Estimating global geographical distribution and ecological niche dynamics of Ammannia coccinea under climate change based on Biomod2

doi:10.21203/rs.3.rs-4652177/v1

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Estimating global geographical distribution and ecological niche dynamics of Ammannia coccinea under climate change based on Biomod2

https://doi.org/10.21203/rs.3.rs-4652177/v1

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Invasive alien plants are a major threat to biodiversity and the agricultural economy. The invasive weed (Ammannia coccinea) can compete with rice in paddy fields, posing a potential threat to rice production. Despite the crucial importance of estimating the global geographical distribution and ecological niche dynamics of A. coccinea in order to implement effective early warning and control strategies and to ensure global agro-rice security, there has been a dearth of relevant research. This study employed biomod2 ensemble model based on environmental and species data to analyze the distribution range shift and ecological niche dynamics of A. coccinea under the current and future climate scenarios. The results showed that the potential suitability area of A. coccinea was mainly located in Southern North America, northern and south-eastern South America, south-western Europe, the Middle East, central Africa, western Asia, south-eastern Asia, with a gradual increase in the mid-high suitability habitat areas with time and radiation levels. Under future climate change scenarios, the centroid of the suitable area of A. coccinea may shift northwards to higher latitudes. The ecological niche of A. coccinea has shifted less and the overall ecological niche has maintained stability under different climate scenarios in the future compared to the current period. Temperature, precipitation, and the human impact index were the primary factors influencing the future distribution of A. coccinea. In conclusion, climate change was contributing to the expansion of A. coccinea's high habitability area and shifts its ecological niche, necessitating the implementation of effective transnational management strategies to mitigate the impact of this invasive species on rice production.

Ammannia coccinea

Biomod2

Climate change

Ecological niche

Habitat distribution

In recent years, with the acceleration of international exchanges and transnational trade, an increasing number of organisms have crossed spatial boundaries to reach other regions, where they have proliferated and caused adverse impacts on the local ecological environment, and consequently, the invasion of exotic organisms has emerged as a significant environmental concern for humanity in the 21st century^[1]. Plants represent a significant component of invasive organisms worldwide, with a diverse array of invasive plant species accounting for approximately 20% of the total flora ^[2]. Invasive alien plants pose a significant threat to native species and have potentially severe biodiversity, ecological, agricultural production and economy ^[3–4]. For example: Mikania micrantha Kunth, a native of South America, was introduced to China in 1919 and has since become a significant invasive species, occupying nearly 20% of the native ecological niche ^[5]; In Australia, the greatest economic losses were caused by invasive alien plants, amounting to approximately $151.68 billion. The invasive species Lolium rigidum, Parthenium hysterophorus and Senecio jacobaea were identified as the primary contributors to these losses ^[6]; In a significant portion of Yunnan, China, the annual crop has been severely impacted by the invasive species Eupatorium odoratum L, which has resulted in more than 20% of the crop failing to yield, causing significant economic losses ^[7].

Ammannia coccinea is a noxious weed that competes with rice and often grows in rice paddies and sometimes in paddy fields, wet meadows, rivers and riverbanks, floodplains, ponds, lakes and marshes ^[8–9]. The seeds of A. coccinea are minute and prolific, with an average of 270 seeds per capsule and a total of over 500,000 seeds per plant ^[10]. Under shade conditions, A. coccinea can adapt by increasing the ratio of crown to root, and the length of the stem node, while simultaneously decreasing the diameter of the stem, the number of branches, and the number of stem nodes ^[10–11]. A. coccinea contains flavonoids, including quercetin ^[12], which are converted to protective flavonoids following exposure to UV-B radiation. These flavonoids have been observed to scavenge free radicals and enhance the plant's resistance to pathogenic fungal infections ^[13]. A. coccinea is capable of thriving in freshwater at a depth of 0.5 metres ^{[8, 14]}. These biological characteristics described above enhance the competitiveness and invasiveness of this weed ^[10]. This weed is highly competitive in rice fields in California, USA, where it can outcompete rice at 45 days after sowing, with densities of 110 plants/m² leading to a 39% reduction in rice yields ^{[10–11, 15]}. It has become one of the most widely distributed nuisance weeds in the region ^{[9, 14, 16]}. A. coccinea, a native of North and Central America, has been introduced to numerous countries outside of its native range. These include France, Spain, Portugal, Italy, Bulgaria, Greece, Turkey, Australia, Africa, Morocco, and others ^{[8, 17–18]}. It was introduced to China, Japan, Korea, Malaysia, and other Asian countries in the 1950s ^{[16, 18–20]}.

The initial step in the control of A. coccinea is the prediction of the potential distribution areas of invasive species ^[21]. The distribution of invasive alien plants is the result of a combination of biotic and abiotic factors at different spatial scales ^[22]. Human activities and land-use types also exert an influence on the distribution of invasive alien plants ^[23–24]. The climate is a key factor that constrains the viability, expansion, and reproduction of plants and animals ^[25–26]. In the context of global warming, future climate change will not only change the land surface temperature and precipitation pattern but also have a great impact on the geographical distribution patterns of invasion plants ^[27]. The shift of species distribution to higher altitudes and latitudes in the context of global climate change may accelerate the invasion process of exotic species ^[27–29]. Once these invasive species have successfully colonized, eradication becomes a challenging endeavor. It is therefore of great importance to investigate the impact of climate change on the global geographic distribution of A. coccinea. This will facilitate the implementation of an effective early warning and control system ^[30].

In order to improve the efficiency of invasive species control, the prediction of potential distribution habitats of invasive plants using species distribution model (SDM) has become one of the hot issues in biological invasion research ^[31]. SDM is the process of predicting the actual and potential distribution state of a species by using certain algorithms to determine the ecological needs of the species and projecting the results of the calculations in a specific spatiotemporal context, taking into account both the known distribution points of the species and the environmental variables associated with the distribution points ^[32].There are dozens of SDMs available, each with different principles, algorithms, and predictive performance ^[33]. The main SDMs that are currently receiving much attention are Generalized Linear Models (GLM), Generalized Boosted Models (GBM), Generalized Additive Models (GAM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), one Rectilinear Envelope Similar to BIOCLIM (SRE), Flexible Discriminant Analysis (FDA), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Maximum Entropy Models (MaxEnt) ^[34]. Biomod2 can combine results from multiple models into a single integrated result, improving the accuracy of species distribution predictions ^[35]. The greater reliability of ensemble model in comparison to single models has led to their extensive use in the study of potential suitable areas for invasive alien plants ^[36], such as Centaurea solstitialis L ^[37], Heracleum mantegazzianum ^[38], ragweed^[39–40], Aegilops tauschii and Ambrosia artemisiifolia ^[41].

Given the high competitiveness and potential for significant damage to paddy fields caused by A. coccinea, there is an urgent need to effectively predict the global potentially suitable areas and understand the niche dynamics of A. coccinea under changing climatic conditions. Nevertheless, there is a paucity of pertinent research. The objective of our study was to provide an in-depth analysis of the distribution and ecological niche shifts of A. coccinea in potential globally suitable areas. This will enable us to reveal the dynamics of its invasion range, thus providing a scientific basis for the development of effective biosecurity measures and management strategies. This methodology involved: (1) the selection of superior single models for integration within biomod2 followed by the identification of the most effective integrated models; (2) using the optimal integrated model to forecast suitable areas for A. coccinea under the SSP126, SSP245, and SSP585 climate scenarios for 2050s, 2070s and 2090s;(3) examining the niche dynamics for A. coccinea and comparing the current invasion zones with its future regions; and (4) exploring the dynamics of potential suitable areas and centroids transfer for A. coccinea. This structured approach was designed to offer insights into the adaptive strategies of A. coccinea, thereby facilitating targeted and effective management practices aimed at mitigating the impacts of this species on global agriculture.

2.1 Acquisition and screening of Occurrence records

The occurrence records for A. coccinea were obtained from literature resource, field survey, the Global Biodiversity Information Network (http://www.GBIF.org), the China Plant Image Bank (https://ppbc.iplant.cn), and the China Digital Herbarium (https://www.cvh.ac.cn/index.php). A total of 3394 occurrence records were collected. In order to ensure the accuracy and precision of the data set, occurrence records that were not on land and those with incorrect latitude and longitude information were excluded. Concurrently, in order to guarantee compatibility with the resolution of our environmental variables, we utilised ENM tools to refine our occurrence data ^[42]. This involved filtering the data to maintain only one occurrence record per 10 × 10 km grid, effectively minimising spatial autocorrelation. If not addressed, spatial autocorrelation can lead to skewed results because of the clustering of data points ^[43]. Finally, we obtained 1138 occurrence records of A. coccinea for modeling the potential geographical distribution in the globe using SDMs (Fig. 1).

2.2 Obtaining and Filtering Environment Variables

The current climate data (1970–2000) as well as future climate data (2050s, 2070s, 2090s) used were downloaded from the website (http://www.worldelimorg). The current climate variables were the WorldClim 2.1. The dataset includes 19 climate variables related to temperature and precipitation, as well as digital elevation data (elev), all with a resolution of 5 minutes. Considering the influence of human activities and land use types on the distribution of invasive alien plants ^[23–24], we also selected Human Impact Index (HII; https://sedac.ciesin.columbia.edu/) and global land cover data (GlobCover 2009; http://due.esrin.esa.int/page_globcover.php).

Future climate data were based on environmental variables for the periods 2050s, 2070s, and 2090s under three climate scenarios: ssp126, ssp245, and ssp585. For future climate projections, we selected data from the BCCCSM2-MR model of the Coupled Model Intercomparison Project (CMIP6), which incorporate a combination of shared socioeconomic pathways (SSPs) with RCPs to reflect future socioeconomic development scenarios ^[44]. SSP126 means a world of sustainability-focused growth and equality, radiative forcing stabilizes at 2.6 W/m² in 2100. SSP245 means that a middle of the road world where trends broadly follow their historical patterns, radiative forcing stabilizes at 4.5 W/m² in 2100. SSP585 means a world of rapid and unconstrained growth in economic output and energy use, radiative forcing stabilizes at 8.5 W/m² in 2100^[45–46].

Given the potential for misinterpretation of the model due to strong collinearity of variables ^[47], the ENMTools software was employed for correlation analysis of the 22 bioclimatic variables. The 22 environment variables acquired were extracted to the sample points using the multi-value extraction module in ArcGIS, and then the ENMTools tool in the R package was invoked for correlation analysis. We used correlation analysis and combined it with the Jackknife importance values in screening the environmental variables, removing the one of the two environmental variables that had a correlation coefficient |r| > 0.8 and contributed less to the model. Finally, only 12 variables were consequently selected as predictors (Table 1, Fig. 2).

Table 1

Screened climatic variables affecting the distribution of *Ammannia coccinea*
Code	Description	Unit	Weather to use Ammannia coccinea for Modeling
bio1	Annual mean temperature	℃	Yes
bio2	Mean diurnal range	℃	Yes
bio3	Isothermality	℃	No
bio4	Temperature seasonality	/	No
bio5	Maximum temperature of the warmest month	/	No
bio6	Minimum temperature of the warmest month	℃	No
bio7	Annual mean temperature range	℃	No
bio8	Mean temperature of the wettest quarter	℃	Yes
bio9	Mean temperature of the driest quarter	℃	Yes
bio10	Mean temperature of the warmest quarter	℃	Yes
bio11	Mean temperature of the coldest quarter	℃	No
bio12	Annual precipitation	mm	No
bio13	Precipitation of the wettest month	mm	Yes
bio14	Precipitation of the driest month	mm	No
bio15	Precipitation seasonality ( CV)	/	No
bio16	Precipitation of the wettest quarter	mm	No
bio17	Precipitation of the driest quarter	mm	Yes
bio18	Precipitation of the warmest quarter	mm	Yes
bio19	Precipitation of the coldest quarter	mm	Yes
HII	Human Influence Index	/	Yes
Globcover	Global land cover	km²	Yes
elev	above sea level	m	Yes

2.3 Construction and Evaluation of ensemble model

The Biomod2 model comprises a total of 10 single models, and a wide range of different models can be constructed from model evaluations to meet different needs ^[48]. By integrating multiple models in biomod2, we can utilize their respective sensitivities and explanatory power to improve the diversity, robustness, and comprehensiveness of predictions. This approach balances the bias of each model, reduces errors, and improves prediction accuracy by combining their outputs to better assess uncertainty ^[35]. Studies indicate that integrating multiple models generally results in higher prediction accuracy than using a single model, thus enhancing the reliability and practicality of scientific research ^[33]. 10 single models (GAM, GBM, GLM, CTA, MARS, ANN, SRE, FDA, RF, MaxEnt) from the R package biomod2 were used to model potentially suitable areas for A. coccinea based on species occurrence records and environmental variables. The disk method in Biomod2 was employed to generate pseudo-absence samples with the same number of points as the distribution points, thereby enhancing the accuracy of classification algorithms such as classification trees and random forests ^[49].In a single species distribution model setup, 75% of the species distribution data was used as training data, and the remaining 25% was used as test data to assess the accuracy and reliability of the models ^[50]. The division of training and test data was repeated randomly 5 times and the model was repeated 10 times. 500 pseudo-negative sample points were randomly selected and repeated three times. A total of 150 species distributions were modeled.

The true skill statistic (TSS), area under the receiver operating characteristic curve (AUC), and Cohen’s kappa (KAPPA) values were used to assess the accuracy of the models, and the closer their values are to 1, the more plausible the predictions are ^[51–53]. The evaluation criteria for AUC, TSS and Kappa were shown in Table 2. Among the 150 models constructed, high-accuracy models with TSS and AUC values greater than 0.75 were selected to construct the ensemble model of species distribution. The weighting in the portfolio model was determined by the level of the model's evaluation value. The greater the rating of the model, the greater the weighting in the combined model ^[51]. The importance of each environmental factor involved in the modeling was evaluated by analysis of the Biomod2 software package. Finally, the assembled ensemble model was used to predict the potential global geographic distribution of A. coccinea under climate change conditions.

We used several integrated methods to construct ensemble model to model the potential distribution areas of A. coccinea, such as EMmean (ensemble mean), and EMca (consensus average) etc. This multifaceted approach permitted a comprehensive evaluation of the models, thereby ensuring the selection of the optimal ensemble model for subsequent prediction.

Table 2

Evaluation standard for AUC, TSS and Kappa
Evaluation index	Fail	Bad	Medium	Good	excellence
AUC	0.50 ~ 0.60	0.60 ~ 0.70	0.70 ~ 0.80	0.80 ~ 0.90	0.90 ~ 1.00
TSS	0.00 ~ 0.40	0.40 ~ 0.55	0.55 ~ 0.70	0.70 ~ 0.85	0.85 ~ 1.00
Kappa	0.00 ~ 0.40	0.40 ~ 0.55	0.55 ~ 0.70	0.70 ~ 0.85	0.85 ~ 1.00

2.4 Division of the suitable areas and calculation of centroids

The critical value of ensemble model was used as the threshold between the suitable and unsuitable zones. The ASCII raster layer, which ranged from 0 to 1,000 of the ensemble model, indicated the occurrence probability (p) of A. coccinea. A larger P-value indicates a higher probability of occurrence of A. coccinea ^[37]. We divided the potential geographic distribution of A. coccinea into four segments using ArcGIS: high-suitability habitat (600 ≤ P ≤ 1000), moderate-suitability habitat (400 ≤ P < 600), low-suitability habitat (200 ≤ P < 400), and unsuitable habitat (0 ≤ P < 200). The centroids shift of potential geographic distribution in invasive alien plants can be captured scientifically and visually using the statistical tools of ArcGIS software ^[54]. We used the raster map of suitable zones of A. coccinea to draw binary maps using the reclassification function in the ArcGIS 10.2, and also used the ArcGIS plug-in SDM toolbox v2.0 to calculate the spatial pattern change and the migration of centroids of the suitable zones. We compared current and future projections of total suitable area and assessed changes in suitable area by calculating areas of expansion, contraction, and rates of gain and loss. Calculation of the direction and distance of spatial movement of the centroids, describing the general changes in the future distributional trends of A. coccinea.

2.5 Ecological Niche Comparison Measures

The Principal Component Analysis (PCA)-based method provided by the ecospat software package ^[55] was used to study the ecological niche shifts of A. coccinea under current and future climatic scenarios. This approach has been widely used to study the ecological niche dynamics of invasive alien species ^[56–57]. The Schoener's D metric was used to determine the extent of ecological niche overlap; the index has a value range from 0 to 1, with larger values indicating a higher overlap rate of ecological niches in the two areas ^[58]. This index was crucial for understanding how the ecological niche of A. coccinea changes or remains constant under current and future climatic scenarios.

Niche equivalency and similarity tests were performed to understand the importance of niche overlap in geographic areas ^[59]. The niche equivalency test assessed whether the niches of the two entities were equal (full overlap), moderately similar (partial overlap), or distinctly different (no overlap).The niche similarity test assessed whether the niches of the two entities being compared were more similar (or different) than expected by chance, also considering the surrounding environmental conditions throughout the geographic area ^[60]. The test was randomly repeated 100 times, and the null hypothesis of ecological niche equivalence or similarity could be rejected if the observed niche values (D) were significantly lower than the overlap value from the null distribution (P ≤ 0.05) ^[59].

To assess niche dynamic metrics, a set of environments represented niche expansion if it was available in both current and future ranges, but was only occupied in the future range ^[60]. Similarly, a set of environments was considered to result from niche stability if it was occupied in both current and future ranges, and niche unfilling if it was used in the current range and was available, but not yet exploited, in the future range ^[61]. Values for expansion, stability, and unfilling ranging from 0 to 100% were considered significant at > 10% ^[62]. Niche expansion is considered the only measure that truly describes shifts in a realized niche ^[56–57].

3.1 Evaluation of model accuracy

GAM, GBM, GLM, CTA, MARS, FDA, RF and MaxEnt were species models that were screened to meet both AUC and TSS greater than 0.75. The AUC values of each single model screened were greater than 0.9, and the TSS and KAPPA values were greater than 0.8 (Fig. 3; Table 3), which indicated that the prediction accuracy of the single models was “good”. We constructed the ensemble model from the screened eight single models. Compared with the single model, the prediction accuracy and stability based on the combined models were substantially improved. The EMca version of the ensemble model exhibited the highest accuracy, with the TSS greater than 0.85 and the AUC exceeding 0.98, the prediction of the suitable area for A. coccinea reached an “excellent level. There are differences in the prediction process and parameter algorithms of single model, and there may be uncertainty in its prediction results ^[36]. Although the AUC and TSS values of the RF model were higher than those of the ensemble model, the stability of the ensemble model was stronger than that of the RF model. Thus to avoid overprediction by the RF model, the ensemble model was selected in this study to predict potential global geographic distribution for A. coccinea.

Table.3 Accuracy test results for different single model algorithms.

Evaluation index	MARS	RF	MAXENT	CTA	FDA	GAM	GBM	GLM
TSS	0.844	0.992	0.857	0.882	0.828	0.836	0.883	0.807
ROC	0.967	0.997	0.944	0.972	0.961	0.974	0.986	0.958
KAPPA	0.847	0.991	0.832	0.884	0.831	0.844	0.892	0.816

3.2 Significance of environmental variables

Eight single models was used to assess the contribution of environmental variables affecting the distribution of A. coccinea, and the results were shown in Fig. 4. Our results showed that the top four environmental variables with the highest average contribution values were HII (0.378), bio1 (0.253), bio10 (0.212), and bio19 (0.091) in the GAM; HII(0.217)、bio10(0.153)、bio19(0.045), and elev(0.030) in the GBM; bio10(0.584), HII(0.214), elev(0.074), and bio2(0.072) in the GLM; HII(0.494), bio10(0.303), bio19(0.134), and bio17(0.070) in the CTA; bio10(0.637), HII(0.239), bio1(0.159), and bio2(0.087) in the FDA; HII(0.952), elev(0.094), bio2(0.034), and bio18(0.029) in the MAXENT; HII(0.127), bio10(0.109), bio1(0.061), and bio19(0.041) in the RF; bio10(0.266), bio1(0.266), HII(0.237), and bio2(0.068) in the MARS. Ensemble model was used to assess the contribution of environmental variables affecting the distribution of A. coccinea, and the results are shown in Fig. 5. The top four environmental variables with the highest average contributions in the ensemble model were consistent with the synthesized results of the eight single models and were all mean annual air temperature (bio1), mean warmest quarter temperature (bio10), coldest quarter precipitation (bio19), and human impact index (HII). Overall, the main environmental variables affecting the distribution of suitable areas for A. coccinea were two temperature, one precipitation and one anthropogenic influence variable.

3.3 Ecological Niche Analysis

Niche analyses contrasting current and future climatic conditions revealed a consistent moderate niche overlap across all scenario comparisons, ranging from 51.8% ( Schoener's D = 0.518 ) for current vs. SSP585-2090s to 80.5% ( Schoener's D = 0.805 ) for current vs. SSP126-2050s (Fig. 6; Table 4). This pattern exhibited a prospective 19.5–48.2% loss of the present climatic niche for A. coccinea within the ensuing climatic framework. The results showed that the Schoener's D values for the SSP126 scenarios are all greater than the SSP245 scenarios, while the Schoener's D for the SSP245 scenarios are all greater than the SSP585 scenarios. In short, there was a decreasing trend in the Schoener's D values with an increasing radiation intensity over time. The PCA-env revealed that the variation retained by principal component 1 (PC1) ranged from a minimum of 37.11% in the case of current vs SSP585-2090s comparison to a maximum of 38.10% for current vs SSP245-270s comparison(Table 4). Similarly, for principal component 2 (PC2), the retained variation ranged from a minimum of 23.01% for the current vs. SSP585-2090s comparison to a maximum of 25.82% for the current vs. SSP126-2090s comparison (Table 4).

Furthermore, we extended the exploration of niche equivalency and similarity. In pairwise analyses of climate ecological niches of species under current and future climate scenarios, the p-values for equivalence and similarity were approximately equal to 0.05, and thus the null hypothesis of ecological niche equivalence was not rejected in any of the pairwise comparisons (Fig. 6; Table 4). This suggested that A. coccinea may have similar ecological niche characteristics in terms of resource utilization and structural features in both current and future scenarios.

Red and green areas indicated expansion and unfilling, respectively, blue areas indicated overlapping ecological niches. The values of ecological niche expansion, stability suggested that the climatic ecological niche of A. coccinea will undergone minimal expansion (< 10%), and in the future (Table 4). With rising radiation intensity over time, the level of ecological niche expansion and unfilling will increase, while the stability of the ecological niche will gradually decline (Table 4).

Table.4 Niche comparisons and variation between the current and future projected distribution ranges of A. coccinea.

Niche comparison pairs	PC1 (%)	PC2 (%)	Niche overlap (D)	Niche Expansion (%)	Niche Stability (%)	Niche Unfilling (%)
Current vs SSP126-2050s	38.08%	23.21%	0.799	0.008	0.992	0.004
Current vs SSP126-2070s	38.10%	23.18%	0.794	0.007	0.993	0.003
Current vs SSP126-2090s	38.01%	23.32%	0.805	0.006	0.994	0.003
Current vs SSP245-2050s	38.05%	23.22%	0.696	0.006	0.994	0.007
Current vs SSP245-2070s	37.91%	23.18%	0.711	0.009	0.991	0.007
Current vs SSP245-2090s	37.75%	23.15%	0.645	0.012	0.987	0.012
Current vs SSP585-2050s	37.78%	23.20%	0.673	0.01	0.99	0.008
Current vs SSP585-2070s	37.40%	23.21%	0.613	0.026	0.974	0.01
Current vs SSP585-2090s	37.11%	23.01%	0.518	0.037	0.963	0.022

3.4 Current potential global geographic distribution of A. coccinea

We predicted the potential global geographic distribution of A. coccinea under the current climate conditions using the ensemble model in biomod2. As shown in Fig. 7. Under current climatic conditions, the potential global geographic distribution of A. coccinea was mainly in southern North America, northern and southeastern South America, southwestern Europe, the Middle East, central Africa, western Asia, Southeast Asia, etc. (150°E-120° W, 40°S-60°N). Specifically, the high-suitable habitat area were 1,759.12 × 10⁴ km², accounting for 11.81% of the global land area, including the United States, Mexico, Cuba, Puerto Rico, Jamaica, northern Colombia, northern Venezuela, Portugal, the eastern coast and south of Brazil, northeastern Argentina, Paraguay, Spain, Italy, Greece, Turkey, the southwestern corner of Russia, Iran, India, northern China, Cambodia, Vietnam, the Philippines, Korea, Japan, southeastern Australia, Guinea, Liberia, the southern coast of Nigeria, and other areas. North, southeastern China, Cambodia, Vietnam, Philippines, Korea, Japan, southeastern Australia, Guinea, Liberia, southern coast of Nigeria and other regions. The mid-suitable habitat area were 101.76 ×10⁴ km², accounting for 8.1% of the global land area, and were mainly distributed around the highly suitable area. The low-suitable habitat area were 1920.86 × 10⁴ km², accounting for 12.89% of the global land area, including the northwestern United States, central Mexico, Peru, Bolivia, central Colombia, Venezuela, Fatima, Germany, Poland, Hungary, Rome, Ukraine, Turkey, Iran, Uzbekistan, Pakistan, central India, Myanmar, Indonesia, Cameroon, Central Africa, the Congo, the Sudan, South Africa, Zimbabwe, Mozambique, Madagascar and other regions.

3.5 Future potential global geographic distribution of A. coccinea

The potential global geographic distribution of A. coccinea under future climate conditions was shown in Fig. 8. The suitable areas for high, medium and low suitable areas were shown in Table 4, and Fig. 9. The changes in the potential global geographic distribution of A. coccinea under future climate change conditions compared to the current were shown in Table 5, and Fig. 10.

Compared with the current, mid-high suitable areas of A. coccinea under future climate change were expanding, with mid-high suitable areas increasing the most under the SSP585 scenario in the 2090s, reaching 921.37×10⁴km² and 202.36×10⁴km², respectively. The distribution of mid-high suitable areas under future climate conditions were similar to that under current conditions. Among them, the contraction areas of the suitable areas were mainly located in northeastern Bolivia, eastern Colombia, central Brazil, Nigeria, South Sudan, Uzbekistan, central India, Myanmar, Thailand, northwestern China, and Australia. The expansion areas of the suitable areas were mainly located in the southwestern corner of Russia, northern Kazakhstan, Zambia, northern China, and the southern part of Canada bordering the United States, among others.

Under SSP126 in 2050s, the highly, moderately, and low suitable areas of A. coccinea were 2174.84×10⁴km², 1326.30×10⁴km², and 1917.08×10⁴km², respectively, accounting for 14.60%, 8.90%, and 12.87% of global land area, respectively; Under SSP126 in 2070s, the highly, moderately, and low suitable areas of A. coccinea were 2173.10×10⁴km², 1335.01×10⁴km², and 1924.14×10⁴km², respectively, accounting for 14.58%, 8.96%, and 12.91% of global land area, respectively; Under SSP126 in 2090s, the highly, moderately, and low suitable areas of A. coccinea were 2184.44×10⁴km², 1354.15×10⁴km², and 1930.49×10⁴km², respectively, accounting for 14.66%, 9.09%, and 12.96% of global land area, respectively.

Compared with the current, the total suitable areas under the SSP126 scenarios all contracted in the future, with the largest contraction of 281.26×10⁴km² under the 2050s. Under the SSP126 scenarios in 2050s, 2070s, and 2090s, the expansion of suitable areas were 349.97 × 10⁴km², 363.15 × 10⁴km², and 421.76 × 10⁴km², respectively; the contraction of suitable areas were 627.70 × 10⁴km², 626.85 × 10⁴km², and 648.65 × 10⁴km².

Under SSP245 in 2050s, the highly, moderately, and low suitable areas of A. coccinea were 2220.47×10⁴km², 1283.22×10⁴km², and 1733.97×10⁴km², respectively, accounting for 14.90%, 8.61%, and 11.64% of global land area, respectively; Under SSP245 in 2070s, the highly, moderately, and low suitable areas of A. coccinea were 2333.41×10⁴km², 1372.91×10⁴km², and 1920.96×10⁴km², respectively, accounting for 15.66%, 9.21%, and 12.89% of global land area, respectively; Under SSP245 in 2090s, the highly, moderately, and low suitable areas of A. coccinea were 2461.24×10⁴km², 1400×10⁴km², and 1918.12×10⁴km², respectively, accounting for 16.52%, 9.4%, and 12.87% of global land area, respectively.

Compared with the current, the total suitable areas under the SSP245 scenarios shrink by 177.24×10⁴km² and 72.22×10⁴km² in the 2050s and 2070s, respectively. Under the SSP245 scenarios in 2050s, 2070s, and 2090s, the expansion of suitable areas were 508.48×10⁴km², 658.62×10⁴km², and 855.80×10⁴km², respectively; the contraction of suitable areas were 682.19×10⁴km², 727.31×10⁴km², and 772.39×10⁴km² respectively.

Under SSP585 in 2050s, the highly, moderately, and low suitable areas of A. coccinea were 2421.71×10⁴km², 1370.13×10⁴km², and 1892.59×10⁴km², respectively, accounting for 16.25%, 9.20%, and 12.70% of global land area, respectively; Under SSP585 in 2070s, the highly, moderately, and low suitable areas of A. coccinea were 2668.32×10⁴km², 1374.73×10⁴km², and 1926.85×10⁴km², respectively, accounting for 17.91%, 9.23%, and 12.93% of global land area, respectively; Under SSP585 in 2090s, the highly, moderately, and low suitable areas of A. coccinea were 2680.5×10⁴km², 1405.13×10⁴km², and 2156.67×10⁴km², respectively, accounting for17.99%, 9.23%, and 14.47% of global land area, respectively.

Compared with the current, the total suitable areas under the SSP585 scenario decreased by 15.06 × 10⁴km² in the 2050s, and increased in both the 2070s and 2090s, by 270.42 × 10⁴km², and 542.83 × 10⁴km², respectively. Under the SSP585 scenarios in 2050s, 2070s, and 2090s, the expansion of suitable areas were 756.37×10⁴km², 1224.47×10⁴km², and 1649.35×10⁴km², respectively; the contraction of suitable areas were 767.90×10⁴km², 950.52×10⁴km², and 1102.99×10⁴km² respectively.

Table 5

Expansion, contraction and stabilization of *A.coccinea* suitable areas under future climate scenarios.
Climate scenarios	total suitable area/km²	contractions /km²	expansions /km²	stabilization /km²
Current	5699.486066	/	/	/
2050s-SSP126	5418.229123	627.701384	349.972219	5068.256904
2050s-SSP245	5522.249956	682.187495	508.479163	5013.770793
2050s-SSP585	5684.43051	767.895827	756.36805	4928.062461
2070s-SSP126	5432.263845	626.847217	363.152775	5069.111071
2070s-SSP245	5627.270788	727.312494	658.624995	4968.645794
2070s-SSP585	5969.90273	950.520826	1224.465268	4745.437462
2090s-SSP126	5469.076345	648.645828	421.763886	5047.31246
2090s-SSP245	5779.368009	772.388883	855.798604	4923.569405
2090s-SSP585	6242.319395	1102.986102	1649.347209	4592.972185

3.6 Centroid migration of the suitable areas

The centroid of the potential geographic distribution of A. coccinea under future climate change conditions quantitatively described changes in its suitable range (Fig. 11). The current centroid was located in Niger (13.21°E, 17.61°N).Under SSP126, the centroid shifted from the current to the point(11.36°E, 20.64°N) in the 2050s, then to the point (11.99°E,20.62°N) in the 2070s, and finally to the point (12.84°E, 21.27°N) in the 2090s; Under SSP245, the centroid shifted from the current to the point(11.95°E, 21.52°N) in the 2050s, then to the point (13.16°E, 22.13°N) in the 2070s, and finally to the point (13.68°E, 23.06°N) in the 2090s; Under SSP585, the centroid shifted from the current to the point(13.04°E, 22.83°N) in the 2050s, then to the point (15.18°E, 25.29°N) in the 2070s, and finally to the point (17.05°E, 27.46°N) in the 2090s. In summary, under future climate change conditions, the centroid of the suitable area for A. coccinea shifted to the northern high latitudes, and moved as far north as Libya.

4.1 Accuracy of model predictions

There are differences in the prediction process and parameter algorithms of single model, and there may be uncertainty in its prediction results ^[36]. Ensemble model constructed by the Biomod2 platform was used to predict the potential habitat distribution of the invasive plant A. coccinea, which can effectively reduce the uncertainty of single model as well as the simulation-induced bias. To mitigate the effect of sampling bias, the occurrence records of A. coccinea were screened. In order to avoid the problem of multicollinearity among environmental variables, Pearson correlation analysis and Jacknife method were adopted to eliminate some environmental factors with high correlation and low importance. A total of nine climate factors, one terrain factor, one human disturbance factor, and one land type coverage factor were ultimately selected for incorporation into the distribution model. The predicted values of AUC, TSS, and Kappa for the single models met the standard of "good," while the predicted values of AUC and TSS for the ensemble model met the standard of "excellent." This indicated that the model predictions were accurate and reliable, and closely aligned with the actual distribution of the species. They can thus be utilized to analyze the potential geographic distribution of A. coccinea across the globe.

4.2 key environmental variables

Annual mean temperature (bio1), mean temperature of the warmest quarter (bio10), precipitation of the coldest quarter (bio19), and human impact index (HII) were the key environmental variables influencing the potential global geographic distribution of A. coccinea. In short, the main determinants of the potential global geographic distribution of A. coccinea were due to the synergistic effects of three environmental variables: temperature, precipitation and anthropogenic impacts. In the zones where A. coccinea has successfully invaded, suitable temperature and precipitation conditions have facilitated its colonization and expansion. This has resulted in individuals from A. coccinea populations having a faster rate of reproduction and higher adaptability than other native populations ^{[8, 12–13]}. There were research findings that A. coccinea was photoblastic and required temperature higher than 15°C and soil burial depth shallower than 3 cm for successful germination and seedling emergence ^[63]. The seeds of dormant A. coccinea required at least 100 days of cold stratification, diurnal temperature fluctuations and light for optimal germination ^[64]. In general, A. coccinea prefers to grow in warm environments, and temperatures above 28°C are favorable for its growth ^[10]. Climate warming has increased the environmental tolerance of A. coccinea, thereby increasing its invasive range. The results of these studies support and validate our findings.

The coldest season precipitation (bio19) also has an effect on the potential geographic distribution of A. coccinea. Climate change alters global precipitation distribution patterns ^[65], while disrupting the invasion of invasive alien species by making populations more aggressive and adaptive ^[66]. Some studies have demonstrated that the invasion of invasive alien plants is facilitated by genetic alterations and accelerated evolution resulting from alterations in precipitation patterns induced by climate change. For example, two annual grass species, Avena barbata and Bromus madritensis, have been observed to exhibit reduced growth and reproduction in habitats with reduced precipitation in the United States ^{[16, 67]}.

Anthropogenic influences have also partially determined the potential geographic distribution of A. coccinea. Studies have found that A. coccinea often occurs in rice fields ^{[8, 16, 18]}, which are areas disturbed by anthropogenic activities. Anthropogenic influences have also allowed A. coccinea to have more ways of spreading. The seeds of A. coccinea can be dispersed everywhere via a variety of pathways, including agricultural trade activities, the flow of water, and wrapped in mud or sticking to the tires of machines used for cultivation ^[8]. The spread of A. coccinea is accelerated by human factors. Consequently, under the appropriate temperature, precipitation, and a certain level of anthropogenic activity, they will flourish.

4.3 Changes in potential geographic distribution

Predicting potential geographical distribution is an essential part of invasive species risk assessments. Our results showed that, under current climatic conditions, the potential geographical distribution of A. coccinea were mainly in Southern North America, northern and southeastern South America, southwestern Europe, West Asia, Southeast Asia, etc. This was consistent with previous research ^[10], indicating a strong agreement between the simulated potential geographic distribution of A. coccinea and its actual range under the current climatic scenarios.

It was demonstrated that under future climatic conditions, the areas of potential mid to high suitability for A. coccinea would increase. The total area of suitable habitats for A. coccinea was projected to increase globally by the 2050s, while the area of suitable habitats was expected to decrease by the 2090s. In the context of future climate change projections for the 2070s, the areas suitable for A. coccinea were expected to decline in the SSP126 and SSP245 scenarios, while the SSP585 scenario indicated an increase in suitable areas. In short, with climate change, especially with an increasing radiation intensity over time, the global invasive range of A. coccinea would gradually increase at an accelerated rate. This suggested that different common socio-economic pathways strongly influenced the global spread of A. coccinea, SSP585 for a development pathway with high fossil fuel consumption would result in the rapid spread of A. coccinea ^[68].

The area was of medium to high suitability and would undergo expansion under climate change conditions. The suitability and invasion risk, in the southwest corner of Russia, northern Kazakhstan, Zambia, northern China, southern Canada, northern United States and other regions, would expand under climate change conditions, which appear to be due to climate warming, thus making temperatures of these areas mentioned above suitable A. coccinea ^[63–64].

Greenhouse gas emissions are significantly altering the global climate, and extreme weather events and altered environmental conditions caused by climate change will facilitate plant invasions ^[69]. With global climate change, the habitats of some plants and animals will shift to higher altitudes and latitudes^{[25, 27, 70]}, Salvia miltiorrhiz and Rosa multiflora has been recognized to shift its centroid to higher latitude (northward) in the global ^{[68, 71]}. Previous studies have suggested that 11 invasive alien plants of the Compositae, Basellaceae, Verbenaceae, Chenopodiaceae, and Convolvulaceae families will shift to higher latitudes (northward) in China under warming conditions ^[66]. The potential geographic distributions of three invasive alien plant may expand in the global under climate change conditions ^[72]. The results of our study suggested that under climate change conditions, the potential geographic distributions of A. coccinea in the global were at risk of shifting to north, which were consistent with this pattern above. Hence, the potential geographic distributions of A. coccinea in the global should be injected given attention to prevent its further spread in north.

4.4 Ecological displacement analysis

Ecological niches play an important role in the prediction of the distribution patterns of invasive species. Invasive alien species adapt to new habitats in a variety of ways and expand their ecological niche space after colonisation, ultimately leading to differences between native and invasive ecological niches, such as thermal niche shifts ^[73–74]. The maximal ecological niche overlap (SSP126-2050s) was 0.805, indicating substantial similarity under certain conditions, whereas the minimal ecological niche overlap (SSP585-20900s) decreased to 0.518, indicating considerable differentiation in other climatic contexts. This suggests that A. coccinea has broad ecological adaptability to various environments. Moreover, the analysis showed a decreasing trend in ecological niche overlap values with an increasing radiation intensity over time. This trend suggests an expanding area invaded by A. coccinea in the future, likely because of its ability to exploit a wider range of habitats as environmental conditions change.

Our results indicated that the ecological niche of A. coccinea has shifted less in the future and the overall ecological niche has maintained high stability. However, we could clearly observe in Fig. 6 that the expansion and unfilling of ecological niches increased with increasing radiation intensity over time. This trend indicated that the range of A. coccinea was likely to expand in the future, potentially due to its capacity to adapt to a broader range of habitats in response to changing environmental conditions. This finding was consistent with our previous conclusion regarding the potential northward migration of the species. It further implied that as the A. coccinea population migrated northward and adapted to new environments due to climate change, there would be competition with local species for survival resources and space ^{[11, 13]}.

4.5 Measures for the Control and Management

A. coccinea is a noxious weed that competes with rice in paddy fields and continues to spread worldwide. At present, globalized economy and climate warming have promoted the global successful invasion of invasive alien species ^[72]. Managing the global invasive species in the face of climate change has become extremely difficult. The global geographic distribution of A. coccinea was primarily due to the synergistic effects of three factors: temperature, precipitation, and anthropogenic impacts. Its seeds can be spread by water currents or by agricultural equipment such as rice harvesters^[8]. In light of the aforementioned considerations, it is recommended that government departments in relevant countries adopt a more robust approach to publicity, management and epidemic prevention, with a view to adjusting agricultural practices or trade regulations. Areas with a high potential for poison ivy invasion are the most suitable areas for its reproduction. It is recommended that different prevention and control strategies be employed in different adaptation areas. Control of A. coccinea should be intensified in highly suitable areas with potential for expansion, such as the United States, Mexico, Cuba, Puerto Rico, Jamaica, northern Colombia, northern Venezuela, Portugal, the east coast and southern Brazil, northeastern Argentina, Paraguay, Spain, Italy, Greece, Turkey, the southwestern corner of Russia, Iran, northern India, Cambodia, and Vietnam, Northeastern Argentina, Paraguay, Spain, Italy, Greece, Turkey, Southwestern corner of Russia, Iran, Northern India, Southeastern China, Cambodia and Vietnam, Philippines, Korea, Japan, Southeastern Australia, Guinea, Liberia, Southern coast of Nigeria. In addition, mechanical, chemical and biological control measures should be used immediately to eliminate weeds in areas of high weed infestation. Finally, intensive control at an early stage usually improves the effectiveness of control ^[66]. Therefore, it is necessary for the organizations or agencies in each of the regions involved to conduct a large-scale survey in areas with high invasion potential and where significant habitat expansion was expected, in order to determine the quantity and damage status of the species and to provide a scientific basis for taking appropriate prevention and control measures. The measures mentioned above will enrich the international body of knowledge on invasive species management.

4.6 Existing deficiencies and future development directions

Species distribution is subject to interactions between biological and abiotic factors ^[75]. Our study mainly considered the influence of climate, HII, and Globalover on the distribution of A. coccinea. However, the interaction between organisms, transportation construction, trade activities, water conservancy projects, and so on play an important role in the distribution of invasive species ^[46]. Therefore, the interaction between invasive species and native biota, the socio-economic implications of invasive species management and so on should be considered into the model, and more accurate prediction results may be obtained. Moreover, although the distribution sites of A. coccinea have been obtained as much as possible, the actual distribution points of the species cannot be fully collected. Future environmental variables used, such as human impact index (HII), Globalover may differ from what is true in the future. It is impossible to accurately predict the changes of human impact index (HII), Globalover, so these data were invariant in the future in the present study (2050s, 2070s, 2090s). The factors influencing the distribution of A. coccinea considered in our study are limited. So the interaction between invasive species and native biota, the socio-economic implications of invasive species management and so on should be considered when studying the distribution of these species in the future.

The habitats of A. coccinea were susceptible to climate change, resulting in changes in suitable areas and ecological niches of the species. The ensemble model, constructed using the best single models, has relatively high accuracy in predicting the potential geographic distributions of A. coccinea. Under current climatic conditions, the potential geographic distributions of A. coccinea were mainly in Southern North America, northern and south-eastern South America, south-western Europe, the Middle East, central Africa, western Asia, south-eastern Asia, etc. The area of mid-high habitability of A. coccinea would be expanded under different climatic scenarios. The ecological niche of A. coccinea has shifted less in the future and the overall ecological niche has maintained high stability. The temperature, precipitation and human impact index (HII) played a key role in invasion and expansion in the global. It is imperative that relevant organizations prioritize the enhancement of prevention and control measures for A. coccinea in regions highly suitable areas with potential for expansion. Additionally, it is crucial to prevent the further spread of A. coccinea in areas that are conducive to regional amplification. This will not only ensure the sustainability of agricultural production, but also contribute to the fulfilment of the United Nations sustainable development goals.

Data availability: The datasets generated and/or analysed during the current study are not publicly available due (our experimental team's policy) but are available from the corresponding author on reasonable request.

Conflicts of Interest: The authors declare no conflicts of interest.

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No competing interests reported.

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Estimating global geographical distribution and ecological niche dynamics of Ammannia coccinea under climate change based on Biomod2

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Acquisition and screening of Occurrence records

2.2 Obtaining and Filtering Environment Variables

2.3 Construction and Evaluation of ensemble model

2.4 Division of the suitable areas and calculation of centroids

2.5 Ecological Niche Comparison Measures

3 Results

3.1 Evaluation of model accuracy

3.2 Significance of environmental variables

3.3 Ecological Niche Analysis

3.4 Current potential global geographic distribution of A. coccinea

3.5 Future potential global geographic distribution of A. coccinea

3.6 Centroid migration of the suitable areas

4 Discussion

4.1 Accuracy of model predictions

4.2 key environmental variables

4.3 Changes in potential geographic distribution

4.4 Ecological displacement analysis

4.6 Existing deficiencies and future development directions

5 Conclusions

Declarations

References

Additional Declarations

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Version 1