Species–habitat associations in an old-growth temperate forest in northeastern China

Study site

The study was carried out in an old-growth temperate forest, which was a 30-ha Forest Dynamics Plot in the Heilongjiang Fenglin National Nature Reserve, Yichun City, Heilongjiang Province, Northeast China. The total area is 18,165 ha. The site is a temperate conifer and broad-leaved mixed forest, the mean annual temperature is − 0.5 °C, and it is affected by the East Asian ocean currents and the Siberian cold current double impact with continental and monsoon climate characteristics and long winters but warm, transitory summers. The mean annual precipitation is 688 mm, mostly falling from June to September. The relative humidity is 73%, frost-free days per year are 120, and growth days per year are 100. The dominant soil is dark brown soil, and the soil depth is 20–50 cm. The region belongs to lowland hilly terrain and is surrounded by water. The main rivers are the Tangwang River, Fenglin River, Pingyuan River and nine other rivers. The seasonal flowing water running through the region constitutes a complete water system. The forest coverage is 95%, which is one of the oldest virgin forests in the broadleaved-Korean pine forest ecosystem. The plot has a complex community composition and high species diversity, which is dominated by Pinus koraiensis and coupled with the broad-leaved species of Acer tegmentosum, Fraxinus mandshurica, Acer mono, Tilia amurensis, and Ulmus laciniata, as well as the conifer species Abies nephrolepis, Picea koraiensis, and Picea jezoensis.

Data collection

In 2009, a 30-ha (600 m × 500 m) permanent plot was established (Fig. 1). The total station was used to divide the plot into 750 20 m × 20 m quadrats. All woody stems with DBH ≥ 1 cm in the plot were identified, measured, had their status recorded (survival, lodging, standing die), mapped and tagged. A recensus was conducted in 2014.

In 2014, approximately 75,586 individuals had a DBH ≥ 1 cm in 22 families, with 37 genera and 44 species. Approximately 79% of the measured woody plants had a DBH of less than 5 cm, whereas 2.73% had a DBH of more than 40 cm. The most abundant species was Corylus mandshurica, accounting for 25.3% of the total individuals. This abundance was followed by Actinidia kolomikta, Abies nephrolepis, Acer ukurunduense, Acer tegmentosum, Acer mono, Lonicera chrysantha, Pinus koraiensis, Acanthopanax senticosus, and Betula costata. The total number of these 10 species accounted for 82.98% of the total number of individuals in the plot. There were 8 species with fewer than 10 individuals, accounting for 18% of the total species.

Habitat classification

The altitude of the four corners of each quadrat were made into a digital elevation model (DEM), and the elevation, slope, aspect, and curvature were derived from the DEM. Aspect was a circular variable, so aspect was transformed to sin (aspect) and cos (aspect) [27]. We used a multiple regression tree (MRT) [31] to divide the 750 quadrats into five habitat types containing similar topographical classifications and species compositions [32]. The multiple regression tree is a statistical technique that can predict the relationship between species and environmental factors [31]. Using the recursive partitioning analysis, the quadrats were divided into homogeneous categories to the greatest extent possible. Tree size was selected by minimizing the cross-validated relative error (CVRE) with 1 SE rules; thus, it was more objective than the common method. Four topographic attributes of 20 m × 20 m quadrats were selected as independent variables. The importance value (IV) of 44 species in 750 quadrats was the dependent variable, and the importance value was calculated as IV = (relative abundance + relative basal area)/2. Categorization was accomplished by the “mvpart” library in R [32]. The 750 quadrats were assigned to five topographic habitat types: ridge, sunny, shady, valley shady side, valley sunny side (Fig. 2). The specific parameters of the five habitat types are shown in Table 1.

Habitat types	Number of quadrats	Total area (ha)	Relative elevation (m)			Slope (°)			Aspect (°)			Curvature (m)
			Min.	Max.	Mean	Min.	Max.	Mean	Min.	Max.	Mean	Min.	Max.	Mean
Ridge	125	5	416.5	489.6	452.9	2.4	25.9	12.9	2.7	359.8	158.9	0.9	5.5	2.3
Sunny	145	5.8	405.8	480.7	437.6	9.2	22.9	14.0	91.8	258.8	161.9	− 5.5	0.7	− 0.9
Shady	247	9.88	407.6	480.7	442.0	9.2	25.3	14.9	0.5	357.6	130.1	− 2.4	0.8	− 0.3
Valley shady side	61	2.44	408.6	463.9	428.1	1.6	9.0	6.0	0.0	359.5	85.3	− 2.2	0.7	− 0.6
Valley sunny side	172	6.88	401.4	453.3	427.7	1.4	9.2	6.5	86.9	276.4	141.5	− 2.3	0.8	− 0.2

Analyses of species–habitat associations

Only those species represented by more than 30 individuals within the plot were included in the analysis. The torus-translation test has proved to be useful in comparing species–habitat associations [21]. The concept is to calculate the probability of a species in a habitat under a random distribution and to determine whether a species is significantly related to this habitat by chance. In contrast to conventional methods, this method is able to consider the spatial autocorrelation of the species distribution, making the test more sensitive [21]. The true habitat map was moved in four directions (up, down and left, right) by 20-m increments, 30 times in the horizontal direction and 25 times in the vertical direction. The horizontal and vertical directions can be moved at the same time. A total of 30 × 25 relative densities were obtained. Three maps are generated by a 180° rotation, mirror image and a 180° rotation of the mirror image, so each species can have 3000 relative densities (2999 simulated relative densities and 1 true relative density). Comparing the position of the true relative density in the simulated relative densities, if it is > 97.5%, then the species is statistically positively associated with this habitat. If it is < 2.5%, then the species is statistically negatively associated with this habitat. If it falls in the middle, then there is no correlation (α = 0.05 significant level, two-tailed test).

To understand the preference of different species for habitat, the species were divided into different life forms (shrubs and trees), shade tolerance (light-demanding, midtolerant, and shade-tolerant) [35], and life stages.

Associations of different species, life forms, and shade tolerance with five habitat types

According to the torus-translation test (P < 0.05), 28 of the 33 species exhibited significant positive or negative associations with at least one habitat type. Picea koraiensis, Phellodendron amurense, Aralia elata, Viburnum burejaeticum, and Populus davidiana did not show significant associations with any type of habitat.

Association of different species, life forms, and shade tolerance in different life stages with five habitat types

Based on the torus-translation test (P < 0.01), there were 34 significant positive associations among the 5 habitats and 16 species at three life stages. Among the 34 positive associations, 11 were detected at the sapling and juvenile stages, and 12 occurred at the mature stage. Picea koraiensis and Fraxinus mandshurica did not exhibit habitat associations at any of the three life stages.

The results showed that Corylus mandshurica, Quercus mongolica, Populus davidiana, Picea jezoensis and Acer ukurunduense were associated with the same habitat at three life stages (G pattern in Fig. 3). Three positive associations appeared at both sapling and juvenile stages but changed at the mature stage (D pattern in Fig. 3). One species maintained the same habitat association during the juvenile and mature stages but had no corresponding associations at the sapling stage (F pattern in Fig. 3). No species had the same positive association at the sapling and mature stages (E pattern in Fig. 3). Three, two, and six species showed positive associations only at sapling, juvenile, and mature stages (A, B, and C patterns in Fig. 3), respectively. Ulmus laciniata was positively associated with ridge at the juvenile stage and positively associated with shady at the mature stage.

The number of species that were positively associated with the five habitats was different across life stages. At the three life stages, most of the species were associated with sunny. At the sapling stage, no species were observed to be associated with ridge and shady. At the juvenile stage, no species were found to be associated with the valley shady side (Fig. 4).

Associations of different species, life forms, shade tolerance with five habitat types

The ability of species to adapt to environmental conditions is different, which may lead to different distribution patterns of species in the environment [36]. Species that are positively associated with a habitat are more competitive than negative and no correlation species [37]. Shade tolerance is also considered to have an impact on the distribution of species [36]. Pinus koraiensis and Tilia amurensis often grow in well-drained, moist areas [38]. Quercus mongolica is a light-demanding species and occurs more in sufficient sunlight areas. Abies nephrolepis and Picea jezoensis are shade-tolerant species and occur more in the valley [35]. The shade-tolerant species were positively associated with less well-lit habitats. The population of shade-tolerant species can maintain or even increase under natural conditions [37]. Light-demanding species and midtolerant species were positively associated with better-lit habitats. The distributions of species were determined by the characteristics of the species. Species characteristics have their own different niches by occupying different space to form coexistence. Shrubs had a preference for well-drained soil and a strong drought tolerance [16]. Sunny and ridge habitats have enough light and are well-drained; thus, this habitat was more suitable for shrubs, while the distribution of shrubs will also be affected and restrained by trees [39]. Canopy species were mostly affected by the topography, while understory species were mostly affected by the forest structure [40]. Trees were mostly distributed in the valley, where shrubs were shaded by trees, affecting the regeneration and growth of shrubs. The functional traits of species, such as the canopy layer and shade tolerance, were important factors affecting species distributions in the CBS temperate plot [36].

In mature boreal forests, interactions between species above 10 m are independent, and the distribution patterns of trees are affected by environmental factors when this scale is exceeded [41]. The spatial heterogeneity at a large scale may be caused by environmental factors such as geology, topography and historical events [6]. Topography is a good predictor of habitat for plants, especially trees [42]. Topography via geomorphic process influences soil, water, heat and nutrient redistribution to form heterogeneous habitat resources [16]. Many studies have verified topographic niche partitioning [21, 22, 24]. In tropical forests, the distribution of species varies greatly as a result of drought sensitivity [43]. In the Korup plot, there was a sharp contrast to the number of habitat associations with valley and ridge habitats, suggesting that niche differentiation of edaphic variables contributed to species distributions and maintained species diversity in African forests [25]. In this study, most of the species were identified in the valley, and fewer species were positively associated with the ridge, which may be because the ridge is characterized by less water, phosphorus, nitrogen and other resources than the valley [44]. Relatively more species preferred the slope, which may be because the steeper areas also have sufficient water, lower nutrient loss for species growth and generally have more soil water and available nutrients than ridge areas.

Habitat has a strong effect on tree species distribution; when the habitat is complex, the proportion of habitat associations is higher. The results revealed that there were 28 positive species, accounting for 84.8% of the tested species, and 27 negative species, accounting for 81.8%. Most species exhibited habitat associations. In a 50-ha BCI plot with little topographic change, 64% of species were positively or negatively associated with at least one habitat, and habitat specialization had a limiting effect on maintaining the species diversity [21]. In a 50-ha Korup plot, 63% of species were associated with at least one habitat type, and the results showed that niche differentiation with respect to edaphic variables contributed to the maintenance of diversity in African forests [25]. In slightly more complex topography in the Jiaohe 21.12-ha plot, 72.3% species were significantly associated with at least one habitat, the results indicated that habitat differentiation contributed to the maintenance of the distribution of species [37]. In a topographically complicated Malaysian 52-ha Lambir plot, of the 10 Sterculiaceae species, 8 species exhibited significant associations with at least one habitat type; these results suggested that edaphic niche differentiation contributed to the coexistence of Sterculiaceae [45]. In a 15-ha maximum height difference Nonggang plot, 85.1% of species examined were associated with at least one habitat; thus, in the karst seasonal rain forest, niche differentiation caused by topography played an important role in the maintenance of species diversity due to the uniqueness of the ecosystem [24]. Since environmental variables are always spatially aggregated, their effects will be reflected in species distributions through species–habitat associations [21, 46]. The proportion of habitat associations are different, which may be due to the changes in environmental factors [6]. Topographic niche differentiation is enhanced with an increase in environmental heterogeneity [47]. The Fenglin plot had a higher percentage of habitat associations, with many species exhibiting habitat preferences. Environmental variables of spatial structures are related to niche processes [48], so we posit that niche differentiation plays an important role in the maintenance of species diversity. In this study, we only considered the effects of topographic factors on species distribution. We need to take into account more factors, such as soil and hydrologic factors, in future studies.

Habitat associations change at different life stages

Species may experience a wide range of environments in their lives, which means that the regeneration stages may be different from the later or more mature stages of the environment [49]. Most species showed different habitat associations at the three life stages, indicating that habitat associations can contribute to regeneration niche and affect species coexistence [50]. For the 19 species, only 5 species showed the same habitat associations at all three life stages. At the sapling stage, 72.7% of species associations were maintained to the juvenile stage, and only 45.5% were consistent at the mature stage. These results demonstrated that habitat preferences of most species in the plot changed with different life stages. These results were largely consistent with previous studies. In a Bornean forest, only 9.1% of species maintained the same habitat preferences at multiple life stages [28]. In the BCI plot, only 6.3% species showed the same habitat preferences at different life stages [22]. In the Gutianshan plot, 26.7% of habitat associations did not change during the three life stages, indicating that the environment of the early stage for most species was not always suitable for the subsequent stage [27].

Corylus mandshurica, Quercus mongolica and Picea jezoensis were positively associated with the same habitat at the three life stages. These 3 species spread through gravitation, and seed dispersal led to spatial limitations and maintained their limited distributions within the same habitat, which was close to the parent tree [28], thereby facilitating the maintenance of the same association at all three life stages. Midtolerant trees and midtolerant shrubs maintained a positive association with sunny at the three life stages. The midtolerant species’ demands for light are between shade-tolerant and light-demanding species; therefore, shade-tolerant and light-demanding species will be more susceptible to the effects of habitat factors than midtolerant species due to these species characteristics. The seed dispersal ability of understory species is weaker than that of overstory species [36]. Therefore, because the seed dispersal ability of shrubs is poor, they maintained the same association at different life stages.

Habitat heterogeneity, dispersal limitation, and biotic interactions can be used to explain species distributions [14, 21]. Relevant research has shown that seed dispersal limitation may also result in changes in habitat associations at different life stages [22, 28]. Dispersal limitation can cover the effect of habitat preference on species distribution to some extent [49]. Differences in the relative importance of environmental factors and dispersal limitation are usually caused by differences in spatial scales or environmental complexity [32, 46]. In early tree stages, the effect of dispersal on species composition is expected to be higher because seed dispersal leads the recruits to a new place before environmental effects begin to act [49]. The environmental effect is small, and the nutrient requirements of saplings can be supplied by themselves in the early stage [51]. As individuals die in unfavourable habitats, the role of environmental effects in tree species distributions is more pronounced [52]. Studies have shown that the distribution of adult tree species may be explained in part by differences in growth and mortality at the seedling and sapling stages in different habitats [53]. The effect of dispersal limitation decreases with the life stage, and the environmental effect increases with the life stage [54]. Due to the dispersal limitation, saplings are mostly distributed around the parent tree. With the growth of trees, the impact of the topography on species distribution is intensified, and the selection of suitable habitat leads to the change of association [30]. It is also possible that the association may change due to negative density dependence, which will reduce the habitat association with later growth [27]. Temperate and tropical studies have shown that seedling survival and seed-to-seedling transition rates were usually due to density and distance dependence, leading to changes in species distributions from one life stage to later stages [55, 56]. The habitat preferences of species will change at different life stages, which may be due to habitat heterogeneity, seed dispersal, negative density dependence or other reasons. Therefore, it is necessary to perform further investigations of seedlings with DBH < 1 cm.