Predicting Impacts of Climate Change on the Distribution of Acacia mearnsii (Black wattle) in the Southern Africa Region.

Predicting Impacts of Climate Change on the Distribution of Acacia mearnsii (Black wattle) in the Southern Africa Region. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting Impacts of Climate Change on the Distribution of Acacia mearnsii (Black wattle) in the Southern Africa Region. Griffin phiri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8509238/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alien invasive species (IASs) are a threat to the ecological systems of southern Africa. Acacia mearnsii , a global top 100 invasive species has become a very invasive tree in the region, displacing native vegetation, altering fire regimes, and influencing water availability. In this study, Ecological Niche Modelling was used using the maximum entropy algorithm to predict the current and future distributions of A. mearnsii southern African underMIROC-6 climate models SSP2-4.5 and SSP5-8.5. Data used for modelling were collected from GBIF and 19 bioclimatic variables from Wordclim. The Model performance assessed with Area Under Cover-ROC metrics. The annual mean temperature, precipitation of the wettest month, and maximum temperature of the warmest month were the primary environmental drivers. Currently, highly suitable areas are concentrated in South Africa, Eswatini, and Lesotho. The projected future suitability suggests potential range expansion, particularly under SSP2-4.5, with highly suitable habitats increasing by 13.7%. However, under SSP5-8.5, extreme warming would lower habitat suitability in some regions with an increase (+ 646.21%). Climate change has a significant effect on A. mearnsii's threat of invasion, emphasizing the importance of early detection, risk identification, and tailored management of susceptible ecosystems. Ecological Modeling Forestry Conservation Biology Systems Biology Population Biology Geographic Information Systems Climatology Climate Analysis and Modeling ecology ecological modelling maxent conservetion biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1.0. Introduction Acacia. mearnsii is a native species from Southeast Australia and poses challenges in southern African ecosystems [ 1 ]. Ranked among the top 100 invasive species, the species quickly outpaces native ecosystems due to multiple dispersal strategies and ability to quickly adapt to new environment[ 1 ][ 2 ]. A. mearnsii produces more seeds than native species higher seed production per unit seed mass in woody invasive plants[ 3 ][ 4 ]. The reproductive success of these plants is characterized by massive flowering, compensating for low fruit production. A. mearnsii rapidly grows 2–4 m per year in savannas, as reported in Tanzania, and it can begin producing seeds as early as 12 months of age[ 5 ]–[ 8 ]. When these traits are combined, the species has the ability to outcompete native vegetation for resources change has been reported as a major driver for biodiversity loss and range expansion of IAS[ 9 ][ 10 ][ 11 ][ 12 ]. Climate change is playing a significant role in shaping aquatic and terrestrial habitats and it is a leading cause of plant species distribution[ 13 ][ 14 ]. Apart from this, climate change is responsible for changes in population migration pattens pushing species with narrow climate range beyond their habitat requirements[ 15 ]. At the curst of this issue is shifts in temperatures and rainfall regimes that have contributed significantly by creating favourable habitat conditions for IAS expansion[ 16 ][ 17 ]. This has enabled IAS to easily expand and invade new areas putting native species at risk[ 18 ][ 19 ] [ 20 ][ 21 ][ 22 ]. These patterns highlight the need to consider the combined effects of IAS and climate change into biodiversity management and policy frameworks[ 23 ] [ 24 ][ 25 ]. Evidence from southern Africa suggests that, despite growing awareness of ecological impacts, existing management efforts remain insufficient and more effective control measures are still needed [ 26 ][ 27 ]. For decades, IAS have been a threat to global biodiversity and biosecurity. Either intentionally or non-intentionally introduced, IAS can be both beneficial and destructive to an ecosystem[ 13 ][ 12 ]. In particular, IAS are used for the control of soil erosion, timber, and energy consumption[ 11 ][ 28 ]. For example, countries such as South Africa, Tanzania, Mozambique and Malawi, jatropha was introduced intentionally for production of medicine, wood and bioenergy. Although IASs have offered economic and social benefits, they are a nuisance and disruptive to ecosystems. IASs such as water hyacinth have been linked to decreased water quality and reduced phytoplankton production [ 11 ]. These effects have severely impacted aquatic biodiversity sectors such as fisheries, irrigation, navigation, and tourism[ 29 ][ 30 ]. IAS disrupt critical ecosystems and have been recognized as contributing factors to poverty[ 31 ] [ 32 ]. This problem has been further exacerbated by increasing anthropogenic activities, climate change, and land use/land change[ 33 ]. Southern Africa has unique and distinct ecological systems, such as miombo woodlands, savanna grasslands, rainforests, and desert ecosystems[ 34 ]. These ecosystems are vulnerable to the expansion of IAS, such as A. mearnsii in South Africa, Zimbabwe, Malawi, and Tanzania[ 2 ]. Some critically affected ecosystems affected by IAS invasions are fynbos and forest ecosystems[ 24 ] [ 35 ]. Therefore, spatial assessment of IAS such as A. mearnsii is crucial for conservation of native species and management Ecological niche modelling uses environmental predictors to assess the impact of climate change on species distributions[ 20 ]. It uses occurrence data and climatic factors to project future shifts in geographic ranges[ 22 ]. Predictive modelling has become an important and crucial objective in ecology[ 36 ]. This approach enables ecologists to understand the drivers affecting the growth of species within the dimensions of space and time[ 23 ][ 37 ];[ 25 ]. These models are particularly vital in the context of understanding biological invasions[ 38 ]. The assessment of potential spread is essential for informing early detection, risk assessment, and targeted management interventions[ 39 ][ 40 ]. Maximum entropy modelling has become one of the most widely used SDM techniques in ENMs[ 41 ]. Maxent estimates the probability distribution of a species’ potential presence on the basis of occurrence data and environmental constraints[ 42 ][ 43 ]. Therefore, the objectives of this study were: To identify the key environmental variables influencing A. mearnsii distribution in southern Africa; To predict the potential habitat suitability of A. mearnsii under current and future scenarios To classify habitat suitability for guiding targeted management, restoration, and early detection efforts on the basis of countries in SADC. 2.0. Materials and Methods 2.1. Study Area Description Figure show countries under the Southern Africa Development Community. The study was conducted in southern Africa (popularly known as SADC), which contains 16 countries, such as South Africa, Malawi, Zambia, and Zimbabwe. The SADC is located at latitudes ranging from 15°S to 35°S and longitudes ranging from 10°E to 40°E. It is known for its savanna grassland, Miombo woodland and desert ecosystems. The regional climate ranges from arid, semiarid and subtropical climates with annual precipitation between 300 mm and 1200 mm. The regional elevations range from 3000 m to 4500 m above sea level, providing optimal growth for IAS. A. mearnsii is disturbing riparian zones, montane grasslands, and forest margins in South Africa, Eswatini, and Zimbabwe. Its aggressive spread poses serious ecological threats by reducing water availability, displacing native flora, and altering fire regimes. 2.2. Input data 2.2.1. Occurrence data Occurrence records were collected from the Global Biodiversity Information Facility ( www.gbif.org ), which was accessed on 15 July 2025 via the pygbif library. The initial dataset contained 8350 occurrence points georeferenced with observations across southern Africa (1990–2024). Owing to nuances in the dataset, Open Refine was used for data cleaning. Open refine is one of the best software packages for cleaning messy data. Data points lacking georeferencing were removed via NumPy and Panda’s library[ 44 ]. This process was repeated for data points that were outside the range of the study area. Spatial analysis was conducted to ensure data accuracy via geopandas and scikit-learn libraries. The aim of conducting spatial thinning was to reduce sampling bias. Therefore, a distance threshold of 5 kilometres is used as the minimum distance so that no more than two data points occur within the set distance minimum threshold [ 30 ]. The final dataset included species, decimal latitude and decimal longitude. This helps minimize the spatial autocorrelation of data collected in densely populated areas. The final curated dataset included 7361 high-confidence occurrence records that accurately represented the distribution of the species in key habitats, such as savannas, plantations, montane grasslands, and riparian zones. The final dataset was saved as a CSV and then exported to Python 3.13 for further data cleaning. In Python programming, the dataset was tested for a normal distribution via analysis of variance (ANOVA), and other exploratory analyses were also conducted[ 45 ][ 46 ]. 2.2.2. Environmental data Nineteen bioclimatic layers for current and future climate projections were obtained from www.worlclim.org/2.1 at a 30 second resolution, accessed and downloaded on 15 July 2025. MIROC6 climate dataset used for modelling of A. mearnsii was, which was developed by the Japanese Space Agency. The MIROC 6 experiments were designed to represent Earth's climate from 1850–2014 and 2014–2099, respectively[ 32 ]. The first experiment was modelled under the representative critical path (RCP), whereas the second experiment was modelled under the shared socioeconomic pathway by the Coupled Model Intercomparison Project phase 6, the latest climate predictor for current and future projections[ 41 ], [ 47 ]–[ 50 ]. Elevation, slope, aspect and topographic indices were also used in this study. Elevation is an important factor because it is used to assess temperature and rainfall changes across diverse ecosystems[ 51 ][ 52 ][ 53 ]. Slopes affect vegetation growth, water movement, soil stability, and water run-off; therefore, slope is an important variable for this study[ 34 ][ 54 ]. Aspect influences the position of the sun and therefore controls solar radiation[ 55 ]. Finally, the topographic index provides information on drainage conditions and soil moisture for plant survival and growth[ 35 ]. Table 1 . shows environmental predictors used in this study used in the modelling of A. mearnsii in Southern Africa. From Bio 1 to bio-11, the unit measurement for each predictor degrees Celsius while from Bio 12-bio17 the unit value is mini-meters(mm). Table 1 Environmental predictors downloaded from www.worlclim.com/2.1 . Variable Description Bio1 Annual Mean Temperature Bio2 Mean Diurnal Range Bio3 Isothermality Bio4 Temperature Seasonality Bio5 Max Temperature of Warmest Month Bio6 Min Temperature of Coldest Month Bio7 Temperature Annual Range Bio8 Mean Temperature of Wettest Quarter Bio9 Mean Temperature of Driest Quarter Bio10 Mean Temperature of Warmest Quarter Variable Description Bio11 Mean Temperature of Coldest Quarter Bio12 Annual Precipitation Bio13 Precipitation of Wettest Month Bio14 Precipitation of Driest Month Bio15 Precipitation Seasonality Bio16 Precipitation of Wettest Quarter Bio17 Precipitation of Driest Quarter Bio18 Precipitation of Warmest Quarter Bio19 Precipitation of Coldest Quarter Elevation Digital Elevation Models asp Derived from DEM Slope Derived from DEM TPI Topographic index 2.2.3. Variable Selection and Multicollinearity Assessment Figure 2 . shows variable selection and multicollinearity assessment. To avoid multicollinearity, the variable inflation factor (VIF) was conducted via Python programming using the pandas, seaborne and statsmodels libraries. The aim was to reduce redundancy among environmental predictors and ensure that there are robust model results and interpretations. We conducted a VIF using pairwise Pearson correlation coefficients (PCCs) to identify environmental layers that had strongly correlated variables. The relationships among the environmental predictors were evaluated via the correlation coefficient of |r 2 |>0.75[ 42 ][ 56 ]. Any predictor showing a strong linear relationship with other predictors is redundant(Guisan, et al., 2017). The results of the process revealed that Bio_2 (mean diurnal temperature), Bio_3 (isothermality), Bio_11 (mean temperature of the coldest quarter), Bio_1 (annual mean temperature) and Bio_1 (precipitation of the driest month) had lower correlations than the other environmental predictors. Therefore, these models were then used to construct a species distribution model for Acacia mearnsii . 2.5. Modelling Approach Figure 3. shows methodology for species distribution modelling of A. mearnsii. Python programming language version 3.14 was used to perform ecological niche modelling of A. mearnsi i in southern Africa[ 31 ]. The environmental layers were exported in Python programming 3.14 via the Jupyter notebook. Raster clipping to the southern Africa boundary was executed in Python programming 3.14 via the rasterio, geopandas, ecopy and xarray libraries[ 37 ]. All raster layers were aligned via WGS 84, EPSG 4326 for projection consistency. We utilized occurrence data in the CSV from the GBIF in CSV format alongside bioclimatic predictors in GeoTIFF. These inputs were used to produce habitat suitability maps and predict potential areas of invasion risk(Poudel, et al., 2024). Maximum entropy modelling, a machine learning algorithm, was integrated in Python via the Jupyter notebook. The occurrence dataset was randomly partitioned into model training and testing. Eighty percent of the occurrence records were portioned for testing, and 30% were portioned for model training (Brunialti, et al., 2021). Model performance was evaluated via the area under the receiver operating characteristic curve (AUC-ROC). According to Phillips (2012), values from 0.9–1 indicate excellent predictive accuracy. A net change analysis was also conducted to assess spatial and temporal changes in habitat suitability. Fifty replicate model runs, all executed and summarized within the Python environment. 3.0. Results 3.1. Analysis of variable contributions Table 2 . presents estimates of the relative contributions of the environmental variables to the Maxent model. The annual mean temperature emerged as the most dominant predictor, contributing 49% to the model’s gain and registering the highest permutation importance of 40.7%. These findings indicate that the daily variability in temperature is a critical ecological factor influencing habitat suitability, potentially affecting species physiological responses and microhabitat stability. The precipitation of the driest month had a strong influence on the model, with a percent contribution of 28% and a permutation importance of 13.4%. These findings underscore the relevance of seasonal thermal conditions, particularly extreme heat periods, in shaping species distributions. Table 2 analysis of variable contributions Variable Percent Contribution Permutation Importance bio_1 49 40.7 bio_14 28.1 13.4 bio_5 14.2 24.5 bio_3 3.6 5 bio_12 2.5 7.3 elev 2.2 6.5 Slope 0.2 0 bio_13 0.2 2.3 TPI 0 0 Aspect 0 0 3.2. Jackknife results Figure 4 . shows the jackknife results for the study. The annual mean temperature was the most important variable influencing the distribution of A. mearnsii . When used in isolation, Bio_1 achieved the highest training gain, indicating that it provided the greatest amount of useful information for predicting species occurrence. Moreover, its exclusion from the model led to a substantial reduction in training gain, further emphasizing its unique contribution. These findings suggest that the annual thermal regime plays a central role in determining habitat suitability for A. mearnsii , which aligns with the known sensitivity of this species to temperature and its preference for moderately warm climates. 3.3. Response curves Figure 5 . shows how each environmental variable affects the predicted probability of presence. They depict a threshold at which the species can survive, assessing its habitat preference. The response curve for the annual mean temperature shows that A. mearnsii responds positively to rising temperatures. The probability of presence at a peak temperature for a. mearnsii was between 15°C and 25°C. These findings indicate that A. mearnsii has an optimal thermal range for establishment and persistence(Shcheglovitova, et al., 2013). The temperatures above 22°C indicate that habitat suitability has declined. These findings suggest that warm climates may limit the growth and reproduction of this species. The maximum temperature of the warmest month revealed that A. mearnsii performs best in regions where the maximum temperature ranges from 26°C to 32°C. The observed decline in habitat suitability above 35°C implies that extremely high temperatures may impact the physiological tolerance of A. mearnsii, affecting seed germination and water-use efficiency. While the Precipitation of the Driest Month showed a unimodal pattern, the probability of presence increased as the precipitation rose from 0 mm to 40 mm. After this point, habitat suitability declined. This finding indicates that A. mearnsii is tolerant of dry season conditions but may be disadvantaged in areas with high year-round precipitation. 3.4. Model performance Figure 6 . Shows the receiver operating characteristic (ROC) curve for this study. Note that the specificity is defined via the predicted area rather than the true commission. This implies that the maximum achievable AUC is less than 1. 3.5. Current habitat suitability model Figure 7 . shows Current distribution of A. mearnsii in southern Africa. The current habitat suitability model revealed that A. mearnsii was highly suitable in Northen region of Malawi, Lesotho, Zimbabwe, Tanzania and South Africa. Lesotho, Zimbabwe, and Eswatini presented more highly suitable and suitable classes. Madagascar, Mozambique, and the DRC Congo ranged from suitable to low suitable. Several areas in Tanzania were highly suitable, moderately suitable and poorly suitable. Madagascar and the DRC in the Congo have few areas that are in the low suitability to medium suitability classes. These findings indicate that these areas have climatic conditions that support the growth of A. mearnsii. Conversely, countries such as Zambia, Mauritius, Seychelles, and the Comoros Islands were largely classified as unsuitable for the establishment of A. mearnsii , likely because of ecological limitations or climatic unsuitability. Furthermore, the spatial predictive maps revealed that Namibia, Botswana, Angola, and Zambia did not have suitable classes. The current habitat suitability map, derived from five classes not suitable, low suitability, medium suitability, suitable, and highly suitable indicated that A. mearnsii is presently most suited to the western and eastern Cape provinces of South Africa. These regions presented environmental conditions aligned with the most influential bioclimatic predictors, especially Bio_1 , Bio_14 , and Bio_5 , which offer optimal conditions for species growth. Other countries, such as Lesotho, Zimbabwe, and Eswatini, also exhibited large areas of high and moderate suitability. Further north, Malawi, Mozambique, and the Democratic Republic of the Congo demonstrated varying degrees of suitability, ranging from high to low, depending on regional climatic variations. In contrast, countries such as Namibia, Botswana, Angola, and Zambia were predominantly categorized as not suitable for the species, likely because of their ecological limitations and unfavourable climatic conditions. Madagascar and the DRC displayed pockets of low to medium suitability, suggesting restricted but potentially viable conditions for future colonization. 3.6. Potential distribution of A. mearnsii under future scenario SSPRCP4.5 Figure 9 . Shows the future suitability map under SSP 45 and 85 climate scenarios. The map reveals an expected shift for A. mearnsii in the SADC region. The potential ranges expansion under suitability class in South Africa, Lesotho, Northern region of Malawi and Tanzania. Under the Medium suitable class SSP 45, the habitat potential suitability maps reveal that countries such as Mozambique and Zimbabwe showed a more sift of potential range increase under this class. The increase in expansion under medium suitability class suggest that these areas have the potential to support the growth of black wattle species. Across the same class, climate change will also create favourable conditions for A. Meamsii species in arid and semi-arid ecosystems such as Angola, Namibia, Lower shire in Malawi and Zambia. This suggest that Black wattle could spread to these areas and create conducive environmental optimal for growth in areas where there no current information. Under the SSP2-4.5 climate change scenario, the results revealed a large expansion in the potential geographical range of the species. Under the highly suitable class, the potential areas increased from 3,841 km² to 28,659 km² representing a 646.21% increase in suitability. The potential areas increased significantly, by 14.69%, from 184,754 km² to 211,884 km². The lower suitability category showed notable losses under climate scenario SSP85 than SSP 45. As observed in Fig. 9 ., the area under the lower class increased by 24.57%, and under the medium suitable class, it decreased by 12.17%. These areas cannot support the growth of A. mearnsii . Notably, countries such as South Africa, Malawi, and Mozambique, which have few areas that are suitable under the current scenario, have now become suitable under this scenario. Furthermore, the shift under these scenarios suggests that changes in rainfall patterns, as shown by bio_14, could result in increased potential expansion of A. mearnsii . Similarly, in countries such as Zambia, Namibia, and Angola, few areas have achieved low suitability under this climate scenario. These findings indicate that A. mearnsii has the potential to colonize these areas. In Malawi, Zambia Tanzania and the DRC, it was also noted that there was a shift in classes from not suitable to the medium class. This suggests that northwards and altitudinal expansion of species niches has occurred in this country. Table 2 habitat suitability analysis of A. mearnsii under five classes across the southern Africa Development Commission. S.N Countries Unsuitable Habitat Low Suitability Moderate Suitability Suitability High Suitability 1 Angola 1,253,284.49 - - - - 2 Botswana 629,051.83 - - - - 3 Comoros - - - - - 4 DR. Congo 2,254,100.58 12,585.77 666.94 - - 5 Eswatini 12,263.06 2,172.93 4,625.54 - - 6 Lesotho 11,036.75 20,997.80 2,517.15 - - 7 Madagascar 501,085.74 49,525.54 9,552.28 - - 8 Malawi 100,793.73 6,411.21 3,399.23 - - 9 Mauritius 43.03 - - - - 10 Mozambique 775,541.58 3,033.49 1,764.16 - - 11 Namibia 868,719.30 - - - - 12 Seychelles - - - - - 13 South Africa 871,838.85 222,542.22 218,540.59 37,864.88 - 14 Tanzania 834,576.37 28,463.20 16,953.14 172.11 - 15 Zambia 776,595.77 43.03 - - - 16 Zimbabwe 375,486.22 29,323.77 9,294.11 - - 3.7. Potential distribution of A. mearnsii under future scenario RCP 8.5 The results under the SSP-8.5 scenario reveal different trajectories than those under the current scenario and SSP-4.5 scenario. The highly suitable class increased from 3,841 km 2 to 27,060 km 2 . Our results revealed a substantial decrease under this scenario under SSP-4.5. The suitability classes expanded by 28,659, whereas the low and medium classes expanded by 900,628 km 2 and 408,970 km 2 respectively. This reduction was observed in Mozambique, where more areas with low suitability changed to the unsuitable class, with similar observations in Malawi, DRC and Namibia. The Potential areas previously recognized as highly suitable, for instance in Eswatini and Lesotho, transitioned into lower suitability classes, highlighting a threshold beyond which warming will become detrimental to optimal growth. South Africa experienced a net increase in potential range under this scenario, with regions that were unsuitable now becoming suitable for invasion of the studied species. Mozambique exhibited a contraction in its medium suitability zones, implying that increased warming may hinder rather than facilitate the spread of A. mearnsii in certain areas. In Malawi, there was a loss of highly suitable zones, although these zones were partially offset by gains in medium suitability. Climate change will facilitate the biological invasion of Acacia mearnsii , as the results suggest, across southern Africa. This is evidence of the changes in suitability classes, especially under highly suitable, suitable and moderately suitable classes. The strong expansion of highly suitable zones under both scenarios underscores the urgent need for proactive monitoring and management strategies, especially in regions newly projected to support the species. Table 2 habitat suitability analysis of A. mearnsii under five classes across the southern Africa Development Commission. S.N Countries Unsuitable Habitat Low Suitability Moderate Suitability High Suitability Very High Suitability 1 Angola 1,282,672.79 - - - - 2 Botswana 629,503.62 - - - - 3 Comoros 1,462.96 64.54 - - - 4 DR. Congo 2,347,127.71 2,495.64 - - - 5 Eswatini 12,177.00 2,194.44 4,690.08 - - 6 Lesotho 10,606.47 15,253.52 8,691.71 - - 7 Madagascar 568,984.36 39,327.84 24,052.80 - - 8 Malawi 115,703.02 7,207.24 752.99 - - 9 Mauritius 1,764.16 215.14 - - - 10 Mozambique 826,745.22 3,119.55 1,398.42 - - 11 Namibia 894,407.18 - - - - 12 Seychelles 193.63 0 - - - 13 South Africa 865,556.72 275,940.31 210,193.11 49,740.68 - 14 Tanzania 917,126.11 34,250.50 3,571.35 - - 15 Zambia 778,811.73 - - - - 16 Zimbabwe 380,886.27 28,011.41 5,206.42 - - 3.8. Net gain and Loss analysis Table 3 gain/loss risk analysis for distribution of A. mearnsii that was averaged by Suitability Class RCP4.5 (Gain/Loss) RCP4.5 (%) RCP8.5 (Gain/Loss) RCP8.5 (%) Highly suitable + 24,818 13.3% + 23,219 15.2% Suitable + 27,130 14.5% + 24,005 15.7% Medium suitable −37,207 19.9% −27,134 17.8% Low suitable −57,592 30.8% −49,250 32.2% Not suitable + 40,475 21.6% + 29,160 19.1% The net gain and net loss analysis revealed an increase in highly suitability class increased by 28, 219 km 2 representing a 15.3% under RCP85. The suitability class expanded by 22,004 km2 showing a partial intensification in the region. The range contraction and fragmentation under RCP85 where substantial losses between low suitability by -27,592 km2 and medium suitable − 49,250 under RCP85 were observed. Under RCP45 scenario, highly suitable class expanded by + 24,818, representing (13.3%) while the suitability class contracted by + 27,130 representing (14.5%). Under the same scenario, larger losses were recorded under medium suitability class with − 37,207 km2 while low suitability zones − 57,592 km2. Notably, both scenarios show an increase in areas classified as not suitable, particularly under RCP4.5 (40,475), highlighting an overall trend toward habitat polarization. The findings suggest that climate change may reduce ecological shifts while concentrating suitability into fewer, more climatically favourable areas, with important implications for species persistence, dispersal, and management under future climate conditions. 4.0. Discussion a. model inference The maximum entropy model has excellent predictive power for species distribution modelling of A. mearnsii in southern Africa. The Maxent model uses the area under the curve (AUC) as a measure of model predictive performance. An AUC of 0.5 indicates random prediction, and an AUC of 0.9-1 represents excellent model performance(Phillips, et al., 2006). The model achieved high accuracy scores, with AUC-ROC values exceeding 0.932, indicating that the model made excellent predictions of 93.2%. This is attributed to the careful preprocessing of occurrence data and environmental variables. A minimum distance threshold of 5 kilometres between data points was applied spatially with the aim of reducing sampling bias and spatial autocorrelation(Stephan, et al., 2020)les. The variable contributions revealed that three bioclimatic variables predominantly control A. mearnsii distribution in southern Africa. In this study, the annual mean temperature. Other variables that were important for the distribution of A. mearnsii were precipitation in the wettest month, which contributed 28.1%. This factor is important because it reveals how climatic factors, especially rainfall, contribute to the degree of water stress[ 41 ]. Furthermore, the other important factor, the maximum temperature of the warmest quarter. This finding suggests that increasing temperatures influence the distribution of A. mearnsii species in southern Africa [ 42 ]. The relatively low contributions from topographic variables such as elevation, slope, and aspect indicate that climatic factors are more influential than terrain variables in A. mearnsii distribution across the SADC region. The geographical distribution displayed by the current distribution across southern Africa reveals various patterns. The suitability class showed more range expansion in South Africa than in other countries in southern Africa. The Mediterranean-type climate in South Africa resembles that of southern Australia, where A. mearnsii is a native species[ 57 ]. This climate provides an optimal climate where temperature and rainfall favour the growth of A. mearnsii [ 58 ]. Highly suitable classes were observed in Eswatini, northern Malawi, northern Tanzania, DRC, and Lesotho. Eswatini, Zimbabwe, and Lesotho have more savanna and montane grasslands and forest ecosystems. Montane and savanna grasslands provide optimal microclimates and water availability conditions for vegetation growth and reproduction[ 59 ]. The presence of probabilities under the suitability and medium suitability classes in Northen Malawi, Eswatini, and Tanzania suggests that A. mearn sii would expand its geographical range in watercourses[ 1 ]. Zambia, Botswana, Namibia, and Angola were largely classified as unsuitable, primarily because of their arid to semiarid climatic conditions and extreme temperature variations. The limited suitable habitat in these regions implies that climate acts as a natural barrier to A. mearnsii establishment and spread. Future climatic scenarios show different patterns of range expansion and contraction. The moderate scenario (SSP-45) predicts geographical range expansion in areas previously under the unsuitable class. Range expansion is observed in Mozambique, Tanzania, Madagascar and Malawi, where transitional zones shift from unsuitable to low-suitability classes, as shown in Fig. 2 . Southern African countries such as Namibia, Botswana, and Zambia have semiarid and arid climatic conditions. Under both scenarios, the shift from the not suitable class to the low suitable class suggests that these areas can support the growth of A. mearnsii . The SSP5-8.5 scenario presents a different trajectory, revealing significant reductions in highly suitable areas. This was observed across Eswatini, Lesotho, and South Africa. This finding indicates that climate factors such as bio_1 may push optimal climatic conditions beyond the tolerance thresholds of a species. This will lead to habitat contraction in currently suitable areas while creating new areas in previously marginal habitats. The quantitative analysis revealed dramatic changes in habitat suitability classes. The highly suitable areas significantly increase under RCP4.5 and RCP8.5, expanding from 3,841 km² to over 27,000 km² under both scenarios. However, these gains are accompanied by substantial losses in the medium- and low-suitability classes, suggesting a polarization towards extreme suitability categories. b. promoting Early detection and Rapid Response for adaptive IAS management. From the management and conservation context, the projected shifts under both scenarios suggest an urgent need for adaptive strategies to slow the further expansion of A. mearnsii in this region. The study proposes Early Detection and Rapid Response (EDRR) especially in the newly transition zones such as Mozambique, DRC and Namibia where there are potential habitats that are at risk of invasion. This objective can be achieved through systematic survey using remote sensing, Ecological niche modelling and an investment in community-based monitoring through citizen science. For countries where biological control has been a success such as south Africa, there is a need for using integrated control approaches. Control mechanisms such as mechanical removal, application of herbicides and burning can reduced seed germination chances and also can prevent re-emergence of A. mearnsii in treated sites. In the treated sites, managers should consider restoring the lands with native vegetation limiting re-invasion of A. mearnsii . The region is under severe threat to IAS due to limited financial instruments to support biodiversity conservation programmes. There is a need for cross-sectorial corroboration between sectorial agencies, local and international actors. This could impact management of A. mearnsii by increasing funding for adaptive management strategies. Due to limited financial resources, member states should work with the academia to develop climate-informed risk maps essential hence it can easily be incorporated into invasive species management. Climate-informed maps could assist land managers to guide areas required for reafforestation, areas at risk of fire disturbances and improve land use land changes by predicting which classes are at risk of invasion. Hence reducing the risk of re-establishment and safeguarding ecosystems services while promoting and conserving native biodiversity. c. Increasing data coverage among non-active member states to improve understanding of the distribution of A. mearnsii under changing climate. Data is important because you can manage what you can’t measure. The study was conducted using published occurrence records from five member states in the region. The rest of the member states particularly Zambia, Botswana, Namibia, and Angola have not published their data, although evidence suggest that Acacia mearnsii is present in these countries. The limited data availability reveals that to the need for better resourced and better coordinated field surveys among these member states. Although South Africa, Malawi, Tanzania, and Madagascar have made efforts to document Acacia mearnsii and publish their data, information remains uneven across the region, constraining effective regional planning and management. To improve and overcome this barrier, The member states with poor data representation such as Malawi, Mozambique and Angola should place much focus on more concrete solution such as community engagement. This can be integrated with research activities using citizen science and environmental education as means towards adaptive IAS management. Some of the strategies for community engagement including the use of local ecological knowledge in monitoring and awareness activities in the established plot where data is collected. In addition, the Southern African Development Community has a key role to play in supporting member states through targeted funding and technical assistance aimed at improving data coverage, consistency, and regional data sharing. Despite maxent predicting excellently (with 0.92 representing 92%), a representative data across the region would enable a better understanding of how Acacia mearnsii would also impact protected areas in the countries with no data published. All in all, the selected climate factors have performed excellently under Maximum Entropy model. However, we did not include other relevant drivers that could inform the management of A. Mearnsii in southern Africa. Future studies will need to include additional environmental layers in the analysis, and dispersal. Climate variables such as land use/land change, soils and other biotic interactions are crucial in understanding species distribution. The temporal scale of climate change projections provided a medium-term (2050) perspective of climatic change. However, longer temporal species environmental layers from 2061–2080 and 2100 for planning not only will require projections of climate but also projections of socio-economic dynamics to better understand the human footprint on future invasions in terms of enhancing or limiting impact. Similarly, in relation to the application of other machine learning and artificial intelligence models which will likely also clarify the past and present impacts of climate change on A. mearnsii . 5.0. Ethical Consideration The Study used secondary data collected from GBIF that is a publicly available database. There was no involvement of animal nor plant specimen. The research study used No personal, sensitive data was used and we adhered to transparency towards the used of open-source data. 6.0. Conclusion This study represents useful information on the current and future distributions of A. mearnsii in southern Africa under changing climates. 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02:55:56","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":152426,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/cb79cbae52c0fb06782b7a44.html"},{"id":99798336,"identity":"bd18334c-a37a-4136-acc2-0307b8fd4b4d","added_by":"auto","created_at":"2026-01-08 13:48:00","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":238180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eOccurrence data for A. mearnsii in southern Africa\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/31e725a7aaf24722dea399ec.jpeg"},{"id":99798304,"identity":"afe4718d-1f24-4389-96ea-5e9625a93599","added_by":"auto","created_at":"2026-01-08 13:47:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196761,"visible":true,"origin":"","legend":"\u003cp\u003eResults of the variable inflation factor for \u003cem\u003eAcacia meansii\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/e072a5f21e947814ee82b079.png"},{"id":99748299,"identity":"863630ee-3232-4a85-ad05-4f09b36231b3","added_by":"auto","created_at":"2026-01-08 02:55:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology for species distribution modelling of \u003cem\u003eA. mearnsii.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/708e6d166ce3240b07c3ad81.png"},{"id":99748309,"identity":"a0b26776-52b6-4f1f-80ec-df33f787fdbd","added_by":"auto","created_at":"2026-01-08 02:55:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eJackknife model of area under curve for A. meansii\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/8c46c228b59b52861896ee83.png"},{"id":99748305,"identity":"9e693ea5-2a0e-4264-9539-0393992e70ab","added_by":"auto","created_at":"2026-01-08 02:55:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89211,"visible":true,"origin":"","legend":"\u003cp\u003eResponse curves of \u003cem\u003eA. mearnsii.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/0199d1b7716db72dd96830d1.png"},{"id":99748316,"identity":"f2005526-fc6b-43d7-9dec-752036238606","added_by":"auto","created_at":"2026-01-08 02:55:55","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":45752,"visible":true,"origin":"","legend":"\u003cp\u003eROC AUC of \u003cem\u003eA. mearnsii\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/c63f23f01f6abbd3439231c5.jpeg"},{"id":99748304,"identity":"1c3aad4c-b9c4-4ece-9566-5eec51ffe40a","added_by":"auto","created_at":"2026-01-08 02:55:55","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":54633,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;current distribution of A. mearnsii in southern Africa\u003c/p\u003e","description":"","filename":"image6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/7078880487da14f163d48e4b.jpg"},{"id":99748307,"identity":"0238ddb0-41c0-44ce-999d-decb785447fe","added_by":"auto","created_at":"2026-01-08 02:55:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":57587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 9. future distributions of A. mearnsii under SSP45 and SSP85 in Southern Africa\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/00148bf86d45ea103ab298b5.jpg"},{"id":99798257,"identity":"f0f9a5b3-08cb-4651-a660-53458a792997","added_by":"auto","created_at":"2026-01-08 13:47:43","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":42711,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"image8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/ce05e163a0216b33a945ff22.jpg"},{"id":99805580,"identity":"04f1b977-6dec-422e-8711-c855934063ce","added_by":"auto","created_at":"2026-01-08 14:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1858870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8509238/v1/b13e9529-8e5e-4129-8f73-30e472654403.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePredicting Impacts of Climate Change on the Distribution of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAcacia mearnsii \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(Black wattle) in the Southern Africa Region.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0. Introduction","content":"\u003cp\u003e \u003cem\u003eAcacia. mearnsii\u003c/em\u003e is a native species from Southeast Australia and poses challenges in southern African ecosystems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Ranked among the top 100 invasive species, the species quickly outpaces native ecosystems due to multiple dispersal strategies and ability to quickly adapt to new environment[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cem\u003eA. mearnsii\u003c/em\u003e produces more seeds than native species higher seed production per unit seed mass in woody invasive plants[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The reproductive success of these plants is characterized by massive flowering, compensating for low fruit production. \u003cem\u003eA. mearnsii\u003c/em\u003e rapidly grows 2\u0026ndash;4 m per year in savannas, as reported in Tanzania, and it can begin producing seeds as early as 12 months of age[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. When these traits are combined, the species has the ability to outcompete native vegetation for resources change has been reported as a major driver for biodiversity loss and range expansion of IAS[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClimate change is playing a significant role in shaping aquatic and terrestrial habitats and it is a leading cause of plant species distribution[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Apart from this, climate change is responsible for changes in population migration pattens pushing species with narrow climate range beyond their habitat requirements[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. At the curst of this issue is shifts in temperatures and rainfall regimes that have contributed significantly by creating favourable habitat conditions for IAS expansion[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This has enabled IAS to easily expand and invade new areas putting native species at risk[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These patterns highlight the need to consider the combined effects of IAS and climate change into biodiversity management and policy frameworks[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Evidence from southern Africa suggests that, despite growing awareness of ecological impacts, existing management efforts remain insufficient and more effective control measures are still needed [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor decades, IAS have been a threat to global biodiversity and biosecurity. Either intentionally or non-intentionally introduced, IAS can be both beneficial and destructive to an ecosystem[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In particular, IAS are used for the control of soil erosion, timber, and energy consumption[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For example, countries such as South Africa, Tanzania, Mozambique and Malawi, jatropha was introduced intentionally for production of medicine, wood and bioenergy. Although IASs have offered economic and social benefits, they are a nuisance and disruptive to ecosystems. IASs such as water hyacinth have been linked to decreased water quality and reduced phytoplankton production [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These effects have severely impacted aquatic biodiversity sectors such as fisheries, irrigation, navigation, and tourism[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. IAS disrupt critical ecosystems and have been recognized as contributing factors to poverty[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This problem has been further exacerbated by increasing anthropogenic activities, climate change, and land use/land change[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSouthern Africa has unique and distinct ecological systems, such as miombo woodlands, savanna grasslands, rainforests, and desert ecosystems[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These ecosystems are vulnerable to the expansion of IAS, such as \u003cem\u003eA. mearnsii\u003c/em\u003e in South Africa, Zimbabwe, Malawi, and Tanzania[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Some critically affected ecosystems affected by IAS invasions are fynbos and forest ecosystems[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, spatial assessment of IAS such as \u003cem\u003eA. mearnsii\u003c/em\u003e is crucial for conservation of native species and management Ecological niche modelling uses environmental predictors to assess the impact of climate change on species distributions[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It uses occurrence data and climatic factors to project future shifts in geographic ranges[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Predictive modelling has become an important and crucial objective in ecology[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This approach enables ecologists to understand the drivers affecting the growth of species within the dimensions of space and time[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e][\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e];[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These models are particularly vital in the context of understanding biological invasions[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The assessment of potential spread is essential for informing early detection, risk assessment, and targeted management interventions[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e][\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Maximum entropy modelling has become one of the most widely used SDM techniques in ENMs[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Maxent estimates the probability distribution of a species\u0026rsquo; potential presence on the basis of occurrence data and environmental constraints[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e][\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the objectives of this study were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTo identify the key environmental variables influencing \u003cem\u003eA. mearnsii\u003c/em\u003e distribution in southern Africa;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo predict the potential habitat suitability of \u003cem\u003eA. mearnsii\u003c/em\u003e under current and future scenarios\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTo classify habitat suitability for guiding targeted management, restoration, and early detection efforts on the basis of countries in SADC.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"2.0. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Area Description\u003c/h2\u003e\n \u003cp\u003eFigure show countries under the Southern Africa Development Community. The study was conducted in southern Africa (popularly known as SADC), which contains 16 countries, such as South Africa, Malawi, Zambia, and Zimbabwe. The SADC is located at latitudes ranging from 15\u0026deg;S to 35\u0026deg;S and longitudes ranging from 10\u0026deg;E to 40\u0026deg;E. It is known for its savanna grassland, Miombo woodland and desert ecosystems. The regional climate ranges from arid, semiarid and subtropical climates with annual precipitation between 300 mm and 1200 mm. The regional elevations range from 3000 m to 4500 m above sea level, providing optimal growth for IAS. \u003cem\u003eA. mearnsii\u003c/em\u003e is disturbing riparian zones, montane grasslands, and forest margins in South Africa, Eswatini, and Zimbabwe. Its aggressive spread poses serious ecological threats by reducing water availability, displacing native flora, and altering fire regimes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Input data\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1. Occurrence data\u003c/h2\u003e\n \u003cp\u003eOccurrence records were collected from the Global Biodiversity Information Facility (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.gbif.org\u003c/span\u003e\u003c/span\u003e), which was accessed on 15 July 2025 via the pygbif library. The initial dataset contained 8350 occurrence points georeferenced with observations across southern Africa (1990\u0026ndash;2024). Owing to nuances in the dataset, Open Refine was used for data cleaning. Open refine is one of the best software packages for cleaning messy data. Data points lacking georeferencing were removed via NumPy and Panda\u0026rsquo;s library[\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. This process was repeated for data points that were outside the range of the study area. Spatial analysis was conducted to ensure data accuracy via geopandas and scikit-learn libraries. The aim of conducting spatial thinning was to reduce sampling bias. Therefore, a distance threshold of 5 kilometres is used as the minimum distance so that no more than two data points occur within the set distance minimum threshold [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe final dataset included species, decimal latitude and decimal longitude. This helps minimize the spatial autocorrelation of data collected in densely populated areas. The final curated dataset included 7361 high-confidence occurrence records that accurately represented the distribution of the species in key habitats, such as savannas, plantations, montane grasslands, and riparian zones. The final dataset was saved as a CSV and then exported to Python 3.13 for further data cleaning. In Python programming, the dataset was tested for a normal distribution via analysis of variance (ANOVA), and other exploratory analyses were also conducted[\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. Environmental data\u003c/h2\u003e\n \u003cp\u003eNineteen bioclimatic layers for current and future climate projections were obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.worlclim.org/2.1\u003c/span\u003e\u003c/span\u003e at a 30 second resolution, accessed and downloaded on 15 July 2025. MIROC6 climate dataset used for modelling of \u003cem\u003eA. mearnsii\u003c/em\u003e was, which was developed by the Japanese Space Agency. The MIROC 6 experiments were designed to represent Earth\u0026apos;s climate from 1850\u0026ndash;2014 and 2014\u0026ndash;2099, respectively[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. The first experiment was modelled under the representative critical path (RCP), whereas the second experiment was modelled under the shared socioeconomic pathway by the Coupled Model Intercomparison Project phase 6, the latest climate predictor for current and future projections[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]\u0026ndash;[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eElevation, slope, aspect and topographic indices were also used in this study. Elevation is an important factor because it is used to assess temperature and rainfall changes across diverse ecosystems[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Slopes affect vegetation growth, water movement, soil stability, and water run-off; therefore, slope is an important variable for this study[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. Aspect influences the position of the sun and therefore controls solar radiation[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. Finally, the topographic index provides information on drainage conditions and soil moisture for plant survival and growth[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. shows environmental predictors used in this study used in the modelling of \u003cem\u003eA. mearnsii\u003c/em\u003e in Southern Africa. From Bio 1 to bio-11, the unit measurement for each predictor degrees Celsius while from Bio 12-bio17 the unit value is mini-meters(mm).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" style=\"width: 281.567px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEnvironmental predictors downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.worlclim.com/2.1\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAnnual Mean Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMean Diurnal Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eIsothermality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTemperature Seasonality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMax Temperature of Warmest Month\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMin Temperature of Coldest Month\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTemperature Annual Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMean Temperature of Wettest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMean Temperature of Driest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 51px;\" align=\"left\"\u003e\n \u003cp\u003eBio10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 220.049px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMean Temperature of Warmest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eMean Temperature of Coldest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eAnnual Precipitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation of Wettest Month\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation of Driest Month\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation Seasonality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation of Wettest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation of Driest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation of Warmest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eBio19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003ePrecipitation of Coldest Quarter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eElevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eDigital Elevation Models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003easp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eDerived from DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eDerived from DEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 52.4815px;\" colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 208px;\" align=\"left\"\u003e\n \u003cp\u003eTopographic index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3. Variable Selection and Multicollinearity Assessment\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. shows variable selection and multicollinearity assessment. To avoid multicollinearity, the variable inflation factor (VIF) was conducted via Python programming using the pandas, seaborne and statsmodels libraries. The aim was to reduce redundancy among environmental predictors and ensure that there are robust model results and interpretations. We conducted a VIF using pairwise Pearson correlation coefficients (PCCs) to identify environmental layers that had strongly correlated variables. The relationships among the environmental predictors were evaluated via the correlation coefficient of |r\u003csup\u003e2\u003c/sup\u003e|\u0026gt;0.75[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e][\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. Any predictor showing a strong linear relationship with other predictors is redundant(Guisan, et al., 2017). The results of the process revealed that Bio_2 (mean diurnal temperature), Bio_3 (isothermality), Bio_11 (mean temperature of the coldest quarter), Bio_1 (annual mean temperature) and Bio_1 (precipitation of the driest month) had lower correlations than the other environmental predictors. Therefore, these models were then used to construct a species distribution model for \u003cem\u003eAcacia mearnsii\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Modelling Approach\u003c/h2\u003e\n \u003cp\u003eFigure 3. shows methodology for species distribution modelling of \u003cem\u003eA. mearnsii.\u003c/em\u003e Python programming language version 3.14 was used to perform ecological niche modelling \u003cem\u003eof A. mearnsi\u003c/em\u003ei in southern Africa[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The environmental layers were exported in Python programming 3.14 via the Jupyter notebook. Raster clipping to the southern Africa boundary was executed in Python programming 3.14 via the rasterio, geopandas, ecopy and xarray libraries[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. All raster layers were aligned via WGS 84, EPSG 4326 for projection consistency. We utilized occurrence data in the CSV from the GBIF in CSV format alongside bioclimatic predictors in GeoTIFF. These inputs were used to produce habitat suitability maps and predict potential areas of invasion risk(Poudel, et al., 2024). Maximum entropy modelling, a machine learning algorithm, was integrated in Python via the Jupyter notebook. The occurrence dataset was randomly partitioned into model training and testing. Eighty percent of the occurrence records were portioned for testing, and 30% were portioned for model training (Brunialti, et al., 2021). Model performance was evaluated via the area under the receiver operating characteristic curve (AUC-ROC). According to Phillips (2012), values from 0.9\u0026ndash;1 indicate excellent predictive accuracy. A net change analysis was also conducted to assess spatial and temporal changes in habitat suitability. Fifty replicate model runs, all executed and summarized within the Python environment.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3.0. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Analysis of variable contributions\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. presents estimates of the relative contributions of the environmental variables to the Maxent model. The annual mean temperature emerged as the most dominant predictor, contributing 49% to the model\u0026rsquo;s gain and registering the highest permutation importance of 40.7%. These findings indicate that the daily variability in temperature is a critical ecological factor influencing habitat suitability, potentially affecting species physiological responses and microhabitat stability. The precipitation of the driest month had a strong influence on the model, with a percent contribution of 28% and a permutation importance of 13.4%. These findings underscore the relevance of seasonal thermal conditions, particularly extreme heat periods, in shaping species distributions.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eanalysis of variable contributions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercent Contribution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePermutation Importance\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eelev\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ebio_13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Jackknife results\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. shows the jackknife results for the study. The annual mean temperature was the most important variable influencing the distribution of \u003cem\u003eA. mearnsii\u003c/em\u003e. When used in isolation, \u003cem\u003eBio_1\u003c/em\u003e achieved the highest training gain, indicating that it provided the greatest amount of useful information for predicting species occurrence. Moreover, its exclusion from the model led to a substantial reduction in training gain, further emphasizing its unique contribution. These findings suggest that the annual thermal regime plays a central role in determining habitat suitability for \u003cem\u003eA. mearnsii\u003c/em\u003e, which aligns with the known sensitivity of this species to temperature and its preference for moderately warm climates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Response curves\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. shows how each environmental variable affects the predicted probability of presence. They depict a threshold at which the species can survive, assessing its habitat preference. The response curve for the annual mean temperature shows that \u003cem\u003eA. mearnsii\u003c/em\u003e responds positively to rising temperatures. The probability of presence at a peak temperature for a. mearnsii was between 15\u0026deg;C and 25\u0026deg;C. These findings indicate that A. mearnsii has an optimal thermal range for establishment and persistence(Shcheglovitova, et al., 2013). The temperatures above 22\u0026deg;C indicate that habitat suitability has declined. These findings suggest that warm climates may limit the growth and reproduction of this species.\u003c/p\u003e\n \u003cp\u003eThe maximum temperature of the warmest month revealed that \u003cem\u003eA. mearnsii\u003c/em\u003e performs best in regions where the maximum temperature ranges from 26\u0026deg;C to 32\u0026deg;C. The observed decline in habitat suitability above 35\u0026deg;C implies that extremely high temperatures may impact the physiological tolerance of A. mearnsii, affecting seed germination and water-use efficiency. While the Precipitation of the Driest Month showed a unimodal pattern, the probability of presence increased as the precipitation rose from 0 mm to 40 mm. After this point, habitat suitability declined. This finding indicates that \u003cem\u003eA. mearnsii\u003c/em\u003e is tolerant of dry season conditions but may be disadvantaged in areas with high year-round precipitation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Model performance\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Shows the receiver operating characteristic (ROC) curve for this study. Note that the specificity is defined via the predicted area rather than the true commission. This implies that the maximum achievable AUC is less than 1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Current habitat suitability model\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. shows Current distribution of A. mearnsii in southern Africa. The current habitat suitability model revealed that \u003cem\u003eA. mearnsii\u003c/em\u003e was highly suitable in Northen region of Malawi, Lesotho, Zimbabwe, Tanzania and South Africa. Lesotho, Zimbabwe, and Eswatini presented more highly suitable and suitable classes. Madagascar, Mozambique, and the DRC Congo ranged from suitable to low suitable. Several areas in Tanzania were highly suitable, moderately suitable and poorly suitable. Madagascar and the DRC in the Congo have few areas that are in the low suitability to medium suitability classes. These findings indicate that these areas have climatic conditions that support the growth of A. mearnsii. Conversely, countries such as Zambia, Mauritius, Seychelles, and the Comoros Islands were largely classified as unsuitable for the establishment of \u003cem\u003eA. mearnsii\u003c/em\u003e, likely because of ecological limitations or climatic unsuitability. Furthermore, the spatial predictive maps revealed that Namibia, Botswana, Angola, and Zambia did not have suitable classes.\u003c/p\u003e\n \u003cp\u003eThe current habitat suitability map, derived from five classes not suitable, low suitability, medium suitability, suitable, and highly suitable indicated that \u003cem\u003eA. mearnsii\u003c/em\u003e is presently most suited to the western and eastern Cape provinces of South Africa. These regions presented environmental conditions aligned with the most influential bioclimatic predictors, especially \u003cem\u003eBio_1\u003c/em\u003e, \u003cem\u003eBio_14\u003c/em\u003e, and \u003cem\u003eBio_5\u003c/em\u003e, which offer optimal conditions for species growth. Other countries, such as Lesotho, Zimbabwe, and Eswatini, also exhibited large areas of high and moderate suitability. Further north, Malawi, Mozambique, and the Democratic Republic of the Congo demonstrated varying degrees of suitability, ranging from high to low, depending on regional climatic variations. In contrast, countries such as Namibia, Botswana, Angola, and Zambia were predominantly categorized as not suitable for the species, likely because of their ecological limitations and unfavourable climatic conditions. Madagascar and the DRC displayed pockets of low to medium suitability, suggesting restricted but potentially viable conditions for future colonization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Potential distribution of \u003cem\u003eA. mearnsii\u003c/em\u003e under future scenario SSPRCP4.5\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. Shows the future suitability map under SSP 45 and 85 climate scenarios. The map reveals an expected shift for \u003cem\u003eA. mearnsii\u003c/em\u003e in the SADC region. The potential ranges expansion under suitability class in South Africa, Lesotho, Northern region of Malawi and Tanzania. Under the Medium suitable class SSP 45, the habitat potential suitability maps reveal that countries such as Mozambique and Zimbabwe showed a more sift of potential range increase under this class. The increase in expansion under medium suitability class suggest that these areas have the potential to support the growth of black wattle species. Across the same class, climate change will also create favourable conditions for \u003cem\u003eA. Meamsii\u003c/em\u003e species in arid and semi-arid ecosystems such as Angola, Namibia, Lower shire in Malawi and Zambia. This suggest that Black wattle could spread to these areas and create conducive environmental optimal for growth in areas where there no current information.\u003c/p\u003e\n \u003cp\u003eUnder the SSP2-4.5 climate change scenario, the results revealed a large expansion in the potential geographical range of the species. Under the highly suitable class, the potential areas increased from 3,841 km\u0026sup2; to 28,659 km\u0026sup2; representing a 646.21% increase in suitability. The potential areas increased significantly, by 14.69%, from 184,754 km\u0026sup2; to 211,884 km\u0026sup2;. The lower suitability category showed notable losses under climate scenario SSP85 than SSP 45. As observed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e., the area under the lower class increased by 24.57%, and under the medium suitable class, it decreased by 12.17%. These areas cannot support the growth of \u003cem\u003eA. mearnsii\u003c/em\u003e. Notably, countries such as South Africa, Malawi, and Mozambique, which have few areas that are suitable under the current scenario, have now become suitable under this scenario. Furthermore, the shift under these scenarios suggests that changes in rainfall patterns, as shown by bio_14, could result in increased potential expansion of \u003cem\u003eA. mearnsii\u003c/em\u003e. Similarly, in countries such as Zambia, Namibia, and Angola, few areas have achieved low suitability under this climate scenario. These findings indicate that \u003cem\u003eA. mearnsii\u003c/em\u003e has the potential to colonize these areas. In Malawi, Zambia Tanzania and the DRC, it was also noted that there was a shift in classes from not suitable to the medium class. This suggests that northwards and altitudinal expansion of species niches has occurred in this country.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ehabitat suitability analysis of A. mearnsii under five classes across the southern Africa Development Commission.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnsuitable Habitat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSuitability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHigh Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,253,284.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBotswana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e629,051.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComoros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDR. Congo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,254,100.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,585.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEswatini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,263.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,172.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,625.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLesotho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,036.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,997.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,517.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadagascar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e501,085.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49,525.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,552.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalawi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100,793.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,411.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,399.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMauritius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMozambique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e775,541.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,033.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,764.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNamibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e868,719.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeychelles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e871,838.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222,542.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218,540.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37,864.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e834,576.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,463.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,953.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e776,595.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375,486.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29,323.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,294.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Potential distribution of \u003cem\u003eA. mearnsii\u003c/em\u003e under future scenario RCP 8.5\u003c/h2\u003e\n \u003cp\u003eThe results under the SSP-8.5 scenario reveal different trajectories than those under the current scenario and SSP-4.5 scenario. The highly suitable class increased from 3,841 km\u003csup\u003e2\u003c/sup\u003e to 27,060 km\u003csup\u003e2\u003c/sup\u003e. Our results revealed a substantial decrease under this scenario under SSP-4.5. The suitability classes expanded by 28,659, whereas the low and medium classes expanded by 900,628 km\u003csup\u003e2\u003c/sup\u003e and 408,970 km\u003csup\u003e2\u003c/sup\u003e respectively. This reduction was observed in Mozambique, where more areas with low suitability changed to the unsuitable class, with similar observations in Malawi, DRC and Namibia.\u003c/p\u003e\n \u003cp\u003eThe Potential areas previously recognized as highly suitable, for instance in Eswatini and Lesotho, transitioned into lower suitability classes, highlighting a threshold beyond which warming will become detrimental to optimal growth. South Africa experienced a net increase in potential range under this scenario, with regions that were unsuitable now becoming suitable for invasion of the studied species. Mozambique exhibited a contraction in its medium suitability zones, implying that increased warming may hinder rather than facilitate the spread of \u003cem\u003eA. mearnsii\u003c/em\u003e in certain areas. In Malawi, there was a loss of highly suitable zones, although these zones were partially offset by gains in medium suitability.\u003c/p\u003e\n \u003cp\u003eClimate change will facilitate the biological invasion of \u003cem\u003eAcacia mearnsii\u003c/em\u003e, as the results suggest, across southern Africa. This is evidence of the changes in suitability classes, especially under highly suitable, suitable and moderately suitable classes. The strong expansion of highly suitable zones under both scenarios underscores the urgent need for proactive monitoring and management strategies, especially in regions newly projected to support the species.\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ehabitat suitability analysis of A. mearnsii under five classes across the southern Africa Development Commission.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnsuitable Habitat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModerate Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eVery High Suitability\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,282,672.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBotswana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e629,503.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComoros\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,462.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDR. Congo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,347,127.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,495.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEswatini\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,177.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,194.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,690.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLesotho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10,606.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,253.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,691.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMadagascar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e568,984.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39,327.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24,052.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalawi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115,703.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,207.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e752.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMauritius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,764.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMozambique\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e826,745.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,119.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,398.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNamibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e894,407.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeychelles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e193.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e865,556.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275,940.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210,193.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49,740.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e917,126.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,250.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,571.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e778,811.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380,886.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28,011.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,206.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ch2 class=\"colspec\" align=\"left\"\u003e3.8. Net gain and Loss analysis\u003c/h2\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003egain/loss risk analysis for distribution of \u003cem\u003eA. mearnsii\u003c/em\u003e that was averaged by\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSuitability Class\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP4.5 (Gain/Loss)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP4.5 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP8.5 (Gain/Loss)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRCP8.5 (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighly suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;24,818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;23,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;27,130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;24,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;37,207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;27,134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;57,592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;49,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot suitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;40,475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u0026thinsp;29,160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe net gain and net loss analysis revealed an increase in highly suitability class increased by 28, 219 km\u003csup\u003e2\u003c/sup\u003e representing a 15.3% under RCP85. The suitability class expanded by 22,004 km2 showing a partial intensification in the region. The range contraction and fragmentation under RCP85 where substantial losses between low suitability by -27,592 km2 and medium suitable \u0026minus;\u0026thinsp;49,250 under RCP85 were observed. Under RCP45 scenario, highly suitable class expanded by +\u0026thinsp;24,818, representing (13.3%) while the suitability class contracted by +\u0026thinsp;27,130 representing (14.5%). Under the same scenario, larger losses were recorded under medium suitability class with \u0026minus;\u0026thinsp;37,207 km2 while low suitability zones \u0026minus;\u0026thinsp;57,592 km2. Notably, both scenarios show an increase in areas classified as not suitable, particularly under RCP4.5 (40,475), highlighting an overall trend toward habitat polarization. The findings suggest that climate change may reduce ecological shifts while concentrating suitability into fewer, more climatically favourable areas, with important implications for species persistence, dispersal, and management under future climate conditions.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4.0. Discussion","content":"\u003cp\u003e \u003cb\u003ea. model inference\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe maximum entropy model has excellent predictive power for species distribution modelling of \u003cem\u003eA. mearnsii\u003c/em\u003e in southern Africa. The Maxent model uses the area under the curve (AUC) as a measure of model predictive performance. An AUC of 0.5 indicates random prediction, and an AUC of 0.9-1 represents excellent model performance(Phillips, et al., 2006). The model achieved high accuracy scores, with AUC-ROC values exceeding 0.932, indicating that the model made excellent predictions of 93.2%. This is attributed to the careful preprocessing of occurrence data and environmental variables. A minimum distance threshold of 5 kilometres between data points was applied spatially with the aim of reducing sampling bias and spatial autocorrelation(Stephan, et al., 2020)les.\u003c/p\u003e \u003cp\u003eThe variable contributions revealed that three bioclimatic variables predominantly control \u003cem\u003eA. mearnsii\u003c/em\u003e distribution in southern Africa. In this study, the annual mean temperature. Other variables that were important for the distribution of A. mearnsii were precipitation in the wettest month, which contributed 28.1%. This factor is important because it reveals how climatic factors, especially rainfall, contribute to the degree of water stress[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, the other important factor, the maximum temperature of the warmest quarter. This finding suggests that increasing temperatures influence the distribution of A. mearnsii species in southern Africa [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The relatively low contributions from topographic variables such as elevation, slope, and aspect indicate that climatic factors are more influential than terrain variables in \u003cem\u003eA. mearnsii\u003c/em\u003e distribution across the SADC region.\u003c/p\u003e \u003cp\u003eThe geographical distribution displayed by the current distribution across southern Africa reveals various patterns. The suitability class showed more range expansion in South Africa than in other countries in southern Africa. The Mediterranean-type climate in South Africa resembles that of southern Australia, where \u003cem\u003eA. mearnsii\u003c/em\u003e is a native species[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This climate provides an optimal climate where temperature and rainfall favour the growth of \u003cem\u003eA. mearnsii\u003c/em\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Highly suitable classes were observed in Eswatini, northern Malawi, northern Tanzania, DRC, and Lesotho. Eswatini, Zimbabwe, and Lesotho have more savanna and montane grasslands and forest ecosystems. Montane and savanna grasslands provide optimal microclimates and water availability conditions for vegetation growth and reproduction[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The presence of probabilities under the suitability and medium suitability classes in Northen Malawi, Eswatini, and Tanzania suggests that \u003cem\u003eA. mearn\u003c/em\u003esii would expand its geographical range in watercourses[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Zambia, Botswana, Namibia, and Angola were largely classified as unsuitable, primarily because of their arid to semiarid climatic conditions and extreme temperature variations. The limited suitable habitat in these regions implies that climate acts as a natural barrier to \u003cem\u003eA. mearnsii\u003c/em\u003e establishment and spread.\u003c/p\u003e \u003cp\u003eFuture climatic scenarios show different patterns of range expansion and contraction. The moderate scenario (SSP-45) predicts geographical range expansion in areas previously under the unsuitable class. Range expansion is observed in Mozambique, Tanzania, Madagascar and Malawi, where transitional zones shift from unsuitable to low-suitability classes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Southern African countries such as Namibia, Botswana, and Zambia have semiarid and arid climatic conditions. Under both scenarios, the shift from the not suitable class to the low suitable class suggests that these areas can support the growth of \u003cem\u003eA. mearnsii\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe SSP5-8.5 scenario presents a different trajectory, revealing significant reductions in highly suitable areas. This was observed across Eswatini, Lesotho, and South Africa. This finding indicates that climate factors such as bio_1 may push optimal climatic conditions beyond the tolerance thresholds of a species. This will lead to habitat contraction in currently suitable areas while creating new areas in previously marginal habitats. The quantitative analysis revealed dramatic changes in habitat suitability classes. The highly suitable areas significantly increase under RCP4.5 and RCP8.5, expanding from 3,841 km\u0026sup2; to over 27,000 km\u0026sup2; under both scenarios. However, these gains are accompanied by substantial losses in the medium- and low-suitability classes, suggesting a polarization towards extreme suitability categories.\u003c/p\u003e \u003cp\u003eb. \u003cb\u003epromoting Early detection and Rapid Response for adaptive IAS management.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom the management and conservation context, the projected shifts under both scenarios suggest an urgent need for adaptive strategies to slow the further expansion of \u003cem\u003eA. mearnsii\u003c/em\u003e in this region. The study proposes Early Detection and Rapid Response (EDRR) especially in the newly transition zones such as Mozambique, DRC and Namibia where there are potential habitats that are at risk of invasion. This objective can be achieved through systematic survey using remote sensing, Ecological niche modelling and an investment in community-based monitoring through citizen science. For countries where biological control has been a success such as south Africa, there is a need for using integrated control approaches. Control mechanisms such as mechanical removal, application of herbicides and burning can reduced seed germination chances and also can prevent re-emergence of \u003cem\u003eA. mearnsii\u003c/em\u003e in treated sites. In the treated sites, managers should consider restoring the lands with native vegetation limiting re-invasion of \u003cem\u003eA. mearnsii\u003c/em\u003e. The region is under severe threat to IAS due to limited financial instruments to support biodiversity conservation programmes. There is a need for cross-sectorial corroboration between sectorial agencies, local and international actors. This could impact management of \u003cem\u003eA. mearnsii\u003c/em\u003e by increasing funding for adaptive management strategies. Due to limited financial resources, member states should work with the academia to develop climate-informed risk maps essential hence it can easily be incorporated into invasive species management. Climate-informed maps could assist land managers to guide areas required for reafforestation, areas at risk of fire disturbances and improve land use land changes by predicting which classes are at risk of invasion. Hence reducing the risk of re-establishment and safeguarding ecosystems services while promoting and conserving native biodiversity.\u003c/p\u003e \u003cp\u003e \u003cb\u003ec. Increasing data coverage among non-active member states to improve understanding of the distribution of A. mearnsii under changing climate.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData is important because you can manage what you can\u0026rsquo;t measure. The study was conducted using published occurrence records from five member states in the region. The rest of the member states particularly Zambia, Botswana, Namibia, and Angola have not published their data, although evidence suggest that Acacia mearnsii is present in these countries. The limited data availability reveals that to the need for better resourced and better coordinated field surveys among these member states. Although South Africa, Malawi, Tanzania, and Madagascar have made efforts to document \u003cem\u003eAcacia mearnsii\u003c/em\u003e and publish their data, information remains uneven across the region, constraining effective regional planning and management. To improve and overcome this barrier, The member states with poor data representation such as Malawi, Mozambique and Angola should place much focus on more concrete solution such as community engagement. This can be integrated with research activities using citizen science and environmental education as means towards adaptive IAS management. Some of the strategies for community engagement including the use of local ecological knowledge in monitoring and awareness activities in the established plot where data is collected. In addition, the Southern African Development Community has a key role to play in supporting member states through targeted funding and technical assistance aimed at improving data coverage, consistency, and regional data sharing. Despite maxent predicting excellently (with 0.92 representing 92%), a representative data across the region would enable a better understanding of how \u003cem\u003eAcacia mearnsii\u003c/em\u003e would also impact protected areas in the countries with no data published.\u003c/p\u003e \u003cp\u003eAll in all, the selected climate factors have performed excellently under Maximum Entropy model. However, we did not include other relevant drivers that could inform the management of \u003cem\u003eA. Mearnsii\u003c/em\u003e in southern Africa. Future studies will need to include additional environmental layers in the analysis, and dispersal. Climate variables such as land use/land change, soils and other biotic interactions are crucial in understanding species distribution. The temporal scale of climate change projections provided a medium-term (2050) perspective of climatic change. However, longer temporal species environmental layers from 2061\u0026ndash;2080 and 2100 for planning not only will require projections of climate but also projections of socio-economic dynamics to better understand the human footprint on future invasions in terms of enhancing or limiting impact. Similarly, in relation to the application of other machine learning and artificial intelligence models which will likely also clarify the past and present impacts of climate change on \u003cem\u003eA. mearnsii\u003c/em\u003e.\u003c/p\u003e"},{"header":"5.0. Ethical Consideration","content":"\u003cp\u003eThe Study used secondary data collected from GBIF that is a publicly available database. There was no involvement of animal nor plant specimen. The research study used No personal, sensitive data was used and we adhered to transparency towards the used of open-source data.\u003c/p\u003e"},{"header":"6.0. Conclusion","content":"\u003cp\u003eThis study represents useful information on the current and future distributions of A. mearnsii in southern Africa under changing climates. The models performed well overall and identified strong, critical environmental drivers of climate change allowing us to be relatively confident when making targeted management decisions. The projected range\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTham LT, Darr D, Pretzsch J (2020) Contribution of small-scale acacia hybrid timber production and commercialization for livelihood development in Central Vietnam. 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November\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrous CJ, Jacobs SM, Esler KJ (2012) Wood anatomical traits as a measure of plant responses to water availability: Invasive Acacia mearnsii De Wild. compared with native tree species in fynbos riparian ecotones, South Africa. Trees 26:1527\u0026ndash;1536\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKharivha T, Ruwanza S, Thondhlana G (2022) Effects of Elevated Temperature and High and Low Rainfall on the Germination and Growth of the Invasive Alien Plant Acacia mearnsii. Plants 11(19):2633\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"N?A","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ecology, ecological modelling, maxent, conservetion biology","lastPublishedDoi":"10.21203/rs.3.rs-8509238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8509238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlien invasive species (IASs) are a threat to the ecological systems of southern Africa. \u003cem\u003eAcacia mearnsii\u003c/em\u003e, a global top 100 invasive species has become a very invasive tree in the region, displacing native vegetation, altering fire regimes, and influencing water availability. In this study, Ecological Niche Modelling was used using the maximum entropy algorithm to predict the current and future distributions of \u003cem\u003eA. mearnsii\u003c/em\u003e southern African underMIROC-6 climate models SSP2-4.5 and SSP5-8.5. Data used for modelling were collected from GBIF and 19 bioclimatic variables from Wordclim. The Model performance assessed with Area Under Cover-ROC metrics. The annual mean temperature, precipitation of the wettest month, and maximum temperature of the warmest month were the primary environmental drivers. Currently, highly suitable areas are concentrated in South Africa, Eswatini, and Lesotho. The projected future suitability suggests potential range expansion, particularly under SSP2-4.5, with highly suitable habitats increasing by 13.7%. However, under SSP5-8.5, extreme warming would lower habitat suitability in some regions with an increase (+\u0026thinsp;646.21%). Climate change has a significant effect on \u003cem\u003eA. mearnsii's\u003c/em\u003e threat of invasion, emphasizing the importance of early detection, risk identification, and tailored management of susceptible ecosystems.\u003c/p\u003e","manuscriptTitle":"Predicting Impacts of Climate Change on the Distribution of Acacia mearnsii (Black wattle) in the Southern Africa Region.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 02:55:50","doi":"10.21203/rs.3.rs-8509238/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17ff31ba-17ca-4770-bf2f-385f7fe5d84e","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60530814,"name":"Ecological Modeling"},{"id":60530815,"name":"Forestry"},{"id":60530816,"name":"Conservation Biology"},{"id":60530817,"name":"Systems Biology"},{"id":60530818,"name":"Population Biology"},{"id":60530819,"name":"Geographic Information Systems"},{"id":60530820,"name":"Climatology"},{"id":60530821,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2026-01-08T02:55:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 02:55:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8509238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8509238","identity":"rs-8509238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}