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Article

Asymmetric Effects of Temperature Change on Herbaceous Seed Germination: Implications for Climate Warming

by
Kai Chen
1,2,3,4,*,
Zi-Hong Chen
1,2,3,4 and
Zhong-Hua Jiang
4
1
Construction Projects about the Key Laboratory of Entomogenous Fungi Resources Conservation and Green Development in Gaoligong Mountains, Baoshan University, Baoshan 678000, China
2
Research Institute of Gaoligong Mountains, Baoshan University, Baoshan 678000, China
3
Baoshan Key Laboratory of Biodiversity Conservation and Utilization of Gaoligong Mountains, Baoshan University, Baoshan 678000, China
4
School of Resources and the Environment, Baoshan University, Baoshan 678000, China
*
Author to whom correspondence should be addressed.
Diversity 2024, 16(10), 649; https://doi.org/10.3390/d16100649
Submission received: 14 September 2024 / Revised: 9 October 2024 / Accepted: 15 October 2024 / Published: 21 October 2024
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Abstract

:
Seed germination is a critical ecological process that governs both the establishment and maintenance of plant diversity and is highly sensitive to temperature fluctuations. As climate change accelerates, particularly through increasing temperatures, the disruption to seed germination could pose significant risks to plant diversity. Therefore, understanding how temperature fluctuations affect seed germination is essential for predicting the future recruitment of mountain plants and for conserving biodiversity in the context of ongoing climate warming scenarios. In this study, we collected seeds from 14 Impatiens species (Balsaminaceae) in the Gaoligong Mountains (Southwest China) at an elevation of approximately 2000 m. Germination tests were conducted on seeds subjected to cold stratification (42 days at 4 °C) across a range of alternating temperatures (6/1, 11/6, 16/11, 21/16, 26/21, and 31/26 °C). We used generalized linear mixed-effects models (random intercept) with temperature and its higher-order terms as the fix-effect terms to construct four models describing the relationship between germination percentage and temperature. Results indicated that (1) the germination percentage varied significantly among species and temperature treatments; (2) the nonlinear and asymmetrical model, incorporating temperature and its quadratic and cubic terms, best fit the germination data; and (3) the decline in germination percentage above the optimum temperature (To) was significantly steeper than the increase below based on the best model. Overall, these findings suggest that seed plants at low elevations in mountain ecosystems may be particularly vulnerable to future climate warming due to the sharp decline in germination percentage at temperatures above To. Thus, protecting low-elevation plants should be a key priority in biodiversity conservation efforts as global temperatures continue to rise.

1. Introduction

Seed germination is crucial for maintaining plant diversity in mountain ecosystems, which host a substantial proportion of the world’s seed plants. This process is highly sensitive to environmental hazards, particularly temperature fluctuations [1,2]. Temperature is a critical environmental factor governing seed germination, and even minor fluctuations can significantly impact plant survival by affecting germination success rates [3,4,5]. As climate warming increases temperatures in mountain habitats, it is likely to exert strong selective pressure on seed germination and plant regeneration [6]. However, our understanding of how climate warming will influence seed germination and plant diversity in these ecosystems remains limited. As such, quantitative analysis of germination sensitivity to temperature is essential for predicting the germination characteristics of mountain plants and ensuring the protection of plant diversity in the context of climate warming [7].
Cardinal temperatures are valuable for predicting seed germination under temperature change scenarios [5]. Seeds germinate within a specific range, defined by a minimum (Tmin) and maximum (Tmax) threshold temperature, outside of which germination does not occur, with the highest germination percentage reached at the optimal temperature (To) [8,9]. These cardinal temperatures can vary spatiotemporally, serving as an efficient adaptation strategy to different ecological factors [10,11]. For instance, Mediterranean low-elevation plants have lower Tmin values compared to high-elevation plants, enabling them to avoid seed germination during brief warm periods in winter [10]. The Tmax values for Peltophorum dubium seeds are higher in the northern provenance of Brazil, which experiences higher temperatures compared to other provenances [12]. The To values for Corymbia calophylla increase with the seed origin temperatures, aiding in maintaining germination success under warmer habitats [13]. The three cardinal temperatures are closely linked to environmental temperatures during seed development [12] and can be quantified using thermal models, which are useful for characterizing germination responses to temperature changes [8].
Various thermal models, such as intersected lines, quadratic polynomial, and five-parameter beta models, have been developed and extensively used to investigate seed germination responses to temperature fluctuations in both cultivated and wild species. These studies suggest that nonlinear models are effective for assessing and predicting shifts in seed germination due to temperature variations [10,14,15,16,17,18]. Conversely, linear models have been successfully employed to quantify the effects of temperature on logit or arcsine-transformed germination percentages, indicating that the relationships can be significantly positive, negative, or non-correlated, depending on the temperature tolerance of plants [4,14,19,20]. However, comparative studies on the effectiveness of nonlinear versus linear models in capturing seed germination responses to temperature are limited, with Watt et al. (2010) reporting that nonlinear models generally provide a more accurate fit [21].
Nonlinear models, used to describe seed germination responses to temperature, can be categorized into two main types based on the rate of change in germination percentage. The first type, represented by models like intersected lines [8,18], assumes a linear relationship between germination percentage (GP) and temperature (T) within the range from Tmin/max to To (i.e., d(GP)/dT = constant). The second type, including models such as quadratic polynomial [16,18], suggests that the rate of change in germination percentage is more abrupt near Tmin/max than near To (i.e., d(GP)/dT = β0 + β1 × T). Nevertheless, beyond these general trends, there remains a significant gap in our detailed understanding of how temperature variations affect seed germination.
Seed germination responses to temperature changes are indirectly regulated by synthetase, hydrolase, and antioxidant enzymes, which govern various key metabolic reactions involved in the germination process [22,23]. These enzyme proteins are susceptible to degradation at temperatures exceeding the optimal range (To), and thus, warming may cause irreversible damage to proteases, unlike cooling [22,24]. According to the enzyme kinetics theory, the rate of enzyme activity decline between To and Tmax is more rapid than the rate of activity increase between Tmin and To [25]. Research also suggests that crop seed production increases gradually with temperature up to To, then sharply declines at temperatures above To, indicating a nonlinear and asymmetric relationship between crop seed production and temperature [26]. Based on these theories and empirical findings, we hypothesize that the relationship between seed germination and temperature is nonlinear and asymmetric, with a steeper decline in germination percentage above To compared to the incline below. To test these hypotheses, we addressed the following questions: (1) Are nonlinear models more effective than linear models in describing the relationship between germination percentage and temperature? (2) Is the reduction in germination percentage due to temperatures increasing above To greater than the reduction caused by temperatures decreasing below To?

2. Materials and Methods

2.1. Species and Seed Collection

The genus Impatiens L. in the Balsaminaceae family is one of the largest among seed plants and is widely distributed in subtropical and temperate zones of the Northern Hemisphere [27]. Consequently, Impatiens species serve as representative herbs in mountain ecosystems. Fully mature seeds from 14 Impatiens species were collected at the onset of natural dispersal between 10 August and 20 October 2023 in the Gaoligong Mountains (97°30′–98°30′ E, 24°40′–25°30′ N), Southwest Yunnan Province, China. For each species, seeds were collected from a single site but from more than 15 individual plants, then packed in paper envelopes and transported to the laboratory within 3 days. All collection sites were distributed within a narrow elevational range of 1930 to 2180 m in similar wet understory habitats on the eastern slopes of the Gaoligong Mountains to minimize maternal seed effects. The maximum distance between the two collection sites was approximately 500 m, while the shortest distance was about 0.5 m. The collection area is influenced by the Indian Ocean monsoon, with an annual rainfall of 648–1591 mm and a mean temperature of 5.4–14.9 °C [28].

2.2. Germination Experiments

Physiological dormancy is a common trait of Impatiens seeds in Southwest China and can be alleviated by the cold–moist stratification [3,29]. To address this, fresh Impatiens seeds were sown five days post-harvest on seven layers of damp filter paper in 90 mm diameter Petri dishes, then placed in a 4 °C refrigerator for 42 days to break dormancy. Cold stratification at 4 °C does not harm Impatiens seeds, and the 42-day period represents the maximum time required for Impatiens seeds at 2200 m elevation to overcome dormancy.
Prior to conducting germination tests, the seeds, having undergone dormancy breaking, were macerated in 0.3% sodium hypochlorite solution (NaClO) for 5 min, then rinsed three times with distilled water to prevent fungal contamination. Three replicates of 30 seeds per species were sown on the surface of five layers of damp filter paper in 90 mm diameter Petri dishes and incubated under alternating temperature regimes (6/1, 11/6, 16/11, 21/16, 26/21, and 31/26 °C) and a 12 h light/dark cycle, with light (4000 Lux) applied during the warm phase. These temperatures included the highest and lowest temperatures of the growing season in the sampling sites. Germination was monitored daily, with seeds displaying visible radicles (≥1 mm) considered germinated. The trials lasted for 30 days until no further germination was observed. Non-germinated seeds were dissected, and those showing complete and hard embryos were considered viable [29,30]. The percentage of germinated seeds was calculated after discarding nonviable seeds. The final dataset contained 252 germination records.

2.3. Data Analysis

The seed germination percentage does not follow a normal distribution. Consequently, a nonparametric two-way analysis of variance (ANOVA) was employed to assess differences in germination percentages among species and temperatures using the “aov” function in the R stats package. Germination percentage was converted to ranks as the response variable using the “rank” function in stats, and temperature was converted to a categorical variable using the “factor” function in stats before performing ANOVA.
Generalized linear mixed-effects models (GLMMs), with a logit link function and binomial error structure, were used to quantify the relationship between seed germination and temperature changes. In each random intercept model, species was included as the random-effect term to account for germination variation among Impatiens species not explained by the fixed-effect terms. To consider possible nonlinear responses of seed germination to temperature changes, the average of day/night temperatures (i.e., 3.5, 8.5, 13.5, 18.5, 23.5, and 28.5 °C) and their higher-order terms (i.e., quadratic, cubic, and quartic) were used as fix-effect terms to construct four alternative models. Model 1 used the average of day/night temperature as a fix-effect term (logit(GP) = β0 + β1 × T + εspecies + ε0, ε ~ N(0, σ2)); model 2 used the average of day/night temperature and its quadratic term as fix-effect terms (logit(GP) = β0 + β1 × T + β2 × T2 + εspecies + ε0, ε ~ N(0, σ2)); model 3 used the average temperature and its quadratic and cubic terms as fix-effect terms (logit(GP) = β0 + β1 × T + β2 × T2 + β3 × T3 + εspecies + ε0, ε~ N(0, σ2)); model 4 used the average temperature and its quadratic, cubic, and quartic terms as fix-effect terms (logit(GP) = β0 + β1 × T + β2 × T2 + β3 × T3 + β4 × T4 + εspecies + ε0, ε ~ N(0, σ2)). GLMM analysis was performed using the “glmer” function in the R package lme4. To compare independent variables, temperature values were transformed to Z scores (mean = 0, SD = 1) before GLMM analysis.
Model selection was conducted through a comprehensive evaluation of the Akaike information criterion (AIC), collinearity, and the coefficient of determination (R2). AIC estimates information loss when a specific model is used to describe the data-generating process [31]. A model with an AIC value at least 2 units lower than another is considered significantly better. The “anova” function in stats was used to compare AIC values among models. Collinearity was assessed using the variance inflation factor (VIF) through the “vif” function in the car package, with VIF values greater than 10 indicating high collinearity among independent variables [32,33]. In addition, marginal and conditional R2 values were calculated using the “r.squaredGLMM” function in the MuMIn package.
To determine the potential asymmetric effects of temperature change on seed germination, we used the best model, denoted as GP = f(T), to find the optimum germination temperature (To) for Impatiens seeds using the “optimize” function in stats. We then adjusted the temperature unit at To (i.e., T = To ± 1) and compared the germination differences between the two adjusted temperatures using the best model (i.e., ΔGP = f(To + 1) − f(To − 1); Figure 1). To estimate the distribution of ΔGP, we employed the bootstrap method, involving two steps. First, we used the “sample” function in stats to sample 252 records with replacement from the original dataset and 1000 repeats. Second, we calculated ΔGP for each bootstrap sample. The normality of ΔGP was determined using the Shapiro–Wilk normality test with the “shapiro.test” function in stats. One-sample Student’s t-test was performed using the “t.test” function in stats to determine whether the mean of the ΔGP distribution differed from zero. All statistical analyses were conducted using R v.4.2.1 [34].

3. Results

3.1. Seed Germination Differences among Species and Germination Temperatures

Variation in germination percentages was observed among the 14 Impatiens species, with I. cyanantha exhibiting the highest germination percentage (average = 87.62%; Figure 2) and I. pianmaensis exhibiting the lowest (average = 26.3%; Figure 2). Significant differences in germination percentages were observed among the Impatiens species when controlling for germination temperature (F(13, 231) = 43.13, p < 0.001; Figure 2).
The To for the germination of most Impatiens seeds was around 18.5 °C (Figure 2). For example, I. siculifer had a germination percentage exceeding 80% at 18.5 °C but less than 40% at 3.5 °C, while I. yaojiapingensis showed 66.67% germination at 13.5 °C and no germination at 28.5 °C (Figure 2). Significant differences in germination percentages were also noted among different germination temperatures for the same Impatiens species (F(6, 231) = 47.64, p < 0.001; Figure 2).

3.2. Modeling the Relationship between Germination Percentage and Temperature

The GLMMs were all significant (p < 0.05) and explained at least 86.21% of the variation in germination percentage, which was largely attributable to the random effect (Table 1). Model 1 had the highest AIC value (2283.9) and explained less variation than the other models, indicating that a simple linear regression was not a good predictor of germination response to temperature change (Table 1 and Figure 3a). Model 4 exhibited severe collinearity with a VIF value of 21.01 (Table 1 and Figure 3d) despite having the lowest AIC value (1710.9) and the highest R2 value (91.15%). Based on the AIC and VIF values, Model 3 (AIC = 1713.3, VIF = 8.02; Table 1) was preferred over Model 2 (AIC = 1753.9, VIF = 1; Table 1). This suggests that the cubic regression model with asymmetry (logit(GP) = β0 + β1 × T + β2 × T2 + β3 × T3 + εspecies + ε0, ε ~ N(0, σ2); Figure 3c) better described the relationship between germination percentage and temperature than the quadratic regression model with symmetry (logit(GP) = β0 + β1 × T + β2 × T2 + εspecies + ε0, ε ~ N(0, σ2); Figure 3b).
Model 3 revealed that the germination percentage decreased nonlinearly with temperatures at both the sub-optimal (<To) and supra-optimal (>To) levels (Table 2 and Figure 3c). Specifically, the change in Impatiens seed germination percentage was less pronounced between Tmin and To than between To and Tmax (d(GP)/dT = 0.64 + 2 × (−0.71) × T + 3 × (−0.26) × T2; Table 2 and Figure 3c).

3.3. Germination Percentage with Temperatures Above and Below TO

The ΔGP values ranged from −1.07 to 0.24 (average = −0.43) and followed a normal distribution (w = 1, p = 0.159; Figure 4). One-sample Student’s t-test showed that the mean value of ΔGP was significantly less than zero (t = −15.6, p < 0.001; Figure 4). This suggests that increasing the temperature by 1 unit above the optimal temperature had a greater inhibitory effect on germination than decreasing the temperature by 1 unit.

4. Discussion

4.1. Seed Germination Variation among Impatiens Species

All 14 Impatiens species exhibited heritability in seed germination, with germination percentages significantly influenced by species. This finding is consistent with Perglová et al. (2009), who reported marked differences in seed germination among four Impatiens species [35]. This observed variation could be attributed to a phylogenetic pattern in seed germination behavior across seed plants globally [11,36]. Our results highlight the necessity of considering genetic differences among species in seed germination studies, even for closely related species.

4.2. Nonlinear and Asymmetric Relationship between Seed Germination and Temperature

An initially increasing and then decreasing trend in Impatiens seed germination percentage with incubation temperature was observed, consistent with previous reports for various cultivated and wild species [4,14,30]. This pattern suggests the existence of a thermal threshold beyond which germination percentages decline in Impatiens species. Seed germination behavior is an adaptation to the climatic conditions of a plant’s natural habitat [3]; therefore, all 14 Impatiens species were able to germinate at 13.5 °C, corresponding to the low temperatures before the rainy season in the Gaoligong Mountains. This indicates convergent adaptation among Impatiens species, ensuring that subsequent seedling establishment occurs when sufficient water is available.
Nonlinear models more accurately captured the relationship between germination percentage and temperature compared to linear models, suggesting that the germination percentage of Impatiens seeds increases with temperature until reaching an optimum, after which it declines. While linear models are straightforward and can be helpful for predicting germination responses to temperature fluctuations, they are generally more prone to bias than nonlinear models [12]. Accumulating evidence has indicated that the relationship between seed germination and temperature exhibits a significant curve, passing through To [18,21,37]. Nonlinear models, such as quadratic polynomial models, intersected-lines models, beta models, and Dent-like models, have been shown to fit germination data well [15,16,18,37]. The suitability of a particular nonlinear model depends on the germination behavior of the species or ecotype [18,37].
The relationship between germination percentage and temperature in Impatiens species was also asymmetric, with the decline in germination percentage above To, which was found to be significantly steeper than the incline below. This may be because temperatures above To can cause seed death through protein denaturation [22]. As temperatures increase above To, the enzyme activity controlling various seed metabolic processes decreases sharply, eventually halting enzyme-catalyzed reactions as enzymes lose their ability to function [25]. Additionally, asymmetric models are more adaptable than symmetric models, as their curvilinear nature allows for a gradual transition between phases, producing a smoother and more realistic curve [37]. This finding partially aligns with previous research showing a gradual increase in corn, soybean, and cotton yields with rising temperatures up to To, followed by a sharp decline beyond that point [26], indicating severe damage to both agricultural seed production and wild plant propagation under climate warming. To the best of our knowledge, this is the first report documenting a nonlinear and asymmetric response of seed germination to temperature changes.

4.3. Implications for Plant Diversity Conservation Under Climate Warming Scenarios

Climate change, particularly rising temperatures, strongly influences seed germination, plant recruitment, and species diversity [38]. Consequently, nonlinear and asymmetric models that describe the relationship between seed germination and temperature are essential tools for predicting the germination characteristics and seedling establishment of mountain plants in the context of global climate change [6]. Plants in the mountains of Southwest China, a global biodiversity hotspot, are generally restricted to specific climatic conditions and are particularly vulnerable to climate warming [39]. The asymmetric effects of temperature on seed germination suggest that rising temperatures due to climate warming could make it difficult for seeds to germinate at low elevations in these mountains, where plants are already near their upper germination temperature limits [5,17]. While most previous studies on plant diversity conservation have focused primarily on the germination responses of alpine plants to climate warming in the mountains of Southwest China [40,41,42,43], our results suggest that plants at low elevations should also be of concern due to the sharp decline in germination percentage at temperatures above the optimum.

5. Conclusions

Nonlinear and asymmetric models more accurately capture the relationship between seed germination and temperature compared to nonlinear and symmetric models, indicating that warming is more detrimental to seed germination than cooling above the optimum temperature. In a warming world, plants are increasingly pushed toward their upper germination temperature limits. This study highlights the importance of conserving plant diversity at low elevations in mountain ecosystems, where many species are already approaching their upper germination temperature limits and experience substantial declines in germination percentage at temperatures above their optimum. To the best of our knowledge, this research is the first to reveal the nonlinear and asymmetric response of seed germination to temperature changes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d16100649/s1.

Author Contributions

K.C. and Z.-H.C. designed this study. Z.-H.J. carried out the germination experiments. K.C., Z.-H.C. and Z.-H.J. conducted fieldwork. K.C. performed statistical analysis, generated the graphs, and wrote this manuscript. Z.-H.C. revised this manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32160078 and 32060638), Middle–Young-Aged Academic and Technical Leader Reserve Talents Projects of Yunnan, China (202205AC160061), and Lower Nu River, Mountain Agroecosystem, Observation, and Research Station of Yunnan Province (202305AM340031).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The datasets used in the study are attached in the Supplementary Materials.

Acknowledgments

We are grateful to Yuan-Yuan Huang, Lin-Yuan Shi, and Xian-Qi Bai for their assistance in fieldwork.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Method for calculating ΔGP. Solid blue line represents germination as a function of temperature, and three green dots from left to right represent values of the function at T = To − 1, T = To, and T = To + 1, respectively.
Figure 1. Method for calculating ΔGP. Solid blue line represents germination as a function of temperature, and three green dots from left to right represent values of the function at T = To − 1, T = To, and T = To + 1, respectively.
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Figure 2. Differences in germination percentage among Impatiens species and germination temperatures. Outlines are violin plots showing distribution of values across the data. Thicker sections represent higher probability, while thinner sections represent lower probability. Points show germination percentage at different temperatures. Top left is a nonparametric two-way ANOVA table showing variance of seed germination attributed to species, germination temperature, and residuals.
Figure 2. Differences in germination percentage among Impatiens species and germination temperatures. Outlines are violin plots showing distribution of values across the data. Thicker sections represent higher probability, while thinner sections represent lower probability. Points show germination percentage at different temperatures. Top left is a nonparametric two-way ANOVA table showing variance of seed germination attributed to species, germination temperature, and residuals.
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Figure 3. Relationship between germination percentage and temperature fitted by linear model (a), quadratic polynomial model (b), cubic polynomial model (c), and quartic polynomial model (d), respectively. Lines in (a–d) represent fitted models for each species, distinguished by different colors.
Figure 3. Relationship between germination percentage and temperature fitted by linear model (a), quadratic polynomial model (b), cubic polynomial model (c), and quartic polynomial model (d), respectively. Lines in (a–d) represent fitted models for each species, distinguished by different colors.
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Figure 4. Histogram of ΔGP values for each bootstrap sample. ΔGP is the difference in germination percentage between the best model at temperatures To + 1 and To − 1. Red dotted line shows a ΔGP of 0 for reference.
Figure 4. Histogram of ΔGP values for each bootstrap sample. ΔGP is the difference in germination percentage between the best model at temperatures To + 1 and To − 1. Red dotted line shows a ΔGP of 0 for reference.
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Table 1. Comparison of four generalized linear random intercept models explaining the relationships between logit germination percentage and temperature, including higher-order terms (i.e., quadratic, cubic, and quartic) using various model selection criteria. GP: germination percentage; T: temperature; AIC: Akaike information criterion; BIC: Bayesian information criterion; R2: coefficient of determination; VIF: variance inflation factor; p: error probability; ε~ N(0, σ2).
Table 1. Comparison of four generalized linear random intercept models explaining the relationships between logit germination percentage and temperature, including higher-order terms (i.e., quadratic, cubic, and quartic) using various model selection criteria. GP: germination percentage; T: temperature; AIC: Akaike information criterion; BIC: Bayesian information criterion; R2: coefficient of determination; VIF: variance inflation factor; p: error probability; ε~ N(0, σ2).
ModelAICBICMarginal R2Conditional R2Max VIFp-Value
logit(GP) = β0 + β1 × T + ɛspecies + ε02283.92294.53.26%86.21%
logit(GP) = β0 + β1 × T + β2 × T2 + εspecies + ε01753.91768.126.86%90.9%1<0.001
logit(GP) = β0 + β1 × T + β2 × T2 + β3 × T3 + εspecies + ε01713.3173128.18%91.12%8.02<0.001
logit(GP) = β0 + β1 × T + β2 × T2 + β3 × T3 + β4 × T4 + εspecies + ε01710.91732.128.38%91.15%21.010.036
Table 2. Results of generalized linear random intercept model estimating the relationship between logit (GP) with T, T2, and T3; GP: germination percentage; T: temperature.
Table 2. Results of generalized linear random intercept model estimating the relationship between logit (GP) with T, T2, and T3; GP: germination percentage; T: temperature.
TermVariableEstimateStandard Errorp-Value
Intercept1.060.27<0.001
Fixed effectT0.640.07<0.001
T2−0.710.03<0.001
T3−0.260.04<0.001
Random effectVariableVariance
Species0.97
Residual3.21
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Chen, K.; Chen, Z.-H.; Jiang, Z.-H. Asymmetric Effects of Temperature Change on Herbaceous Seed Germination: Implications for Climate Warming. Diversity 2024, 16, 649. https://doi.org/10.3390/d16100649

AMA Style

Chen K, Chen Z-H, Jiang Z-H. Asymmetric Effects of Temperature Change on Herbaceous Seed Germination: Implications for Climate Warming. Diversity. 2024; 16(10):649. https://doi.org/10.3390/d16100649

Chicago/Turabian Style

Chen, Kai, Zi-Hong Chen, and Zhong-Hua Jiang. 2024. "Asymmetric Effects of Temperature Change on Herbaceous Seed Germination: Implications for Climate Warming" Diversity 16, no. 10: 649. https://doi.org/10.3390/d16100649

APA Style

Chen, K., Chen, Z. -H., & Jiang, Z. -H. (2024). Asymmetric Effects of Temperature Change on Herbaceous Seed Germination: Implications for Climate Warming. Diversity, 16(10), 649. https://doi.org/10.3390/d16100649

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