Niche shift and conservatism in Solanum rostratum's global invasion Drivers and invasion risk assessment implications

Niche shift and conservatism in Solanum rostratum's global invasion Drivers and invasion risk assessment implications | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 1 April 2026 V1 Latest version Share on Niche shift and conservatism in Solanum rostratum's global invasion Drivers and invasion risk assessment implications Authors : Yabin Liu 0009-0006-4763-5902 , Weitian Meng , Lizhu Guo 0000-0002-8211-2879 , Yuyu Li , Rui Wang 0000-0003-1317-8767 , Chuan Wang , Yu Ji , Tian Tian , Lifen Hao [email protected] , and Kejian Lin Authors Info & Affiliations https://doi.org/10.22541/au.177502932.26628175/v1 135 views 83 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Whether invasive species retain ancestral climatic niches or undergo niche shifts remains a central question in invasion ecology, directly affecting the reliability of risk predictions based solely on native-range data. We integrated ensemble species distribution models (SDMs) with the Centroid Shift, Overlap, Unfilling, and Expansion (COUE) framework and multivariate environmental similarity surface (MESS) analysis to quantify global invasion risk and climatic niche dynamics of Solanum rostratum across four major invaded regions: China, Australia, Eastern Europe, and Western Europe. The species exhibits a heat-dominated yet precipitation-constrained niche. Annual mean temperature and extreme heat were the principal limiting factors, whereas excessive precipitation imposed an upper threshold on habitat suitability. Cross-regional comparisons revealed a continuum of niche dynamics rather than a uniform invasion strategy, shaped by invasion history, environmental availability, and adaptive processes. Australian populations showed near-complete niche conservatism (Stability = 0.98), indicating long-term persistence within historical introduction limits. European populations displayed pronounced niche unfilling (Western Europe: Unfilling = 0.69), likely reflecting dispersal constraints and environmental heterogeneity. In contrast, Chinese populations exhibited substantial niche expansion (Expansion = 0.48), suggesting adaptive shifts into colder and drier climates beyond native conditions. These findings demonstrate that ignoring niche dynamics leads to systematic underestimation of invasion risk. We propose a region-specific assessment framework that explicitly incorporates dynamic niche processes, thereby improving predictive accuracy and informing precision management of invasive plants under ongoing global change. 1. Introduction Biological invasions are a major driver of global biodiversity loss, with far-reaching consequences for agricultural productivity, ecosystem resilience, and socio-economic stability (Jaureguiberry et al., 2022; Turbelin et al., 2023). Intensifying global trade and transportation have accelerated human-mediated species dispersal across biogeographic boundaries (Early et al., 2016; Hulme, 2009, 2021). Simultaneously, climate change is altering temperature and precipitation regimes, enabling alien species to overcome historical climatic constraints, establish viable populations, and expand into newly suitable regions (Deslippe & Veenendaal, 2025; Hellmann et al., 2008). Understanding climate-driven range dynamics is therefore essential for forecasting invasion trajectories and guiding effective management (Flickinger & Dukes, 2024; Rubenstein et al., 2023; Wallingford et al., 2020). Accumulating evidence shows that invasive species may undergo niche shifts, expansions, or contractions in response to novel environmental conditions, rapid adaptive evolution, phenotypic plasticity, and changes in genetic structure (Dlugosch & Parker, 2008; Whitney & Gabler, 2008; Guisan et al., 2014; Turner et al., 2015; Qiao et al., 2017; Sherpa et al., 2019). These mechanisms often interact over short timescales, particularly in invasions characterized by multiple introductions and strong anthropogenic disturbance, allowing populations to occupy climatic conditions distinct from those of their native range (Dlugosch & Parker, 2008; Blumenfeld et al., 2021). Disentangling true niche evolution from shifts in environmental availability requires rigorous analytical frameworks. The PCA-env approach corrects for background environmental differences by projecting occurrences into a standardized environmental space, enabling robust niche comparisons (Broennimann et al., 2012). However, it primarily quantifies niche overlap. The COUE framework extends this approach by decomposing niche dynamics into stability, expansion, and unfilling, while also capturing centroid shifts, thereby providing a more comprehensive representation of invasion processes. These methods are increasingly applied to evaluate invasion dynamics and climate-driven range reorganization (Di Cola et al., 2017; Melton et al., 2022; Santamarina et al., 2023). Despite widespread use of niche-based approaches in invasion research, systematic cross-continental analyses remain limited for Solanum rostratum Dunal., a highly aggressive global invader. Native to North America, S. rostratum has established populations across climatically contrasting regions, including Europe, Australia, and China, and is designated a high-risk alien species in many countries (Yu et al., 2021; Zhao et al., 2013). This annual spiny herb in the genus Solanum (Solanaceae) produces toxic alkaloids such as solanine, posing poisoning risks to livestock and reducing rangeland productivity (Lin & Tan, 2007). It also serves as an intermediate host for Leptinotarsa decemlineata and multiple plant viruses, threatening agricultural biosecurity (Izzo et al., 2014; Matzrafi et al., 2023). Long-distance dispersal via contaminated fodder, grain, and vehicle tires facilitates transboundary spread and establishment in disturbed habitats (Y. Zhang et al., 2023; Zhou et al., 2023). Existing studies on S. rostratum largely focus on regional distribution modeling or local ecological traits, typically within single geographic contexts. For example, several studies have modeled its potential distribution exclusively in China, while others have examined trait variation in localized populations (T. Huang et al., 2024; Solís-Montero et al., 2022; Wang et al., 2025). Comparative analyses across invaded continents within a unified niche-dynamics framework remain lacking. Given the pronounced climatic contrasts and distinct introduction histories among regions—such as the arid environments of northern China versus the humid conditions of Western Europe—static niche models calibrated solely on native-range data may fail to detect region-specific niche shifts, thereby underestimating invasion risk. Whether S. rostratum exhibits niche conservatism or adaptive niche divergence during global expansion thus remains unresolved, with direct implications for biosecurity and risk assessment. Here, we present a global evaluation of climatic niche dynamics in S. rostratum to clarify its environmental adaptation strategies across major invaded regions. Specifically, we (1) modeled its global potential distribution using a multi-model ensemble framework to identify key climatic constraints; (2) applied PCA-env and COUE analyses to quantify niche overlap, stability, expansion, and unfilling between the native range and four invaded regions (Eastern Europe, Western Europe, China, and Australia); and (3) developed a region-specific invasion risk framework integrating niche dynamics and MESS analysis to support targeted prevention and management under global change. 2. Materials and Methods 2.1 Occurrence data and environmental variables Global occurrence records of S. rostratum were compiled from major biodiversity databases (GBIF, iNaturalist, CABI, CalFlora, Atlas of Living Australia, and the Southwest Environmental Information Network) and supplemented with targeted field surveys. Data quality was ensured through a stringent filtering workflow. Duplicate entries were removed, and records with erroneous, imprecise, or inconsistent coordinates were excluded. To reduce temporal mismatches with bioclimatic variables (1970–2000) and minimize georeferencing uncertainty associated with historical herbarium specimens, only records collected after 1970 were retained. Taxonomic reliability was verified by cross-checking uncertain identifications with photographic evidence, and herbarium-only records lacking spatial precision were discarded. After quality control, 25,423 high-confidence records remained. To mitigate spatial sampling bias and standardize resolution, occurrences were aggregated onto a global 5-arc-minute grid (~10 km at the equator). Each grid cell containing at least one record was coded as a presence, yielding 4,579 presence grid cells for subsequent analyses. Climatic predictors comprised 19 bioclimatic variables representing long-term temperature and precipitation patterns (1970–2000) from WorldClim, resampled to 5 arc-minute resolution. To reduce multicollinearity and enhance model stability, Pearson correlation analyses were performed. When |r| > 0.7, one variable from each correlated pair was retained based on ecological relevance. In line with the ecological characteristics of S. rostratum and best-practice recommendations (Brun et al., 2020; Feng et al., 2019), twelve alternative low-collinearity variable sets were constructed, each including nine to ten predictors (Supplementary Table S1). 2.2 Global niche modeling and climatic variable selection Global potential distribution was modeled using nine species distribution algorithms applied to each of the twelve climatic variable combinations. Analyses were conducted in R v4.3.3 using the Biomod2 package and included regression-based methods (GLM, MARS, GAM), recursive partitioning (CTA), machine-learning algorithms (ANN, RF, MAXENT), and an envelope-based method (SRE). Model performance was assessed using the area under the receiver operating characteristic curve (AUC). Pseudo-absence points were randomly sampled within the global accessible environmental space following the default BIOMOD2 strategy, widely adopted in large-scale SDM studies to reduce sampling bias. For each run, 70% of presence data were used for calibration and 30% for validation; this procedure was repeated five times to obtain mean cross-validated metrics. Final models were recalibrated using the full dataset to generate spatial predictions. For each variable set, ensemble models were constructed using an AUC-weighted mean approach, in which individual models were ranked and weighted according to evaluation score decay (Thuiller, 2014; Thuiller et al., 2009). Ensemble outputs from the twelve variable sets were evaluated using AUC, sensitivity, specificity, and the true skill statistic (TSS) (Supplementary Table S2). Based on overall performance, four combinations (5, 6, 8, and 12) were retained for visualization and comparison. Combination 12 showed the highest and most consistent predictive accuracy (AUC, TSS, sensitivity, specificity; Supplementary Table S2) and was selected for subsequent analyses. This set included nine bioclimatic variables: annual mean temperature (bio1), mean diurnal range (bio2), temperature annual range (bio7), mean temperature of the wettest quarter (bio8), mean temperature of the warmest quarter (bio10), precipitation of the wettest month (bio13), precipitation of the driest quarter (bio17), precipitation of the warmest quarter (bio18), and precipitation of the coldest quarter (bio19). 2.3 Regional niche modeling and projection Based on the global distribution of S. rostratum , the study area was partitioned into five regions: the native range (North America) and four invaded regions (Eastern Europe, Western Europe, China, and Australia; Fig. 1). For each invaded region, the accessible area (M) was approximated by a rectangular geographic extent encompassing the concentration of occurrence records, thereby defining the regional environmental background. After spatial aggregation, presence grid cells numbered 3,842 (North America), 68 (Eastern Europe), 102 (Western Europe), 434 (China), and 31 (Australia). To characterize climatic differences among regions, principal component analysis (PCA) was conducted using regional environmental backgrounds. Kernel density estimation was then applied to visualize niche occupancy within the reduced environmental space (Fig. S1, Fig. S2). These analyses were performed using the R packages FactoMineR (v2.12), factoextra (v1.0.7), and ks (v1.15.1). To compare native and invaded niches directly, regional SDMs were constructed following the same workflow as the global model. Models calibrated with North American occurrences were projected onto invaded regions to simulate potential distributions under strict niche conservatism (“native models”). These projections were contrasted with models independently calibrated using occurrence data from each invaded region (“invaded models”). For comparability, continuous suitability outputs were converted to binary presence–absence predictions using the Youden Index, which maximizes the sum of sensitivity and specificity. Grid cells exceeding this threshold were classified as suitable and treated as simulated presence points. Based on the variable importance results in Section 2.2, four high-contribution variables (bio1, bio2, bio10, bio13) were selected. Their values were extracted from simulated presence cells to quantify frequency differences between native and invaded model predictions (Fig. 3b). A PCA was then applied to these simulated presences to provide an initial evaluation of climatic niche differentiation between native and invaded populations. Fig. 1 Current global occurrence records of S. rostratum (top) and the probabilistic global distribution predicted by ensemble SDMs (bottom). Blue (native) and red (introduced) rectangles delineate the regional study extents used to define environmental backgrounds for climatic niche dynamics analyses. 2.4 Quantifying Climatic Niche Dynamics and Background-Corrected Shifts To assess climatic niche dynamics, we compared the realized niche in the native range with potential niches expressed in invaded regions using simulated presence points derived from regional SDMs. Threshold-independent metrics were implemented by integrating kernel density–based PCA with environmental background correction in the ecospat R package, enabling robust quantification of niche overlap and change while accounting for differences in environmental availability. 2.4.1 Kernel density–based niche analysis Smoothed niche density distributions for native and invaded models were estimated along the first two PCA axes using kernel density estimation (KDE). Niche overlap was quantified with Schoener’s D (Schoener, 1968), which measures similarity between two probability density distributions in environmental space. A global niche change index ( I NC ) was calculated as: where P INT,i and P NAT,i denote the occurrence probabilities predicted by the invaded and native models, respectively, for grid cell i . I NC ranges from 0 (complete overlap) to 1 (no overlap). To evaluate changes in predicted suitable area associated with niche dynamics, we additionally calculated an area-based niche change index following (Christina et al., 2020): 2.4.2 Background-corrected niche dynamics and statistical testing To partition niche dynamics into stability, expansion, and unfilling, and to test their significance, we conducted background-corrected analyses in ecospat. For each region, 10,000 background points were randomly sampled within a unified study extent and combined with simulated presence points to construct a shared environmental space using PCA. Within this standardized space, Schoener’s D and niche dynamic indices (stability, expansion, unfilling) were computed. Observed niche differences were evaluated using permutation-based niche equivalency and similarity tests (1,000 iterations). To further distinguish apparent niche shifts caused by regional differences in environmental availability from genuine shifts in environmental response, we performed multivariate environmental similarity surface (MESS) analyses in geographic space, comparing climatic conditions of invaded regions with those of the North American native range (Elith et al., 2010; Zurell et al., 2012). 3 Results 3.1 Global distribution patterns of Solanum rostratum The global ensemble SDM demonstrated excellent predictive accuracy (AUC = 0.976; TSS = 0.844; sensitivity = 0.9595; specificity = 0.8854). The optimal probability threshold, determined by maximizing sensitivity and specificity, was 0.44. Only 4.7% of observed occurrences fell below this threshold, indicating strong agreement between predicted suitability and known distributions. Model projections revealed extensive climatically suitable areas across both native and invaded ranges, including North America, Western and Eastern Europe, northern China, Morocco, South Africa, and southeastern Australia (Fig. 1). The model also identified highly suitable regions without confirmed records, notably parts of South America (e.g., Argentina and Bolivia) and northern Africa, suggesting potential areas at risk of future invasion. 3.2 Global climatic niche characteristics Variable importance analyses identified annual mean temperature (bio1), mean temperature of the warmest quarter (bio10), mean diurnal temperature range (bio2), and precipitation of the wettest month (bio13) as the dominant climatic drivers of global distribution. Density profiles from ensemble predictions indicate that S. rostratum is primarily associated with temperate continental climates (Fig. 2). Suitable habitats corresponded to annual mean temperatures of 2.1–25.6 °C. Suitability declined sharply when the mean temperature of the warmest quarter exceeded ~33.9 °C, suggesting thermal limitation under extreme heat. Mean diurnal temperature range clustered between 5.5 and 21.4 °C, reflecting preference for pronounced day–night variability. Precipitation exerted a clear upper constraint: suitability was rare where precipitation of the wettest month exceeded heat-dominated but precipitation-limited climatic niche. Fig. 2 Global climatic niche of S. rostratum predicted by the ensemble SDM. Density distributions are shown for the nine climatic variables included in the selected model. Black curves represent the global climatic background; grey curves represent the species’ climatic niche. Presence was defined using the sensitivity–specificity maximization threshold ( P = 0.44). 3.3 Niche shifts between native and invaded ranges Comparisons between native-range projections and region-specific invaded-range models revealed marked geographic heterogeneity in niche responses (Fig. 3a). Kernel density–based niche change indices ( I NC ) and background-corrected COUE analyses consistently demonstrated strong regional contrasts. Uncorrected kernel density comparisons indicated substantial niche differentiation (Fig. 4; Fig. S3). Western Europe showed the greatest niche change ( I NC = 0.90), followed by Eastern Europe ( I NC = 0.72) and China ( I NC = 0.68), whereas Australia exhibited the lowest change ( I NC = 0.35). After correcting for environmental availability using the ecospat framework (Fig. 5; Supplementary Table S3), patterns became clearer. Corrected Schoener’s D values revealed highest niche overlap in Australia (D = 0.71), moderate overlap in China (D = 0.37), and extremely low overlap in Eastern Europe (D = 0.05) and Western Europe (D = 0.08). Niche dynamic components differed strikingly among regions. China displayed pronounced niche expansion (Expansion = 0.48) without detectable unfilling, indicating occupation of novel climatic space. Australia showed near-complete niche stability (Stability = 0.98) with minimal expansion (Expansion = 0.02), consistent with strong niche conservatism. European populations exhibited moderate stability and expansion but substantial unfilling, particularly in Western Europe (Unfilling = 0.69), indicating extensive suitable yet unoccupied climatic space. Permutation tests supported these findings. Niche equivalency was rejected for all invaded regions. Niche similarity tests were significant for China ( P = 0.01) and Australia ( P = 0.001), but not for Eastern Europe ( P = 0.42) or Western Europe ( P = 0.39), further highlighting divergent niche dynamics across regions. Fig. 3 Predicted potential distribution of S. rostratum and environmental response patterns. (a) Probability of presence predicted by native-range and introduced-range models, using the same threshold as in Fig. 1. (b) Density distributions of four key climatic variables for introduced-range presence points ( P = 0.44) under native (blue) and introduced (red) model projections. Fig. 4 Climatic niche comparisons between the native range and four introduced regions based on SDM-derived simulated presences ( P = 0.44). Kernel density plots in PCA-derived environmental space illustrate niche occupancy. The niche change index (I NC ) ranges from 0 (complete overlap) to 1 (no overlap). Fig. 5 Dynamic climatic niche changes between the native range and introduced regions based on the COUE framework. Blue indicates niche stability, green indicates unfilling, and red indicates expansion. Dashed lines delineate the 99% climatic background. 3.4 Comparison of native and invaded niches in geographic space Model projections based on invaded-range occurrences consistently predicted broader suitable areas than those calibrated solely with native-range data (Fig. 3a). Incorporating niche shifts further expanded the total predicted suitable area across all invaded regions (Fig. 6), with the largest proportional increases observed in Eastern Europe ( I NC-area = 278%) and China ( I NC-area = 291%). MESS analyses indicated that these newly suitable areas were largely located in climates dissimilar to the native range (MESS < 0) (Fig. 6; Fig. S4). In contrast, Western Europe ( I NC-area = 97.5%) and Australia ( I NC-area = 9.9%) exhibited more moderate expansions. Here, newly suitable areas occurred in both climatically similar (MESS > 0) and dissimilar (MESS < 0) environments. Geographic expansions corresponded with broader occupation of climatic space. Regions with higher niche change indices supported suitability across wider temperature and precipitation gradients. In Eastern Europe, predicted suitable areas extended toward higher mean temperatures of the warmest quarter and lower precipitation of the wettest month. In China, suitability expanded along both temperature and precipitation axes, with some occurrences exceeding native climatic limits (Fig. 4). Principal component analysis confirmed a clear displacement of the niche centroid for Chinese populations relative to the native range. Fig. 6 Similarities and differences between the invasive climatic niche and the native climatic niche of S. rostratum . Multivariate environmental similarity surface (MESS) analysis compares native and introduced models. Bars show the number of 5 arc-minute grid cells predicted as suitable (threshold P = 0.44). Positive values indicate climatic similarity; negative values indicate dissimilarity. 4 Discussion Our results demonstrate that the global invasion trajectory of S. rostratum is not governed by a single rule but emerges from the interaction of climate filtering, environmental availability, and invasion history. By integrating ensemble distribution modeling, the COUE framework, and MESS analysis, we distinguished genuine niche evolution from environmentally driven apparent differentiation, resolving a longstanding methodological challenge in invasion biology. S. rostratum exhibits three heterogeneous patterns along this continuum: (1) strict niche conservatism (Australia), reflecting early invasion stages or lag; (2) apparent niche differentiation (Europe), where distribution changes are driven by environmental heterogeneity rather than physiological evolution; and (3) adaptive niche expansion (China), where the species has breached native climatic barriers. This continuum underpins a region-specific risk assessment framework, emphasizing that management strategies must align with underlying niche mechanisms—from containment in lag-phase regions to rapid adaptive control in expansion zones. 4.1 Climatic conditions shaping suitable habitats for Solanum rostratum Model analyses indicate that temperature, rather than precipitation, is the primary constraint on the global distribution of S. rostratum . Its range boundaries are largely determined by thresholds of mean annual and extreme high temperatures, reflecting the evolutionary imprint of its North American continental origin (Li et al., 2018; Liu et al., 2022).Mean diurnal temperature range also plays a critical role, with the species favoring environments with pronounced day-night variation. This thermal strategy supports survival in arid and semi-arid habitats, including Central Asia and the agro-pastoral ecotones of Northern China (Yu et al., 2021, 2024). Unlike invasive species adapted to stable, moist climates (e.g., Solidago canadensis ), S. rostratum thrives in disturbed habitats where intense thermal fluctuations enhance photosynthesis by day and reduce respiration and water loss at night, optimizing carbon gain and water use efficiency under drought stress (Guo & Fang, 2003; Chen et al., 2012; Zhang H. et al., 2015). Although drought-tolerant, S. rostratum is constrained by upper precipitation limits, with virtually no suitable habitat predicted in humid subtropical regions. Excess soil moisture impairs root respiration and increases susceptibility to soil-borne pathogens (Jackson & Colmer, 2005; Klironomos, 2002; Manghwar et al., 2024), restricting southward expansion. Thus, its distribution is largely temperature-driven but strictly limited by precipitation. Climate warming may relax low-temperature constraints, promoting potential expansion into high-latitude or high-altitude cold regions (Wiens & Graham, 2005; Y. Huang et al., 2024). Conversely, increases in extreme precipitation may restrict abundance, as high soil moisture elevates root rot risk, creating ephemeral refuges for native communities even in thermally suitable areas (Donat et al., 2016; Jackson & Colmer, 2005; Legg, 2021; Manghwar et al., 2024; Tabari, 2020). 4.2 Niche dynamics between native and invaded regions Our analysis reveals substantial regional heterogeneity in the global spread of S. rostratum . While most exotic plants exhibit niche conservatism (Petitpierre et al., 2012), S. rostratum spans a continuum from strict conservatism to rapid niche shift, consistent with the coexistence of conservatism and shift during cross-regional dispersal (Atwater et al., 2018). Niche equivalency and similarity tests clarify the mechanisms driving these patterns. Rejection of niche equivalency across all regions indicates realized niches diverge from the native range (Broennimann et al., 2012; Warren et al., 2008). Similarity tests reveal whether divergence reflects ancestral preferences or environmental availability: in Australia and China, significant similarity suggests retention of ancestral climatic preferences despite different environments (Petitpierre et al., 2012). In Europe, non-significant similarity implies divergence arises from non-analogous climates rather than physiological shifts (Guisan et al., 2014). In Australia, invasion reflects strict niche conservatism. Populations remain largely within native climatic limits (Stability = 0.98) with minimal expansion. Historical records since 1896 (Atlas of Living Australia) fall entirely within the current core distribution (post-1970), indicating century-long spatiotemporal stability. Limited genetic variation due to founder effects likely constrained adaptive responses (Gallagher et al., 2010). Models and sporadic historical records in Western Australia support this, as introductions failed to establish despite climatic suitability, indicating demographic or genetic barriers, rather than climate, limit expansion. In Europe, invasion is characterized by environmentally driven apparent niche differentiation. Statistical divergence from the native range reflects differences in environmental availability, not fundamental shifts in preferences. High unfilling in Western Europe (Unfilling = 0.69) highlights large areas of suitable but unoccupied habitat, consistent with dispersal limitation or invasion lag (Early & Sax, 2014), supporting the apparent niche shift hypothesis (Warren et al., 2008; Peterson, 2011; Broennimann et al., 2012; Guisan et al., 2014). In China, populations exhibit adaptive niche expansion, with high rates of niche expansion and minimal unfilling. Northern China’s arid and semi-arid climates exceed native limits, yet S. rostratum fully occupies these novel habitats (Yu et al., 2024). This expansion likely reflects both rapid evolution and phenotypic plasticity. Multiple introductions and genetic admixture may resolve the genetic paradox, providing variation for adaptation (Lombaert et al., 2010; Zhao & Lou, 2017). while plasticity buffers against environmental stress, allowing tolerance to novel arid and cold conditions even before genetic adaptation occurs (Davidson et al., 2011). However, SDM analyses cannot fully distinguish realized niche shifts due to genetic evolution from those driven by phenotypic plasticity. While results indicate adaptive shifts, confirming mechanisms requires population genomics and common-garden experiments to assess whether these populations have evolved functional traits for cold and arid conditions. 4.3 Implications for management and biodiversity conservation The dynamic risk assessment framework developed here departs fundamentally from traditional static approaches based solely on native niches. Its innovation lies in three advances: the integration of a three-dimensional niche metric system (expansion, unfilling, and stability) rather than a single overlap index; the use of MESS analysis to distinguish true niche shifts from environmentally driven apparent shifts; and the explicit incorporation of invasion stage, contrasting the prolonged lag phase in Australia with the established expansion phase in China. Applying this framework demonstrates that the complex niche dynamics of S. rostratum challenge conventional risk assessments. Models calibrated only on native-range data systematically underestimate invasion risk in regions with shift potential (Broennimann et al., 2007; Early & Sax, 2014). In adaptive shift regions such as China, management must move beyond static climatic thresholds toward a dynamic assessment of adaptive capacity. Given the species’ high tolerance to heat and drought, risk evaluations should account for pre-adaptation and evolutionary potential(Atwater et al., 2018; Colautti & Lau, 2015). Because long-distance spread is strongly associated with agricultural machinery, forage transport, and logistics networks, pathway-based quarantine and equipment sanitation are critical to prevent its dissemination as an agricultural contaminant (Hulme et al., 2008). Ecological restoration strategies should also reflect competitive dynamics. Establishing dense, multi-layered vegetation (e.g., shrubs combined with perennials) to impose light limitation is likely more effective than planting single native grasses (e.g., Leymus chinensis ), particularly under nitrogen deposition, which enhances the invader’s competitiveness(Funk et al., 2008; Sun et al., 2023). Reducing nutrient runoff in agro-pastoral systems is therefore essential to limit habitat suitability. In unfilling regions such as Europe, dispersal limitation rather than climatic suitability constrains distribution. Once barriers are removed, rapid infilling is possible under existing climates. Because propagule pressure strongly influences invasion success (Lockwood et al., 2005), surveillance should prioritize ports, logistics hubs, and forage trade nodes to disrupt introduction pathways. In high-risk but currently unoccupied areas, Early Detection and Rapid Response systems are essential. Cost-effective monitoring can integrate citizen science platforms (e.g., iNaturalist) with remote sensing to identify large-scale soil disturbances that often precede establishment (Rejmánek & Pitcairn, 2003; Westbrooks, 2004). In niche-conservative regions such as Australia, populations remain in prolonged evolutionary stasis. Although this century-long lag may obscure future outbreak risk, it provides a cost-effective window for eradication (Crooks et al., 1999; Panetta, 2009). Strict biosecurity is required to prevent new genetic introductions that could trigger evolutionary rescue and renewed expansion. Targeted eradication of isolated populations should therefore remain a management priority. From a broader conservation perspective, the interaction between climate change and biological invasion warrants particular attention. As thermal constraints weaken at higher latitudes, S. rostratum may expand northward, threatening temperate grassland and desert margins (Bellard et al., 2013; Y. Huang et al., 2024). Given the difficulty of fully preventing species introductions, long-term strategies should shift from single-species removal toward strengthening ecosystem resilience. Systematic restoration that enhances native community resistance will be central to mitigating invasion impacts on ecosystem services across large spatial and temporal scales (Chaffin et al., 2016; Hobbs et al., 2006). 4.4 Limitations Despite providing a global perspective on the niche dynamics of S. rostratum , this study has several limitations. First, models incorporated only bioclimatic variables, excluding edaphic factors and biotic interactions that may constrain realized distributions at local scales. Second, the 5 arc-minute (~10 km) resolution may overlook microclimatic refugia that permit persistence within otherwise unsuitable macroclimates. Third, inferences regarding adaptive shifts are based on niche-space patterns in the absence of molecular evidence. Future research should address these gaps in two directions. First, beyond climatic constraints, studies should clarify how invasion history, including multiple introductions and genetic admixture, drives adaptive niche shifts. Second, to distinguish phenotypic plasticity from genetic evolution, global population genomic analyses should be integrated with reciprocal transplantation or common garden experiments. These approaches will provide robust evidence for local adaptation and evolutionary potential in invasive populations. 5 Conclusions This extensible framework provides three key insights into the global expansion of S. rostratum . First, we identify a distribution mechanism characterized by “heat drive–precipitation constraint,” with mean diurnal temperature range emerging as a critical determinant of adaptation to arid environments. Second, we reveal a global niche dynamic continuum: strict conservatism in Australia reflecting evolutionary stasis, environmentally driven apparent differentiation in Europe, and adaptive expansion in China. Third, we propose a region-specific risk assessment framework integrating dynamic niche metrics, enabling precision prevention and control strategies tailored to invasion stage and regional niche dynamics under global change. Author contributions Lifen Hao, and Kejian Lin designed the study. Yabin Liu and Weitian Meng performed the modeling and analyses. Lizhu Guo, Yuyu Li, Chuan Wang, Yu Ji, and Tian Tian collected and quality-controlled the data. Rui Wang assisted with visualization. Yabin Liu wrote the manuscript, which was critically revised by Lifen Hao and Kejian Lin. All authors approved the final version. Funding This work was supported by the Earmarked Fund for National Key Research and Development Program of China (2024YFC2607702), China Forage and Grass Research System CARS (CARS-34), Inner Mongolia Autonomous Region Sci-ence & Technology Plan Project (2025YFHH0163). Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability Statement I confirm that the Data Availability Statement is included in the main file of my submission, and that access to all necessary data files is provided to editors and reviewers. Supplementary Material The Supplementary Material for this article can be found online at: Supplementary Material.zip. 1. Supplementary Table S1 Combination of climate variables in species distribution models undergoing testing. 2. Supplementary Table S2. Accuracy evaluation of 12 combinations. 3. Supplementary Table S3. Niche overlap, dynamics, and hypothesis testing across introduction regions based on environmental background correction. 4. Supplementary Figure S1. Climatic space of observed occurrences of S. rostratum in the native range and four introduced regions. 5. Supplementary Figure S2. PCA variable contribution plots for pairwise comparisons between the native range (North America) and four introduced regions of S. rostratum. 6. Supplementary Figure S3. PCA variable contribution plots for pairwise comparisons between the native range (North America) and four introduced regions of S. rostratum based on SDM-derived simulated presences. 7. Supplementary Figure S4. Multivariate environmental similarity (MESS) between the native range (North America) and introduced regions of S. rostratum . Reference Atwater, D. Z., Ervine, C., & Barney, J. N. (2018). Climatic niche shifts are common in introduced plants. 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Keywords comparative ecosystem ecosystem ecology plants theoretical theory Authors Affiliations Yabin Liu 0009-0006-4763-5902 Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Weitian Meng Anhui Agricultural University View all articles by this author Lizhu Guo 0000-0002-8211-2879 Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Yuyu Li Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Rui Wang 0000-0003-1317-8767 Chinese Academy of Agricultural Sciences Institute of Plant Protection View all articles by this author Chuan Wang Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Yu Ji Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Tian Tian Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Lifen Hao [email protected] Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Kejian Lin Chinese Academy of Agricultural Sciences Grassland Research Institute View all articles by this author Metrics & Citations Metrics Article Usage 135 views 83 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yabin Liu, Weitian Meng, Lizhu Guo, et al. 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