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Article

Establishment of Fitted Models for Topographical Factors and Coexisting Plants Influencing Distribution of Natural Wild Jujube

by
Yansheng Wu
1,2,*,
Yanchao Wang
1,
Weitao Niu
1,2,
Pengfei Zhang
1,2,
Lina Wu
1,2,
Huan Li
1,2 and
Senghu Wang
1,2
1
College of Bioscience and Engineering, Xingtai University, Xingtai 054001, China
2
Hebei Xingzaoren Utilization Technology Innovation Center, Xingtai 054001, China
*
Author to whom correspondence should be addressed.
Forests 2023, 14(3), 439; https://doi.org/10.3390/f14030439
Submission received: 27 November 2022 / Revised: 2 February 2023 / Accepted: 18 February 2023 / Published: 21 February 2023
(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)
Browse Figures
Review Reports Versions Notes

Abstract

:
Fitting mathematical models to describe the influence of topographic factors and coexisting plants on wild jujube distribution was performed to provide a scientific basis for wild jujube forestation. Investigation quadrats, with straight-line distances between adjacent quadrats of longer than 100 m, were set up in areas of wilderness or low human disturbance, which were rich in wild plant species. Data concerning altitude, slope aspect, slope position and slope degree of each investigation quadrat, as well as the type and number of coexisting wild plants, were collected. Based on this, correlations with the average number, occurrence probability and density of wild jujube and these variables were analyzed, and data models were established. Results of analyses show that topographic factors such as altitude, aspect, gradient, slope and position, play an important role in the distribution of wild jujube; and that Vitex negundo var. heterophylla (Franch.) Rehd. coexistence is related to wild jujube distribution. Both average number and occurrence probability of wild jujube conform to a GaussAmp model with altitude. The highest average number was recorded at 581.24 ± 13.78 m above sea level, and the highest occurrence probability at 462.53 ± 36.67 m above sea level. Average number and occurrence probability of wild jujube were fit to a linear model with slope aspect—with mathematical slope 0.49 ± 0.16—indicating that wild jujube is a light-loving and drought-tolerant plant. Average number and density of wild jujubes were fit to GaussAmp models with slope position. The highest average number and the highest density of wild jujube appears on the upper part of the middle slope. Wild jujube occurrence probability was correlated to slope degree in a quadratic equation model. With an increase in slope degree, the distribution number of wild jujube increased sharply. The survey data of slope position and slope degree further reinforced the observed drought-resistance qualities of wild jujube. Average number and density of wild jujubes were correlated to the number of Vitex negundo var. heterophylla by quadratic equation models. No other plants investigated conformed to a statistically significant relationship with wild jujube distribution. Our results suggest altitude, slope aspect, slope position and slope degree play an important role in wild jujube distribution, and that Vitex negundo var. heterophylla is an important coexistent plant species for wild jujube.

1. Introduction

Wild jujubes are widely distributed throughout the low mountains and hilly areas of northern China, and have outstanding drought resistance [1]. Wild jujube seeds are an important Chinese herbal medicine with varied therapeutic effects, such as calming nerves, aiding sleep, and displaying antidepressant, anti-anxiety, anticancer, anti-inflammatory and anti-Alzheimer’s disease properties [2,3,4,5]. In addition, the flesh, leaves, roots, wood, etc. of wild jujube have high utilization value [6]. In recent years, with the increase in the market value of wild jujube seeds, the scale of artificial planting of wild jujube has been expanding year by year; however, the optimal growth conditions for wild jujube are largely unknown. Topographic factors play an important role in vegetation coverage [7], plant distribution [8,9,10], plant diversity [11] and plant landscape [12,13,14]. It is benefitial to understand the impact of topographic conditions and coexisting plants on wild jujube growth in order to successfully plant and cultivate wild jujube. At present, there are few published studies reporting research on the suitable growth conditions of wild jujube plants. Zhao et al. have studied the effect of climate change on distribution of wild jujube [15], Liu has studied temperature, precipitation, soil and other environmental factors suitable for the growth of wild jujube [16] and Wang et al. studied the factors influencing jujube fruition [17]. To date we could find no reported research on the influence of terrain or coexisting plants on wild jujube growth.
Hebei Province is a favorable growth area for wild jujube [18], and within Hebei Province, Xingtai City is particularly well suited to wild jujube growth, as is evidenced by the abundance of wild jujube resources found there. All the above provide convenient conditions for us to carry out experiments. Since 2012, our research team has conducted a large number of surveys on wild plant resources in the Xindu District (formerly Xingtai County), Shahe City, Neiqiu County and Lincheng County of Xingtai City, as well as some areas of adjacent counties, and has obtained a large amount of data. This study focuses on the statistical analysis and data modeling of altitude, slope aspect, slope position, slope degree and coexisting plants that affect wild jujube growth, in order to provide a scientific basis for the ecological research and forestation of wild jujube.

2. Materials and Methods

2.1. Study Area

The area investigated is located in Xingtai City, Hebei Province, at the southern foot of the Taihang Mountains and includes the Xindu District, Shahe City, Neiqiu County, Lincheng County and some areas bordering adjacent counties—covering an area of more than 3500 km2 ranging from 113°48′8.7″ to 114°37′44.5″ E and from 36°52′43.5″ to 37°26′14.4″ N (Figure 1). The area investigated has a warm, temperate, subhumid, monsoon climate. It is dry with less rainfall in spring; humid, hot and rainy in summer; cool in autumn; and dry and cold in winter. There are large differences in temperature and humidity, soil temperature and humidity, and soil pH values throughout the year. Measurements recorded in 2021 by our research group in the Suanzao Ecological Factor Observation Station in Xindu District, Xingtai City, show that the average annual temperature is 19.3 ± 11.7 °C, with extremes of−16.8 °C and 44.9 °C. The average annual air humidity is 55.3 ± 26.3%, with extremes of 6.2% and 105.7%. Soil temperature at 20 cm below the surface is 16.0 ± 8.4 °C, with extremes of −6.9 °C and 31.2 °C. Soil humidity at 20 cm below the surface is 18.6 ± 4.5%, with extremes of 2.0% and 41.2%; the soil pH is 7.2 ± 0.4, with extremes of 6.6 and 10.7. The region consists of four types of terrain: mountain, low mountain, hill and plain with an altitude range of 48.2~1395.5 m. The surface is rugged and terrain features are heterogenous. The surface is extensively corroded, and the soil is mixed with a large amount of sand and gravel, with low fertility and poor water retention.

2.2. Investigation Data

The investigation was carried out in areas of wilderness or those with less human disturbance, which are rich in wild plant species. The quadrat method was used for this investigation. An investigation quadrat was defined as an area of 100 m2 and demarcated using rope. Straight-line distance between adjacent investigation quadrats was longer than 100 m. Topographic data such as altitude, slope aspect, slope position, slope degree, as well as the type and quality of coexisting plants, were recorded. During the investigation, team members were not informed of the study’s purpose, so as to avoid subjective influence. A total of 525 quadrats were investigated.

2.3. Statistical Methods

Statistical analyses were conducted on the occurrence probability, average number and density data of wild jujubes in a quadrat where the occurrence probability (%) = number of quadrat with wild jujube/total quadrats investigated; average number (/100 m2) = total wild jujube recorded/total quadrats investigated; and density (/100 m2) = total wild jujube recorded/quadrats with wild jujube. IBM SPSS (Version 23) statistical software was used for correlation analysis, and Origin 2018 was used for fitting to establish a mathematical model.
In order to facilitate statistical analyses, the continuous data such as altitude, slope degree and numbers of wild jujube, Vitex negundo var. heterophylla, etc., are grouped, and the median value is used to represent each group of data. Investigation quadrats are divided into nine groups according to altitude: 100(0~200 m), 250(200~300 m), 350 (300~400 m), 450 (400~500 m), 550 (500~600 m), 650 (600~700 m), 750 (700~800 m), 850 (800~900 m), 950 (900~1000 m), 1050 (1000~1100 m) and 1150 (>1100 m). Slopes are divided into four groups: 10 (0~20°), 30 (20~40°), 50 (40~60°) and 70 (60~80°) according to the difference in slope degree. Numbers of wild jujube, Vitex negundo var. heterophylla, etc., are grouped as 0, 3(1~5), 8(6~10), 13(11~15), 18(16~20), etc.
The recorded data of slope aspect and slope position are discrete. Light intensity, as well as temperature, water content and acidity of the soil, all change gradually from the northern to the southern slopes (following a north-northwest-west-southwest-south direction) [19]. We therefore quantified the north, northeast/northwest, east/west, southeast/southwest, no aspect and south directions into slope aspect values (SAVs) of 0, 1, 2, 3, 4 and 5, respectively. With an increase in slope position, soil water content [20,21,22,23,24], organic matter and total phosphorus content [25], and species richness [22] decrease gradually. In this study, the slope bottom, down slope, middle slope, upper slope and top slope are quantized into slope position values (SPVs) 0, 1, 2, 3 and 4, respectively.
A linear equation [26,27,28], quadratic equation [27,29] and GaussAmp equation [30,31] can be used to represent the quantitative relationship between biological distribution and environmental factors well. So, in this study, these three data models are used to fit our survey data.
In the linear equation (Equation (1)) and quadratic equation (Equation (2)), y represents the distribution of wild jujube (average number, occurrence probability, density, the same below); x represents the quantitative terrain factor; a represents the lowest jujube distribution in investigated quadrats; b and c indicate the degree of dependence of wild jujube distribution on topographic factors.
y = a + bx
y = a + bx + cx 2
In the GaussAmp equation (Equation (3) and Figure 2) y0 represents the minimum value of wild jujube distribution; A represents the maximum change value of wild jujube distribution; xc represents the value of topographic factors (e.g., altitude, SPV, etc.) when the wild jujube distribution value is highest; and ω is the width value of ecological factors. In this study, FWHM (Full Width at Half Maximum) represents the main distribution range of wild jujube: FWHM = sqrt(ln(4)) × 2ω. FWHM distribution range is (xc − sqrt(ln(4)) × ω, xc + sqrt(ln(4)) × ω).
y = y 0 + A × e − 0.5 x − x c ω 2

3. Results

3.1. Effect of Altitude on Wild Jujube Distribution

In a scatter plot, it was found that wild jujube distribution concentrates between 200 m and 800 m above sea level. The scatter plot is almost bell-shaped as a whole (Figure 3A), so normal curve fitting should be adopted. The GaussAmp equation of Origin 2018 is adopted for fitting, and the correlation coefficient is 0.3023 (n = 525, p < 0.001).
The GaussAmp equation is better for fitting grouped altitude data and wild jujube numbers, with a correlation coefficient 0.97 (p < 0.001) (Figure 3B). The equation shows that xc is 581.24 ± 13.78, ω is 131.20 ± 17.06, and the FWHM range is (426.82, 735.66).The relationship between wild jujube occurrence probability and the grouped altitude data conforms to the GaussAmp equation, with a correlation coefficient 0.92 (p < 0.001). The equation shows that xc is 462.53 ± 36.57, ω is 278.41 ± 76.51 and the FWHM range is (154.42, 790.22). In the range of 154.42~790.22 m above sea level, the occurrence probability of wild jujube is high; in the range of 426.82~735.66 m above sea level, the average number of wild jujube was highest.

3.2. Effect of Slope Aspect, Slope Position and Slope Degree on Wild Jujube Distribution

Correlation analysis showed that the average number and occurrence probability of wild jujube were significantly correlated with SAV, with correlation coefficients of 0.84 (p < 0.05) and 0.87 (p < 0.05), respectively. From this we inferred that wild jujube species tend to be preferentially distributed in places with sufficient sunlight. By observing the scatter plot, it is appropriate to use linear equation fitting, as shown in Equation (1) where a is the intercept, representing the minimum number of trees in the quadrat and b is the slope. The correlation coefficient was 0.84 (p < 0.05), a was 3.77 ± 0.49 and b was 0.49 ± 0.16 (Figure 4A, Equation (4)). In order to further understand the quantitative relationship between wild jujube occurrence probability and SAV, a linear equation was fit between the two. The results showed that correlation coefficients was 0.87 (p <0.05), with a = 40.43 ± 2.16 and b = 2.57 ± 0.71 (Figure 4B, Equation (5)). These data reflect that there is a close linear relationship between the distribution of wild jujube and SAV. The wild jujube species tends to be distributed in areas with strong light intensity and low soil moisture content. The distribution density of wild jujube is the highest on the southern slope (with the strongest light intensity), followed by the eastern and western slopes, and finally, the northern slope.
y = 3.77 + 0.49 x
y = 40.43 + 2.57 x
Fitting results showed that the relationship between the average number and density of wild jujube and SPV was in accordance with GaussAmp equation (Figure 5A,B), and the correlation coefficients were 0.98 and 0.94 for average number and density, respectively (p < 0.05). The equation showed that the mean number of wild jujubes was highest at 2.36 ± 0.17 of SPV, and the density of wild jujube was the highest at 2.10 ± 0.22 of SPV. Therefore, wild jujube tended to be distributed in the upper part of the middle slope. No statistically significant mathematical model for wild jujube occurrence probability and SPV was found (data not shown).
Curve fitting results showed that wild jujube occurrence probability could form a quadratic regression equation with slope degree (Equation (6), Figure 6), with a correlation coefficient of 0.97 (p < 0.05). The probability of wild jujube occurrence increased with an increase in the degree of the slope. We could not establish a statistically significant data model between either the average number of wild jujube or their density, and the slope degree (data not shown).
y = 45.74 − 0.09 x + 0.002 x 2

3.3. Effect of Coexisting Plants on Wild Jujube Distribution

During this investigation, 388 species of plant were recorded, 38 of which were significantly related to the distribution of wild jujube (Table 1). In order to further clarify quantitative relationships between wild jujube and these plants (according to wild jujube numbers per investigation quadrat) they were divided into six groups: 0, 1~5, 6~10, 11~15, 16~20 and 21 and above. Results of analyses (Table 2) show that the coexistence probability of three species (Vitex negundo var. heterophylla, Rubia cordifolia Linn. and Melilotus officinalis (Linn.) Pall.) with wild jujube is relatively stable at more than 40%. Among them, the coexistence probability of Vitex negundo var. heterophylla and Rubia cordifolia with wild jujube is more than 70%, and the coexistence probability of Melilotus officinalis with wild jujube is between 40% and 60%. However, in investigation quadrats without wild jujube, occurrence probability of Vitex negundo var. heterophylla and Rubia cordifolia is between 40% and 50%, and the occurrence probability of Melilotus officinalis is between 30% and 40% (data not shown). Other plants with occurrence probability less than 30% may appear randomly so they were not investigated further.
To further analyze the quantitative relationship between wild jujube and the three plants, the three plants were divided into different sections according to their numbers. Each section was represented by the median, as indicated: 0, 3 (1–5), 8 (6–10), 13 (11–15), 18 (16–20), 23 (21–25), 28 (26–30), 33 (31–35) and 38 (36–40). Different equations in Origin2018 were used for fitting, and results showed that only the average number and density of Vitex negundo var. heterophylla and wild jujube had a close quantitative relationship, which could be fitted to a quadratic equation (Figure 7A,B, Equations (7) and (8)), with correlation coefficients of 0.93 and 0.88 (p < 0.01).The other two plants could not be fitted to a representative equation.
y = 1.96 + 0.23 x + 0.003 x 2
y = 7.15 + 0.02 x + 0.008 x 2

4. Discussion

4.1. The Influence of Topographic Factors Such as Altitude, Slope Aspect, Slope Position and Slope Degree on Wild Jujube Distribution

In the macro-regional scope, meteorological factors such as precipitation and temperature are the main factors affecting the distribution of tree species [32,33,34,35,36]. Within the local scope, topographic factors such as elevation, slope and aspect play a decisive role in the distribution pattern of tree species [10,37,38]. In this study, the distribution of wild jujube was subdivided into three variables: occurrence probability, average number and density. The effects of four topographic factors—altitude, aspect, position and gradient—on the occurrence probability, average number and density of wild jujube were investigated. Results showed that these four topographical factors had an important influence on the distribution of wild jujube, but there were some differences in the mechanism of influence. Altitude and slope position conformed to GaussAmp equations, slope degree conformed to a quadratic equation, and slope aspect conformed to a linear equation.
A change in altitude results in a comprehensive change of temperature, humidity, light, wind speed, ultraviolet radiation and other meteorological factors [39]. It especially has a significant impact on soil moisture content [40], which in turn has an important impact on crop growth, morphological characteristics, quality and yield [41,42], and is a key factor limiting the distribution of tree species within a local scope [10].The numbers and occurrence probability of wild jujube have significant dependence on altitude (Figure 3), a phenomenon which is also seen in the relationship between the distribution of Prunus armeniaca and altitude [43].Wild jujubes mostly occur as shrubs which conform to a single peak pattern of first increasing and then decreasing with elevation [44]. The altitudes with the highest average number and the highest occurrence probability of wild jujube were 581.24 ± 13.78 m and 462.53 ± 36.67 m, respectively; their FWHM ranges were [154.42, 790.22] and [426.82, 735.66], respectively. These data are consistent with Zhao’s prediction that the most suitable altitude for jujube distribution is lower than 750 m [18], as well as with Zhu’s conclusion that the most suitable altitude is 400~700 m [45].
Wild jujube is mostly propagated in the form of its root sprout, which grows on the horizontal, fine roots of wild jujube. A rise in altitude intensifies the effects of drought [46], which can promote thin root growth and branching of wild jujube [47]. These effects are consistent with the results of this study. As the influence of altitude on temperature is about 1000 times faster than the influence of a change in latitude [48,49,50,51], the wild jujube’s main production area (ranging from Yan’an, Shaanxi Province at 35°21′ N, 1200 m above sea level in the south, to Chaoyang, Liaoning Province in the north at 41°34′ N, 41 m above sea level) falls within the suitable range for wild jujube growth. Based on this, we can also make a reasonable speculation that climate warming will make the distribution area of natural wild jujube migrate to higher altitude and higher latitude areas, which is consistent with Zhao et al.’s research results [15]. With a change of altitude, meteorological factors change accordingly, so the upper and lower altitude thresholds of wild jujube distribution should not be determined only by altitude. It has been reported that the limiting factors of the upper altitude thresholds of plant distribution are low temperature and growing season, and the limiting factors of the lower altitude thresholds are humidity and biological factors [52,53].
Slope aspect causes a difference in light conditions [54], which influences factorsof water and heat and thereby affects the distribution and growth of plants [55,56]. In this study, the average number and occurrence probability of wild jujube exhibit a positive, linear relationship with the slope aspect—the stronger the light, the higher the average number and occurrence probability of wild jujube (Figure 4), which is consistent with the results of Pei [57]. In addition, the soil acidity increases with increasing SAV, with the northern slope close to a pH of 6 and the southern slope close to a pH of 8 [19].This is consistent with our group’s conclusion that a pH of 8 is most conducive to the germination of wild jujube seeds [58]. These data reflect that the wild jujube is a photophilic, drought-tolerant and basophilic plant species.
Different plants have different requirements of slope aspect, for instance: Robinia pseudoacacia Linn. [10,59], Pinus tabulaeformis Carr. [60,61] and Hippophae rhamnoides Linn. [10] prefer shady slopes, whereas Parashurea chinensis Wang Hsie [62], Prunus armeniaca L. [43], Armeniaca sibirica (L.) Lam [10] and Ormosia hosiei Hemsl. et Wils. [63] prefer sunny slopes. Slope aspect therefore has a great impact on plant diversity [40]. In northern China, the vegetation [10,59,60] and species richness [40] on shady slopes is obviously better than those on sunny slopes. On sunny slopes, the plant community has fewer species and simple structure [64]. Shrubs tend to be distributed on shady slopes, while trees tend to be distributed on sunny slopes [56]. The influence of slope aspect on plant distribution is affected by climate. In the subtropical, humid, monsoon climate of southern China, the plant species found on sunny slopes are more abundant than those found on shady slopes [8], and there is no significant impact on the distribution of tree species in the subtropical, mixed coniferous and broad-leaved forests [11]. In northern China, the temperature on sunny slopes is high, and the intensities of its physical and chemical processes are greater than those on shady slopes. This causes the organic matter content [64] and water content [40] of the soil on the sunny slope to be lower than on the shady slope. The preference of wild jujube for sunny slopes is in line with the drought resistant characteristics of wild jujube. As a sun-loving tree species, the wild jujube is an ideal tree species for afforestation applications and ecological environment improvement in northern mountainous and hilly areas.
Slope position affects the diversity of plant distribution [65], numbers, evenness [11], growth [63], as well as the distribution richness of tree species [22]. Slope position also has a great impact on soil moisture content and soil fertility. Most reports show that water content is greatest on lower slopes, decreases on middle slopes, and is lowest on upper slopes (lower slope > middle slope > upper slope) [20,21,22,23]. The water holding capacity at the top of the slope is the worst [24]. However, it has also been reported that, in the soil layer with the most active water content change, the water content follows the pattern: lower slope > upper slope > middle slope [20,66], where the water content of the middle slope is lowest. Slope position is the dominant topographic factor influencing the total phosphorus and organic matter content in soil. With the rise of slope position, the content of total phosphorus and organic matter in soil decreases gradually [25]. Species richness is negatively correlated with slope position—an observation which reflects the expected soil water content and fertility at different slope positions [22]. The results of this study show that wild jujube is mainly distributed in the upper part of the middle slope (Figure 5), and that it has strong drought resistance and barren tolerance.
Different plants adapt to different slope degrees [67,68], thereby impacting plant species diversity and community richness—the greater the slope, the higher the species diversity [13,56,69,70] and the greater the community richness [25]. In addition, different slope degrees also have an impact on plant morphology and structure [71]. In this study, the occurrence probability and slope degree of wild jujube can be fit to a quadratic equation. The occurrence probability of wild jujube increases sharply with an increase in slope (Figure 6), which is consistent with the distribution trend of some plant species [25,56]. There is a close relationship between slope degree and soil water content—generally, the greater the slope degree, the lower the water content [8]. Wild jujube distribution therefore reflects the pattern of a typical drought-tolerant plant species.

4.2. Vitex negundo var. heterophylla Is a Coexisting Plant Species

Coexistence is a common phenomenon [72], which occurs either due to niche overlap [73] or to mutualism. Reports show that such association between species plays a role in reducing pest incidence, improving soil quality and improving fruit quality [74,75]. Until now, there have been few studies reporting on plants associated with wild jujube. In this study, it was found that there is a stable, quantitative relationship between Vitex negundo var. heterophylla and wild jujube (Figure 7).This is consistent with the observed trend of their changing main root lengths and carbon content in different slope directions [76]. Vitex negundo var. heterophylla is a drought-tolerant plant [77], although its drought resistance is weaker than that of the wild jujube when viewed in terms of its slope distribution tendency, water consumption characteristics, stem xylem structure and the effectiveness of water transport [76,78]. These factors do not prevent the two plants from forming a coexistence relationship. Further, the stems, leaves, flowers and seeds of Vitex negundo var. heterophylla contain a variety of volatile oil components [79,80] which have an insect-repellent effect [81] and create conditions for the establishment of a mutually beneficial symbiotic relationship between wild jujube and Vitex negundo var. heterophylla. However, the underlying biological mechanism of the association between Vitex negundo var. heterophylla and wild jujube needs to be further studied.

5. Conclusions

Using the quadrat method, the relationship between the wild jujube distribution and topographic factors was studied in the Xindu District, Shahe City, Neiqiu County and the Lincheng County of Xingtai City in Hebei Province, as well as some adjacent regions in bordering counties. This area has a warm, temperate, sub−humid monsoon climate—which is the most suitable climate for the growth of wild jujube [12,16]—and has complex terrain, so the research results have strong universality. In the data analyses, the distribution of wild jujube is investigated using three characteristics: average number, probability of occurrence and density. The relationship between these three characteristics and topographic factors such as altitude, slope aspect, slope position, slope degree and the number of associated plants is analyzed. Results show that there is a close quantitative relationship between topographic factors (such as altitude, slope aspect, slope position, slope degree) as well as between Vitex negundo var. heterophylla distribution and wild jujube. The average number and occurrence probability of wild jujube conform to the GaussAmp model with regards to altitude, and to a positive correlation linear model with SAV. Both average number and density of wild jujube conform to GaussAmp models in relation to SPV. The occurrence probability of wild jujube conforms to a quadratic equation model with slope degree. Finally, the average number and density of wild jujube correlate to numbers of Vitex negundo var. heterophylla using a quadratic model. These three models are suitable for representing quantitative relationships between the distribution of higher plants and both ecological factors as well as the presence of coexisting plants. However, it is worth noting that due to the unique advantages of the model method in the analysis of single factors, this study only studied the impact of different topographical factors on the distribution of natural wild jujube, but did not explore the interaction between these factors. In fact, there might be interaction among these factors, and the topographical factors play a comprehensive role in the distribution of jujube, which requires us to do more in-depth research.
This study revealed that the highest average number of wild jujube is found at an altitude of 581.24 ± 13.78 m, and the highest occurrence probability is found at an altitude of 462.53 ± 36.67 m. Based on the quantitative relationship between altitude change and latitude change [48,49,50,51], the main jujube-producing areas in China fall within the numerical range recorded in this study. From the quantitative relationships between the distribution of wild jujube and slope aspect, slope position and slope degree, wild jujube tends to be distributed on sun-facing, upper-middle and inclined slopes—showing obvious characteristics of photophilia, drought-tolerance and basophilia.
This study gives the specific values or orientations for altitude range, slope aspect, slope position, slope degree and the types of associated plants typically found in areas suitable for wild jujube growth.

Author Contributions

Conceptualization, Y.W. (Yansheng Wu); methodology, Y.W. (Yansheng Wu) and Y.W. (Yanchao Wang); software, Y.W. (Yansheng Wu) and Y.W. (Yanchao Wang); validation, Y.W. (Yansheng Wu) and S.W.; formal analysis, Y.W. (Yansheng Wu), Y.W. (Yanchao Wang) and S.W.; investigation, S.W., Y.W. (Yansheng Wu), W.N., P.Z. and H.L.; resources, Y.W. (Yansheng Wu) and S.W.; data curation, Y.W. (Yansheng Wu) and Y.W. (Yanchao Wang); writing—original draft preparation, Y.W. (Yansheng Wu) and L.W.; writing—review and editing, Y.W. (Yansheng Wu) and P.Z.; visualization, Y.W. (Yansheng Wu) and Y.W. (Yanchao Wang); supervision, S.W.; project administration, S.W. and Y.W. (Yansheng Wu), funding acquisition, Y.W. (Yansheng Wu) and S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the science and technology planning projects from Xingtai Science Technology Bureau (2020ZZ030), the startup fund for doctoral research from Xingtai University (2205005012) and the special funds after acceptance of Hebei Xingzaoren utilization technology innovation center (2205001009). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wu, Y.; Wu, L.; Niu, W.; Zhang, P.; Liu, H.; Li, H.; Wang, S. Analysis of drought-tolerance of Ziziphus jujuba var. spinosa. For. Sci. Technol. 2021, 3, 88–91. [Google Scholar] [CrossRef]
  2. Park, H.J.; Jung, I.H.; Kwon, H.; Yu, J.; Jo, E.; Kim, H.; Park, S.J.; Lee, Y.C.; Kim, D.H.; Ryu, J.H. The ethanol extract of Zizyphus jujuba var. spinosa seeds ameliorates the memory deficits in Alzheimer’s disease model mice. J. Ethnopharmacol. 2019, 233, 73–79. [Google Scholar] [PubMed]
  3. Zhang, M.; Liu, J.; Zhang, Y.; Xie, J. Ziziphi spinosae Semen: A natural herb resource for treating neurological disorders. Curr. Top. Med. Chem. 2022, 22, 1379–1391. [Google Scholar] [CrossRef] [PubMed]
  4. Song, L. Chinese Materia Medica; Shanghai Science and Technology Press: Shanghai, China, 1999. [Google Scholar]
  5. Wang, D.; Ho, C.T.; Bai, N. Ziziphi spinosae Semen: An updated review on pharmacological activity, quality control, and application. J. Food Biochem. 2022, 46, e14153. [Google Scholar] [CrossRef] [PubMed]
  6. Hua, Y.; Xu, X.; Guo, S.; Xie, H.; Yan, H.; Ma, X.; Niu, Y.; Duan, J. Wild jujube (Ziziphus jujuba var. spinosa): A review of its phytonutrients, health benefits, metabolism, and applications. J. Agric. Food Chem. 2022, 70, 7871–7886. [Google Scholar] [CrossRef]
  7. Zhang, H.; Li, Y.; Zhang, N. Relationship between Temporal and Spatial Characteristics of Vegetation Coverage and Topographic Factors in Fenhe River Basin. Bull. Soil Water Conserv. 2022, 42, 353–359. [Google Scholar]
  8. Sun, L.; Li, Q.; Pei, L.; Wu, H.; Chen, L. Effects of topographic factors on soil physical and chemical properties and plant species. J. Irrig. Drain. 2020, 39, 120–127. [Google Scholar]
  9. Wang, Y.; Xiao, B.; Wang, W.; Yu, X.; Zhang, X. Bryophyte diversity and microhabitat characteristics of bryophyte-dominated biological soil crusts development in water−wind erosion crisscross region of the northern Loess Plateau, China. Chin. J. Appl. Environ. Biol. 2022, 33, 1729–1737. [Google Scholar]
  10. Jiang, L.; Wei, T.; Li, Y.; Wei, A. Effects of topographical factors on tree species distribution of shelter forest in Loess hilly region of northern Shaanxi. Arid. Land Geogr. 2021, 44, 1763–1771. [Google Scholar]
  11. Zhu, J.; Wei, X.; Yang, J.; Zhang, J. Effects of topographic factors on tree species diversity in subtropical coniferous and broad-leaved mixed Forests. J. Nanjing For. Univ. (Nat. Sci. Ed.) 2022, 46, 153–161. [Google Scholar]
  12. Yang, Q.; Li, K.; Yu, L. The relationship between forest landscape and terrain factors in Qiannan prefecture of Guizhou province. For. Sci. Technol. Inf. 2022, 54, 1–4. [Google Scholar]
  13. Liu, T.; Hou, L. Effects of terrain factors on land use and landscape pattern change in terrain transition zone of Northern Sichuan province. Bull. Soil Water Conserv. 2020, 40, 75–82, 90. [Google Scholar]
  14. Pan, Y.; Bai, R.; Shi, Y. Research on the landscape characteristics of traditional settlement under the influence of topographic factors—A case of Weizhou Island in Beibu gulf. Chin. Landscpe Archit. 2021, 37, 6. [Google Scholar]
  15. Zhao, G.; Cui, X.; Sun, J.; Li, T.; Wang, Q.; Ye, X.; Fan, B. Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models. Ecol. Indic. 2012, 132, 108256. [Google Scholar] [CrossRef]
  16. Liu, A. Suitable Ecological Regionalization of Ziziphi spinosae Semen in the Hebei Province; Hebei University of Chinese Medicine: Shijiazhuang, China, 2019. [Google Scholar]
  17. Wang, F.; Sun, X.; Dong, J.; Cui, R.; Liu, X.; Lim, X.; Wang, H.; He, T.; Zheng, P.; Wang, R. A primary study of breeding system of Ziziphus jujuba var. spinosa. Sci. Rep. 2021, 11, 10318. [Google Scholar] [CrossRef]
  18. Zhao, G.; Cui, X.; Wang, Z.; Jing, H.; Fan, B. Prediction of potential distribution of Ziziphus jujuba var. spinosa in China under context of climate change. Sci. Silvae Sin. 2021, 57, 158–168. [Google Scholar]
  19. Nan, X. Study on the Functional Diversity of Plant Communities on Different Slopes in the Alpine Meadow of Gannan; Northwest Normal University: Lanzhou, China, 2020. [Google Scholar]
  20. Zhao, X.; Zhai, S.; Li, J.; Sun, S. Effect of different slope conditions on soil moisture of Amygdalus pedunculate woodland in Mu Us sandy land. Bull. Soil Water Conserv. 2020, 40, 45–52. [Google Scholar]
  21. Meng, W.; Wang, T.; Zhao, X.; Zhu, L. Effects of different slope positions on soil moisture and physiological indicators of Artemisia ordosica root zone in the Mu Us sandy land. Biotechnol. Bull. 2019, 35, 57–63. [Google Scholar]
  22. Zhuo, W.; Li, L.; Pan, Y.; Jiang, Y.; Lin, H.; He, Y.; Fang, Y.; Liang, S.; Su, Q. Effects of different slope positions on shrub species diversity and soil factors in Guilin hilly area. J. Guangxi Norm. Univ. (Nat. Sci. Ed.) 2021. [Google Scholar] [CrossRef]
  23. Jing, T.; Hu, C.; Qiu, C.; Li, C.; Zhou, J.; Zhang, Q.; Bai, C. Spatial variability and its controlling topographic factors of soil moisture during spring plowing season in black soil hilly region of Northeast China. Res. Soil Water Conserv. 2022, 29, 58–65. [Google Scholar]
  24. Li, W.; Liu, M.; Yi, J.; Zhang, H.; Lu, S.; Bai, Y. Soil water retention curve and hydraulic properties at different slope positions in the Three Gorges Mountains area. Bull. Soil Water Conserv. 2021, 35, 135–142. [Google Scholar]
  25. Cui, B.; An, Y.; Zhang, Q.; Pu, M.; Chen, K. Effect of slope on plant diversity of a Stellera chamaejasme community in subalpine meadow. Pratacultural Sci. 2020, 38, 823–834. [Google Scholar]
  26. Zheng, C.; Duan, J.; Shi, W.; Bao, G.; Qiu, X. Phytoplankton community structure and its relationship with water environmental factors in Xinghai lake. Chin. J. Fish. 2020, 33, 46–52. [Google Scholar]
  27. Liu, X.; Yang, G.; Chen, H.; Yu, Z. Spectral characteristics of plant-soil mixture with spectral reflection measurement. J. Northeast. For. Univ. 2020, 48, 54–60. [Google Scholar]
  28. Yu, C.; Liu, C.; Liao, G.; Zhang, Y.; Chen, P.; Zhou, S.; Xu, D. A biomass estimation by regression method for herbaceous plant Spartina alterniflora in coastal wetlands. J. Appl. Oceanogr. 2022, 41, 149–155. [Google Scholar]
  29. Li, Z.; Liu, C.; Peng, X.; Xue, J.; Deng, H.; Zhou, L. Effects of different plant growth regulators on rapid propagation of three grape rootstocks based on quadratic regression orthogonal rotation. J. Fruit Sci. 2022, 1–23. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Wang, X.; Zhan, Y. Effects of Cu(Ⅱ) on the growth of marine algae. Asian J. Ecotoxicol. 2009, 4, 114–122. [Google Scholar]
  31. Zhang, Y.; Yang, R.; Wang, X.; Zhan, Y. Effects of Cr(Ⅵ) on the growth of marine algae. Acta Sci. Circumstantiae 2011, 31, 865–872. [Google Scholar]
  32. Amissah, L.; Mohren, G.M.J.; Bongers, F.; William, D.; Poorter, L. Rainfall and temperature affect tree species distribution in Ghana. J. Trop. Ecol. 2014, 30, 435–446. [Google Scholar] [CrossRef]
  33. Wang, W.; He, H.; Thompson, F.R.; Fraser, J.S.; Dijak, W.D. Changes in forest biomass and tree species distribution under climate change in the northeastern United States. Landsc. Ecol. 2017, 32, 1399–1413. [Google Scholar] [CrossRef]
  34. Pelletier, J.; Chidumayo, E.; Trainor, A.; Siampale, A.; Mbindo, K. Distribution of tree species with high economic and livelihood value for Zambia. For. Ecol. Manag. 2019, 441, 280–292. [Google Scholar] [CrossRef]
  35. Yan, H.; Peng, L.; Tang, X. Modeling and impact analysis on distribution prediction of forest tree species in northeast China based on climate change. Sci. Silvae Sin. 2014, 50, 132–139. [Google Scholar]
  36. Zhang, X.; Wang, J.; Wang, M.; Yang, Y.; Zhao, C. Dominant climatic factors influencing the geographical distribution pattern of picea in China. Sci. Silvae Sin. 2020, 56, 1–11. [Google Scholar]
  37. Liu, Q.; Kang, M.; Liu, Q. Environmental gradient analysis and species group division of tree species distribution in Eastern Zhongtiao mountain. Chin. J. Ecol. 2006, 25, 1306–1311. [Google Scholar]
  38. Zhou, W.; Wei, T.; Liu, G.; Zhu, Q. Coupling relationship between Hippophae rhamnoides community and soil factor in typical returning farmland to forest area in northern Shaanxi province. Sci. Soil Water Conserv. 2020, 18, 1–9. [Google Scholar]
  39. Körner, C. The use of ‘altitude’ in ecological research. Trends Ecol. Evol. 2007, 22, 569–574. [Google Scholar] [CrossRef]
  40. Liu, Y.; Liu, W.; Feng, B.; Shi, G.; Sun, C.; Li, C.; Zhang, X.; Dong, Q. Effect of slope aspect and elevation on vegetation distribution pattern of alpine mountain meadow. Acta Agrestia Sin. 2021, 29, 1166–1173. [Google Scholar]
  41. Wang, Y.; Wang, Y.; Chen, H.; Zhang, J.; Fan, Q.; Xai, Q.; Chen, J.; An, P. Advance in influence of altitudinal gradient on the distribution and quality formation of medicinal plants. Mod. Chin. Med. 2012, 14, 41–43. [Google Scholar]
  42. Xu, S.; He, X.; Yang, P.; Bai, T.; Fan, H.; Yin, K.; Yang, B.; Zheng, S. Effect of climatic factors on the banana growth and its fruit quality at different altitudes. J. Yunnan Agric. Univ. (Nat. Sci.) 2020, 35, 102–107. [Google Scholar]
  43. Chen, C.; Cui, D.; Yan, J.; Kasim, N.; Zhang, J.; Bayarta. The effect of altitude on spatial distribution of Prunus armeniaca L. in Turgen river basin. Chin. Wild Plant Resour. 2022, 41, 79–84. [Google Scholar]
  44. Yuan, Y.; Sun, G.; Yuan, M.; Zhang, Z. Distributional patterns of plant species richness along an elevational gradient in Saihanba. J. Anhui Agric. Univ. 2017, 44, 496–501. [Google Scholar]
  45. Zhu, G.; Chen, X.; Irshad, A.; Liu, J.; Zhi, W.; Jiao, X.; Zhou, G. Mechanism of plasticity of root vessel structure of Ziziphus jujuba var. spinosa adapting ecotopes along a natural drought gradient. Acta Pedol. Sin. 2018, 55, 764–773. [Google Scholar]
  46. Gao, K. Spatial and Temporal Variation of Soil Moisture in the Source Region of the Yellow River and Its Factors of Influence; Gansu Agricultural University: Lanzhou, China, 2018. [Google Scholar]
  47. Zhu, H.; Deng, R.; Wei, X. Spatial distribution of the root system of Ziziphus jujuba var. spinosa in response to a natural drought gradient ecotope. Acta Ecol. Sin. 2016, 36, 1539–1546. [Google Scholar]
  48. Wang, G.; Zhou, G.; Yang, L.; Li, Z. Distribution, species diversity and life-form spectra of plant communities along an altitudinal gradient in the northern slopes of Qilianshan Mountains, Gansu, China. Plant Ecol. 2003, 165, 169–181. [Google Scholar] [CrossRef]
  49. Gaston, K. Global patterns in biodiversity. Nature 2000, 405, 220–227. [Google Scholar] [CrossRef]
  50. Xu, M.; Ma, L.; Jia, Y.; Liu, M. Integrating the effects of latitude and altitude on the spatial differentiation of plant community diversity in a mountainous ecosystem in China. PLoS ONE 2017, 12, e0174231. [Google Scholar] [CrossRef] [Green Version]
  51. Kessler, M. Elevational gradients in species richness and endemism of selected plant groups in the central Bolivian Andes. Plant Ecol. 2000, 149, 181–193. [Google Scholar] [CrossRef]
  52. Macarthur, R. Geographical Ecology: Patterns of the Distribution of Species; Harper and Row: New York, NY, USA, 1972. [Google Scholar]
  53. Kluge, J.; Kessler, M.; Dunn, R.R. What drives elevational patterns of diversity? A test of geometric constraints, climate and species pool effects for pteridophytes on an elevational gradient in Costa Rica. Glob. Ecol. Biogeogr. 2006, 15, 358–371. [Google Scholar] [CrossRef]
  54. Fu, M.; Yang, H.; Yang, X. Quercus variabilis plantation community biomass structure characteristics under different site conditions. For. Resour. Manag. 2016, 3, 67–73. [Google Scholar] [CrossRef]
  55. Ferrer-Castán, D.; Vetaas, O.R. Pteridophyte richness, climate and topography in the Iberian Peninsula: Comparing spatial and nonspatial models of richness pattern. Glob. Ecol. Biogeogr. 2005, 14, 155–165. [Google Scholar] [CrossRef]
  56. Liu, Y.; Yu, Y.; Qu, L.; Zhou, D.; Dong, X. Characteristics of plant community structure and its relationship with aspect on Houji Island. J. Qingdao Agricul. Univ. (Nat. Sci.) 2022, 39, 208–211. [Google Scholar]
  57. Pei, X.; Li, X.; Ding, W.; Yan, Y.; Shang, C.; Wang, Y.; Du, C. Investigation and analysis on the germplasm resources of Ziziphus Jujuba Mill. Var. spinosa in Shanxi province. Chin. J. Ethnomed. Ethnopharm. 2022, 31, 49–54, 66. [Google Scholar]
  58. Wu, Y.; Hua, M.; Gong, J.; Dong, X.; Li, H.; Cao, M.; Wang, S. Study on biological characteristics of wild jujube seeds. Seeds 2022, 41, 80–87. [Google Scholar]
  59. Tang, Y.; Wen, Z.; Liu, J.; Yang, Y.; Wang, Y. Adaptation mechanism of Robinia pseudoacacia under different site conditions in loess hill and gully region. Bull. Soil Water Conserv. 2019, 39, 46–53. [Google Scholar]
  60. Zhang, X.; Shangguan, Z.; Wang, J.; Mao, N. The regeneration characteristics and affecting factors of Pinus tabulaeformis artificial forests of Ziwu Mountains, China. Mt. Res. 2014, 32, 561–567. [Google Scholar]
  61. Jiao, X.; Liu, G. Growth and its influencing factors of Pinus tabulaeformis in the Loess Plateau of Shaanxi Province. Acta Bot. Boreali-Occident. Sin. 2009, 29, 1026–1032. [Google Scholar]
  62. Liu, K.; Zhu, Y.; Yang, M.; Wu, M.; Cheng, F.; Wu, Y. Diameter and height class structure distribution of Parashorea chinensis Wang Hsie. Plantations in different slope directions. J. South. Agric. 2018, 49, 763–767. [Google Scholar]
  63. Yu, X. Effects of different slope directions and positions on the growth of Ormosia hosiei in west Hubei. Anhui Agric. Sci. Bull. 2022, 28, 47, 129. [Google Scholar]
  64. Zhang, X. Slope aspect distribution of mountain vegetation in Guozigou, Yili. J. Bingtuan Educ. Inst. 1999, 3, 5–7. [Google Scholar]
  65. Sun, Q.; Wang, S.; Zhao, S.; Li, J.; Dao, R.; Zhang, J.; Wang, Y. Species diversity of different plant communities at estuary of Guanmenlazi reservoir, Liao river basin. J. Inn. Mong. Univ. 2014, 45, 591–598. [Google Scholar]
  66. Pan, Z.; Yu, F.; Wang, Z.; Li, S.; Zhang, Y. Effects of slope aspect and position on temporal and spatial variation of soil water content on Alfalfa Land in loess hilly region of South Ningxia Hui autonomous region. Res. Soil Water Conserv. 2010, 17, 141–144. [Google Scholar]
  67. Liu, J.; Xue, J. Impacts of slope gradient on the characteristics of Vetiveria zizanioides community in early-stage. Grassl. Turf 2010, 30, 56–61. [Google Scholar]
  68. Fang, C.; Yuan, Z.; Liu, X.; Pei, J.; Zhao, D.; Ye, J. Effect of slope on soil properties and the Interaction of Caragana tibetica shrub Species in Loess plateau of China. J. Desert Res. 2015, 35, 1607–1611. [Google Scholar]
  69. Zhang, Q. Effect of Topography on Plant Diversity Patterns in Alpine Meadow Using Multi-Scale Analysis—Taking Hezuo City as an Example; Nanjing Normal University: Nanjing, China, 2019. [Google Scholar]
  70. Li, Q.; Rong, L.; Wang, M. Effects of topography on diversity and distribution patters of plant species in Karst mountains area. Bull. Soil Water Conserv. 2019, 39, 27–34. [Google Scholar]
  71. Wang, X.; Hu, D.; Rao, J.; Tang, X. Primary study on reproductive traits of Hosta ventricosa and its relationship with slope of distribution land. J. Guizhou Norm. Univ. (Nat. Sci.) 2019, 37, 19–23. [Google Scholar]
  72. Zhang, X.; Zhao, L.; Kang, L. Evolutionary mechanisms of species coexistence in ecological communities. Biodivers. Sc. 2001, 9, 8–17. [Google Scholar]
  73. Li, J.; Pan, P.; Ouyang, X.; Peng, S.; Deng, W.; Ning, J. Interspecific association and species coexistence mechanism of evergreen broad-leaved forest in Lushan Mountain. Chin. J. Ecol. 2022, 41, 1474–1481. [Google Scholar]
  74. Shu, R.; Yin, X.; Li, L.; Long, Y.; Gu, G.; Long, H.; Wu, Q. Regulation of companion plants on actinidia Heterodera glycines and their effects on the quality and yield of kiwifruit. China Plant Prot. 2021, 41, 63–70. [Google Scholar]
  75. Wu, H.; Yang, Q.; Zeng, J.; Xiong, Z.; Liu, Y.; Xiao, J. Effects of different grass intercropping treatments on citrus rrchard temperature and yield. Acta Agric. Univ. Jiangxiensis 2015, 37, 239–248. [Google Scholar]
  76. Wang, N. Effect of Slope Aspect of Biomass and Carbon Storage of Vitex negundo var. heterophylla Shrub in Low Mountains and Hilly Area of Taihang Mountains; Hebei Agricultural University: Baoding, China, 2020. [Google Scholar]
  77. Chen, J.; Zhang, L.; Zhang, G.; Zhang, C.; Zhen, W.; Liu, D. Drought tolerance of six garden species. J. Trop. Subtrop. Bot. 2020, 28, 310–316. [Google Scholar]
  78. Liu, C.; Ma, C.; Wang, L.; Li, X.; Li, X. Water-consumption characteristics and Stem Xylem Anatomical Structure of Ziziphus jujuba and Vitex negundo var. heterophylla. Bull. Soil Water Conserv. 2017, 37, 92–97. [Google Scholar]
  79. Wei, J.; Tang, L.; Lou, X.; Hou, T.; Chu, Y.; Liu, Y.; Liu, B.; Wang, N.; Qian, H. GC-MS analysis of constituents of volatile oil in different parts of Vitex negundo var. heterophylla. Sci. Technol. Food Ind. 2022, 43, 310–316. [Google Scholar]
  80. Liu, X.; Cao, H.; Tian, G. Study on essential oil obtained from seeds of wild Vitex negundo. Amino Acids Biot. Resour. 2010, 32, 75–78. [Google Scholar]
  81. Bai, P.; Hu, R.; Bian, D.; Liu, B.; Li, N.; Hong, C. Laboratory toxicity Evaluation of Vitex negundo Extracts on Lipaphis erysimi. Tianjin Agric. Sci. 2022, 28, 57–60. [Google Scholar]
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Figure 1. Quadrat distribution in areas investigated.
Figure 1. Quadrat distribution in areas investigated.
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Figure 2. Model of GaussAmp equation.
Figure 2. Model of GaussAmp equation.
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Figure 3. Scatter plot (A) of wild jujube numbers at various altitude and fitted GaussAmp models of occurrence probability (B) and density (C) of wild jujube and altitude.
Figure 3. Scatter plot (A) of wild jujube numbers at various altitude and fitted GaussAmp models of occurrence probability (B) and density (C) of wild jujube and altitude.
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Figure 4. Fitted linear models of average number (A) and occurrence probability (B) of wild jujube and SAV.
Figure 4. Fitted linear models of average number (A) and occurrence probability (B) of wild jujube and SAV.
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Figure 5. Fitted GaussAmp models of occurrence probability (A) and density (B) of wild jujube and SPV.
Figure 5. Fitted GaussAmp models of occurrence probability (A) and density (B) of wild jujube and SPV.
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Figure 6. Fitted quadratic equation model of wild jujube occurrence probability and slope degree.
Figure 6. Fitted quadratic equation model of wild jujube occurrence probability and slope degree.
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Figure 7. Fitted quadratic equation models of average number (A) and density (B) of wild jujube of and Vitex negundo var. heterophylla.
Figure 7. Fitted quadratic equation models of average number (A) and density (B) of wild jujube of and Vitex negundo var. heterophylla.
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Table
Table 1. Plant species significantly related to the distribution of wild jujube.
Table 1. Plant species significantly related to the distribution of wild jujube.
OrderLatin NameCorrelation Coefficient to Wild Jujube
1Cynanchum thesioides (Freyn) K. Schum.0.259 **
2Orostachys fimbriatus (rostachys fir)0.227 **
3 Vitex negundo var. heterophylla0.226 **
4 Melilotus officinalis 0.223 **
5Speranskiae tuberculatae (Bge.) Baill.0.200 **
6Patrinia scabiosaefolia Fisch. ex Trev.0.192 **
7 Rubia cordifolia 0.185 **
8Ampelopsis aconitifolia Bge.0.184 **
9Ipomoea nil (Linn.) choisy0.158 **
10 Dianthus superbus L. 0.152 **
11Salsola collina Pall.0.147 **
12Rhaponticum uniflorum (L.) DC.0.144 **
13Leptodermis oblonga Bunge0.141 **
14Clematis chinensis Osbeck0.136 **
15Bupleurum chinense DC.0.134 **
16Potentilla conferta Bge.0.133 **
17 Erigeron canadensis L. 0.131 **
18 Phragmites australis (Cav.) Trin. ex Steud. 0.131 **
19Zanthoxylum bungeanum Maxim.0.131 **
20Potentilla discolor Bge.0.122 **
21Geranium wilfordii Maxim.0.121 **
22Ailanthus altissima (Mill.) Swingle0.117 **
23Scorzonera sinensis Lipsch. et Krasch. ex Lipsch.0.115 **
24Grewia biloba G. Don0.111 *
25Stellaria dichotoma L. var. lanceolata Bge.0.110 *
26 Morus alba Linn. 0.108 *
27Crepidiastrum sonchifolium (repidiastrum sonc)0.105 *
28Arthraxon hispidus (Thunb.) Makino0.105 *
29Speranskia tuberculata (Bunge) Baill.0.104 *
30Tribulus terrestris L.0.098 *
31Pyrus betulifolia Bge.0.091 *
32Indigofera kirilowii Maxim. ex Palibin0.091 *
33Selaginella sinensis (Desv.) Spring0.088 *
34Oxalis corniculata L.0.088 *
35 Clematis hexapetala Pall. var. tchefouensis (Debeaux) S.Y.Hu 0.087 *
36Leonurus japonicus Houtt.−0.087 *
37Echinops sphaerocephalus L.−0.089 *
38Chenopodium glaucum L.−0.120 **
Illustration: * means p < 0.05, ** means p < 0.01.
Table
Table 2. The relationship between wild jujube density and probability of occurrence of other plant species.
Table 2. The relationship between wild jujube density and probability of occurrence of other plant species.
Number of Wild JujubeOccurrence Probability (F/%)
F ≥ 8070 ≤ F < 8060 ≤ F < 7050 ≤ F < 6040 ≤ F < 50
0 Rubia cordifolia, Vitex negundo var. heterophylla
1~5Vitex negundo var. heterophyllaRubia cordifolia Bidens pilosa L., Selaginella sinensis, Melilotus officinalis
6~10Vitex negundo var. heterophyllaRubia cordifolia Melilotus officinalis, Selaginella sinensisSetaria viridis (L.) Beauv, Scorzonera sinensis, Cynanchum thesioides, Bidens pilosa, Arthraxon hispidus, Potentilla discolor
11~15Vitex negundo var. heterophylla Rubia cordifolia, Melilotus officinalis Scorzonera sinensis, Crepidiastrum sonchifolium, Bidens pilosa, Selaginella sinensis, Viola philippica Cav.
16~20Rubia cordifolia, Vitex negundo var. heterophylla Melilotus officinalis, Cynanchumthesioides, Setaria viridisScorzonera sinensisArthraxon hispidus, Orostachys fimbriatus
≥21Vitex negundo var. heterophyllaRubia cordifolia Bidens pilosaMelilotus officinalis, Salsola collina, Cynanchum thesioides, Patrinia scabiosaefolia, Crepidiastrum sonchifolium
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Wu, Y.; Wang, Y.; Niu, W.; Zhang, P.; Wu, L.; Li, H.; Wang, S. Establishment of Fitted Models for Topographical Factors and Coexisting Plants Influencing Distribution of Natural Wild Jujube. Forests 2023, 14, 439. https://doi.org/10.3390/f14030439

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Wu Y, Wang Y, Niu W, Zhang P, Wu L, Li H, Wang S. Establishment of Fitted Models for Topographical Factors and Coexisting Plants Influencing Distribution of Natural Wild Jujube. Forests. 2023; 14(3):439. https://doi.org/10.3390/f14030439

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Wu, Yansheng, Yanchao Wang, Weitao Niu, Pengfei Zhang, Lina Wu, Huan Li, and Senghu Wang. 2023. "Establishment of Fitted Models for Topographical Factors and Coexisting Plants Influencing Distribution of Natural Wild Jujube" Forests 14, no. 3: 439. https://doi.org/10.3390/f14030439

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