than that between birch adults and larch juveniles (Figure 7a, e), which was also stronger than the competition between birch juveniles and larch juveniles. This result revealed that birch saplings and larch juveniles could maintain optimal co-growth patterns. Conversely, negative associations were found when comparing birch saplings and juveniles with larch adults, and the intensity between birch saplings and larch adults was weaker than that between birch juveniles and larch adults (Figure 7b, d).

Overall, the interspecific competition in the mixed stand was weaker than the intraspecific competition in the pure birch stand but stronger than that in the pure larch stand (Table 4). Among the different life stages, the interspecific facilitation between saplings and juveniles in the mixed stand was stronger than the intraspecific facilitation in the pure birch stand. However, for saplings and adults, more interspecific competition effects were observed in the mixed stand than the intraspecific interactions in the pure birch stand (Figure A4). Moreover, the facilitation between juveniles and adults in the pure birch stand was stronger than interspecific interactions between those life stages in the mixed stand. Whether competition or facilitation, tree interactions between different life stages in the mixed stand were frequently stronger than those in the pure larch stand.

4 Discussion

4.1 Stand structure

In the study area, birch was the main native tree species, while fast-growing larch was mostly artificially planted (Song et al. 2018). For a clear knowledge of tree interactions, our study started by analyzing the spatial structures. As major stand attributes, stand structure and community composition interdependently affect forest ecosystem function (Carvalho 2011). Studies on the stand structure of boreal forests in China have been reported (Fang et al. 2012; Gao et al. 2013). However, our results also compared the differences between pure and mixed forests. Meanwhile, as three main forest types in northern Hebei, pure birch secondary forests, pure larch plantations, and birch-larch mixed forests were rarely studied. Our results found that both DBH size structure and tree height structure showed a normal distribution (Figure 2). It has been demonstrated that the normal distribution was closest to the actual DBH structures for stands in the growing up or break-up stage (Podlaski 2006). It should be noted that the distribution skewness in the mixed stand was frequently larger than that in pure stands. Interestingly, more seedlings were found in the mixed stand, and more saplings were observed for birch than that for larch (Table 2). A reasonable explanation was that the formation of mixed forests can also change the original stand structure; for example, plantations facilitated forest succession and produced a conducive environment for the regeneration of native trees (Shoyama 2013). Moreover, both DBH size and tree height structures were always characterized by positive kurtosis (Figure 2). In contrast, Orzel et al. (2017) found that the DBH distribution of majority stands had right-sided asymmetry and negative kurtosis. However, these variations in stand structure could result from differences in species community and even alter the spatial distribution.

Heatmap was applied to reveal the spatial distribution of tree height, and values of tree height presented patchy distributions across the three stands (Figure 2). Trees tended to form complex patches with different sizes, species, and ages (Dale 1999). Patchy distributions existed in most ecosystems, and the locations of high- and low-density patches frequently vary with time (Effenberger et al. 2011). Similarly, Yuan et al. (2020) provided the spatial variability maps of biomass, with a patchy distribution of C for all species. Goode et al. (2021) found that shortleaf pine was clustered into distinct patches at the stand scale. In line with our findings, polarization of the tree height distribution was observed in two pure stands. The vegetation structure in fragmented landscapes is mostly determined by the location and size of patches, as well as disturbances (Schippers et al. 2014). According to the survey, there was overgrazing in local forests a few years ago. Meanwhile, environmental pollution and human disturbance have affected the forest structure. Hence, the results reported in this study could be influenced by the above factors and caused the polarization of patches in pure stands.

The underlying ecological processes, e.g., competition and facilitation, can be deduced by analyzing spatial distribution (Pillay & Ward 2012; Hesselbarth et al. 2018). However, regardless of the stand structure or heatmaps, they simply characterized the overall pattern. One important extension for spatial distribution is the consideration of spatial scales (Weigand & Moloney 2014).

 

4.2 Adjustment of tree competition and facilitation in pure and mixed stands

Competition for light is a central process that offers opportunities for niche differentiation and contributes to the coexistence of tree species (Grote et al. 2013). In forests, the light environment brightens with increasing tree height (Osada et al. 2004), and tree height is related to their ability to compete for above-canopy light (Elmendorf & Moore 2007; Noyer et al. 2019). The spatial association of tree height may imply light competition within forest ecosystems. Therefore, the competition and facilitation in this study were mainly associated with the utilization of light resources. Most importantly, we utilized spatial point pattern analysis to elucidate whether species coexistence of birch and larch regulated tree interactions. Here, we found that competition and facilitation operate simultaneously, which can be confirmed by similar findings from other boreal forests (Omelko et al. 2018; Liu et al. 2020).

Previous studies have shown that aggregation was a widespread pattern in nature (Li et al. 2009). In our study, the spatial distributions of tree positions were aggregated across all plots (Figure A3). One of the major mechanisms contributing to aggregation was reported as neighborhood competition (Canham et al. 2006; Getzin et al. 2008; Liu et al. 2021). Conversely, facilitation between conspecific individuals was also considered a key driver of small-scale aggregation (Montesinos et al. 2007; Gimeno et al. 2015). Simultaneously, observed competition and facilitation were consistent with these theories (Table 3), which allowed us to reasonably speculate that aggregation was a suitable distribution pattern in local forests. Tree competition and facilitation contribute to the formation of spatial patterns, as well as species coexistence (Barabás et al. 2016; Abd-ElGawad et al. 2020; Brewer et al. 2021; Eigentler 2021).

In principle, species coexistence could also determine tree interactions (Ormeño et al. 2007). Therefore, we investigated whether there were differences in interactions between pure and mixed stands. Notably, on one hand, the mixed forest in our study has existed locally for decades, and the two species could coexist stably. On the other hand, the mixed forest was the result of a natural conversion of pure forests. Our findings showed that tree interactions were dominated by intraspecific competition in pure birch and larch stands and by intraspecific facilitation in mixed birch-larch stands (Table 3). This result confirmed that interspecific competition was stronger than intraspecific competition. We conjectured that trees select intraspecific facilitation to resist interspecific competition. In mixed forests, with increasing environmental stress caused by interspecies or water levels, the strength of competition decreased while facilitation increased (Maestre et al. 2009; Luo et al. 2010). Although there were some differences from previous studies (Tilman 1982; Chesson 2000; Xia et al. 2016), our finding was consistent with that of Goldberg & Barton (1992), who reported that intraspecific competition was sometimes weaker than interspecific competition. Recently, Guo et al. (2022) also found that interspecific competition was greater than intraspecific competition between two host plants through AM fungi interacting with litter. Improvement of the stress factor, ecophysiological requirements of different species, and tolerances determined the outcome of interactions (Maestre et al. 2005).

Our results suggested that each species limited the growth of its competitors more than the limits on its own population. For example, the facilitation within birch juveniles was largely stronger than the interspecific competition with larch trees (Figure A4, Table 4). In this way, it was assumed that the mixed forest created better conditions that favor birch juveniles, while the growth of nearby larch trees was limited. Previous studies reported that birch trees got a long juvenile stage, inducing a delay in reproduction (Truong et al. 2007; Sun et al. 2012). Therefore, the number of birch juveniles in mixed forests should be strictly controlled. However, birch adults and all larch trees could facilitate each other, and this coexistence pattern also had implications for the interactions among other life stages. Moreover, birch saplings were subjected to the competitive effects of adults in the pure stand while facilitated by both juveniles and adults in the mixed stand (Table 3). A similar result was also reported by Liu et al. (2020), although the studied species were different. The significant competition between birch saplings and larch adults indicated that birch saplings struggle to survive under the canopy of larch adults (Table 4). Birch saplings have a strong growth response to the canopy openings that are important for sapling growth (Takahashi & Rustandi 2006). This result may be explained by the fact that birch trees have larger canopy openings than larch trees. In summary, differences in tree interactions between pure and mixed forests reveal the regulation of species coexistence on tree competition and facilitation.

 

4.3 Implications and management suggestions 

Species coexistence and tree interactions have drawn considerable attention in plant ecology due to their key role in preserving biodiversity. An implication of our findings is that the coexistence of birch and larch regulated intra- and interspecific interactions within and between different life stages. A better understanding of the tree interactions among species will improve our exploration of ecosystem conservation. The findings will be useful in identifying the shifting of ecological processes and providing insight for studies on the regulatory mechanism of multispecies coexistence. Despite promising results, further work is still required to investigate the influence of environmental factors (e.g., degradation, climate variables, and soil nutrients) and other plants such as shrubs and grasses. Our results supply an instructive and theoretical basis for the management of local forests and facilitate the determination of reasonable thinning and planting strategies. For example, 

(1) in pure birch secondary forests, adults need to be removed regularly at some distance to maintain the growth of saplings; 

(2) in pure larch plantations, distances between trees should be noted to prevent fragile stand structures caused by excessive independence, and planting birch saplings in the gap is a good operation to make full use of stand space; and 

(3) in mixed birch-larch forests, birch juveniles and larch adults need to be thinned at some distance to ensure stable coexistence and regeneration. Furthermore, manual thinning should also be manipulated to ensure the proper distance between trees.

5 Conclusions

There has been a great focus on understanding the effects of tree interactions on species coexistence. However, the adjustment of tree competition and facilitation when two species coexist has not been documented previously in forest ecosystems. By using spatial point pattern analysis in mountainous forests of northern Hebei, this study demonstrated that tree interactions were dominated by intraspecific competition in pure stands while by intraspecific facilitation to resist interspecific competition in the mixed stand. The findings of our study revealed the spatial patterns of local typical forests and offer detailed information about the ecological process of how species coexistence regulates tree interactions. Specifically, spatial patterns and species coexistence mechanisms will have implications for species diversity and forest regeneration. Our understanding of the underlying dynamics and selection strategies of tree competition and facilitation in such pure and mixed forests can be used in practical forest management. Furthermore, the current data implied that the conversion from pure plantations to mixed birch–larch forests would be the optimal strategy in local forest ecosystems.

Despite the regional limitations, the present study employed an efficient method to evaluate the regulation of species coexistence on tree interactions and provided support for further studies on multispecies coexistence. Future research should consider the potential effects of environmental factors such as climate variables and soil nutrients.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, [Huayong Zhang], upon reasonable request.

Acknowledgements

We thank Dr. Edmond Sanganyado from Northumbria University for polishing the language. Thanks are due to Yu Zheng, Xinxin Zhen, and Zhuoqing Bai for assistance with experiments and data processing.

Funding

This work was supported by the “National Water Pollution Control and Treatment Science and Technology Major Project”, grant number “2017ZX07101-002”.

Declaration of interest statement

The authors declare no competing interests.

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