A Weibull Distribution-Based Method for Estimating Soil Seed Bank Longevity in Annual Invasive Plants

A Weibull Distribution-Based Method for Estimating Soil Seed Bank Longevity in Annual Invasive Plants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Weibull Distribution-Based Method for Estimating Soil Seed Bank Longevity in Annual Invasive Plants Zhili Yuan, Weidong Fu, Zhen Song, Zhonghui Wang, Chengyu Sun, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6742628/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Seed longevity is a key determinant of population persistence, spread, and outbreak potential in annual invasive plant species. Understanding seed bank dynamics is crucial for determining colonization timing and assessing invasion potential, thereby supporting sustainable weed management strategies. While soil seed bank fluctuations have become a focus in invasion biology area, efficient and accurate methods for evaluating seed bank longevity in annual invasive plants remain scarce so far. Results In this study, we focus on a representative annual globally malignant invasive plant buffalo bur Solanum rostratum , investigating seed viability dynamics under accelerated aging conditions (60°C and 85% relative humidity) across multiple regions and collection years. We developed a three-parameter Weibull distribution model to characterize seed aging and applied it to assess S. rostratum seed bank viability in both grassland and abandoned farmland habitats. The results showed that S. rostratum seeds lost viability rapidly within three days under accelerated aging condition. Seeds from different regions in the same year exhibited similar aging patterns, while interannual variation led to significantly divergent aging curves. Polynomial regression of viability data estimated natural seed longevity at approximately 9.91 years. This study demonstrates that combining accelerated aging with the three-parameter Weibull distribution provides an effective approach for evaluating seed longevity and seed bank persistence. Our findings highlight the feasibility of combining accelerated aging and the three-parameter Weibull distribution model to evaluate seed longevity and seed bank viability. Conclusion It proposes a practical and efficient approach to estimate seed bank persistence in annual invasive plants and highlights the critical role of persistent seed banks in facilitating S. rostratum 's invasion success, offering a practical framework for assessing invasion risks. These results contribute important theoretical foundations for developing ecologically sustainable weed control strategies. Seed aging test Solanum rostratum Seed viability Weibull distribution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Persistent soil seed banks serve as a fundamental driver of invasion success in annual invasive plant species. Defined as reservoirs of viable seeds and vegetative propagules capable of regenerating plant communities [ 1 ], seed banks play a vital role in maintaining future weed populations [ 2 ]. Previous studies demonstrates that many annual invasive species produce seeds capable of surviving extended periods in soil [ 3 ], maintaining viability under adverse conditions while rapidly germinating when conditions improve [ 4 ]. This delayed germination strategy preserves population genetic diversity and enhances adaptability to environmental variability [ 5 ]. The soil seed banks of annual invasive plants not only sustain invasive population persistence but also severely impact native seed banks. Research demonstrates that plant invasions significantly reduce soil seed bank species richness and seed density, while particularly depleting native seed reserves [ 6 ]. Ecological restoration resistance increases with the duration of invasion [ 7 ], as substantial quantities of invasive seeds stored in natural ecosystems create significant barriers to vegetation recovery. Consequently, preventing invasive seed bank replenishment is critical for effective ecological restoration. Systematic investigations into invasive plant seed bank characteristics serve dual purposes: predicting future invasion risks [ 8 ] and informing science-based agricultural management and control strategies [ 9 , 10 ]. A comprehensive understanding of seed quantity and viability in annual invasive plants is essential for evaluating re-invasion potential and long-term control efficacy [ 11 ]. Such knowledge enables policymakers to develop region-specific, cost-effective management strategies with long-term viability [ 2 , 12 ]. However, current methods for assessing seed viability and longevity in annual invasive plant species remain far from adequate. Most soil seed bank studies prioritize species composition and abundance [ 13 – 15 ], factors influencing seed bank density and diversity [ 16 , 17 ], and ecological roles of seed banks [ 18 , 19 ], while research on seed longevity and viability is notably limited. Traditional methods face significant limitations. Germination assays [ 20 , 21 ]: These involve soil sampling, controlled germination, and seedling counting to estimate seed bank viability. However, this approach fails to determine seed age or original quantities and introduces bias in species with complex dormancy mechanisms (e.g., S. rostratum ) due to prolonged germination periods. Another method seed burial experiments [ 12 , 22 ]: While partially simulating natural seed bank dynamics, these labor-intensive methods require burying known seed quantities and periodic retrieval for viability testing. Their artificial setup and time demands – particularly for deeply dormant species – limit their utility in assessing natural seed longevity. These shortcomings underscore the urgent need for efficient, reliable methods to evaluate viability and longevity in invasive plant seed banks. Studies demonstrate that high-temperature/humidity aging treatments effectively simulate natural seed deterioration through shared physiological mechanisms [ 23 , 24 ], providing a practical approach to rapidly assess seed viability and longevity in annual invasive species. The Accelerated aging (AA) test – a standard method for evaluating seed vigor – expose seeds to elevated temperatures (typically 40 ~ 50°C) and approximately 100% relative humidity (RH), inducing rapid degradation [ 25 , 26 ]. High-vigor seeds exhibit slower viability decline under these conditions while maintaining higher germination rates. Unlike crop seeds, certain invasive species exhibit significantly slower aging rates at 40 ~ 50°C, requiring temperatures above 50°C to effectively accelerate deterioration [ 27 ]. Monitoring viability changes under such conditions allows researchers to infer seed storage potential, longevity, and physiological quality. Recent applications of the AA test in invasive plant control studies [ 28 ] highlight its utility in ecological management. The three-parameter Weibull distribution has proven particularly effective for modeling seed longevity. For instance, it successfully characterized vigor and lifespan in Carpobrotus edulis ' seeds [ 27 ]. This distribution’s flexibility and superior model fit [ 29 ] make it ideal for describing asymmetric viability decline patterns. Its probability density function (PDF) is defined as: $$\:\begin{array}{c}f\left(x;\lambda\:,k,c\right)={e}^{{-\left(\frac{x-c}{\lambda\:}\right)}^{k}}\end{array}$$ In this distribution, 𝑥 ≥ 𝑐, and the density function 𝑓(𝑥; 𝜆, 𝑘, 𝑐) equals zero when x < 𝑐. The primary distinction between the three parameter Weibull distribution and the standard two-parameter version lies in the inclusion of a location parameter 𝑐, which enhances model flexibility and allows for better fitting of skewed data sets. This enables precise quantification of seed storage potential and longevity through vigor analysis, with particular effectiveness in modeling asymmetric viability decline patterns [ 30 ]. Although originally developed for reliability analysis in engineering fields – such as memory devices, fatigue resistance in mechanical systems, and aerospace structures – the three-parameter Weibull distribution has also been shown to effectively describe seed germination rates and germination speed [ 31 ]. In seed science, its simplicity and adaptability [ 29 ] have made it an important mathematical tool for modeling seed survival time and longevity. Compared with other nonlinear models, such as the Morgan-Mercer-Flodin, Richards, Mitscherlich, Gompertz, and logistic functions, the three-parameter Weibull distribution demonstrates superior fitting accuracy and lower sensitivity to initial seed vigor values, resulting in more stable and reliable estimates [ 32 ]. Therefore, it is well-suited for accurately estimating the seed longevity of annual invasive plant species. Traditional methods for assessing seed vigor historically relied on physical or physiological traits such as color, morphology, volume, weight, density, electrical conductivity, and respiration rate. However, these methods frequently produced inconsistent outcomes [ 33 , 34 ]. To enhance the precision of vigor evaluation in accelerated aging tests, the tetrazolium (TZ) test has emerged as a complementary approach. Widely adopted for seed viability testing due to its rapidity, sensitivity, and broad applicability [ 35 – 37 ], this method was specifically optimized in this study for the annual invasive species S. rostratum . Building on protocols established for its close relative species Solanum melongena [ 38 ], the refined tetrazolium staining protocol enabled precise tracking of seed vigor dynamics during aging. Buffalo bur, S. rostratum , a globally malignant invasive annual weed native to Mexico and the central United States [ 39 ], has colonized regions across North America, Europe, Africa, Asia, and Oceania, threatening ecosystem stability and agroecological security [ 40 , 41 ]. This species exhibits exceptional reproductive capacity, with individual plants producing 1,600 ~ 43,800 seeds [ 42 ], and readily forms persistent soil seed banks. Approximately 55% of seeds germinate in the first post-sowing spring, while the remainder germinate in subsequent growing seasons [ 43 ], around 20% entering long-term dormancy to sustain seed bank longevity. The seeds (2.2 ~ 2.8 mm diameter) possess a dense, honeycomb-patterned coat that enhances permeability and mechanical resistance [ 44 ], coupled with notable stress tolerance [ 45 ]. These traits facilitate a dual physical-physiological dormancy mechanism [ 46 ], enabling the establishment of large, persistent seed banks – a critical driver of its ongoing global invasion success. Seed longevity serves as a critical indicator for assessing the persistence, spread, and outbreak potential of annual invasive plants within specific habitats. Despite its ecological significance, systematic evaluation of seed longevity in such species remains lacking. S. rostratum was selected as a model organism for this study due to its representative seed traits, ease of sampling, and dual theoretical-practical relevance. This research aims to: (1) develop an accelerated aging system for S. rostratum seeds under high-temperature/humidity conditions to rapidly assess viability, (2) analyze viability patterns across seed collection years and geographic origins to identify key vigor determinants, (3) model aging dynamics using the three-parameter Weibull distribution, calculating L 50 (time to 50% viability loss) and estimating natural longevity via polynomial regression, (4) assess invasion lifespan by analyzing soil seed bank L 50 values across habitats. By elucidating seed longevity mechanisms and spatiotemporal viability trends, this study provides a scientific framework for long-term invasive weed management, technical support for tracing invasion histories, evidence-based strategies for evaluating control measures, and region-specific management protocols for invasive species. Methods Plant materials collection Seeds of S. rostratum were collected from seven Chinese provinces and municipalities: Inner Mongolia Autonomous Region, Xinjiang Uygur Autonomous Region, Shanxi, Jilin, Liaoning, Hebei, and Beijing during early autumn from 2008 to 2022 (detailed location information including longitude and latitude for each collection site are provided in Tables 1 and 2 ). Formal taxonomic identification was performed by Dr. Guoliang Zhang. Voucher specimens are deposited in the Invasive Alien Plants Control and Management Laboratory at the Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing. This research complies with international conventions including the Convention on Biological Diversity and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES); no endangered or protected species were involved. Condition Screening Optimization of tetrazolium staining duration for S. rostratum seed viability assessment. To optimize the tetrazolium staining protocol for S. rostratum seeds, mature seeds collected in October 2022 from Huai’an County, Zhangjiakou City, Hebei Province, China (114°23′8″E, 40°40′27″N) were used. Fifty seeds were placed into each of nine 5 mL centrifuge tubes. A 1% 2,3,5-triphenyltetrazolium chloride (TTC) solution was added to each tube, followed by incubation in a 40°C water bath. At time points of 12, 24, and 36 hours, three replicates per time point were removed from the bath. After discarding the TTC solution via pipetting, seeds were rinsed 1 ~ 2 times with distilled water, longitudinally sectioned with a scalpel and forceps, and microscopically examined for staining patterns. The influence of staining duration on coloration quality was assessed, and representative staining images were compiled to determine the optimal staining time for use in subsequent accelerated aging experiments. Determination of optimal accelerated aging temperature. To shorten the experimental duration and improve efficiency, an evaluation was conducted to determine the optimal temperature for accelerated aging of S. rostratum seeds. Mature seeds collected in October 2022 from Huai’an County, Zhangjiakou City, Hebei Province, China, were subjected to aging treatments at four temperatures: 45°C, 50°C, 55°C, and 60°C, under a constant relative humidity of 85%. For each temperature treatment, 18 replicates were prepared, each consisting of 50 seeds evenly placed in mesh bags and laid flat in an aging incubator (Model LH-1509). For the 45°C, 50°C, and 55°C treatments, samples were taken every 3 days; for the 60°C treatment, sampling was performed daily. At each sampling point, three replicates were assessed using the tetrazolium test method to evaluate seed viability. Data were organized using Excel 2023, and viability analysis and significance testing were performed using SPSS 27.0. The percentage of viable seeds was calculated to determine the optimal temperature for accelerated aging. Data analysis and visualization. For all condition-screening experiments mentioned above, data were compiled and organized using the Microsoft Excel 2023. Viability analysis and statistical tests were performed using the SPSS version 27.0. Graphs were generated using the Origin software. Establishment of the aging model Effect of geographical origin on seed longevity. To evaluate the impact of geographical variation on seed longevity and establish an aging model for S. rostratum , seeds collected in the same year from six distinct populations (geographical regions; Table 1 ) underwent accelerated aging experiments. For each population, three replicates (50 seeds per replicate) were prepared in mesh bags across all sampling time points. Using optimized conditions (Section 2.1.2), seeds were aged in an incubator maintained at 60°C and 85% relative humidity. Sampling commenced at 0 hours, with subsequent collections at 24-hour intervals over a 96-hour period. Viability at each interval was assessed via the standardized tetrazolium staining protocol. Table 1 Accelerated aging test sampling locations of Solanum rostratum seeds from distinct geographical regions Region Latitude and longitude Collection date Abbreviations Guyang County, Baotou City, Inner Mongolia Autonomous Region 110°3′24″ E, 41°1′47″ N 2021.09 GY Huimin District, Hohhot city, Inner Mongolia Autonomous Region 111°37′26″ E, 40°48′29″ N 2021.09 HT Changji city, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region 87°18′0″ E, 44°1′12″ N 2021.09 CJ Tianzhen county, Datong city, Shanxi Province 114°4′48″ E, 40°25′12″ N 2021.09 TZ Tongyu county, Baicheng city, Jilin province 123°4′48″ E, 44°49′12″ N 2021.09 TY Urat Front Banner, Bayannur City, Inner Mongolia Autonomous Region 108°39′0″ E, 40°43′11″ N 2021.09 BY Correlation between seed collection year and longevity. To refine the aging model of S. rostratum seeds and investigate the temporal influence on seed longevity, seeds collected across distinct years were subjected to accelerated aging tests (see Table 2 for details). Experimental protocols mirrored those in Section 2.1.2: seeds were incubated in a controlled aging chamber maintained at 60°C and 85% relative humidity. Sampling intervals spanned 0–96 hours at 24-hour increments, with three biological replicates per time point and year. Post-aging viability was assessed using the optimized TTC staining protocol established in Section 2.1.1 (1% TTC solution, 40°C, 24 hour incubation), and quantitative viability metrics were systematically recorded. Table 2 Accelerated aging test sampling locations of Solanum rostratum seeds from different years Region Latitude and longitude Collection date Abbreviations Shuangta District, Chaoyang City, Liaoning Province 120˚28'47" E, 41˚36'36" N 2008.09 2008 Zhenlai County, Baicheng City, Jilin Province 122°51'0" E, 45°34'12" N 2015.09 2015 Tuquan county, Xing 'an League, Inner Mongolia Autonomous Region 121˚35'24" E, 45˚22'48" N 2019.09 2019 Beipiao City, Chaoyang City, Liaoning Province 120˚55'34" E, 41˚30'25" N 2020.09 2020 Tianzhen county, Datong city, Shanxi Province 114°4′48″ E, 40°25′12″ N 2021.09 2021 Huaian County, Zhangjiakou City, Hebei Province 114°23′8″ E, 40°40′27″ N 2022.09 2022 Yanqing District, Beijing City 115°51′22″ E, 40°23′38″ N 2023.09 2023 Seed aging modeling framework. In this study, L₅₀ is defined as the duration required for seed viability to decline to 50%, serving as a quantitative metric to compare longevity across S. rostratum populations. The aging kinetics were modeled with a three-parameter Weibull distribution, whose probability density function is: $$\:\begin{array}{c}f\left(x;\lambda\:,k,c\right)={e}^{{-\left(\frac{x-c}{\lambda\:}\right)}^{k}}\end{array}$$ Where \(\:x\ge\:c\) , and when \(\:x<c\) , the density function \(\:f\left(x;\:\lambda\:,\:k,\:c\right)\) is equal to 0. Here, \(\:\lambda\:\) represents the scale parameter of the three-parameter Weibull distribution, \(\:k\) represents the shape parameter of the three-parameter Weibull distribution, and \(\:c\) is the threshold parameter of the three-parameter Weibull distribution. \(\:x\) represents the accelerated aging time, and \(\:f\left(x\right)\) represents the seed viability at the corresponding time. Raw viability data were collated in Microsoft Excel 2023, with population-specific longevity metrics computed for each accession. Parameter estimation and the three-parameter Weibull distribution curve fitting were implemented in MATLAB R2024a. Model diagnostics and distribution plots were concurrently generated to validate goodness-of-fit. Seed longevity calculation method. All data from the accelerated aging experiment of S. rostratum seeds from different years were organized with the Excel 2023. The seed aging model for each year's S. rostratum population was established based on the three-parameter Weibull distribution outlined in section 2.2.3 using the Matlab R2024a software, and the L 50 value for seeds from different years was obtained. The L 50 values for seeds from different years were then polynomially fitted using Origin 2023 software to calculate seed longevity. The seed longevity calculation model was established to estimate and infer the seed lifespan of S. rostratum , and the corresponding model graph was plotted. Model application experiment Application of Accelerated Aging Tests in Soil Seed Bank Research. To evaluate the applicability of the accelerated aging test conditions and the three-parameter Weibull distribution method established in this study under field-relevant conditions, soil seed bank samples of S. rostratum were collected from two distinct habitats: Grassland habitat: Ke’erqin Grassland in Taobei District, Baicheng City, Jilin Province (45°34′12″N, 122°51′0″E). Abandoned farmland habitat: Huai’an County, Zhangjiakou City, Hebei Province (40°40′27″N, 114°23′8″E). Both of these locations belong to the temperate continental monsoon climate zone. Soil samples were collected from 0 ~ 10 cm and 10 ~ 20 cm depths at each site. After cleaning, drying, and sieving (through 10 and 14 mesh), black kidney-shaped S. rostratum seeds with distinct characteristics were manually selected. A total of 400 seeds were screened from each location for the experiment. The S. rostratum seeds were placed in mesh bags, with each group of seeds taken from the aging chamber at each sampling time point consisting of 3 replicates, each containing 50 seeds. According to the results of the previous experiment 2.1.2, the seeds were placed in a seed aging chamber at 60°C with 85% relative humidity. Samples were taken every 24 hours from 0 hours to 96 hours, with 3 replicates from each location's group at each observation time point. The seed vitality was assessed using the tetrazolium test, following the method outlined in 2.1.1. Field-adapted longevity model development. All data from the accelerated aging experiment of S. rostratum seeds from different years were organized using Excel 2023. Based on the Matlab R2024a software, the three-parameter Weibull distribution model (described in 2.2.3) was applied to establish the seed aging model for each year’s population of S. rostratum seeds, and the L 50 values for seeds from different years were obtained. The L 50 values of seeds from different years were polynomially fitted using Origin 2023 software to calculate seed longevity and establish a longevity calculation model. The seed longevity of S. rostratum was then estimated and inferred, and the corresponding model plot was generated. The data organization and analysis were performed in the same way as described in step 2.2.4. Results Criteria and optimal staining duration for evaluating S. rostratum seed viability using tetrazolium test To establish a standardized protocol for assessing S. rostratum seed viability via the tetrazolium test, this study defined three distinct viability categories based on staining patterns (Fig. 1 ). Viable seeds exhibited uniform bright red staining of both the embryo and endosperm with intact tissue morphology. Seeds displaying partial or unstained embryos combined with more than 50% unstained or structurally compromised storage tissues were classified as non-viable (mottled-stained). Fully unstained seeds with softened, decayed, or damaged tissues were categorized as non-viable (dead). Staining duration significantly influenced diagnostic accuracy. Time-course experiments (40°C, 1% TTC) revealed suboptimal results at 12 hours, with only 51.8% viable seeds, 44.8% mottled-stained, and 3.2% dead (Fig. 2 a). The high mottled-stained proportion indicated incomplete enzymatic reduction of TTC. In contrast, 24-hour and 48-hour treatments achieved 96.6% and 96.8% viable seeds, respectively, with complete elimination of mottled staining. Given negligible improvement beyond 24 hours, a 24-hour staining duration at 40°C was selected as the optimal balance between efficiency and precision for subsequent experiments. Optimal temperature for rapid viability assessment of S. rostratum seeds Figure 2 b illustrates the viability decline curves of S. rostratum seeds under varying temperature treatments (all maintained at 85% relative humidity). The results demonstrate that temperature profoundly influences the aging process, modulating both the survival curve morphology and the median viability period L 50 . At the minimal test temperature of 45°C, seeds exhibited progressive deterioration, showing approximately 40% viability reduction over 15 days. Comparative analysis revealed comparable deterioration rates between 45°C and 50°C conditions, both treatments reached 50% viability on day 9, though diverging thereafter – 45°C seeds maintained gradual decline, while 50°C specimens underwent rapid depletion to complete non-viability by day 15. Thermal acceleration became pronounced at 55°C, with viability initiating exponential decay on day 3 (L 50 attainment: day 4; full viability loss: day 9). The most extreme aging occurred at 60°C, where viability demonstrated precipitous decline within 24 hours, culminating in complete depletion by day 4. Collectively, under sustained 85% humidity, incremental temperature elevation (from 45°C to 60°C) reduced L 50 values from 15 days to less than 2 days, confirming thermal sensitivity as a critical driver of viability loss. To maximize efficiency in viability assessment and aging sensitivity analysis, 60°C with 85% relative humidity was selected as the optimal accelerated aging condition. This protocol enables comprehensive seed viability evaluation within one week. Geographic consistency in seed viability dynamics of S. rostratum Under standardized accelerated aging conditions (60°C, 85% RH), interregional seed seeds of S. rostratum collected within the same year exhibited conserved viability loss patterns. As illustrated in Fig. 2 (c), all populations demonstrated triphasic degradation kinetics: gradual viability reduction during the initial 24-hour phase (0 ~ 1 day) followed by precipitous decline from day 2 onward, culminating in complete viability loss by days 2 ~ 3. The consistent pattern across populations indicates that population heterogeneity has little impact on seed longevity, suggesting that seed lifespan is an inherent trait. To quantitatively characterize these dynamics, viability time-series data were fitted to the three-parameter Weibull survival function. The resultant models (Fig. 2 (d)) demonstrated excellent goodness-of-fit, with near-identical decay parameters observed among geographically distinct populations. This convergence underscores the robustness of the aging mechanism under controlled environmental stress. Based on the the three-parameter Weibull distribution, L₅₀ values (time required for viability to decline to 50%) were calculated for each of the six populations (Fig. 2 (d) and Table 3 ). All L₅₀ values were below 2 days, indicating that S. rostratum seeds are highly sensitive to aging under high temperature and humidity. While minor differences in L₅₀ were observed among populations, the variation was minimal and not statistically significant, confirming that seeds collected in the same year exhibit consistent longevity under accelerated aging conditions. These findings establish the three-parameter Weibull distribution as an effective descriptor of S. rostratum seed aging under combined thermal-hydric stress. The demonstrated geographic invariance in longevity parameters provides critical baseline data for predictive modeling of soil seed bank persistence and retrospective estimation of invasion chronologies. Table 3 Quantitative parameters of Three-parameter Weibull distribution and L 50 values characterizing Solanum rostratum seed viability across geographical populations Population abbreviations \(\:\lambda\:\) \(\:k\) \(\:c\) Model L 50 (days) GY 44.33 107.08 -42.64 \(\:V=exp\left({-\left(\frac{x+42.64}{44.33}\right)}^{107.08}\right)\) 1.54 HT 10.21 39.18 -8.53 \(\:V=exp\left({-\left(\frac{x+8.54}{10.21}\right)}^{39.18}\right)\) 1.57 CJ 26.20 54.35 -24.42 \(\:V=exp\left({-\left(\frac{x+24.42}{26.20}\right)}^{54.35}\right)\) 1.60 TZ 28.35 83.28 -26.70 \(\:V=exp\left({-\left(\frac{x+26.70}{28.35}\right)}^{83.28}\right)\) 1.53 TY 222.98 571.01 -221.39 \(\:V=exp\left({-\left(\frac{x+221.39}{222.98}\right)}^{571.01}\right)\) 1.45 BY 10.867 37.16 -9.50 \(\:V=exp\left({-\left(\frac{x+9.5}{10.87}\right)}^{37.16}\right)\) 1.26 Note: \(\:V\) represents viability (%), and \(\:x\) represents aging time in days. \(\:\lambda\:\:\) represents the scale parameter of the three-parameter Weibull distribution, \(\:k\) represents the shape parameter of the three-parameter Weibull distribution, and \(\:c\) represents the threshold parameter of the three-parameter Weibull distribution. Geographical codes: GY: Guyang County (Inner Mongolia), HT: Hohhot City (Inner Mongolia), CJ: Changji City (Xinjiang), TZ: Tianzhen County (Shanxi), TY: Tongyu County (Jilin), BY: Bayannur City (Inner Mongolia). Chronological aging patterns in S. rostratum seeds Accelerated aging trials revealed divergent viability decay kinetics among S. rostratum seed seeds stratified by collection year (Fig. 3 (a)). Freshly harvested seeds and one-year-old seeds exhibited progressive deterioration commencing on day 1 of thermal-hydric stress. In stark contrast, seeds aged 2–4 years displayed catastrophic viability collapse within the first 24 hours, reaching near-complete non-viability by day 2. Notably, 8–15 year-old seeds entered experiments with substantially compromised initial viability and achieved total non-viability within 24 hours. Temporal viability profiles were robustly modeled using the three-parameter Weibull survival function (Fig. 3 (b), Table 4 ), demonstrating excellent goodness-of-fit. Derived L 50 values exhibited strong negative correlation with storage duration: maximal longevity occurred in fresh seeds (L 50 = 2.51 days), followed by 1-year-old seeds (2.08 days). Seeds stored for less than 2 years had L₅₀ values greater than 2 days, while those stored for 3–4 years had L₅₀ values between 1–2 days. For seeds stored for more than 5 years, L₅₀ values fell below 1 day, indicating extreme sensitivity to aging. These findings establish a quantifiable inverse relationship between chronological age and aging resistance in S. rostratum seeds. The L₅₀ metric functions as a dual indicator of both storage history and physiological competence, providing critical insights for seed bank longevity modeling and invasion timeline reconstructions. Table 4 Three-parameter Weibull distribution model parameters and L₅₀ values characterizing Solanum rostratum seed viability across collection years Collection year \(\:\lambda\:\) \(\:k\) \(\:c\) Model L 50 (days) 2008 0.39 1.62 -0.32 \(\:V={e}^{{-\left(\frac{x+0.32}{0.39}\right)}^{1.62}}\) 0 2015 0.43 2.05 -0.26 \(\:V={e}^{{-\left(\frac{x+0.26}{0.43}\right)}^{2.05}}\) 0.10 2019 38.94 108.04 -37.49 \(\:V={e}^{{-\left(\frac{x+37.49}{38.94}\right)}^{108.04}}\) 1.32 2020 21.34 70.96 -19.78 \(\:V={e}^{{-\left(\frac{x+19.78}{21.34}\right)}^{70.96}}\) 1.45 2021 28.35 83.26 -26.70 \(\:V={e}^{{-\left(\frac{x+26.70}{28.35}\right)}^{83.26}}\) 1.53 2022 112.42 210.98 -110.14 \(\:V={e}^{{-\left(\frac{x+110.14}{112.42}\right)}^{210.98}}\) 2.08 2023 23.54 63.75 -20.9 \(\:V={e}^{{-\left(\frac{x+20.90}{23.54}\right)}^{63.75}}\) 2.51 Note: \(\:V\) represents viability (%), and \(\:x\) represents aging time in days. \(\:\lambda\:\:\) represents the scale parameter of the three-parameter Weibull distribution, \(\:k\) represents the shape parameter of the three-parameter Weibull distribution, and \(\:c\) represents the threshold parameter of the three-parameter Weibull distribution. Estimation of S. rostratum seed longevity Accelerated aging trials of S. rostratum seed cohorts collected between 2008–2023 revealed an inverse relationship between chronological age and L₅₀ values. Polynomial regression of this temporal decline (Fig. 4 , Table 5 ) produced a robust predictive model, evidenced by a near-unity coefficient of determination (R² = 0.979) and minimal residual error (RSS = 0.110), confirming exceptional model fidelity. The regression identified two theoretical longevity thresholds where L₅₀ approaches zero: 9.91 years (primary intercept) and 15.21 years (secondary intercept). Empirical validation using 2008-collected seeds (chronological age = 15 years) demonstrated strong alignment with the latter threshold, reinforcing model accuracy. Based on the primary intercept, we estimate S. rostratum seed bank persistence at 9.91 years under natural soil conditions. Gibberellic acid (GA₃) treatment effectively promotes rapid germination in S. rostratum seeds [ 46 ]. To validate the ecological relevance of the calculated 9.91-year seed longevity, we applied GA₃ to seeds collected in 2008, 2015, and 2023 (n = 6 replicates per year, 30 seeds per replicate). The 2008 seeds showed complete viability loss with no germination after 30 days. The 2015 seeds exhibited limited germination (approximately 10% per replicate) within 7 days, but with significantly reduced seedling vigor compared to 2023 seeds (P < 0.05 for height difference). In contrast, the 2023 seeds demonstrated rapid germination (more than 80% within 7 days) and robust seedling growth. These results demonstrates that S. rostratum seeds from 2015 still possess germination capacity albeit with markedly declined viability, whereas 2008 seeds have completely lost viability. These findings confirm the ecological relevance of the 9.91-year seed longevity estimate. Complementary histochemical analysis (Fig. 5 ) revealed age-dependent viability patterns. Seeds aged less than 6 years maintained stable initial viability but exhibited progressive staining attenuation during aging, while older cohorts (≥ 7 years) showed accelerated degradation kinetics. Distinct temporal gradients in tetrazolium staining intensity correlated precisely with seed age, enabling visual differentiation of senescence stages. This study provides technical support for rapid determination of S. rostratum seed age. In practical applications, seeds recovered from soil samples can be subjected to accelerated aging treatment, and their viability patterns compared to the reference images in Fig. 5 to estimate their age range. By integrating viability curves with tailored control strategies, targeted management measures can be developed for different invasion stages of S. rostratum , thereby enhancing the effectiveness of weed control and informing ecological management decisions for invasive species. Table 5 Polynomial fitting equations and parameter estimates Parameter Value Standard Error Equation \(\:y=Intercept+B1\times\:x+B2\times\:{x}^{2}\) Intercept 2.4887 ± 0.11942 B1 -0.41537 ± 0.04919 B2 0.01654 ± 0.00312 Residual Sum of Squares (RSS) 0.10957 R-squared (COD) 0.97923 Adjusted R-squared 0.96885 Note: The residual sum of squares (RSS) represents the sum of the squared differences between the observed and predicted values in the regression model. A smaller RSS indicates a better model fit. The coefficient of determination (R²) reflects the proportion of variance in the dependent variable explained by the model, ranging from 0 to 1, with values closer to 1 indicating a better fit. Vriation in seed viability of S. rostratum across different habitats The seed viability of S. rostratum exhibited significant variation across habitats. As illustrated in Fig. 6 and Table 6 , seeds collected from abandoned farmland demonstrated an L 50 value of 1.59 days. Polynomial fitting analysis estimated the seed bank longevity (y-value) in this habitat at 2.39 years, indicating viability comparable to seeds stored for approximately two years. In contrast, seeds from grassland habitats showed a lower L 50 value (1.36 days) with a corresponding longevity estimate of 3.11 years, equivalent to seeds stored for around three years. Although ecological differences in seed viability exist between habitats, the three-parameter Weibull distribution model developed in this study demonstrated good fit and parameter stability for both habitat types, indicating strong explanatory power. This seed bank longevity estimation model not only enables quantitative evaluation of the survival potential of invasive species seeds in specific regions but also shows good cross-regional applicability. Application experiments using soils from two ecologically distinct locations confirmed the model's robustness under varying environmental conditions. These findings suggest that the model can be reliably used to predict the outbreak potential of invasions across multiple regions, thereby providing a scientific basis for the development of site-specific management strategies. Table 6 Three-parameter Weibull distribution (TPWD) model parameters and L 50 values quantifying Solanum rostratum seed viability dynamics across different habitats \(\:\lambda\:\) \(\:k\) \(\:c\) Model L 50 (days) Abandoned farmland 32.26 91.67 -30.54 \(\:V=\text{e}\text{x}\text{p}\left({-\left(\frac{x+30.54}{32.26}\right)}^{91.67}\right)\) 1.59 Grassland 273.18 453.69 -271.82 \(\:V=\text{e}\text{x}\text{p}\left({-\left(\frac{x+271.82}{273.18}\right)}^{453.69}\right)\) 1.36 Note: \(\:V\) represents viability (%), and \(\:x\) represents aging time in days. \(\:\lambda\:\:\) represents the scale parameter of the three-parameter Weibull distribution, \(\:k\) represents the shape parameter of the three-parameter Weibull distribution, and \(\:c\) represents the threshold parameter of the three-parameter Weibull distribution. Discussion Optimal conditions for artificial accelerated aging The tetrazolium test, widely used for detecting infarct areas in mammalian tissues (Bederson et al., 1986)[ 47 ], is also effective for assessing seed viability (Towill and Mazur, 1975)[ 48 ]. The staining agent, 2,3,5-triphenyl tetrazolium chloride (TTC), is a lipid-soluble, photosensitive compound. In seeds or plant tissues, viable tissues are stained varying shades of red, whereas dead or non-viable tissues remain unstained. Studies on Hordeum vulgare (Lopez Del Egido et al., 2017; Ma et al., 2019)[ 49 , 50 ] and the artificial, self-pollinated cereal crop species × Triticosecale Wittmack (triticale) (Souza et al., 2010)[ 51 ] demonstrate that the tetrazolium test generates viability estimates comparable to germination tests but with significantly greater efficiency. During preliminary viability testing for S. rostratum , both germination and tetrazolium tests were attempted. However, after 20 days of germination trials, only untreated seeds germinated, achieving a rate of merely 1%. For annual invasive plants like S. rostratum – which exhibit hard seed coats and mixed dormancy – germination tests are unsuitable for rapid viability assessment in accelerated aging experiments. Consequently, the tetrazolium test, as employed in this study, offers a more efficient and reliable alternative. Notably, due to S. rostratum ’s unique seed coat structure, piercing the coat at a non-critical site with a scalpel is essential prior to staining to ensure adequate TTC solution penetration [ 46 ]. This step is crucial for accurate staining results. By optimizing staining conditions and protocols, the tetrazolium test effectively evaluates S. rostratum seed viability, providing robust data for subsequent research. In agricultural research, seed aging conditions have been extensively studied across crops. For example, rice ( Oryza sativa ) seeds exhibited a more than 60% reduction in germination and vigor indices after 9 days at 50°C [ 52 ], while barley ( Hordeum vulgare ) seeds lost viability within 48 hours at 42°C [ 53 ]. Similarly, chickpea ( Cicer arietinum ) seeds became non-viable after 48 hours of soaking in a saturated NaCl solution at 41℃ [ 54 ]. These findings informed the selection of aging conditions for S. rostratum . Given its complex, dense seed coat, this study initially screened temperatures starting at 45℃ (based on crop seed research) and increased in 5℃ increments, with relative humidity fixed at 85%. Results revealed that S. rostratum seeds lost viability entirely within 3 days under 85% humidity and 60℃. These findings underscore the species’ acute sensitivity to hot, humid environments and establish a rapid protocol for informing soil seed bank management strategies. Regional variation and seed longevity Invasive plants frequently undergo rapid adaptive evolution in novel environments, driving modifications in life-history traits – such as seed germination strategies – to optimize ecological alignment with local conditions. This process often involves phenotypic plasticity and genetic differentiation, both critical determinants of invasion success [ 55 ]. In this study, S. rostratum exhibited negligible regional variation in seed longevity. Although environmental heterogeneity may induce shifts in specific traits (e.g., germination timing) [ 56 ], seed aging dynamics remained consistent across populations. The L 50 showed minimal interregional variation, reflecting low environmental sensitivity of seed longevity. This suggests that S. rostratum has evolutionarily stabilized its seed longevity strategy during invasion, potentially enhancing its capacity to establish persistent seed banks in diverse habitats. Additionally, previous studies report minimal geographic variation in seed traits among populations of some annual invasive species. Instead, high within-population phenotypic and genetic diversity, strong germination potential, and diverse seed traits are critical for their widespread dispersal and colonization [ 57 ]. For example, the annual invasive weed Senecio vernalis exhibits little variation in seed traits and germination behavior across habitats with differing disturbance regimes, yet maintains high intraspecific phenotypic and genetic diversity [ 58 ]. Similar to S. rostratum , S. vernalis produces dormant seeds capable of forming large, persistent soil seed banks. Combined with high reproductive output and environmental adaptability, this trait enables effective responses to disturbances [ 46 , 59 ]. Research on another annual invader, Avena fatua , demonstrates that drought and shading reduce reproductive allocation and primary dormancy levels but do not significantly affect seed viability [ 60 ]. These findings collectively support the hypothesis that invasive plants exhibit substantial phenotypic plasticity, with seed longevity being relatively insensitive to environmental variation. The stability of seed longevity in annual invasive species may therefore enhance their persistence in newly colonized environments. Implications of seed aging models for managing annual weeds Effective management of invasive plant species necessitates balancing direct costs (e.g., removal, monitoring) and indirect ecological impacts (e.g., disrupted ecosystem functions, secondary spread risks) [ 2 ]. Accurate cost-benefit analyses of control measures enable optimized resource allocation, improving eradication feasibility [ 61 ]. Persistent soil seed banks drive reinvasion in invasive species, rendering short-term control strategies ineffective without long-term seed bank monitoring and adaptive management [ 9 , 62 ]. Seed bank size and longevity are thus critical predictors of invasion resilience and spread potential [ 12 ]. Seed aging rates and survival capacities vary among invasive species, reflecting differences in ecological adaptability. Accelerated aging tests enable researchers to construct aging models that elucidate degradation dynamics in annual invaders. These models not only reveal mechanisms of invasion success but also serve as predictive tools for evaluating spread risks. In this study, accelerated aging experiments were used to develop a seed aging model for S. rostratum . Results indicate that this species forms a persistent soil seed bank. Seeds from abandoned farmland exhibited viability equivalent to two-year-old stored seeds, while grassland seeds matched three-year-old seeds. Based on longevity estimates, S. rostratum seeds in abandoned farmland may persist for ≥ 8 years, whereas grassland seeds remain viable for ≤ 7 years. This suggests greater population stability in abandoned farmland, likely due to favorable conditions such as higher soil moisture, reduced exposure, and lower disturbance – factors known to decelerate seed aging [ 63 ]. Persistent seed banks imply that annual invasive species can remain dormant near the soil surface and resurge explosively under favorable climatic or disturbance conditions [ 64 , 65 ]. Controlling such species thus demands long-term strategies. Soil seed banks of annual invaders should be integrated into dynamic risk assessment frameworks, prioritizing periodic monitoring, targeted interventions, and region-specific protocols over one-time eradication efforts. The accelerated aging approach and seed longevity model developed here provide a framework for predicting seed bank persistence. By modeling aging dynamics in annual invaders, researchers can infer their reinvasion potential and persistence in target regions. These models offer critical insights into belowground seed viability and lifespan, clarifying how invasive plants establish and maintain seed banks in novel environments. Such knowledge is vital for designing effective control schedules and replacement strategies to mitigate reinvasion [ 66 ]. Feasibility and advantages of the experimental approach While prior studies suggest limited environmental influence on seed longevity, this investigation specifically assessed whether the invasive species S. rostratum maintains stable seed lifespans across heterogeneous environmental conditions. Seeds collected from multiple geographic origins and collection years were subjected to accelerated aging tests, with viability decay modeled via the three-parameter Weibull distribution for nonlinear fitting. Aging models were constructed for six geographically distinct populations under standardized conditions (60°C, 85% relative humidity). The L 50 served as a quantitative metric to compare seed longevity, enabling evaluation of environmental variability and invasion risk projection. Results demonstrated minimal interpopulation L 50 variation, confirming that S. rostratum maintains environmentally robust seed longevity – a trait likely stabilized during invasion. As a widely adopted metric in seed biology, L 50 reflects median survival under controlled aging stress [ 67 , 68 ]. Our findings validate its applicability for viability and longevity assessment in this species. From a weed management perspective, determining seed longevity and its decline rate is critical for designing soil seed bank regulation strategies and implementing alternative control methods. Establishing aging models not only elucidates viability loss patterns but also provides a theoretical framework for understanding seed storage physiology. However, the seed aging models developed here require further validation. Future studies should expand sampling to include more collection years and locations while increasing temporal resolution of viability measurements during aging to enhance L 50 accuracy and model robustness. Although accelerated aging offers a practical method for estimating longevity, it remains an artificial simulation and may not fully replicate natural decay processes. Thus, we recommend integrating natural storage experiments and long-term monitoring to achieve comprehensive seed lifespan evaluations. Conclusion The accelerated aging experiments under controlled high-temperature and high-humidity conditions revealed that S. rostratum seeds experienced complete viability loss within 3 days at 60°C with 85% relative humidity. Notably, the temporal progression of viability decline exhibited a triphasic pattern: gradual reduction during the initial 0–1 day period, followed by an abrupt decrease on day 1, culminating in near-total viability loss by days 2–3. Crucially, this aging trajectory remained consistent across geographically distinct seed sources when tested under standardized conditions, demonstrating negligible regional variation in seed longevity parameters. Analysis of seeds from multiple collection years established clear longevity gradients: newly harvested seeds displayed the highest L 50 values, with progressive reductions observed in one-year-old seeds. Specifically, seeds less than 2 years old maintained L 50 values exceeding 2 days, while 3–4 year-old specimens exhibited L 50 values between 1–2 days. Seeds surpassing 5 years of age showed markedly reduced L 50 values below 1 day. Polynomial regression modeling of L 50 against chronological age predicted seed longevity at 9.91 years, defined as the temporal intercept where L 50 approaches zero. This mathematical projection indicates an estimated maximum seed lifespan of approximately 9.91 years for S. rostratum under natural storage conditions. These empirical findings establish a robust framework for evaluating viability dynamics in annual invasive species such as S. rostratum . The derived longevity parameters enable precise estimation of invasion persistence timelines, predictive modeling of seed bank turnover rates under environmental change scenarios, and quantitative assessment of management intervention efficacy. The developed aging model provides actionable insights for designing targeted seed bank depletion strategies, with potential applicability to other annual invasive taxa to improve ecological management outcomes Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not Applicable. Availability of data and materials Not applicable. Competing interests The authors declare that they have no conflict of interests. Funding This work was supported by the National Key R&D Program of China (2021YFD1400300), and Central Public-interest Scientific Institution Basal Research Fund (BSRF202408). Authors' contributions ZY conducted data curation, formal analysis, investigation, and visualization, developed the methodology and software, and drafted the manuscript. WF and ZS contributed to data collection, resource provision, and validation, and participated in manuscript review and editing. ZW assisted in investigation, provided resources, and contributed to validation and manuscript review. CS performed data analysis and software development, and contributed to validation and manuscript revision. 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Bewley JD, Bradford KJ, Hilhorst HWM, Nonogaki H. Germination. In: Bewley JD, Bradford KJ, Hilhorst HWM, Nonogaki H, editors. Seeds: Physiology of Development, Germination and Dormancy, 3rd Edition. New York, NY: Springer; 2013. p. 133–81. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6742628","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467808617,"identity":"94e4ad41-0431-4fed-a48e-ef2659ba4630","order_by":0,"name":"Zhili Yuan","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhili","middleName":"","lastName":"Yuan","suffix":""},{"id":467808618,"identity":"9e344ed9-7faf-4d08-851d-182599b9c3e7","order_by":1,"name":"Weidong Fu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Weidong","middleName":"","lastName":"Fu","suffix":""},{"id":467808619,"identity":"4ea8ac2a-72ee-4a77-bce2-0bc9fad31f3c","order_by":2,"name":"Zhen Song","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Song","suffix":""},{"id":467808620,"identity":"60048922-1258-4540-937f-c10b1f538f56","order_by":3,"name":"Zhonghui Wang","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhonghui","middleName":"","lastName":"Wang","suffix":""},{"id":467808621,"identity":"b8423e04-36fa-40c5-9843-1680b26e89ca","order_by":4,"name":"Chengyu Sun","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chengyu","middleName":"","lastName":"Sun","suffix":""},{"id":467808622,"identity":"f48418f0-5190-4ea2-8948-92f555a9f9c1","order_by":5,"name":"Yue Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACCSjNxsx8gEQt/OxsCSRqkeznMSBOh/zs5mcPv7YdljM4zPPxw4+aOgb+2Q34tTDOOWZuLNt22NjgMO9myZ5jhxkk7hzAr4VZIsFMWrLtcOKGw7zbGHjYDjAYSCTg18Imkf4NpKV+w2GeZ4x//tUR1sIjkWMm+bHtcIJkMw8bM28bM2EtEhI5ZdIM59IN+5nZjKVl+w7zSNwgoEV+Rvo2yR9l1vJs/IcffnzzrU6OfwYBLSDAzMuG5FLC6oGA8ccfotSNglEwCkbBSAUAo7g9rjt941EAAAAASUVORK5CYII=","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhang","suffix":""},{"id":467808623,"identity":"d313c26a-4b40-41a7-81a0-dc0fd6217d24","order_by":6,"name":"Guoliang Zhang","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-25 08:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6742628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6742628/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84163349,"identity":"0011372c-916d-451d-a55b-fb4cea4ee687","added_by":"auto","created_at":"2025-06-08 15:00:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239817,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic correlation between tetrazolium staining patterns and physiological viability in \u003cem\u003eSolanum rostratum\u003c/em\u003e seeds\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/0b8220c50181ae5e479b8135.png"},{"id":84163347,"identity":"0ea02cfd-0b7e-42e5-8bb7-e2d6392139b2","added_by":"auto","created_at":"2025-06-08 15:00:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64440,"visible":true,"origin":"","legend":"\u003cp\u003eSpatiotemporal dynamics of\u003cem\u003e Solanum rostratum\u003c/em\u003e seed viability under accelerated aging conditions. (a) Temporal viability dynamics quantified by tetrazolium staining rate (%) during 0-96 h aging. (b) Correlation between aging duration (h), thermal stress, and viability decay trajectories. (c) Regional divergence in viability loss kinetics across six biogeographical populations under standardized aging protocols. (d) Three-parameter Weibull distribution fitting curves for regional seed seeds, demonstrating parametric heterogeneity in longevity thresholds.\u003c/p\u003e\n\u003cp\u003eNote: Lowercase letters denote statistically significant intergroup differences ( P \u0026lt; 0.05). Geographical codes: GY: Guyang County (Inner Mongolia), HT: Hohhot City (Inner Mongolia), CJ: Changji City (Xinjiang), TZ: Tianzhen County (Shanxi), TY: Tongyu County (Jilin), BY: Bayannur City (Inner Mongolia).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/80663e12bb8f87fcc9329c46.png"},{"id":84163348,"identity":"cd82bc28-358d-47e2-aa0d-379c1cf7794b","added_by":"auto","created_at":"2025-06-08 15:00:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62277,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual Variability in \u003cem\u003eSolanum rostratum\u003c/em\u003e seed longevity dynamics under accelerated aging. (a) Temporal decay of seed viability across multi-year seeds (2008-2023) under standardized aging protocols (60 °C, 85% RH). (b) Three-parameter Weibull distribution fits for interannual viability trajectories, highlighting parametric divergence in longevity thresholds.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/fe1213d650fcb11f2c77e6cd.png"},{"id":84163949,"identity":"70a7ad2c-643a-4714-af76-ae0dbedb5d5c","added_by":"auto","created_at":"2025-06-08 15:16:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12603,"visible":true,"origin":"","legend":"\u003cp\u003ePolynomial regression modeling of median seed survival time (L₅₀) in relation to chronological age for \u003cem\u003eSolanum rostratum\u003c/em\u003e seed seeds. The analysis quantifies longevity trajectories across differentially aged seed populations.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/4990ec247b1a85385e57f7ef.png"},{"id":84163350,"identity":"00d4f918-ea9a-4c1a-9476-688428deb5c3","added_by":"auto","created_at":"2025-06-08 15:00:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":328196,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual viability dynamics of \u003cem\u003eSolanum rostratum\u003c/em\u003e seeds under accelerated aging condition. Tetrazolium staining profiles of seeds from 2008-2023 seeds after 96-hour aging (60℃, 85% RH). Seeds with both embryo and non-embryonic tissues stained bright red are considered viable, while those with mottled or no staining in these tissues are classified as non-viable.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/7aaab583207bc266ac807f4e.png"},{"id":84163363,"identity":"6376a155-38f2-4e5c-ab02-21b5a75c579d","added_by":"auto","created_at":"2025-06-08 15:00:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31644,"visible":true,"origin":"","legend":"\u003cp\u003eViability of \u003cem\u003eSolanum rostratum \u003c/em\u003esoil seed bank viability under accelerated aging conditions across two habitat types. (a) Comparative viability attrition between grassland and abandoned farmland habitats during 0-96 h aging (60°C, 85% RH). (b) Three-parameter Weibull distribution fits demonstrating parametric divergence in seed persistence thresholds across habitats.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/ccc5272f7344888ef47102ce.png"},{"id":86297047,"identity":"dc6944df-e9b7-4f55-b614-14dee30dd10e","added_by":"auto","created_at":"2025-07-09 05:33:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2111404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6742628/v1/ee2ac7ec-b3c9-4e88-ab06-a45b62f531c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Weibull Distribution-Based Method for Estimating Soil Seed Bank Longevity in Annual Invasive Plants","fulltext":[{"header":"Background","content":"\u003cp\u003ePersistent soil seed banks serve as a fundamental driver of invasion success in annual invasive plant species. Defined as reservoirs of viable seeds and vegetative propagules capable of regenerating plant communities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], seed banks play a vital role in maintaining future weed populations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previous studies demonstrates that many annual invasive species produce seeds capable of surviving extended periods in soil [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], maintaining viability under adverse conditions while rapidly germinating when conditions improve [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This delayed germination strategy preserves population genetic diversity and enhances adaptability to environmental variability [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe soil seed banks of annual invasive plants not only sustain invasive population persistence but also severely impact native seed banks. Research demonstrates that plant invasions significantly reduce soil seed bank species richness and seed density, while particularly depleting native seed reserves [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Ecological restoration resistance increases with the duration of invasion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], as substantial quantities of invasive seeds stored in natural ecosystems create significant barriers to vegetation recovery. Consequently, preventing invasive seed bank replenishment is critical for effective ecological restoration. Systematic investigations into invasive plant seed bank characteristics serve dual purposes: predicting future invasion risks [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and informing science-based agricultural management and control strategies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A comprehensive understanding of seed quantity and viability in annual invasive plants is essential for evaluating re-invasion potential and long-term control efficacy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Such knowledge enables policymakers to develop region-specific, cost-effective management strategies with long-term viability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, current methods for assessing seed viability and longevity in annual invasive plant species remain far from adequate. Most soil seed bank studies prioritize species composition and abundance [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], factors influencing seed bank density and diversity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and ecological roles of seed banks [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], while research on seed longevity and viability is notably limited. Traditional methods face significant limitations. Germination assays [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]: These involve soil sampling, controlled germination, and seedling counting to estimate seed bank viability. However, this approach fails to determine seed age or original quantities and introduces bias in species with complex dormancy mechanisms (e.g., \u003cem\u003eS. rostratum\u003c/em\u003e) due to prolonged germination periods. Another method seed burial experiments [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]: While partially simulating natural seed bank dynamics, these labor-intensive methods require burying known seed quantities and periodic retrieval for viability testing. Their artificial setup and time demands \u0026ndash; particularly for deeply dormant species \u0026ndash; limit their utility in assessing natural seed longevity. These shortcomings underscore the urgent need for efficient, reliable methods to evaluate viability and longevity in invasive plant seed banks.\u003c/p\u003e \u003cp\u003eStudies demonstrate that high-temperature/humidity aging treatments effectively simulate natural seed deterioration through shared physiological mechanisms [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], providing a practical approach to rapidly assess seed viability and longevity in annual invasive species. The Accelerated aging (AA) test \u0026ndash; a standard method for evaluating seed vigor \u0026ndash; expose seeds to elevated temperatures (typically 40\u0026thinsp;~\u0026thinsp;50\u0026deg;C) and approximately 100% relative humidity (RH), inducing rapid degradation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. High-vigor seeds exhibit slower viability decline under these conditions while maintaining higher germination rates. Unlike crop seeds, certain invasive species exhibit significantly slower aging rates at 40\u0026thinsp;~\u0026thinsp;50\u0026deg;C, requiring temperatures above 50\u0026deg;C to effectively accelerate deterioration [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Monitoring viability changes under such conditions allows researchers to infer seed storage potential, longevity, and physiological quality. Recent applications of the AA test in invasive plant control studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] highlight its utility in ecological management. The three-parameter Weibull distribution has proven particularly effective for modeling seed longevity. For instance, it successfully characterized vigor and lifespan in \u003cem\u003eCarpobrotus edulis\u003c/em\u003e' seeds [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This distribution\u0026rsquo;s flexibility and superior model fit [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] make it ideal for describing asymmetric viability decline patterns. Its probability density function (PDF) is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}f\\left(x;\\lambda\\:,k,c\\right)={e}^{{-\\left(\\frac{x-c}{\\lambda\\:}\\right)}^{k}}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this distribution, \u0026#119909; \u0026ge; \u0026#119888;, and the density function \u0026#119891;(\u0026#119909;; \u0026#120582;, \u0026#119896;, \u0026#119888;) equals zero when x \u0026lt; \u0026#119888;. The primary distinction between the three parameter Weibull distribution and the standard two-parameter version lies in the inclusion of a location parameter \u0026#119888;, which enhances model flexibility and allows for better fitting of skewed data sets. This enables precise quantification of seed storage potential and longevity through vigor analysis, with particular effectiveness in modeling asymmetric viability decline patterns [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although originally developed for reliability analysis in engineering fields \u0026ndash; such as memory devices, fatigue resistance in mechanical systems, and aerospace structures \u0026ndash; the three-parameter Weibull distribution has also been shown to effectively describe seed germination rates and germination speed [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In seed science, its simplicity and adaptability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] have made it an important mathematical tool for modeling seed survival time and longevity. Compared with other nonlinear models, such as the Morgan-Mercer-Flodin, Richards, Mitscherlich, Gompertz, and logistic functions, the three-parameter Weibull distribution demonstrates superior fitting accuracy and lower sensitivity to initial seed vigor values, resulting in more stable and reliable estimates [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Therefore, it is well-suited for accurately estimating the seed longevity of annual invasive plant species.\u003c/p\u003e \u003cp\u003eTraditional methods for assessing seed vigor historically relied on physical or physiological traits such as color, morphology, volume, weight, density, electrical conductivity, and respiration rate. However, these methods frequently produced inconsistent outcomes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. To enhance the precision of vigor evaluation in accelerated aging tests, the tetrazolium (TZ) test has emerged as a complementary approach. Widely adopted for seed viability testing due to its rapidity, sensitivity, and broad applicability [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], this method was specifically optimized in this study for the annual invasive species \u003cem\u003eS. rostratum\u003c/em\u003e. Building on protocols established for its close relative species \u003cem\u003eSolanum melongena\u003c/em\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], the refined tetrazolium staining protocol enabled precise tracking of seed vigor dynamics during aging.\u003c/p\u003e \u003cp\u003eBuffalo bur, \u003cem\u003eS. rostratum\u003c/em\u003e, a globally malignant invasive annual weed native to Mexico and the central United States [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], has colonized regions across North America, Europe, Africa, Asia, and Oceania, threatening ecosystem stability and agroecological security [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This species exhibits exceptional reproductive capacity, with individual plants producing 1,600\u0026thinsp;~\u0026thinsp;43,800 seeds [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and readily forms persistent soil seed banks. Approximately 55% of seeds germinate in the first post-sowing spring, while the remainder germinate in subsequent growing seasons [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], around 20% entering long-term dormancy to sustain seed bank longevity. The seeds (2.2\u0026thinsp;~\u0026thinsp;2.8 mm diameter) possess a dense, honeycomb-patterned coat that enhances permeability and mechanical resistance [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], coupled with notable stress tolerance [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These traits facilitate a dual physical-physiological dormancy mechanism [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], enabling the establishment of large, persistent seed banks \u0026ndash; a critical driver of its ongoing global invasion success.\u003c/p\u003e \u003cp\u003eSeed longevity serves as a critical indicator for assessing the persistence, spread, and outbreak potential of annual invasive plants within specific habitats. Despite its ecological significance, systematic evaluation of seed longevity in such species remains lacking. \u003cem\u003eS. rostratum\u003c/em\u003e was selected as a model organism for this study due to its representative seed traits, ease of sampling, and dual theoretical-practical relevance. This research aims to: (1) develop an accelerated aging system for \u003cem\u003eS. rostratum\u003c/em\u003e seeds under high-temperature/humidity conditions to rapidly assess viability, (2) analyze viability patterns across seed collection years and geographic origins to identify key vigor determinants, (3) model aging dynamics using the three-parameter Weibull distribution, calculating L\u003csub\u003e50\u003c/sub\u003e (time to 50% viability loss) and estimating natural longevity via polynomial regression, (4) assess invasion lifespan by analyzing soil seed bank L\u003csub\u003e50\u003c/sub\u003e values across habitats. By elucidating seed longevity mechanisms and spatiotemporal viability trends, this study provides a scientific framework for long-term invasive weed management, technical support for tracing invasion histories, evidence-based strategies for evaluating control measures, and region-specific management protocols for invasive species.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials collection\u003c/h2\u003e \u003cp\u003eSeeds of \u003cem\u003eS. rostratum\u003c/em\u003e were collected from seven Chinese provinces and municipalities: Inner Mongolia Autonomous Region, Xinjiang Uygur Autonomous Region, Shanxi, Jilin, Liaoning, Hebei, and Beijing during early autumn from 2008 to 2022 (detailed location information including longitude and latitude for each collection site are provided in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Formal taxonomic identification was performed by Dr. Guoliang Zhang. Voucher specimens are deposited in the Invasive Alien Plants Control and Management Laboratory at the Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing. This research complies with international conventions including the Convention on Biological Diversity and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES); no endangered or protected species were involved.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCondition Screening\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eOptimization of tetrazolium staining duration for\u003c/b\u003e \u003cb\u003eS. rostratum\u003c/b\u003e \u003cb\u003eseed viability assessment.\u003c/b\u003e To optimize the tetrazolium staining protocol for \u003cem\u003eS. rostratum\u003c/em\u003e seeds, mature seeds collected in October 2022 from Huai\u0026rsquo;an County, Zhangjiakou City, Hebei Province, China (114\u0026deg;23\u0026prime;8\u0026Prime;E, 40\u0026deg;40\u0026prime;27\u0026Prime;N) were used. Fifty seeds were placed into each of nine 5 mL centrifuge tubes. A 1% 2,3,5-triphenyltetrazolium chloride (TTC) solution was added to each tube, followed by incubation in a 40\u0026deg;C water bath. At time points of 12, 24, and 36 hours, three replicates per time point were removed from the bath. After discarding the TTC solution via pipetting, seeds were rinsed 1\u0026thinsp;~\u0026thinsp;2 times with distilled water, longitudinally sectioned with a scalpel and forceps, and microscopically examined for staining patterns. The influence of staining duration on coloration quality was assessed, and representative staining images were compiled to determine the optimal staining time for use in subsequent accelerated aging experiments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetermination of optimal accelerated aging temperature.\u003c/b\u003e To shorten the experimental duration and improve efficiency, an evaluation was conducted to determine the optimal temperature for accelerated aging of \u003cem\u003eS. rostratum\u003c/em\u003e seeds. Mature seeds collected in October 2022 from Huai\u0026rsquo;an County, Zhangjiakou City, Hebei Province, China, were subjected to aging treatments at four temperatures: 45\u0026deg;C, 50\u0026deg;C, 55\u0026deg;C, and 60\u0026deg;C, under a constant relative humidity of 85%. For each temperature treatment, 18 replicates were prepared, each consisting of 50 seeds evenly placed in mesh bags and laid flat in an aging incubator (Model LH-1509). For the 45\u0026deg;C, 50\u0026deg;C, and 55\u0026deg;C treatments, samples were taken every 3 days; for the 60\u0026deg;C treatment, sampling was performed daily. At each sampling point, three replicates were assessed using the tetrazolium test method to evaluate seed viability. Data were organized using Excel 2023, and viability analysis and significance testing were performed using SPSS 27.0. The percentage of viable seeds was calculated to determine the optimal temperature for accelerated aging.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData analysis and visualization.\u003c/b\u003e For all condition-screening experiments mentioned above, data were compiled and organized using the Microsoft Excel 2023. Viability analysis and statistical tests were performed using the SPSS version 27.0. Graphs were generated using the Origin software.\u003c/p\u003e\n\u003ch3\u003eEstablishment of the aging model\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eEffect of geographical origin on seed longevity.\u003c/b\u003e To evaluate the impact of geographical variation on seed longevity and establish an aging model for \u003cem\u003eS. rostratum\u003c/em\u003e, seeds collected in the same year from six distinct populations (geographical regions; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) underwent accelerated aging experiments. For each population, three replicates (50 seeds per replicate) were prepared in mesh bags across all sampling time points. Using optimized conditions (Section 2.1.2), seeds were aged in an incubator maintained at 60\u0026deg;C and 85% relative humidity. Sampling commenced at 0 hours, with subsequent collections at 24-hour intervals over a 96-hour period. Viability at each interval was assessed via the standardized tetrazolium staining protocol.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccelerated aging test sampling locations of \u003cem\u003eSolanum rostratum\u003c/em\u003e seeds from distinct geographical regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude and longitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollection date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuyang County, Baotou City, Inner Mongolia Autonomous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u0026deg;3\u0026prime;24\u0026Prime; E, 41\u0026deg;1\u0026prime;47\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuimin District, Hohhot city, Inner Mongolia Autonomous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u0026deg;37\u0026prime;26\u0026Prime; E, 40\u0026deg;48\u0026prime;29\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChangji city, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87\u0026deg;18\u0026prime;0\u0026Prime; E, 44\u0026deg;1\u0026prime;12\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCJ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTianzhen county, Datong city, Shanxi Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u0026deg;4\u0026prime;48\u0026Prime; E, 40\u0026deg;25\u0026prime;12\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTZ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongyu county, Baicheng city, Jilin province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u0026deg;4\u0026prime;48\u0026Prime; E, 44\u0026deg;49\u0026prime;12\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrat Front Banner, Bayannur City, Inner Mongolia Autonomous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u0026deg;39\u0026prime;0\u0026Prime; E, 40\u0026deg;43\u0026prime;11\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation between seed collection year and longevity.\u003c/b\u003e To refine the aging model of \u003cem\u003eS. rostratum\u003c/em\u003e seeds and investigate the temporal influence on seed longevity, seeds collected across distinct years were subjected to accelerated aging tests (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details). Experimental protocols mirrored those in Section 2.1.2: seeds were incubated in a controlled aging chamber maintained at 60\u0026deg;C and 85% relative humidity. Sampling intervals spanned 0\u0026ndash;96 hours at 24-hour increments, with three biological replicates per time point and year. Post-aging viability was assessed using the optimized TTC staining protocol established in Section 2.1.1 (1% TTC solution, 40\u0026deg;C, 24 hour incubation), and quantitative viability metrics were systematically recorded.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccelerated aging test sampling locations of \u003cem\u003eSolanum rostratum\u003c/em\u003e seeds from different years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude and longitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCollection date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShuangta District, Chaoyang City, Liaoning Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120˚28'47\" E, 41˚36'36\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2008.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhenlai County, Baicheng City, Jilin Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122\u0026deg;51'0\" E, 45\u0026deg;34'12\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2015.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuquan county, Xing 'an League, Inner Mongolia Autonomous Region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121˚35'24\" E, 45˚22'48\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeipiao City, Chaoyang City, Liaoning Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120˚55'34\" E, 41˚30'25\" N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTianzhen county, Datong city, Shanxi Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u0026deg;4\u0026prime;48\u0026Prime; E, 40\u0026deg;25\u0026prime;12\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuaian County, Zhangjiakou City, Hebei Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114\u0026deg;23\u0026prime;8\u0026Prime; E, 40\u0026deg;40\u0026prime;27\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYanqing District, Beijing City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115\u0026deg;51\u0026prime;22\u0026Prime; E, 40\u0026deg;23\u0026prime;38\u0026Prime; N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2023.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSeed aging modeling framework.\u003c/b\u003e In this study, L₅₀ is defined as the duration required for seed viability to decline to 50%, serving as a quantitative metric to compare longevity across \u003cem\u003eS. rostratum\u003c/em\u003e populations. The aging kinetics were modeled with a three-parameter Weibull distribution, whose probability density function is:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}f\\left(x;\\lambda\\:,k,c\\right)={e}^{{-\\left(\\frac{x-c}{\\lambda\\:}\\right)}^{k}}\\end{array}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\ge\\:c\\)\u003c/span\u003e\u003c/span\u003e, and when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\u0026lt;c\\)\u003c/span\u003e\u003c/span\u003e, the density function \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(x;\\:\\lambda\\:,\\:k,\\:c\\right)\\)\u003c/span\u003e\u003c/span\u003e is equal to 0. Here, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e represents the scale parameter of the three-parameter Weibull distribution, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e represents the shape parameter of the three-parameter Weibull distribution, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e is the threshold parameter of the three-parameter Weibull distribution. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents the accelerated aging time, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the seed viability at the corresponding time. Raw viability data were collated in Microsoft Excel 2023, with population-specific longevity metrics computed for each accession. Parameter estimation and the three-parameter Weibull distribution curve fitting were implemented in MATLAB R2024a. Model diagnostics and distribution plots were concurrently generated to validate goodness-of-fit.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSeed longevity calculation method.\u003c/b\u003e All data from the accelerated aging experiment of \u003cem\u003eS. rostratum\u003c/em\u003e seeds from different years were organized with the Excel 2023. The seed aging model for each year's \u003cem\u003eS. rostratum\u003c/em\u003e population was established based on the three-parameter Weibull distribution outlined in section 2.2.3 using the Matlab R2024a software, and the L\u003csub\u003e50\u003c/sub\u003e value for seeds from different years was obtained. The L\u003csub\u003e50\u003c/sub\u003e values for seeds from different years were then polynomially fitted using Origin 2023 software to calculate seed longevity. The seed longevity calculation model was established to estimate and infer the seed lifespan of \u003cem\u003eS. rostratum\u003c/em\u003e, and the corresponding model graph was plotted.\u003c/p\u003e\n\u003ch3\u003eModel application experiment\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eApplication of Accelerated Aging Tests in Soil Seed Bank Research.\u003c/b\u003e To evaluate the applicability of the accelerated aging test conditions and the three-parameter Weibull distribution method established in this study under field-relevant conditions, soil seed bank samples of \u003cem\u003eS. rostratum\u003c/em\u003e were collected from two distinct habitats:\u003c/p\u003e \u003cp\u003eGrassland habitat: Ke\u0026rsquo;erqin Grassland in Taobei District, Baicheng City, Jilin Province (45\u0026deg;34\u0026prime;12\u0026Prime;N, 122\u0026deg;51\u0026prime;0\u0026Prime;E).\u003c/p\u003e \u003cp\u003eAbandoned farmland habitat: Huai\u0026rsquo;an County, Zhangjiakou City, Hebei Province (40\u0026deg;40\u0026prime;27\u0026Prime;N, 114\u0026deg;23\u0026prime;8\u0026Prime;E).\u003c/p\u003e \u003cp\u003eBoth of these locations belong to the temperate continental monsoon climate zone. Soil samples were collected from 0\u0026thinsp;~\u0026thinsp;10 cm and 10\u0026thinsp;~\u0026thinsp;20 cm depths at each site. After cleaning, drying, and sieving (through 10 and 14 mesh), black kidney-shaped \u003cem\u003eS. rostratum\u003c/em\u003e seeds with distinct characteristics were manually selected. A total of 400 seeds were screened from each location for the experiment. The \u003cem\u003eS. rostratum\u003c/em\u003e seeds were placed in mesh bags, with each group of seeds taken from the aging chamber at each sampling time point consisting of 3 replicates, each containing 50 seeds. According to the results of the previous experiment 2.1.2, the seeds were placed in a seed aging chamber at 60\u0026deg;C with 85% relative humidity. Samples were taken every 24 hours from 0 hours to 96 hours, with 3 replicates from each location's group at each observation time point. The seed vitality was assessed using the tetrazolium test, following the method outlined in 2.1.1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eField-adapted longevity model development.\u003c/b\u003e All data from the accelerated aging experiment of \u003cem\u003eS. rostratum\u003c/em\u003e seeds from different years were organized using Excel 2023. Based on the Matlab R2024a software, the three-parameter Weibull distribution model (described in 2.2.3) was applied to establish the seed aging model for each year\u0026rsquo;s population of \u003cem\u003eS. rostratum\u003c/em\u003e seeds, and the L\u003csub\u003e50\u003c/sub\u003e values for seeds from different years were obtained. The L\u003csub\u003e50\u003c/sub\u003e values of seeds from different years were polynomially fitted using Origin 2023 software to calculate seed longevity and establish a longevity calculation model. The seed longevity of \u003cem\u003eS. rostratum\u003c/em\u003e was then estimated and inferred, and the corresponding model plot was generated. The data organization and analysis were performed in the same way as described in step 2.2.4.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eCriteria and optimal staining duration for evaluating\u003c/b\u003e \u003cb\u003eS. rostratum\u003c/b\u003e \u003cb\u003eseed viability using tetrazolium test\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo establish a standardized protocol for assessing \u003cem\u003eS. rostratum\u003c/em\u003e seed viability via the tetrazolium test, this study defined three distinct viability categories based on staining patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Viable seeds exhibited uniform bright red staining of both the embryo and endosperm with intact tissue morphology. Seeds displaying partial or unstained embryos combined with more than 50% unstained or structurally compromised storage tissues were classified as non-viable (mottled-stained). Fully unstained seeds with softened, decayed, or damaged tissues were categorized as non-viable (dead).\u003c/p\u003e \u003cp\u003eStaining duration significantly influenced diagnostic accuracy. Time-course experiments (40\u0026deg;C, 1% TTC) revealed suboptimal results at 12 hours, with only 51.8% viable seeds, 44.8% mottled-stained, and 3.2% dead (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The high mottled-stained proportion indicated incomplete enzymatic reduction of TTC. In contrast, 24-hour and 48-hour treatments achieved 96.6% and 96.8% viable seeds, respectively, with complete elimination of mottled staining. Given negligible improvement beyond 24 hours, a 24-hour staining duration at 40\u0026deg;C was selected as the optimal balance between efficiency and precision for subsequent experiments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOptimal temperature for rapid viability assessment of\u003c/b\u003e \u003cb\u003eS. rostratum\u003c/b\u003e \u003cb\u003eseeds\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb illustrates the viability decline curves of \u003cem\u003eS. rostratum\u003c/em\u003e seeds under varying temperature treatments (all maintained at 85% relative humidity). The results demonstrate that temperature profoundly influences the aging process, modulating both the survival curve morphology and the median viability period L\u003csub\u003e50\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eAt the minimal test temperature of 45\u0026deg;C, seeds exhibited progressive deterioration, showing approximately 40% viability reduction over 15 days. Comparative analysis revealed comparable deterioration rates between 45\u0026deg;C and 50\u0026deg;C conditions, both treatments reached 50% viability on day 9, though diverging thereafter \u0026ndash; 45\u0026deg;C seeds maintained gradual decline, while 50\u0026deg;C specimens underwent rapid depletion to complete non-viability by day 15. Thermal acceleration became pronounced at 55\u0026deg;C, with viability initiating exponential decay on day 3 (L\u003csub\u003e50\u003c/sub\u003e attainment: day 4; full viability loss: day 9). The most extreme aging occurred at 60\u0026deg;C, where viability demonstrated precipitous decline within 24 hours, culminating in complete depletion by day 4.\u003c/p\u003e \u003cp\u003eCollectively, under sustained 85% humidity, incremental temperature elevation (from 45\u0026deg;C to 60\u0026deg;C) reduced L\u003csub\u003e50\u003c/sub\u003e values from 15 days to less than 2 days, confirming thermal sensitivity as a critical driver of viability loss. To maximize efficiency in viability assessment and aging sensitivity analysis, 60\u0026deg;C with 85% relative humidity was selected as the optimal accelerated aging condition. This protocol enables comprehensive seed viability evaluation within one week.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeographic consistency in seed viability dynamics of\u003c/b\u003e \u003cb\u003eS. rostratum\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUnder standardized accelerated aging conditions (60\u0026deg;C, 85% RH), interregional seed seeds of \u003cem\u003eS. rostratum\u003c/em\u003e collected within the same year exhibited conserved viability loss patterns. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c), all populations demonstrated triphasic degradation kinetics: gradual viability reduction during the initial 24-hour phase (0\u0026thinsp;~\u0026thinsp;1 day) followed by precipitous decline from day 2 onward, culminating in complete viability loss by days 2\u0026thinsp;~\u0026thinsp;3. The consistent pattern across populations indicates that population heterogeneity has little impact on seed longevity, suggesting that seed lifespan is an inherent trait.\u003c/p\u003e \u003cp\u003eTo quantitatively characterize these dynamics, viability time-series data were fitted to the three-parameter Weibull survival function. The resultant models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(d)) demonstrated excellent goodness-of-fit, with near-identical decay parameters observed among geographically distinct populations. This convergence underscores the robustness of the aging mechanism under controlled environmental stress.\u003c/p\u003e \u003cp\u003eBased on the the three-parameter Weibull distribution, L₅₀ values (time required for viability to decline to 50%) were calculated for each of the six populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(d) and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All L₅₀ values were below 2 days, indicating that \u003cem\u003eS. rostratum\u003c/em\u003e seeds are highly sensitive to aging under high temperature and humidity. While minor differences in L₅₀ were observed among populations, the variation was minimal and not statistically significant, confirming that seeds collected in the same year exhibit consistent longevity under accelerated aging conditions.\u003c/p\u003e \u003cp\u003eThese findings establish the three-parameter Weibull distribution as an effective descriptor of \u003cem\u003eS. rostratum\u003c/em\u003e seed aging under combined thermal-hydric stress. The demonstrated geographic invariance in longevity parameters provides critical baseline data for predictive modeling of soil seed bank persistence and retrospective estimation of invasion chronologies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantitative parameters of Three-parameter Weibull distribution and L\u003csub\u003e50\u003c/sub\u003e values characterizing \u003cem\u003eSolanum rostratum\u003c/em\u003e seed viability across geographical populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation abbreviations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003csub\u003e50\u003c/sub\u003e (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-42.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=exp\\left({-\\left(\\frac{x+42.64}{44.33}\\right)}^{107.08}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=exp\\left({-\\left(\\frac{x+8.54}{10.21}\\right)}^{39.18}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-24.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=exp\\left({-\\left(\\frac{x+24.42}{26.20}\\right)}^{54.35}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-26.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=exp\\left({-\\left(\\frac{x+26.70}{28.35}\\right)}^{83.28}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e222.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e571.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-221.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=exp\\left({-\\left(\\frac{x+221.39}{222.98}\\right)}^{571.01}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=exp\\left({-\\left(\\frac{x+9.5}{10.87}\\right)}^{37.16}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e represents viability (%), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents aging time in days. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the scale parameter of the three-parameter Weibull distribution, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e represents the shape parameter of the three-parameter Weibull distribution, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e represents the threshold parameter of the three-parameter Weibull distribution. Geographical codes: GY: Guyang County (Inner Mongolia), HT: Hohhot City (Inner Mongolia), CJ: Changji City (Xinjiang), TZ: Tianzhen County (Shanxi), TY: Tongyu County (Jilin), BY: Bayannur City (Inner Mongolia).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cb\u003eChronological aging patterns in\u003c/b\u003e \u003cb\u003eS. rostratum\u003c/b\u003e \u003cb\u003eseeds\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccelerated aging trials revealed divergent viability decay kinetics among \u003cem\u003eS. rostratum\u003c/em\u003e seed seeds stratified by collection year (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a)). Freshly harvested seeds and one-year-old seeds exhibited progressive deterioration commencing on day 1 of thermal-hydric stress. In stark contrast, seeds aged 2\u0026ndash;4 years displayed catastrophic viability collapse within the first 24 hours, reaching near-complete non-viability by day 2. Notably, 8\u0026ndash;15 year-old seeds entered experiments with substantially compromised initial viability and achieved total non-viability within 24 hours.\u003c/p\u003e \u003cp\u003eTemporal viability profiles were robustly modeled using the three-parameter Weibull survival function (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b), Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), demonstrating excellent goodness-of-fit. Derived L\u003csub\u003e50\u003c/sub\u003e values exhibited strong negative correlation with storage duration: maximal longevity occurred in fresh seeds (L\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.51 days), followed by 1-year-old seeds (2.08 days). Seeds stored for less than 2 years had L₅₀ values greater than 2 days, while those stored for 3\u0026ndash;4 years had L₅₀ values between 1\u0026ndash;2 days. For seeds stored for more than 5 years, L₅₀ values fell below 1 day, indicating extreme sensitivity to aging.\u003c/p\u003e \u003cp\u003eThese findings establish a quantifiable inverse relationship between chronological age and aging resistance in \u003cem\u003eS. rostratum\u003c/em\u003e seeds. The L₅₀ metric functions as a dual indicator of both storage history and physiological competence, providing critical insights for seed bank longevity modeling and invasion timeline reconstructions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-parameter Weibull distribution model parameters and L₅₀ values characterizing \u003cem\u003eSolanum rostratum\u003c/em\u003e seed viability across collection years\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollection year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003csub\u003e50\u003c/sub\u003e (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+0.32}{0.39}\\right)}^{1.62}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+0.26}{0.43}\\right)}^{2.05}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-37.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+37.49}{38.94}\\right)}^{108.04}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-19.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+19.78}{21.34}\\right)}^{70.96}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-26.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+26.70}{28.35}\\right)}^{83.26}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-110.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+110.14}{112.42}\\right)}^{210.98}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V={e}^{{-\\left(\\frac{x+20.90}{23.54}\\right)}^{63.75}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e represents viability (%), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents aging time in days. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the scale parameter of the three-parameter Weibull distribution, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e represents the shape parameter of the three-parameter Weibull distribution, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e represents the threshold parameter of the three-parameter Weibull distribution.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimation of\u003c/b\u003e \u003cb\u003eS. rostratum\u003c/b\u003e \u003cb\u003eseed longevity\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccelerated aging trials of \u003cem\u003eS. rostratum\u003c/em\u003e seed cohorts collected between 2008\u0026ndash;2023 revealed an inverse relationship between chronological age and L₅₀ values. Polynomial regression of this temporal decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) produced a robust predictive model, evidenced by a near-unity coefficient of determination (R\u0026sup2; = 0.979) and minimal residual error (RSS\u0026thinsp;=\u0026thinsp;0.110), confirming exceptional model fidelity.\u003c/p\u003e \u003cp\u003eThe regression identified two theoretical longevity thresholds where L₅₀ approaches zero: 9.91 years (primary intercept) and 15.21 years (secondary intercept). Empirical validation using 2008-collected seeds (chronological age\u0026thinsp;=\u0026thinsp;15 years) demonstrated strong alignment with the latter threshold, reinforcing model accuracy. Based on the primary intercept, we estimate \u003cem\u003eS. rostratum\u003c/em\u003e seed bank persistence at 9.91 years under natural soil conditions.\u003c/p\u003e \u003cp\u003eGibberellic acid (GA₃) treatment effectively promotes rapid germination in \u003cem\u003eS. rostratum\u003c/em\u003e seeds [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. To validate the ecological relevance of the calculated 9.91-year seed longevity, we applied GA₃ to seeds collected in 2008, 2015, and 2023 (n\u0026thinsp;=\u0026thinsp;6 replicates per year, 30 seeds per replicate). The 2008 seeds showed complete viability loss with no germination after 30 days. The 2015 seeds exhibited limited germination (approximately 10% per replicate) within 7 days, but with significantly reduced seedling vigor compared to 2023 seeds (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for height difference). In contrast, the 2023 seeds demonstrated rapid germination (more than 80% within 7 days) and robust seedling growth. These results demonstrates that \u003cem\u003eS. rostratum\u003c/em\u003e seeds from 2015 still possess germination capacity albeit with markedly declined viability, whereas 2008 seeds have completely lost viability. These findings confirm the ecological relevance of the 9.91-year seed longevity estimate.\u003c/p\u003e \u003cp\u003eComplementary histochemical analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) revealed age-dependent viability patterns. Seeds aged less than 6 years maintained stable initial viability but exhibited progressive staining attenuation during aging, while older cohorts (\u0026ge;\u0026thinsp;7 years) showed accelerated degradation kinetics. Distinct temporal gradients in tetrazolium staining intensity correlated precisely with seed age, enabling visual differentiation of senescence stages.\u003c/p\u003e \u003cp\u003eThis study provides technical support for rapid determination of \u003cem\u003eS. rostratum\u003c/em\u003e seed age. In practical applications, seeds recovered from soil samples can be subjected to accelerated aging treatment, and their viability patterns compared to the reference images in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e to estimate their age range. By integrating viability curves with tailored control strategies, targeted management measures can be developed for different invasion stages of \u003cem\u003eS. rostratum\u003c/em\u003e, thereby enhancing the effectiveness of weed control and informing ecological management decisions for invasive species.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePolynomial fitting equations and parameter estimates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:y=Intercept+B1\\times\\:x+B2\\times\\:{x}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.11942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.41537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.04919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.00312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Sum of Squares (RSS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared (COD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: The residual sum of squares (RSS) represents the sum of the squared differences between the observed and predicted values in the regression model. A smaller RSS indicates a better model fit. The coefficient of determination (R\u0026sup2;) reflects the proportion of variance in the dependent variable explained by the model, ranging from 0 to 1, with values closer to 1 indicating a better fit.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVriation in seed viability of S. rostratum across different habitats\u003c/h2\u003e \u003cp\u003eThe seed viability of \u003cem\u003eS. rostratum\u003c/em\u003e exhibited significant variation across habitats. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, seeds collected from abandoned farmland demonstrated an L\u003csub\u003e50\u003c/sub\u003e value of 1.59 days. Polynomial fitting analysis estimated the seed bank longevity (y-value) in this habitat at 2.39 years, indicating viability comparable to seeds stored for approximately two years. In contrast, seeds from grassland habitats showed a lower L\u003csub\u003e50\u003c/sub\u003e value (1.36 days) with a corresponding longevity estimate of 3.11 years, equivalent to seeds stored for around three years.\u003c/p\u003e \u003cp\u003eAlthough ecological differences in seed viability exist between habitats, the three-parameter Weibull distribution model developed in this study demonstrated good fit and parameter stability for both habitat types, indicating strong explanatory power. This seed bank longevity estimation model not only enables quantitative evaluation of the survival potential of invasive species seeds in specific regions but also shows good cross-regional applicability. Application experiments using soils from two ecologically distinct locations confirmed the model's robustness under varying environmental conditions. These findings suggest that the model can be reliably used to predict the outbreak potential of invasions across multiple regions, thereby providing a scientific basis for the development of site-specific management strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree-parameter Weibull distribution (TPWD) model parameters and L\u003csub\u003e50\u003c/sub\u003e values quantifying \u003cem\u003eSolanum rostratum\u003c/em\u003e seed viability dynamics across different habitats\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003csub\u003e50\u003c/sub\u003e (days)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbandoned farmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-30.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=\\text{e}\\text{x}\\text{p}\\left({-\\left(\\frac{x+30.54}{32.26}\\right)}^{91.67}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e273.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e453.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-271.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=\\text{e}\\text{x}\\text{p}\\left({-\\left(\\frac{x+271.82}{273.18}\\right)}^{453.69}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\)\u003c/span\u003e\u003c/span\u003e represents viability (%), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\)\u003c/span\u003e\u003c/span\u003e represents aging time in days. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\:\\)\u003c/span\u003e\u003c/span\u003erepresents the scale parameter of the three-parameter Weibull distribution, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e represents the shape parameter of the three-parameter Weibull distribution, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e represents the threshold parameter of the three-parameter Weibull distribution.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOptimal conditions for artificial accelerated aging\u003c/h2\u003e \u003cp\u003eThe tetrazolium test, widely used for detecting infarct areas in mammalian tissues (Bederson et al., 1986)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], is also effective for assessing seed viability (Towill and Mazur, 1975)[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The staining agent, 2,3,5-triphenyl tetrazolium chloride (TTC), is a lipid-soluble, photosensitive compound. In seeds or plant tissues, viable tissues are stained varying shades of red, whereas dead or non-viable tissues remain unstained. Studies on \u003cem\u003eHordeum vulgare\u003c/em\u003e (Lopez Del Egido et al., 2017; Ma et al., 2019)[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and the artificial, self-pollinated cereal crop species \u0026times;\u003cem\u003eTriticosecale Wittmack\u003c/em\u003e (triticale) (Souza et al., 2010)[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] demonstrate that the tetrazolium test generates viability estimates comparable to germination tests but with significantly greater efficiency. During preliminary viability testing for \u003cem\u003eS. rostratum\u003c/em\u003e, both germination and tetrazolium tests were attempted. However, after 20 days of germination trials, only untreated seeds germinated, achieving a rate of merely 1%. For annual invasive plants like \u003cem\u003eS. rostratum\u003c/em\u003e \u0026ndash; which exhibit hard seed coats and mixed dormancy \u0026ndash; germination tests are unsuitable for rapid viability assessment in accelerated aging experiments. Consequently, the tetrazolium test, as employed in this study, offers a more efficient and reliable alternative. Notably, due to \u003cem\u003eS. rostratum\u003c/em\u003e\u0026rsquo;s unique seed coat structure, piercing the coat at a non-critical site with a scalpel is essential prior to staining to ensure adequate TTC solution penetration [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This step is crucial for accurate staining results. By optimizing staining conditions and protocols, the tetrazolium test effectively evaluates \u003cem\u003eS. rostratum\u003c/em\u003e seed viability, providing robust data for subsequent research.\u003c/p\u003e \u003cp\u003eIn agricultural research, seed aging conditions have been extensively studied across crops. For example, rice (\u003cem\u003eOryza sativa\u003c/em\u003e) seeds exhibited a more than 60% reduction in germination and vigor indices after 9 days at 50\u0026deg;C [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], while barley (\u003cem\u003eHordeum vulgare\u003c/em\u003e) seeds lost viability within 48 hours at 42\u0026deg;C [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Similarly, chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e) seeds became non-viable after 48 hours of soaking in a saturated NaCl solution at 41℃ [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These findings informed the selection of aging conditions for \u003cem\u003eS. rostratum\u003c/em\u003e. Given its complex, dense seed coat, this study initially screened temperatures starting at 45℃ (based on crop seed research) and increased in 5℃ increments, with relative humidity fixed at 85%. Results revealed that \u003cem\u003eS. rostratum\u003c/em\u003e seeds lost viability entirely within 3 days under 85% humidity and 60℃. These findings underscore the species\u0026rsquo; acute sensitivity to hot, humid environments and establish a rapid protocol for informing soil seed bank management strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRegional variation and seed longevity\u003c/h2\u003e \u003cp\u003eInvasive plants frequently undergo rapid adaptive evolution in novel environments, driving modifications in life-history traits \u0026ndash; such as seed germination strategies \u0026ndash; to optimize ecological alignment with local conditions. This process often involves phenotypic plasticity and genetic differentiation, both critical determinants of invasion success [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eS. rostratum\u003c/em\u003e exhibited negligible regional variation in seed longevity. Although environmental heterogeneity may induce shifts in specific traits (e.g., germination timing) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], seed aging dynamics remained consistent across populations. The L\u003csub\u003e50\u003c/sub\u003e showed minimal interregional variation, reflecting low environmental sensitivity of seed longevity. This suggests that \u003cem\u003eS. rostratum\u003c/em\u003e has evolutionarily stabilized its seed longevity strategy during invasion, potentially enhancing its capacity to establish persistent seed banks in diverse habitats.\u003c/p\u003e \u003cp\u003eAdditionally, previous studies report minimal geographic variation in seed traits among populations of some annual invasive species. Instead, high within-population phenotypic and genetic diversity, strong germination potential, and diverse seed traits are critical for their widespread dispersal and colonization [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. For example, the annual invasive weed \u003cem\u003eSenecio vernalis\u003c/em\u003e exhibits little variation in seed traits and germination behavior across habitats with differing disturbance regimes, yet maintains high intraspecific phenotypic and genetic diversity [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Similar to \u003cem\u003eS. rostratum\u003c/em\u003e, \u003cem\u003eS. vernalis\u003c/em\u003e produces dormant seeds capable of forming large, persistent soil seed banks. Combined with high reproductive output and environmental adaptability, this trait enables effective responses to disturbances [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on another annual invader, \u003cem\u003eAvena fatua\u003c/em\u003e, demonstrates that drought and shading reduce reproductive allocation and primary dormancy levels but do not significantly affect seed viability [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. These findings collectively support the hypothesis that invasive plants exhibit substantial phenotypic plasticity, with seed longevity being relatively insensitive to environmental variation. The stability of seed longevity in annual invasive species may therefore enhance their persistence in newly colonized environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImplications of seed aging models for managing annual weeds\u003c/h2\u003e \u003cp\u003eEffective management of invasive plant species necessitates balancing direct costs (e.g., removal, monitoring) and indirect ecological impacts (e.g., disrupted ecosystem functions, secondary spread risks) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accurate cost-benefit analyses of control measures enable optimized resource allocation, improving eradication feasibility [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePersistent soil seed banks drive reinvasion in invasive species, rendering short-term control strategies ineffective without long-term seed bank monitoring and adaptive management [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Seed bank size and longevity are thus critical predictors of invasion resilience and spread potential [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeed aging rates and survival capacities vary among invasive species, reflecting differences in ecological adaptability. Accelerated aging tests enable researchers to construct aging models that elucidate degradation dynamics in annual invaders. These models not only reveal mechanisms of invasion success but also serve as predictive tools for evaluating spread risks.\u003c/p\u003e \u003cp\u003eIn this study, accelerated aging experiments were used to develop a seed aging model for \u003cem\u003eS. rostratum\u003c/em\u003e. Results indicate that this species forms a persistent soil seed bank. Seeds from abandoned farmland exhibited viability equivalent to two-year-old stored seeds, while grassland seeds matched three-year-old seeds. Based on longevity estimates, \u003cem\u003eS. rostratum\u003c/em\u003e seeds in abandoned farmland may persist for \u0026ge;\u0026thinsp;8 years, whereas grassland seeds remain viable for \u0026le;\u0026thinsp;7 years. This suggests greater population stability in abandoned farmland, likely due to favorable conditions such as higher soil moisture, reduced exposure, and lower disturbance \u0026ndash; factors known to decelerate seed aging [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePersistent seed banks imply that annual invasive species can remain dormant near the soil surface and resurge explosively under favorable climatic or disturbance conditions [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Controlling such species thus demands long-term strategies. Soil seed banks of annual invaders should be integrated into dynamic risk assessment frameworks, prioritizing periodic monitoring, targeted interventions, and region-specific protocols over one-time eradication efforts.\u003c/p\u003e \u003cp\u003eThe accelerated aging approach and seed longevity model developed here provide a framework for predicting seed bank persistence. By modeling aging dynamics in annual invaders, researchers can infer their reinvasion potential and persistence in target regions. These models offer critical insights into belowground seed viability and lifespan, clarifying how invasive plants establish and maintain seed banks in novel environments. Such knowledge is vital for designing effective control schedules and replacement strategies to mitigate reinvasion [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFeasibility and advantages of the experimental approach\u003c/h2\u003e \u003cp\u003eWhile prior studies suggest limited environmental influence on seed longevity, this investigation specifically assessed whether the invasive species \u003cem\u003eS. rostratum\u003c/em\u003e maintains stable seed lifespans across heterogeneous environmental conditions. Seeds collected from multiple geographic origins and collection years were subjected to accelerated aging tests, with viability decay modeled via the three-parameter Weibull distribution for nonlinear fitting. Aging models were constructed for six geographically distinct populations under standardized conditions (60\u0026deg;C, 85% relative humidity). The L\u003csub\u003e50\u003c/sub\u003e served as a quantitative metric to compare seed longevity, enabling evaluation of environmental variability and invasion risk projection.\u003c/p\u003e \u003cp\u003eResults demonstrated minimal interpopulation L\u003csub\u003e50\u003c/sub\u003e variation, confirming that \u003cem\u003eS. rostratum\u003c/em\u003e maintains environmentally robust seed longevity \u0026ndash; a trait likely stabilized during invasion. As a widely adopted metric in seed biology, L\u003csub\u003e50\u003c/sub\u003e reflects median survival under controlled aging stress [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Our findings validate its applicability for viability and longevity assessment in this species.\u003c/p\u003e \u003cp\u003eFrom a weed management perspective, determining seed longevity and its decline rate is critical for designing soil seed bank regulation strategies and implementing alternative control methods. Establishing aging models not only elucidates viability loss patterns but also provides a theoretical framework for understanding seed storage physiology.\u003c/p\u003e \u003cp\u003eHowever, the seed aging models developed here require further validation. Future studies should expand sampling to include more collection years and locations while increasing temporal resolution of viability measurements during aging to enhance L\u003csub\u003e50\u003c/sub\u003e accuracy and model robustness. Although accelerated aging offers a practical method for estimating longevity, it remains an artificial simulation and may not fully replicate natural decay processes. Thus, we recommend integrating natural storage experiments and long-term monitoring to achieve comprehensive seed lifespan evaluations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe accelerated aging experiments under controlled high-temperature and high-humidity conditions revealed that \u003cem\u003eS. rostratum\u003c/em\u003e seeds experienced complete viability loss within 3 days at 60\u0026deg;C with 85% relative humidity. Notably, the temporal progression of viability decline exhibited a triphasic pattern: gradual reduction during the initial 0\u0026ndash;1 day period, followed by an abrupt decrease on day 1, culminating in near-total viability loss by days 2\u0026ndash;3. Crucially, this aging trajectory remained consistent across geographically distinct seed sources when tested under standardized conditions, demonstrating negligible regional variation in seed longevity parameters.\u003c/p\u003e \u003cp\u003eAnalysis of seeds from multiple collection years established clear longevity gradients: newly harvested seeds displayed the highest L\u003csub\u003e50\u003c/sub\u003e values, with progressive reductions observed in one-year-old seeds. Specifically, seeds less than 2 years old maintained L\u003csub\u003e50\u003c/sub\u003e values exceeding 2 days, while 3\u0026ndash;4 year-old specimens exhibited L\u003csub\u003e50\u003c/sub\u003e values between 1\u0026ndash;2 days. Seeds surpassing 5 years of age showed markedly reduced L\u003csub\u003e50\u003c/sub\u003e values below 1 day. Polynomial regression modeling of L\u003csub\u003e50\u003c/sub\u003e against chronological age predicted seed longevity at 9.91 years, defined as the temporal intercept where L\u003csub\u003e50\u003c/sub\u003e approaches zero. This mathematical projection indicates an estimated maximum seed lifespan of approximately 9.91 years for \u003cem\u003eS. rostratum\u003c/em\u003e under natural storage conditions.\u003c/p\u003e \u003cp\u003eThese empirical findings establish a robust framework for evaluating viability dynamics in annual invasive species such as \u003cem\u003eS. rostratum\u003c/em\u003e. The derived longevity parameters enable precise estimation of invasion persistence timelines, predictive modeling of seed bank turnover rates under environmental change scenarios, and quantitative assessment of management intervention efficacy. The developed aging model provides actionable insights for designing targeted seed bank depletion strategies, with potential applicability to other annual invasive taxa to improve ecological management outcomes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2021YFD1400300), and Central Public-interest Scientific Institution Basal Research Fund (BSRF202408).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZY conducted data curation, formal analysis, investigation, and visualization, developed the methodology and software, and drafted the manuscript. WF and ZS contributed to data collection, resource provision, and validation, and participated in manuscript review and editing. ZW assisted in investigation, provided resources, and contributed to validation and manuscript review. CS performed data analysis and software development, and contributed to validation and manuscript revision. YZ proposed the research idea, contributed to experimental design, supervised the study, and participated in validation and manuscript revision. GZ contributed to conceptualization and methodology, supervised the project, and was involved in validation and manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbella SR, Chiquoine LP, Vanier CH. Characterizing soil seed banks and relationships to plant communities. 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Front Plant Sci. 2017;8:747.\u003c/li\u003e\n\u003cli\u003eSouza CR de, Ohlson O de C, Gavazza MIA, Panobianco M. Tetrazolium test for evaluating triticale seed viability. Rev bras sementes. 2010;32:163\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eGuo Y, Tong H, Liu Y, Lu X, Zhang H, Yan S, et al. A rapid and sensitive method for evaluating aging life of rice seeds. Hubei Agricultural Sciences. 2022;61:5\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eEbone LA, Goncalves IM, Langaro NC. Accelerated aging test and image analysis for barley seed. Australian Journal of Crop Science. 2019.\u003c/li\u003e\n\u003cli\u003eAra\u0026uacute;jo J de O, Dias DCF dos S, Miranda RM de, Nascimento WM. Adjustment of the electrical conductivity test to evaluate the seed vigor of chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.). J Seed Sci. 2022;44:e202244003.\u003c/li\u003e\n\u003cli\u003eGong W, Wang Y, Chen C, Xiong Y, Zhou Y, Xiao F, et al. The rapid evolution of an invasive plant due to increased selection pressures throughout its invasive history. Ecotoxicology and Environmental Safety. 2022;233:113322.\u003c/li\u003e\n\u003cli\u003eYu H, Zhang R, Huang W, Liu W, Zhan J, Wang R, et al. Seed Traits and Germination of Invasive Plant \u003cem\u003eSolanum rostratum\u003c/em\u003e (Solanaceae) in the Arid Zone of Northern China Indicate Invasion Patterns. Plants. 2024;13:3287.\u003c/li\u003e\n\u003cli\u003eArcher D, Toledo D, Blumenthal DM, Derner J, Boyd C, Davies K, et al. Invasive annual grasses\u0026mdash;Reenvisioning approaches in a changing climate. Journal of Soil and Water Conservation. 2023;78:95\u0026ndash;103.\u003c/li\u003e\n\u003cli\u003eHantsch L, Bruelheide H, Erfmeier A. High phenotypic variation of seed traits, germination characteristics and genetic diversity of an invasive annual weed. Seed Science Research. 2013;23:27\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eComes HP. 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New Phytologist. 2007;176:635\u0026ndash;43.\u003c/li\u003e\n\u003cli\u003eJoosen RVL, Kodde J, Willems LAJ, Ligterink W, van der Plas LHW, Hilhorst HWM. germinator: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination. The Plant Journal. 2010;62:148\u0026ndash;59.\u003c/li\u003e\n\u003cli\u003eBewley JD, Bradford KJ, Hilhorst HWM, Nonogaki H. Germination. In: Bewley JD, Bradford KJ, Hilhorst HWM, Nonogaki H, editors. Seeds: Physiology of Development, Germination and Dormancy, 3rd Edition. New York, NY: Springer; 2013. p. 133\u0026ndash;81.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Seed aging test, Solanum rostratum, Seed viability, Weibull distribution","lastPublishedDoi":"10.21203/rs.3.rs-6742628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6742628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSeed longevity is a key determinant of population persistence, spread, and outbreak potential in annual invasive plant species. Understanding seed bank dynamics is crucial for determining colonization timing and assessing invasion potential, thereby supporting sustainable weed management strategies. While soil seed bank fluctuations have become a focus in invasion biology area, efficient and accurate methods for evaluating seed bank longevity in annual invasive plants remain scarce so far.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn this study, we focus on a representative annual globally malignant invasive plant buffalo bur \u003cem\u003eSolanum rostratum\u003c/em\u003e, investigating seed viability dynamics under accelerated aging conditions (60\u0026deg;C and 85% relative humidity) across multiple regions and collection years. We developed a three-parameter Weibull distribution model to characterize seed aging and applied it to assess \u003cem\u003eS. rostratum\u003c/em\u003e seed bank viability in both grassland and abandoned farmland habitats. The results showed that \u003cem\u003eS. rostratum\u003c/em\u003e seeds lost viability rapidly within three days under accelerated aging condition. Seeds from different regions in the same year exhibited similar aging patterns, while interannual variation led to significantly divergent aging curves. Polynomial regression of viability data estimated natural seed longevity at approximately 9.91 years. This study demonstrates that combining accelerated aging with the three-parameter Weibull distribution provides an effective approach for evaluating seed longevity and seed bank persistence. Our findings highlight the feasibility of combining accelerated aging and the three-parameter Weibull distribution model to evaluate seed longevity and seed bank viability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIt proposes a practical and efficient approach to estimate seed bank persistence in annual invasive plants and highlights the critical role of persistent seed banks in facilitating \u003cem\u003eS. rostratum\u003c/em\u003e's invasion success, offering a practical framework for assessing invasion risks. These results contribute important theoretical foundations for developing ecologically sustainable weed control strategies.\u003c/p\u003e","manuscriptTitle":"A Weibull Distribution-Based Method for Estimating Soil Seed Bank Longevity in Annual Invasive Plants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-08 15:00:47","doi":"10.21203/rs.3.rs-6742628/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c0f0877-d21d-4075-9f05-841e747e962e","owner":[],"postedDate":"June 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-09T05:09:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-08 15:00:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6742628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6742628","identity":"rs-6742628","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}