Trace Element Geochemical Characteristics of Plants and Their Role in Indicating Concealed Ore Bodies outside the Shizhuyuan W–Sn Polymetallic Deposit, Southern Hunan Province, China

Abstract

To explore the potential of plant trace elements as indicators in the search for concealed deposits within the W–Sn polymetallic mining area of Shizhuyuan, Hunan Province, this study focused on the geochemical characterization of 21 trace elements, including Ag, As, B, Bi, Cd, Mo, Ni, Pb, and U, in the stem and leaf tissues of three predominant plants in the area. A total of 126 plant samples were collected, covering an area of about 10 km², and analyzed using ICP-MS. The best indicator plants and sampling sites were selected using multiple indicators, including the biological absorption coefficient (X_BAC), the enrichment coefficient (K_NJ), and the contrast coefficient (K_CD). The results showed that plant leaf tissues represent the most effective sampling components for phyto-geochemical surveys in this region. Dicranopteris dichotoma exhibited markedly pronounced geochemical anomalies of Ag (0.137 µg/g), As (86.12 µg/g), Mo (0.963 µg/g), Pb (15.4 µg/g), Sb (2.03 µg/g), and Se (0.547 µg/g) and demonstrated superior absorption capabilities for Ni, Sn, Sb, Pb, and Bi in the soil, with X_BAC values of 12.0, 54.2, 23.3, 2.9, and 83.9, respectively. R-type cluster analysis and factor analysis identified four distinct mineralization element combinations: (1) Sn–As, (2) Ag–Cu–Mo, (3) Pb, and (4) Bi–Sb–Se. Consequently, D. dichotoma is a viable indicator plant for the phyto-geochemical detection of concealed Ag, Bi, Mo, Pb, Sb, Se, and Sn mineralization in mining areas. The results demonstrate that using phyto-geochemical methods for mineral prospecting is feasible and has significant application value in the Shizhuyuan mining area, which is characterized by dense vegetation and complex geological conditions.

1. Introduction

Located on the northern edge of the central section of the Nanling latitudinal tectonic zone, Shizhuyuan, in Hunan Province, hosts a globally renowned, extensive W–Sn–Mo–Bi deposit, which is characterized by well-developed fold and fracture structures and forms a crucial component of the Nanling metallogenic belt [1,2,3]. To date, exploration efforts in the region have extensively covered surface and near-surface mineral deposits, underscoring the necessity to shift focus toward the discovery of deeper, concealed deposits. However, due to the dense vegetation, substantial Quaternary cover, and limited outcrops in this region, it is difficult to obtain reliable results with traditional exploration methods. Therefore, developing more effective exploration techniques has become particularly crucial.

Traditional geochemical exploration methods often struggle to effectively detect deeply hidden ore resources, whereas phyto-geochemical exploration methods offer distinct advantages [4]. Plant roots can penetrate thick overburden layers, absorbing water, nutrients, and mineral elements from deep underground, thereby generating geochemical anomaly signals that provide valuable clues about the locations of ore bodies [5,6]. This approach is well suited to complex surface conditions, avoiding the need for extensive soil stripping or drilling, making it both environmentally friendly and cost-effective. Additionally, the extensive reach of plant root systems can indicate the presence of ore bodies over a much broader area. As a result, phyto-geochemical methods represent a potentially effective approach that has already been successfully applied in the search for concealed deposits in various countries and regions, demonstrating their unique advantages [7,8,9,10]. A pilot study was conducted on simultaneous prospecting using both surface soil and plants in an Australian mining area. The results showed that the soil exhibited weak and ambiguous mineralization signals, while Acacia aneura demonstrated strong multi-element mineralization signals due to the uptake of mobile mineralized elements. For instance, Zn concentrations were 29 ppm higher in the leaves and 35 ppm higher in the roots than the mineralization background value [11]. Empetrum nigrum L., a dwarf shrub flourishing above Finland’s Juomauso Au–Cu deposit, provided geochemical data potentially revealing minerals situated over 200 m beneath the surface [12]. In the Yorke Peninsula region of South Australia, researchers conducted large-scale regional sampling to analyze Cu concentrations in the leaves of Mallee Eucalypts. The study revealed anomalously high Cu concentrations (6–10.04 ppm) in the leaves within a 3 km radius of known Cu-mineralized bodies [13]. In a study of the Twin Lakes deposit in Canada, researchers used phyto-geochemical and multivariate statistical techniques to evaluate the distribution patterns of various mineral elements in samples of Picea mariana. The samples showed average concentrations of 2100 ppm Zn, 81 ppm Cu, and Au ranging from 0.2 ppb to 303 ppb, all significantly exceeding background levels, with the highest elemental anomalies observed in plants located directly above the mineralized areas [14]. A study in southern Norway observed a distinct signal in vegetation overlying Pb and Mo mineralizations, identifying birch and the dwarf shrub Vaccinium vitis-idaea as the most effective species for biogeochemical exploration due to their strong signal and wide distribution [15]. Substantial progress has been made in phyto-geochemical exploration research in China, conducted primarily on known deposits located in the Gobi Desert; glacial deposits of northwest China; and southern regions with well-developed red soils, lush vegetation, and limited outcrops [16,17,18]. Geochemical anomalies integrated by common plants, such as Seriphidium terrae-albae, above the metal deposits in the desert regions of northern East Junggar can accurately pinpoint the locations of concealed deposits. [18]. In the Gyabjeka mining area, the northern Rhododendron nivale exhibited anomalous peak values for Li, Be, Rb, Cs, and W, reaching 5.25 × 10⁻⁹, 13.62 × 10⁻⁹, 34.66 × 10⁻⁹, 1.00 × 10⁻⁹, and 0.43 × 10⁻⁹, respectively. These pronounced anomalies of mineralization elements effectively indicate the presence of concealed ore bodies at varying depths [17]. Numerous studies have confirmed that certain plants located above mining areas exhibit trace element geochemical anomalies corresponding to concealed ore deposits beneath [19,20,21].

Currently, there is no systematic study to evaluate the effectiveness of phyto-geochemical methods for exploring deeply concealed ore resources in the Shizhuyuan mining area of Hunan Province. Traditional exploration techniques are not only expensive but may also be ineffective or unfeasible, particularly when ore bodies are buried beneath the thick overburden. Therefore, this study sought to fill this gap by investigating the feasibility of using local dominant plants as indicators for mineral exploration and promoting the application of phyto-geochemical methods in geological surveys. Based on the theory of plant geochemistry, this study hypothesized that by analyzing trace elements in the dominant plants of the study area, it is possible to identify optimal indicator plants and their sampling sites for detecting deep ore bodies and that the elements showing geochemical anomalies will exhibit a certain degree of correlation with each other. To test this hypothesis, three dominant plant species in the Shizhuyuan mining area (Artemisia argyi, Maesa japonica, and Dicranopteris dichotoma) were selected to analyze their absorption of and enrichment characteristics related to trace elements across different plant organs. Multivariate statistical techniques, including R-type cluster analysis and factor analysis, were used to evaluate the effectiveness of these plants as indicators for ore detection. The study aimed to identify the most suitable indicator plants, verify the applicability of phyto-geochemical methods in this area, and provide new approaches and insights for the exploration of deep, concealed deposits in Shizhuyuan and similar regions.

2. Materials and Methods

2.1. Description of the Study Area

The Shizhuyuan deposit is located roughly 15 km southeast of Chenzhou City, Hunan Province. The study area is located around the Jinshiling area, which is on the periphery of the Shizhuyuan deposit. Early data indicate the presence of concealed polymetallic Pb–Zn–Ag ore bodies, suggesting that it is a potential area for mineral prospecting [22,23]. The soil in the study area is in situ soil, with no transported soil, primarily consisting of red soil, with a pH ranging from 4.6 to 6.8. The vegetation cover in the study area surpasses 95%, mainly including the Compositae family, followed by the Pteridaceae family. The region supports a diverse array of annual and perennial herbaceous plants, while shrubs also thrive extensively throughout the area. Key local plant species comprise Artemisia argyi, Dicranopteris dichotoma, Cynodon dactylon, Pinus massoniana, and Maesa japonica. It is demonstrated that A. argyi and D. dichotoma possess great capabilities for the absorption and accumulation of heavy metals [24]. Furthermore, M. japonica is widespread, with a deep-rooted system. Therefore, these three species were primarily selected for sampling.

2.2. Geology of the Study Area

The surface strata of the Shizhuyuan deposit are predominantly composed of Upper Devonian carbonate rocks arranged into four distinct layers from bottom to top: (1) the Tiaomajian Formation, with a thickness of 358 m, consists of conglomerates and sandstones; (2) the Qiziqiao Formation is mainly composed of limestones, micritic dolomites, and dolomitic limestones, with a thickness exceeding 520 m; (3) the Shetianqiao Formation (thickness > 296 m), which serves as the most favorable ore-bearing host, mainly consists of micritic dolomites interspersed with layers of argillaceous bands; and (4) the Xikuangshan Formation (thickness > 363 m) consists of limestones and dolomitic limestones [25]. Additionally, Sinian metamorphic clastic sediments are exclusively found in the eastern part of the Shizhuyuan deposit, while limited Quaternary sediments are only present in the northern part of the area (Figure 1). Numerous NE-, NNE-, and near-NS-trending faults govern the distribution of granites and ore bodies (skarns) (Figure 1). The Qianlishan granitic pluton, covering an area of approximately 10 km², plays a dominant role in local mineralization. Three magmatic phases are identified: (1) the initial phase, marked by porphyritic biotite granite (~160–156 Ma); (2) the second phase, i.e., highly differentiated equigranular biotite granite (~158–153 Ma); and (3) the final phase, i.e., granite porphyry (~154 Ma) [1,26,27]. Among these, the first- and third-phase granites are considered unrelated to mineralization, while the second-phase granite is crucial for the primary W–Sn–Mo–Bi mineralization. Skarn- and greisen-type W–Sn–Bi–Mo deposits predominantly occur in the granite contact zone, whereas hydrothermal-type Cu, Pb, Zn, and Ag deposits arise distal to the rock mass.

2.3. Sample Collection and Preparation

A sampling transect, extending in the northwest direction, was established on the northeastern side of the Jinshiling area. The sampling points were set 20 m apart, and approximately 300 g of a sample was collected at each point. In total, 126 samples were collected from 28 sites, covering an area of about 10 km². This included simultaneous collections of stem and leaf samples from M. japonica, A. argyi, and D. dichotoma at 8 sites and solely leaf samples from these three species at another 20 sites. Twenty-four control samples of the corresponding plants were randomly collected from an area distant from the mining site, free from artificial contamination. The samples were first washed with tap water, subsequently rinsed 3–5 times with ultrapure water, and dried at 60 °C. After that, they were ground to a particle size of 0.2 mm and then stored in bags.

2.4. Analysis of Samples

Prior to quantitative analysis, the samples were processed using a wet-digestion method with HNO₃-HClO₄. Approximately 0.2 g of the sample was weighed into a 50 mL beaker, and 10 mL of a mixed acid (V(HNO₃):V(HClO₄) = 4:1) was added. The beaker was covered with a watch glass and allowed to soak for 30 min and then placed on a hot plate at 175 °C for digestion until a large amount of white smoke was emitted and the digestate became clear and colorless. After complete digestion, the solution was further heated to evaporate the acid until 1–2 mL of liquid remained in the beaker, and then the digestate was allowed to cool to room temperature. The digestate was then transferred to a 25 mL volumetric flask, diluted to volume, mixed thoroughly, and left to stand before analysis. All reagents used in the experiment were of guaranteed reagent grade (GR). The samples collected in this study were analyzed at the State Key Laboratory of Isotope Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. The trace elements examined included Ag, As, B, Bi, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, Se, Sn, Th, Tl, U, and Zn. The content of each element refers to the ratio of the mass of the element to the dry weight of the plant (ug/g). The analysis was performed using an inductively coupled plasma mass spectrometer (ICP-MS) (Thermo X Series, Waltham, MA, USA).

2.5. Quality Control and Assessment

During the sample submission process, four national standard plant samples and four duplicate samples were randomly inserted to monitor analysis quality. Quality control was performed according to the requirements for geochemical exploration analysis.

Accuracy was checked using the formula $∆\mathrm{l}\mathrm{g}{\mathrm{C}}_{\mathrm{j}}-∆\mathrm{l}\mathrm{g}{\mathrm{C}}_{\mathrm{s}}\le \pm 0.13$, where C_j and C_s represent the average value of n measurements and the standard value of the national standard sample, respectively. The relative deviation of the elemental mass fraction between two analyses of the same sample should meet the standard of RD ≤ 20%.

The results of this quality check showed that the control samples passed the pre-test criteria, with the relative deviation (RD) of repeated measurements of the same sample ranging from 0.2% to 3.5%, which meets the standard of RD ≤ 20%, indicating that the test results are reliable.

2.6. Data Analysis

(1) Normalized values (Z) for trace element concentrations in plant stem and leaf tissues were calculated using the following equation:

$$Z={\displaystyle \frac{{\omega }_{stem/leaf}}{{\omega }_{stem}+{\omega }_{leaf}}}$$

(1)

where ω_stem/leaf denotes the average concentration of an element in either a plant stem or leaf sample.

(2) The contrast coefficient (K_CD), enrichment coefficient (K_NJ), and variability coefficient (C_V) for trace elements in plant data were calculated using the subsequent equations:

$$K_{\mathrm{C}\mathrm{D}}={\displaystyle \frac{{\omega }_o}{{\omega }_b}}$$

(2)

$$K_{\mathrm{N}\mathrm{J}}={\displaystyle \frac{{\omega }_o}{{\omega }_g}}$$

(3)

$$C\mathrm{v}={\displaystyle \frac{\sigma }{{\omega }_o}}$$

(4)

where ω_o is the mean concentration of an element in a plant sample, ω_b is the average concentration of the element in control area plant samples, σ is the standard deviation, and ω_g is the global reference value for plant element concentrations [28].

(3) The biological absorption coefficient (X_BAC) for the uptake of each element by the plant from the soil was expressed by the formula below:

$$X_{\mathrm{B}\mathrm{A}\mathrm{C}}={\displaystyle \frac{{\omega }_{plant}}{{\omega }_{soil}}}$$

(5)

where ω_plant denotes the concentration of the element in the plant and ω_soil is the concentration of the element in the active state fraction of the soil.

(4) R-type cluster and factor analysis of the 21 trace elements produced spectrograms and orthogonal rotated factor loading matrices.

3. Results

3.1. Trace Element Characteristics in Plants

3.1.1. Optimal Sampling Position for Different Plants

Elements absorbed by the plant, either actively or passively from the environment, migrate and are stored across various organs. Owing to variations in elemental activity, migration ability, and organ-specific carrying capacities, the distribution of the same element varies among different plant organs. During phyto-geochemical exploration, identifying the optimal sampling position is crucial to enhancing both the accuracy and the efficacy of mineral searches. Consequently, the average concentration (ω_o) and contrast coefficient (K_CD) of trace elements in the stem and leaf tissues of A. argyi, M. japonica, and D. dichotoma from eight sites within the study area were analyzed. Given the substantial variation in the concentrations of the 21 elements, normalization of the trace element averages was necessary for straightforward plotting in Origin 2022 software.

In A. argyi, the ω_o values of 18 trace elements, excluding Cd, Tl, and Zn, were notably higher in the leaf tissues. In M. japonica, the leaf tissues displayed comparatively higher ω_o values of Ag, As, B, Bi, Cr, Cu, Fe, Hg, Mn, Mo, Sb, Se, Sn, Th, Tl, and Zn than those in the stem tissues. Similarly, in D. dichotoma, leaf tissues showed higher ω_o values of Ag, As, B, and 19 other trace elements (Figure 2). This suggests that the majority of trace elements were predominantly enriched in the leaf tissues of these three plants. Though the percentage concentrations of elements in different organs somewhat influence the analytical outcomes, a higher percentage does not necessarily correlate with suitability as a sampling medium. Furthermore, the significance of elemental anomalies in tissues regarding concealed deposits must be considered to identify optimal sampling parts.

Compared with stem tissues, the leaf tissues of A. argyi, M. japonica, and D. dichotoma demonstrated elevated K_CD values for 18, 15, and 18 trace elements, respectively (Figure 2). This suggests that most trace elements in the leaves of these plants are more likely to form distinct geochemical anomalies. In summary, the leaf tissues of A. argyi, M. japonica, and D. dichotoma possess a pronounced capacity to not only become enriched in trace elements but also to create distinct geochemical anomalies, offering significant indicative potential. Given the convenience and efficiency of sample collection, leaf tissues are deemed optimal for investigating the geochemical traits of local plants.

3.1.2. Characteristics of Soil Trace Element Uptake by Different Plants

In phyto-geochemical mineral exploration, it is crucial to select plants with a high capacity for target element enrichment, as these plants typically exhibit a pronounced contrast, effectively displaying anomalies from background levels [15]. The biological absorption coefficient (X_BAC) is defined as the ratio of elemental concentration in leaves to that in soil, signifying the extent of elemental migration from soil to plant. This coefficient reflects a plant’s capacity or propensity to absorb and accumulate specific elements [29]. During growth, plants primarily absorb and use elements from the soil in their bioavailable forms. Therefore, the plant’s transfer factor calculated using the bioavailable fractions of elements in the soil reflects the plant’s ability or tendency to absorb and accumulate specific elements more effectively. The X_BAC value for plant trace elements was calculated using soil fractionation data collected and tested at the same location [22], with results presented in

Table 1. The X_BAC values of trace elements in different plants in the Shizhuyuan mining area.

Element	Plants (N = 20)	XBAC	σ	Element	Plants (N = 20)	XBAC	σ
Ni	A. argyi	1.8~39.7 (11.9) *	15.8	Sb	A. argyi	0.4~138.1 (18.1)	45.9
	M. japonica	1.1~38.3 (9.1)	7.3		M. japonica	1.0~104.9 (16.3)	20.9
	D. dichotoma	0.35~47.8 (12.0)	2.9		D. dichotoma	0.5~262.2 (23.3)	87.2
Cu	A. argyi	6.6~189.8 (53.6)	61.1	Pb	A. argyi	0.1~13.4 (2.7)	3.4
	M. japonica	2.9~83.5 (19.1)	16.1		M. japonica	0.1~13.8 (2.3)	2.7
	D. dichotoma	4.8~111.1 (23.1)	26.6		D. dichotoma	0.1~13.3 (2.9)	3.1
Zn	A. argyi	9.3~146.4 (45.9)	45.7	Bi	A. argyi	0.7~602.2 (75.8)	200.5
	M. japonica	4.0~44.1 (15.1)	8.0		M. japonica	1.2~172 (42.4)	34.2
	D. dichotoma	6.3~65.2 (27.3)	14.7		D. dichotoma	0.5~692 (83.7)	230.5
Mo	A. argyi	1.7~144.8 (41.0)	80.1	Th	A. argyi	0.03~1.13 (0.3)	3.3
	M. japonica	0.84~52.3 (13.2)	10.3		M. japonica	0.007~0.162 (0.06)	0.03
	D. dichotoma	2.7~54.69 (18.7)	16.8		D. dichotoma	0.02~0.21 (0.06)	0.05
Cd	A. argyi	2.5~242.8 (65.9)	76.7	U	A. argyi	0.07~0.6 (0.24)	0.16
	M. japonica	0.744~12.37 (6.6)	2.3		M. japonica	0.029~0.24 (0.1)	0.03
	D. dichotoma	1.3~141.8 (26.2)	35.1		D. dichotoma	0.04~0.28 (0.15)	0.06
Sn	A. argyi	6.9~237.0 (50.0)	36.0
	M. japonica	10.6~118.5 (33.7)	21.6
	D. dichotoma	5.6~284.4 (54.2)	92.9

* The values in parentheses are the average.

.

In the study area, the three plants demonstrated strong capabilities to absorb and accumulate the bioavailable fractions of metal elements (Ni, Cu, Zn, Mo, Cd, Sn, Sb, Pb, Bi) in the soil, with their average X_BAC values exceeding 5. Conversely, these plants showed minimal uptake of radioactive elements (U, Th) from the soil, with average X_BAC values below 1. Among the three species, A. argyi significantly absorbed and accumulated trace elements, such as Cu, Zn, Mo, and Cd, with average X_BAC values of 53.6, 45.9, 41.0, and 66.0, respectively. D. dichotoma exhibited the strongest capacity to absorb and accumulate Ni, Sn, Sb, Pb, and Bi, with average X_BAC values of 12.0, 54.2, 23.3, 2.9, and 83.9, respectively (Table 1). In contrast, M. japonica had relatively weaker absorption and accumulation capabilities for these trace elements. Overall, the enrichment capabilities of A. argyi and D. dichotoma for most trace elements were superior to those of M. japonica.

3.1.3. Trace Element Indicator Characteristics of Different Plants

This study assessed the trace element indicator characteristics of three plants by analyzing their leaf trace element concentrations and contrast coefficient of deviation (K_CD) at a consistent sampling location. Generally, the mean concentration (ω_o) serves as an approximate indicator of the extent of element enrichment in plants, whereas the K_CD values for trace elements in plants reveal the magnitude of geochemical anomalies. Furthermore, the variation in trace element concentrations in plants provides insights into the robustness of their ability to signal environmental anomalies in trace elements.

Among the three plants, A. argyi exhibited the highest concentrations of B, Bi, Cd, Co, Cr, Cu, Fe, Mo, Ni, Sn, Th, U, Tl, and Zn, with ω_o values of 43.50, 1.91, 4.45, 0.601, 0.94, 22.0, 495.9, 1.673, 3.87, 0.327, 0.143, 0.084, 0.076, and 84.80, respectively. M. japonica demonstrated the highest concentrations of Hg, Sb, and Se, with ω_o values of 0.180, 0.695, and 0.966, respectively. D. dichotoma had the highest concentrations of Ag, As, Mn, and Pb, with ω_o values of 0.137, 86.12, 995.7, and 15.4, respectively (

Table 2. Trace element concentrations (ω_o) and contrast coefficients of deviation (K_CD) across different plants at identical sampling points.

Element	Plants	ωo (ug·g−1)	σ	KCD	Element	Plants	ωo (ug·g−1)	σ	KCD
	(N = 20)					(N = 20)
Ag	A. argyi	0.029~0.093 (0.059) *	0.016	1.33	Mo	A. argyi	0.62~3.36 (1.67)	0.69	3.41
	M. japonica	0.011~0.088 (0.048)	0.019	1.07		M. japonica	0.34~0.76 (0.513)	0.11	1.05
	D. dichotoma	0.028~1.33 (0.137)	0.65	3.50		D. dichotoma	0.28~2.47 (0.963)	1.10	3.70
As	A. argyi	2.12~9.44 (4.22)	1.83	2.20	Ni	A. argyi	0.82~7.73 (3.87)	2.30	2.39
	M. japonica	0.609~4.34 (2.357)	0.93	1.23		M. japonica	1.56~5.03 (2.47)	0.87	1.36
	D. dichotoma	0.636~1525 (86.1)	762.18	51.3		D. dichotoma	0.36~9.71 (3.79)	3.12	2.43
B	A. argyi	20~70 (43.5)	25.0	4.35	Pb	A. argyi	4.92~20.7 (9.91)	3.95	1.77
	M. japonica	10~29.9 (15.5)	4.98	1.55		M. japonica	3.62~13.1 (7.60)	2.37	1.36
	D. dichotoma	10~29.9 (12)	4.98	1.33		D. dichotoma	1.94~70.8 (15.4)	6.18	2.76
Bi	A. argyi	0.83~3.98 (1.91)	1.05	2.18	Sb	A. argyi	0.328~1.19 (0.06)	0.26	1.47
	M. japonica	0.59~2.55 (1.55)	0.49	1.77		M. japonica	0.278~1.315 (0.695)	0.39	1.71
	D. dichotoma	0.16~5.54 (1.11)	2.69	2.03		D. dichotoma	0.107~1.67 (0.46)	0.72	2.03
Cd	A. argyi	0.32~12.6 (4.45)	6.14	9.43	Se	A. argyi	0.39~1.31 (0.758)	0.26	1.76
	M. japonica	0.24~0.78 (0.509)	0.14	1.08		M. japonica	0.36~1.4 (0.966)	0.14	1.86
	D. dichotoma	0.14~7.46 (2.06)	0.23	4.36		D. dichotoma	0.16~1.74 (0.547)	0.29	2.10
Co	A. argyi	0.07~1.64 (0.601)	0.12	4.52	Sn	A. argyi	0.18~0.65 (0.327)	0.12	1.63
	M. japonica	0.05~0.15 (0.107)	0.03	0.81		M. japonica	0.06~0.5 (0.273)	0.05	1.37
	D. dichotoma	0.049~2.03 (0.504)	0.5	3.80		D. dichotoma	0.13~0.78 (0.282)	0.33	2.66
Cr	A. argyi	0.28~2.3 (0.942)	0.41	3.36	Th	A. argyi	0.015~0.668 (0.143)	0.22	7.50
	M. japonica	0.16~0.95 (0.354)	0.20	1.27		M. japonica	0.006~0.066 (0.03)	0.02	1.57
	D. dichotoma	0.07~1.6 (0.48)	0.38	1.71		D. dichotoma	0.013~0.058 (0.026)	0.01	1.38
Cu	A. argyi	10.8~33.7 (22.0)	7.63	3.60	Tl	A. argyi	0.031~0.224 (0.084)	0.05	2.88
	M. japonica	4.76~8.26 (6.74)	0.88	1.02		M. japonica	0.028~0.09 (0.053)	0.02	1.56
	D. dichotoma	4.95~17.3 (8.73)	3.09	1.32		D. dichotoma	0.012~0.493 (0.063)	0.16	1.96
Fe	A. argyi	99~1402 (495.9)	434.33	4.63	U	A. argyi	0.023~0.148 (0.076)	0.03	2.09
	M. japonica	38~275 (139)	59.25	1.30		M. japonica	0.005~0.051 (0.032)	0.01	0.94
	D. dichotoma	74~222 (124.5)	42.33	1.16		D. dichotoma	0.029~0.089 (0.049)	0.02	1.35
Hg	A. argyi	0.037~0.08 (0.055)	0.08	1.19	Zn	A. argyi	24~202 (84.8)	44.5	2.70
	M. japonica	0.008~0.403 (0.18)	0.08	3.92		M. japonica	18.5~48 (24.6)	7.38	0.99
	D. dichotoma	0.018~0.098 (0.05)	0.02	1.11		D. dichotoma	22.2~91.3 (46.15)	23.03	2.86
Mn	A. argyi	153.5~1980 (696.0)	608.83	1.18
	M. japonica	152~1120 (856.4)	252.5	1.45
	D. dichotoma	57.3~1860 (995.7)	469.2	1.68

* The values in parentheses are the average.

). A. argyi displayed the most pronounced geochemical anomalies for B, Bi, Cd, Co, Cr, Cu, Fe, Th, Tl, and U, with respective K_CD values of 4.350, 2.182, 9.431, 4.522, 3.364, 3.590, 4.634, 7.503, 2.879, and 2.090. M. japonica displayed the most pronounced geochemical anomalies for Hg, with a K_CD of 3.918. Similarly, D. dichotoma exhibited marked geochemical anomalies for Ag, As, Mn, Mo, Ni, Pb, Sb, Se, Sn, and Zn, with K_CD values of 3.497, 51.260, 1.682, 3.702, 2.430, 2.763, 2.028, 2.104, 2.660, and 2.861, respectively.

Geochemical anomalies at the Shizhuyuan W–Sn polymetallic deposit have revealed mineralizing elements, such as W, Sn, Mo, Bi, Cu, Pb, Zn, Cd, As, Sb, Ag, and Au [30]. Although A. argyi is notably enriched in 14 trace elements, like B, Bi, and Cd, displaying significant geochemical anomalies for 10 of these elements, D. dichotoma spans the widest range of concentrations for Ag, As, Bi, Mo, Ni, Pb, Sb, Se, and Sn. It also highlights geochemical anomalies for Ag, As, Mn, Mo, Ni, Pb, Sb, Se, Sn, and Zn, effectively indicating potential mineralizing anomalies in the study area. However, M. japonica solely indicates Hg anomalies effectively. Consequently, D. dichotoma is identified as the most suitable indicator plant for phyto-geochemical mineralization in the study area.

3.1.4. Statistics on Indicator Plant Geochemical Parameters

Based on the aforementioned analysis and discussion, this study focused on D. dichotoma (leaves) to analyze its trace element geochemical properties and evaluate their significance in mineral indication. The analytical results included the anomaly intensity, standard deviation, enrichment coefficient (K_NJ), contrast coefficient (K_CD), and variability coefficient (C_V) for each trace element. K_NJ, using the average concentration of global plant elements as the regional background, approximately represents the background of elemental mineralization [28]; K_CD, based on the mean average concentration of plant elements in the control area as the regional background, indicates the extent of phyto-geochemical anomalies. Additionally, C_V serves as a critical parameter for assessing the degree of elemental differentiation. Under identical conditions (with the same K_NJ), elements exhibiting a high C_V demonstrate significant differentiation and are readily enriched locally for mineralization.

Analysis results are shown in

Table 3. Geochemical parameters of key plants in the Shizhuyuan mining area.

Element (N = 42)	ωo (ug/g)	ωmax (ug/g)	ωmin (ug/g)	σ	KNJ	KCD	CV	ωb (ug/g)
Ag	0.286	1.585	0.028	0.463	14.30	7.526	1.618	0.038
As	146.6	1970	0.636	417.370	1466	89.390	2.847	1.640
B	14.88	40.00	10.00	9.404	0.372	1.667	0.632	8.926
Bi	1.053	5.540	0.065	1.076	1053	1.932	1.022	0.545
Cd	1.693	7.460	0.134	1.757	33.86	3.657	1.038	0.463
Co	0.463	2.030	0.043	0.469	2.317	3.534	1.014	0.131
Cr	0.494	1.600	0.060	0.371	0.329	1.777	0.752	0.278
Cu	9.786	22.70	4.150	5.891	0.979	1.493	0.602	6.555
Fe	129.6	254.0	74.00	130.378	0.864	1.217	1.006	106.5
Hg	0.055	0.130	0.018	0.026	2.745	1.196	0.481	0.046
Mn	847.3	2070	55.60	597.347	4.236	1.438	0.705	589.2
Mo	1.130	2.940	0.170	1.161	2.260	4.431	1.427	0.255
Ni	4.171	12.40	0.350	4.963	0.278	2.726	1.19	1.530
Pb	14.71	70.80	1.940	8.105	14.71	2.679	0.551	5.491
Sb	0.454	1.670	0.056	0.568	4.537	2.000	1.252	0.227
Se	0.550	1.740	0.160	0.279	27.49	2.124	0.507	0.259
Sn	0.228	0.780	0.030	0.144	1.139	2.621	0.631	0.087
Th	0.026	0.058	0.008	0.011	5.127	1.368	0.416	0.019
Tl	0.052	0.493	0.010	0.073	0.010	1.625	1.411	0.032
U	0.051	0.115	0.027	0.021	5.054	1.342	0.407	0.038
Zn	49.98	97.10	22.20	19.292	1.250	2.029	0.386	24.63

. In D. dichotoma samples, elements such as Ag, As, Bi, Co, Hg, Mn, Mo, Pb, Sb, Se, Sn, Th, and U exhibited strong mineralization backgrounds, with K_NJ values of 14.30, 1466.0, 1053.0, 2.317, 2.745, 4.236, 2.260, 14.71, 4.537, 27.49, 1.139, 5.127, and 5.054, respectively. The geochemical anomalies for elements such as Ag, As, Bi, Cd, Co, Mo, Ni, Pb, Sb, Se, Sn, and Zn were pronounced, with K_CD values of 7.526, 89.390, 1.932, 3.657, 3.534, 4.431, 2.726, 2.679, 2.000, 2.125, 2.621, and 2.029, respectively. Elements such as Ag, As, Bi, Cd, Co, Fe, Mo, Ni, Sb, and Tl demonstrated significant geochemical differentiation, with Cv values of 1.618, 2.847, 1.022, 1.038, 1.014, 1.006, 1.027, 1.190, 1.252, and 1.411, respectively. D. dichotoma samples containing Ag, Bi, Mo, Pb, Sb, Se, and Sn not only display favorable metallogenic backgrounds, distinct geochemical anomalies, and marked differentiation characteristics but also hold high potential for local enrichment in mineralization, aligning with the metallogenic conditions of the study area.

3.2. Multivariate Statistical Analyses

Due to the overlapping or partially overlapping effects of multiple geological processes in the same region, regional geochemical data are typically characterized by multi-element or multi-variable features [31]. To differentiate the impacts of various geological processes, researchers commonly use R-type cluster analysis and R-type factor analysis on elements to elucidate their interrelationships, co-occurrence patterns, and the characteristics of regional mineralization [32,33]. To date, research on the application of R-type factor analysis and R-type cluster analysis to phyto-geochemical data remains scarce.

3.2.1. R-Type Cluster Analysis

R-type cluster analysis was performed on z-score-normalized geochemical data for 21 trace elements from D. dichotoma samples (N = 42) using SPSS 20.0, yielding spectrograms and providing insights into elemental interconnections [34]. As illustrated in Figure 3, with a distance coefficient of 10, the 21 trace elements are classified into five distinct groups. The first group, comprising transition elements Ag, Cu, B, Mo, Ni, and Zn, reflects diverse hydrothermal overlays, with Ag, Cu, and Mo exhibiting closer spatial associations. The second group includes transition elements Cd and Mn, which represent a combination of oxidophile elements [35]. The third group, containing metal elements Fe, Th, U, As, and Sn, indicates the influence of medium- and high-temperature hydrothermal fluids, with Sn demonstrating greater spatial independence. The fourth group, comprising non-ferrous metals Bi, Sb, and Se, shows a strong affinity for sulfur, readily forming covalent bonds with sulfur ions, and exhibits close spatial relationships. The fifth group, consisting of transition elements Co, Hg, Cr, Pb, and Tl, reflects various hydrothermal effects, with these elements displaying relative spatial independence. Additionally, the potential ore-forming elements Sn, Mo, Bi, Zn, Cu, and Pb are distributed across various elemental groups, exhibiting strong independence. This suggests spatial zoning of ore-forming elements in the study area.

3.2.2. Factor Analysis

This study used factor analysis to explore the spatial assemblage patterns of potential mineralizing elements in greater depth. According to Bartlett’s sphericity test and the Kaiser–Meyer–Olkin (KMO) measure, the significance value (Sig) obtained from Bartlett’s test was 0.00, and the KMO index reached 0.788, greater than the Kaiser-recommended threshold of 0.6. These findings reveal a significant interrelationship within the dataset, rendering it appropriate for factor analysis [36]. Principal component analysis and varimax rotation were applied, based on eigenvalues > 1 and cumulative variance contribution rate > 81.366% [37]. Factors were derived from factor loadings that exceeded an absolute value of 0.6, resulting in four distinct factor sets, as detailed in

Table 4. Orthogonal rotation factor load matrix of trace elements of the dominant plant in the Shizhuyuan mining area.

Element	F1	F2	F3	F4
Ag	0.194	0.035	0.698	0.557
As	0.818	0.240	0.107	0.318
B	0.447	0.318	−0.061	0.640
Bi	0.674	0.139	0.450	0.465
Cd	0.550	0.731	−0.184	−0.011
Co	0.622	0.650	0.033	−0.094
Cr	0.773	0.335	0.102	−0.088
Cu	0.335	0.831	−0.022	0.156
Fe	0.894	0.378	0.086	0.072
Hg	0.259	0.371	0.559	0.443
Mn	0.199	0.546	0.512	−0.157
Mo	−0.071	−0.302	0.144	0.706
Ni	0.056	0.823	0.196	−0.078
Pb	0.696	0.545	0.218	0.162
Sb	0.654	0.158	0.476	0.507
Se	0.281	0.631	0.280	0.401
Sn	0.709	0.150	0.502	0.407
Th	0.892	0.361	0.105	0.026
Tl	0.105	−0.101	0.921	0.028
U	0.736	0.106	0.407	0.219
Zn	0.407	0.852	−0.105	0.000
% of Variance (rotated)	31.819	23.359	14.437	11.752
Cumulative % of variance (rotated)	31.819	55.177	69.614	81.366

.

F1 represents As–Cr–Fe–Sn–Th–U, with a variance contribution rate of 31.819%, characterized as a combination of low-, medium-, and high-temperature elements, reflecting the superposition of multiple hydrothermal events [38]. F2 represents Cd–Cu–Ni–Zn, with a variance contribution rate of 23.359%, identified as a combination of low- and medium-temperature elements, reflecting magmatic-hydrothermal activities associated with acidic and moderately acidic intrusive rocks in the region [39]. F3 and F4 represent Tl and Mo, respectively, with variance contribution rates of 14.437% and 11.752%, respectively. Both of them are independently existing high-temperature elements, which further demonstrates high-temperature hydrothermal mineralization [40]. The congruence between the results from R-type cluster analysis and factor analysis confirms their consistent origins.

4. Discussion

4.1. Factors Influencing Trace Element Content in Plants

The organs of most plants can be categorized into roots, stems, branches, bark, leaves, and fruits, each serving distinct physiological functions and showing significant differences in elemental composition and concentration. A biogeochemical study at the Twin Lakes deposit in the northwestern Superior Province using the bark, twigs, and needles of Picea mariana revealed that elements such as Au, As, Bi, Se, Sb, Tl, Fe, Co, Ni, Cr, Mo, Cd, Pb, Zn, Ca, Ba, and Cu preferentially accumulate in the twigs and bark, while B, K, Mg, Mn, and P are predominantly concentrated in the needles [14]. These variations reflect the differing capacities of plant organs to absorb and concentrate trace elements. In this study, as shown in Table 3, the concentrations of Cu (4.2~22.7 µg/g), Fe (74~254 µg/g), Mn (56~2070 µg/g), Mo (0.2~3 µg/g), and Zn (22~97 µg/g) in the leaf organs of Dicranopteris dichotoma were relatively high, likely because these elements are essential trace elements for plant growth. For example, Fe and Mn play critical roles in the photosynthetic processes of plants. Generally, plants develop resistance mechanisms against elements that are highly radioactive or toxic to them. However, Dicranopteris dichotoma appears to exhibit a strong tolerance to harmful heavy metals, such as As (1–1970 µg/g), Cd (0.2–7.5 µg/g), and Pb (2–71 µg/g), which may be related to its adaptive physiological mechanisms [41,42]. The K_NJ and K_CD values for the elements, including Ag, As, Bi, Cd, Co, Cr, Cu, Fe, Mo, Ni, Pb, Sb, Se, Sn, Th, Tl, U, and Zn, exceeded 1, indicating that D. dichotoma in the Shizhuyuan polymetallic mining area is responding to the stress from high local metal concentrations in soil, rock, or groundwater, leading to polymetallic enrichment [43]. Soil pH is a key factor influencing the bioavailability of trace elements due to its strong relationship with their activity [44]. In low-pH soils, abundant hydrogen ions promote the release of trace element ions from the hydroxyl surfaces of clay minerals, increasing their concentration and activity [45,46], which facilitates their accumulation in plants. The study area in this study is characterized by acidic soils with a pH range of 4.6 to 6.8, which may partly explain the ease with which plants accumulate trace elements.

4.2. Result Validation Based on Mineralization and Geological Evidence

Through the analysis of trace elements in the three dominant plant species, the leaf organs of Dicranopteris dichotoma were identified as the key indicator plant and sampling site. This study confirmed the reliability of the results through various analytical methods. Ag, As, Bi, Cd, Pb, Mo, Sn, Ti, U, and Zn showed clear enrichment patterns and geochemical anomalies in the plant samples, with K_NJ values above 5 and K_CD values above 2 and all Cv values exceeding 1. The geochemical anomaly characteristics of the Shizhuyuan W–Sn polymetallic deposit identified mineralizing elements, such as W, Sn, Mo, Bi, Cu, Pb, Zn, Cd, As, Sb, Ag, and Au [30]. Comparing with the known distribution characteristics of mineralizing elements, Dicranopteris dichotoma exhibits significant geochemical anomalies for elements such as Ag, As, Bi, Mo, Ni, Pb, Sb, Se, and Sn. Combined with the results of R-type cluster analysis and factor analysis, the mineralization system in the study area demonstrates complex hydrothermal evolution characteristics. The independent distribution of potential mineralizing elements, such as Sn, Mo, Bi, Zn, Cu, and Pb, within their respective groups suggests they may have originated from different hydrothermal events or mineralization stages. The diverse combinations of medium-, high-, and low-temperature hydrothermal activities in the study area further highlight the complexity of the mineralizing environment and potential ore deposits [47,48,49]. These findings collectively confirm the effectiveness of Dicranopteris dichotoma as an indicator plant for mineral exploration.

4.3. Limitations of the Study and Future Perspectives

While this study demonstrates the potential of phyto-geochemical methods for exploring deeply concealed ore bodies, several limitations remain. Despite extensive biogeochemical exploration efforts globally, the processes by which mineralizing elements diffuse from underground deposits, migrate to the surface, and are absorbed, transported, and accumulated by plants in different organs are still highly complex and not well understood. Future research should focus more on the mechanisms of plant enrichment for various trace elements to improve data accuracy and reliability.

5. Conclusions

This study investigated the geochemical characteristics of trace elements in dominant plant species within the Shizhuyuan mining area, demonstrating that plant samples exhibited a favorable mineralization background, geochemical anomalies, and differentiation patterns, thereby proving the feasibility of using phyto-geochemical methods for mineral exploration.

On analyzing the average dry weight concentration (ω_o) and contrast coefficient (K_CD) in the stems and leaves of three local dominant plant species—Artemisia argyi, Maesa japonica, and Dicranopteris dichotoma—it was found that leaf organs not only have a stronger capacity for trace element enrichment but also display more distinct geochemical characteristics. Therefore, leaf organs are identified as the optimal sampling site for studying the phyto-geochemical characteristics of local plants.

The absorption coefficient (X_BAC) for bioavailable metal elements in soil and the contrast coefficient (K_CD) relative to the background area indicated that D. dichotoma exhibits more pronounced geochemical anomaly characteristics for potential mineralizing elements (Ag, As, Mn, Mo, Ni, Pb, Sb, Se, Sn) within the study area. This indicates that D. dichotoma is particularly well suited as an indicator plant for mineral exploration within this region.

Through R-type cluster and factor analyses of the plant geochemical data, the potential mineralizing elements were classified into four groups: (1) Sn–As, (2) Ag–Cu–Mo, (3) Pb, and (4) Bi–Sb–Se. This indicates a distinct spatial zoning of mineralizing elements within the study area, offering valuable guidance for future mineral exploration.

These findings offer a new, effective tool for exploring deeply concealed ore bodies in similar geological settings and provide a crucial basis for future mineral resource exploration efforts. While this study confirms the effectiveness of phyto-geochemical methods for detecting hidden ore resources, further research is needed to refine plant enrichment mechanisms and assess method applicability in various geological contexts.