Introduction

Eragrostis curvula is a species that has attracted government, public and scientific interest due to its impacts on ecological integrity and agricultural productivity1,2,3. E. curvula is a C4 perennial tussock grass in the family Poaceae. The Eragrostis genus has 300 species worldwide, with 66 present in Australia, 52 native, and 14 considered non-native species4. E. curvula was intentionally introduced into Australia as early as 1910 by the Australian Government as part of a larger scheme to introduce soil-stabilising plant species that may also offer a source of feed to grazing livestock5,6. In Australia, E. curvula has become an undesirable plant species in agricultural and ecological contexts, impacting species diversity, richness, and agricultural productivity7,8. However, evidence suggests that E. curvula can sustain agricultural enterprises but requires intensive management to be a moderate pasture grass, regarding sustaining livestock health and weight for productive agricultural output while still impacting native species diversity5,6,9,10. Since its introduction, E. curvula has spread and is now present in every state in Australia, including high densities in agricultural regions7,8,11.

Factors driving E. curvula’s ability to distribute so widely in Australia and across the world include its high propagule pressure and phenotypic plasticity, allowing it to establish itself in a wide range of climatic conditions6,10,12,13,14. The extent to which a species can alter its morphological or physiological traits to suit various landscape conditions defines the amount of phenotypic plasticity a species possesses14,15,16. Firn et al.14 found E. curvula to have greater variability in six measured plant traits when growing conditions were altered compared to two other co-occurring native species (Eragrostis sororia and Aristida personata), indicating that E. curvula has greater phenotypic plasticity. However, this phenotypic plasticity has been shown to act as a double-edged sword; where when nutrients are applied to E. curvula, it will increase its resource use efficiency, thus absorbing nutrients desirable to foraging organisms and becoming a more desirable forage for herbivores and subsequent decrease in E. curvula abundance14. The findings by Firn et al.14 support the ability of E. curvula to adapt its phenological development to a wide range of conditions but also offer unique insight into potential management strategies. More broadly, E. curvula’s continent-wide distribution may result from its ability to adapt to various stressful and varied environmental conditions16. This phenotypic plasticity appears common across cultivars of E. curvula and is reflected by relatively distinct yet variable properties such as appearance palatability and phenology within the species17,18.

Holmes19 reported on the impact of both the perennial weeds E. curvula and serrated tussock on the Monaro grazing industries suggesting that the difference between the cumulative cash surplus of an uninvaded farm and an invaded farm can be up to $300 000, primarily due to the difference in return from livestock. In an invaded farm situation, the land’s carrying capacity can be as low as two dry sheep equivalent (dse) per hectare, which according to Holmes19, is insufficient to “break-even” and results in an income deficiency. Holmes19 categorises uninvaded native pastures as being able to sustain four des per hectare and eight des per hectare in modified pastures. Holmes19 argues that the benefits of managing invasive plants, despite the monetary cost of labour and resources required, far outweigh the financial cost impacts of no management due to invasive species’ impact on an area’s carrying capacity decreasing a farm’s gross income.

Due to its ability to impact both native ecosystems as well as economically important grazed grasslands, research has been conducted that relates the ecological factors that influence the distribution of E. curvula and which may be integrated into a single model used for the prediction of distribution across larger spatial areas6,10,20. Predictive models are dependent on relevant predictor variables, and in the case of species distribution models, understanding what predictor variables are most likely to influence a species distribution is vital in the development of accurate models21,22,23. Thanks to developments in the accessibility of open-source data through data collectors such as Copernicus, Earth Explorer, and the Bureau of Meteorology, predictive models have become more accurate and widespread24,25. E. curvula is considered an invasive species in Australia, among other countries, and predictor variables influencing its distribution relate to its invasive traits, such as the production of many small seeds and the ability to grow under a wide range of climatic conditions3,6,10. E. curvula is primarily dispersed by hydrochory (water dispersal), anemochory (wind dispersal) and zoochory (animal dispersal), and as such, predictor variables relating to climate, distance to waterways and distance to human and animal dispersal pathways (such as roads) are likely to be the strongest predictors of E. curvula distribution6,10.

Random Forest models (RF) are a highly flexible and robust machine learning technique that has gained prominence in various fields of study, including research on drought, land-use change and predictive species distribution modelling26,27,28,29,30,31. Random forests are an ensemble classifier formed from many classification and regression trees26. Roozbeh Valavi, Elith, Lahoz‐Monfort, and Guillera‐Arroita (2023) highlight the advantage of flexible models, such as RF, in their ability to generalise data and model complex, non-linear relationships. An issue often present in machine learning techniques is overfitting26. The inherent model architecture of RF, such as the incorporation of bootstrapping, random feature selection and the model’s ensemble mechanisms, work to overcome overfitting issues26. However, RF are not immune to overfitting data issues, and appropriate performance metrics analysis is required to evaluate RFs26. Jalayer et al.27 use RF and a neural network architecture for supervised learning, fussy ARTMAP, to model land cover change in the Chalus Watershed, Iran. Using these models, Jalayer et al.27 provide evidence of increases in agricultural land and barren areas and declines in grasslands and forested areas where the major land use changes between 2001 and 2021. Further, the model’s prediction was validated on the known land use change and found to be an accurate simulation, with K-index values ranging from 0.92 to 0.94. Here, the K-index values approaching one, reflect a high degree of accuracy between the predicted and actual land use change maps. Jalayer et al.27 used these models to forecast land use change into 2040 and predict a further decrease in forest cover and increase in expansion of barren areas, agricultural land and build-up area.

There is a general consensus among invasion ecologists that simply eradicating invasive plants is not feasible in landscape management of social, environmental, and economic values where a species has been established for many years and has developed substantial aboveground biomass and soil seed bank and replaced native species that fulfil a similar role in the ecosystem32,33. Rather, mitigation of impacts on the most susceptible areas and preventing the further spread into uninvaded areas is the true goal, especially for species established for decades, such as E. curvula32,34. With an overarching goal of limiting the spread of invasive species into uninvaded areas, our research objective is to investigate the extent of easily accessible abiotic variables’ ability to predict the presence or absence of E. curvula at a landscape scale in the Snowy Monaro region, Australia, using random forest modelling techniques. Random forests (hereafter RFM) are powerful classification and regression methods that function by combining multiple randomised decision trees and aggregates their predictions by averaging35,36. We achieve the research objective through observational studies using E. curvula in the Snowy Monaro Regional Council area as a case study species and region.

Methods

Study area

The extent of the study area covers the New South Wales Local Government Area of Snowy Monaro Regional Council (hereafter SMRC), which has an approximate area of 1,516,000 hectares (top: −35.579312, right: 149.602741, bottom: −37.262980, left: 148.200678) (Fig. 1). The mean annual temperature within the SMRC between 1976 and 2005, derived from monthly mean temperature data, is 9.4 °C, with a standard deviation of 1.5 °C37. The notable rivers of the region are the Murrumbidgee and Snowy, and many more sub-water ways connecting to the river systems. Two major highways run within the SMRC; the Monaro highway runs approximately north–south, and Snowy Mountains highway runs from the northwest, meeting up with the Monaro highway at Cooma, then continues east approximately 9 km south of Nimmitabel, which provides transport of livestock, fodder, residence and tourists. The SMRC ranges in elevation from a minimum of 213 m above sea level to a maximum of 2223 m above sea level38,39. The region’s mean elevations are approximately 1000 m above sea level, with a standard deviation of 259.2 m38,39. The region’s higher elevations are located towards the east along the portion of the Great Dividing Range commonly known as the Australian Alps and home to Mt Kosciuszko. This study excluded nature conservation areas from potential sites due to the research scope focusing on grazing systems that spatially dominate the SMRC. Excluding nature conservation areas removes large areas of the Australian Alps from the study area and focuses on areas within the Monaro tableland, which is approximately represented by the Interim Biogeographic Regionalisation for Australia40, Version 7 sub-region “Monaro”41.

Figure 1
figure 1

Map of the study locations within the Snowy Monaro Local Government Area. The blue crosses indicate areas surveyed that did not have Eragrostis curvula present, while the green crosses indicate areas where E. curvula was absent. Temperature (°C) is represented as the annual mean temperature between 1976 and 200542.

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Field method

Field data collection occurred from September to December 2019 and again from August to October 2021 (Supplementary Information). The 2019 data collection method involved placing a transect in a location representing the vegetation community of interest and perpendicular to established roads of varying use. Transects were established in areas that contained either high or no/low density of E. curvula. Low-density sites are defined as not having any E. curvula present, or where E. curvula is present; it is not present in swards larger than 10 m2. High-density sites are defined as having E. curvula in dense swards covering the majority of the sampled site. The transects were 100 m long with 20, 50 × 50 cm quadrats stratified along the length, with GPS points recorded at each quadrat (Fig. 2). The number of replicates at each site depended on the property’s area, with larger properties having more transects, with between 20 and 80 quadrats per property.

Figure 2
figure 2

Schematic of the two methods used to collect data for one of the 21 properties used in this study. (a) The random points within polygons using the polygon method. Each quadrat in (a) is at least 20 m apart from another quadrat. The site polygons are at least 5 hectares in area and were used to bound the random placement of the quadrates within. (b) The transect method with quadrate spaced 5 m apart along a 100-m transect. The transects in (b) run perpendicular to roads or tracks within the property.

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The 2021 data collection method differed from that of 2019 in that out of the 21 properties originally sampled, six were selected as sites having a particularly high or low density of E. curvula or sites that have areas of high E. curvula density in some locations within the property and low density in others. At these sites, three areas of at least five hectares were outlined as a polygon using QGIS software, and ten points were randomly chosen within each polygon, with no points being within 20 m of another (Fig. 2). At the location of each of the points, a 1 × 1-m quadrat was placed, and a vegetation survey was conducted.

Ancillary data

Due to black summer bushfires across this region during Dec 2019–Mar 2020 and institutional COVID-19 travel restrictions, the data collection on additional properties in 2020 was greatly impeded. Due to these restrictions, a decision was made to use ancillary data in the form of the Atlas of Living Australia11 E. curvula dataset (Supplementary Information). The Atlas of Living Australia11 acts as an E. curvula presence dataset with information on location within the Snowy Monaro Local Government Area, bolstering the number of E. curvula present data points. The ancillary data was refined to only include records from the year 2000 onwards and had a coordinate uncertainty of less than 100 m.

Copernicus Sentinel data43 provide global satellite imagery at spatial resolutions of ten meters for bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared). Bands 4 and 8 can be used for the calculation of NDVI, an index of the greenness of vegetation and a proxy of vegetation density using the formula:

$$\frac{(Band\,8-Band\,4)}{(Band\,8+Band\,4)}.$$
(1)

The calculation is performed on each pixel within the satellite imagery using the open software provided by QGIS.org44. This research utilises Copernicus Sentinel data43 collected on 01/11/2019 on sentinel tiles T55HFA, T55HFV, T55HGA and T55HGV. This date was chosen as it falls within the sampling period for this study’s first data collection period. Using the open software QGIS.org44, values for NDVI were extracted at each data point using the sample raster values function, resulting in each point used to train the model having a corresponding NDVI value recorded at that location.

Using the open software QGIS (version 3.16.9), the distance (in metres) from each survey location to the nearest road was determined44. Roads included all named roads within NSW and farm tracks used to navigate the interior of properties individually added for each property surveyed; in addition, the role that highways may have played as a vector of spread was included in the RF as a separate predictor45. Using QGIS (version 3.16.9), the distance from each survey location to the nearest hydroline was obtained using the distance to nearest hub (line to hub) function. The Hydroline data includes surface water features such as lakes, rivers, creeks, canal drains, spillways, races, tunnel-siphons and connectors46. The values recorded are to the nearest meter Hydroline was subset only to include rivers, and the same methodology used to calculate the distance to hydroline was used.

Mean annual temperature (°C) and rainfall (mm) data for the period 1976–2005 at the spatial resolution of 1 Arc-second were obtained37. One arc second is approximately 30 m but varies due to the earth’s irregular oblate spheroid, or geoid, shape. The mean temperature and rainfall values were extracted at each survey point using the sample raster values function. Eragrostis curvula has been suggested to be present in the study area for at least 70 years; as such, it has spread over the area under a changing climate as a result of anthropogenic climate change47. As such, this research has chosen to use mean annual temperature data and monthly mean precipitation from 1976 to 2005 as it would best reflect the climatic conditions present in the region during a large time period of its establishment and spread5,47. For each site, the elevation (m) above sea level (asl) was determined using a one Arc-second resolution digital elevation model based on data collected in February 2000 Gallant et al.38.

Random forest model

Random Forest creates many (e.g. 500) bootstrap samples, and the data that does not appear in each bootstrap sample is known as the out-of-bag (OOB) sample35,36. The bootstrapped samples are passed through a classification tree with randomised binary splits at each node with a random subset of the provided predictor variables35,36. The OOB sample from each bootstrap sample is then run through a classification tree, with each OOBs sample classification being recorded, with the final classification of a given sample being the majority vote of all of the classification trees35,36. We developed an RFM with binary response variables (hereafter RF P/A) for classifying the presence/absence of E. curvula based on seven predictor variables. The predictor variables for the RF P/A are elevation, distance to road, distance to highway, distance to river, distance to any watercourse, Sentinel 2 NDVI, mean temperature over 40 years and mean rainfall over 40 years. All predictor variables are continuous.

The data set for the RF P/A comprises 1292 observations, a combination of in-field measurements using field methods described above, totalling 1096 data points, and E. curvula presence data gained from the Atlas of Living Australia11, totalling 196 data points. The Atlas of Living Australia11 data comprises 196 presence data points. The data set has 612 instances of E. curvula absence and 680 instances of E. curvula presence.

The RF P/A hyperparameter tuning was conducted using a grid search, where the researcher compared a combination of different hyperparameters chosen to determine the optimal model. As the RF P/A is created for prediction, the metric used to tune the hyperparameters is the error rate or the number of correct predictions the model performs on a data set48.

Statistical analyses

All mapping outputs were created using open-source GIS software version 3.16.9-Hannover produced by QGIS.org44. All model calculations were performed in the open-source R software Version 4.2.1 produced by RStudio Team49. The model was trained and evaluated using the train function inside the R package Ranger. Metrics to assess model performance are AUC, R squared for the out-of-bag samples (R squared OOB), pseudo R squared, Cohen’s Kappa, sensitivity, specificity and F1 score48. Predictor variables of elevation and NDVI were found to be highly correlated (> 0.65) with other predictor variables in the model and thus removed as they had the lowest importance of the correlated variables when included in the RF P/A. Further, the ecological justification of retaining the correlated predictor variables of mean annual rainfall and mean annual temperature relates to the ecology of E. curvula as species tolerant of dry conditions but potentially intolerant to certain temperature extremes6,10,50. Due to the fluctuating nature of performance metrics when conducting RFM, the optimal RF P/A underwent 30 repetitions, with the performance metric being recorded from each interaction and allowing for a measure of spread using standard deviation. Predictions were generated using the predict function from the inbuilt stats package in R.

Results

Presence/absence random forest model performance

The RF P/A for E. curvula has an AUC of 0.9995, R squared OOB of 0.67, and a pseudo-R squared of 0.96 (Table 1). The relatively low standard deviation of the model after 30 repetitions indicates the model is performing consistently across the 30 repetitions. The model had a very good agreement regarding Kappa with a value of 0.97, other performance metrics of sensitivity, specificity and F1 scores of 0.98, 0.99 and 0.98, respectively (Table 2). However, as Lantz48 notes, the term very good agreement is accompanied by subjectivity. Comparing the pseudo-R squared, AUC and Kappa to the R squared OOB indicate that the model performs with high accuracy when classifying data that it was trained on but lost accuracy when classifying completely new data. Regardless, a random model that arbitrarily assigns points as having E. curvula present or absent would be expected to have an AUC of 0.5 and an R-squared OOB of 0, assuming a binary response and equal samples in the presence and absence dataset.

Table 1 Performance metrics used to assess random forest performance.
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Table 2 Confusion matrix of the random forest binary classification model predicting presence (1) and absence (0).
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Presence/absence random forest model predictors

All predictor variables had some influence on the RF P/A, with the removal of any one predictor increasing the mean error when permuted (Fig. 3). Temperature, distance from highway and distance from river were the most important predictor variables when minimising error in the RF P/A, respectively. The relationship between E. curvula and the three most important predictor variables does not appear linear when all other predictor variables are held constant, reflecting the complex interactions among predictor variables in predicting the distribution of E. curvula (Fig. 4). Variation in the response curve of the three most important predictor variables has minimal deviation from the general trend (Fig. 4).

Figure 3
figure 3

Variable importance plot showing the increase in mean error computed on the out-of-bag data across trees when predictors are permuted during the development of the random forest.

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Figure 4
figure 4

Response curves of the random forest model showing the top three important predictor variables derived from the model, (a) mean annual temperature (°C) 1976–2005, (b) distance from highway (m) and (c) distance from river (m). The bold black line indicates the response of E. curvula presence as a result of changes in the x-axis predictor variable, with all other predictor variables held at their 50% quantile. The thin grey lines show the variation of the response of E. curvula to the x-axis predictor variable after 30 repetitions.

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Discussion

Adopting a modelling approach to predicting the potential presence and absence of E. curvula has the potential to significantly benefit efforts to suppress its spread throughout the region by assisting targeted, proactive management strategies. Our model is empirically based on physical and ecological parameters offering further value in identifying potential mitigation strategies to reduce or contain its spread. The predictor variables used in the RF P/A were chosen to be a balance between easily accessible data available for public download and ecologically relevant variables that would likely impact the distribution of an invasive species6,10. As such, the observed influence of individual parameters (and their interactions) gives functional insight into potential mechanisms that may be used to constrain spread. Figure 4 highlights the complex non-linear relationship between temperature, distance from highway and distance from river and E. curvula presence. Roozbeh Valavi et al.28 conclude that flexible models, such as RF, can effectively predict a range of response variables in spatially distinct areas, such as those found in the SMRC. Roozbeh Valavi et al.28 define a flexible model as a modal that can adapt to simple and complex data, fitting closely to the data when needed but avoiding overfitting by not capturing random noise.

Tables 1 and 2 highlight high model performance, with a notable decrease in R squared (out of bag) compared to the other performance metrics. This notable decrease reflects the RF P/A limitations in modelling the presence or absence of E. curvula beyond the data it has been trained on. However, the RF P/A highlights the model’s explanatory power due to the high Kappa and F1 scores. Specifically, the RF P/A produces few false positives and fewer false negatives (Table 2). Few false negatives are preferred in this context, as under the precautionary principle, it would be considered better to assume the presence of an invasive species, check and be incorrect rather than believe that there are no invasive species in an area, risk being incorrect and the invasive species continuing to spread51. Using a similar workflow, De Simone et al.52 obtained an overall accuracy of 94.76% and a kappa of 88% when using RF for species distribution modelling of the invasive perennial grass Brachypodium genuense in an Italian grassland system. De Simone et al.52 find slope, elevation and solar radiation to be the most important predictor variable of B. genuense distribution, as opposed to temperature, distance from roads and distance from rivers in our research. Differentiating this research from that of De Simone et al.52 is that the selection the predictor variables used in this research were chosen based on their ecological relevance to E. curvula’s occurrence6,10,53. The predictor variables of De Simone et al.52 were chosen based on topographic variables available using the Sentinel-2 image database. Regardless, both De Simone et al.52 and this research highlight the utility of RF in developing accurate species distribution models for modelling invasive species within grassland ecosystems.

Temperature as a predictor of Eragrostis curvula

The predictor variable of temperature was the most important variable in the RF P/A. Removing temperature as a variable from the model had the most detrimental effect on the RF P/A performance regarding error rate. Ngoy and Shebitz20 found a similar association between E. curvula and temperature, where the mean annual temperature was one of the most significant predictors of E. curvula across New Jersey in the United States of America. The ecological relevance of temperature to plant growth relates to the optimal conditions a plant requires for the most efficient metabolic processes54,55.

Temperature has been identified as an important predictor variable in the RF P/A, but a simple linear relationship cannot be assumed due to the nature of RFM. For example, the sample point located at longitude 148.57 and latitude −36.66 has an average annual temperature of 10.6 °C and has E. curvula present, while another sample point located at longitude 149.20 and latitude −36.29 also has an average annual temperature of 10.6 °C but does not have E. curvula present. Temperature in isolation is, therefore, not a reliable predictor in the RF P/A. However, when considered in the context of the other predictor variables, it becomes important in minimising error and maximising the accuracy of the RF P/A. Figure 4 highlights this complex relationship with no clear trend in the temperature response curve when all other predictor variables are held constant. A peak between 11 and 12 °C may indicate that E. curvula prefers this annual mean temperature within the study region. Roberts et al.6,10 found that temperatures between 17/7 °C for E. curvula seed collected from Maffra, Tenterfield, and Shepparton, Australia, germination time and survival rate were lower than at higher temperature ranges when investigating the germination biology for four different populations of E. curvula. Roberts et al.6,10 observed that temperature did not directly influence E. curvula seeds obtained from Midvale, Australia. The finding of Roberts et al.6,10 provides further evidence to support the phenotypic plasticity of E. curvula and its ability to adapt to a wide range of environmental conditions. Thus, making E. curvula a species well adapted to spatial dispersal across landscapes if given appropriate dispersal corridors such as roads and waterways.

Roads as a predictor of Eragrostis curvula

The distance to highway was the next most important predictor variable in the RF P/A. Similar to temperature, due to the nature of RFM, the model does not make generalisations regarding the relationship between E. curvula and distance to highway. However, the ecological context suggests that as the distance from frequently used roads increases, the cover of invasive species such as E. curvula is likely to decrease, a trend supported by Fig. 4. Figure 4B shows a pattern of forecasting higher predictions of E. curvula present when the distance from highways is low, and all other predictor variables are constant. The distance to highway acts as a proxy to human dispersal and, by extension, a vector for E. curvula dispersal. Distance from human dispersal corridors, such as roads, consistently appears to influence invasive plant presence and density16,28, 30,35,44, 56,57,58. Gelbard and Belnap59 found that as road use increases, the cover and richness of invasive species also increase adjacent to these roads and decrease as distance increases from the road into the interior of the surrounding area. Hansen and Clevenger60 suggest that corridor edges, such as roads and railway lines, are more prone to invasion as they may act as microhabitats or microclimates suitable for invasion recruitment. These microclimates are suitable due to areas adjacent to roads often having barriers to invasion removed, such as competition for light and soil moisture60. The concept of ecosystem invasibility is relevant here as it pertains to roads acting as a vector of disturbance, thus removing competition and opening niche space for invasive species to exploit61,62. For this reason, areas adjacent to roads can be seen as more invasable, allowing invasive species to establish and spread into surrounding areas. Furthermore, roads can increase the propagule pressure of invasive plants where seeds are spread long distances by vehicle travel63,64,65.

Rivers as a predictor of Eragrostis curvula

Rivers are a dynamic, inherently spatial system as water is transported through the landscape, with material actively or passively transported in conjunction66. The dynamic and spatial nature of riverine systems alleviates one of the major barriers to invasive species recruitment: the dispersal of propagules to sites outside of their native range66,67. Furthermore, riverine systems and the associated riparian systems undergo regular disturbance in the form of flooding and livestock watering67. Nakayama, Nishihiro, Kayaba, Muranaka, and Washitani68 investigated the potential for E. curvula to spread via riverine systems by comparing the fall velocity of E. curvula seeds with different sediment particle sizes. Nakayama et al.68 found that sediment particle size of < 0.25 mm, here considered fine sand, had a similar fall velocity to E. curvula. Furthermore, a positive correlation between fine sand and E. curvula seed occurred at the study site of the Kinu River, Japan. Nakayama et al.68 provide evidence for E. curvula to disperse via hydrochory and link to our findings that show a trend for lower E. curvula presence prediction as the distance from rivers increases.

Limitations of the predicted presence and absence of Eragrostis curvula

Due to the large number of seeds E. curvula produces and its high germination rate, there will likely be other E. curvula plants within at least 1 hectare of any given plant6,10. Further, sites close to each other are likely to have more similar abiotic conditions than sites further away which is the basis of spatial interpolation69. Therefore, sites, where E. curvula was not observed when conducting the vegetation survey cannot be ruled out as incapable of supporting E. curvula, particularly if an E. curvula plant was observed within close proximity. Further, due to the relative course resolution of predictor variables of the climatic variable of temperature and rainfall, compared to the sampling spatial resolution, finer microclimatic variations are generalised over a larger area, potentially overlooking important fine scale influence on E. curvula distribution70,71. Despite several limitations to data collection imposed by COVID-19 travel restrictions and the Black Summer bushfires, our RF P/A showed value in outperforming the neutral random model and therefore has great potential to assist in targeted efforts for monitoring and managing E. curuvla presence. However, these limitations potentially influenced the availability and quality of data used to train the model due to the reliance on ancillary data, impacting the reliability of predictions.

Further, due to the intentional decision to use open-source, free-available data as predictor variables, there are inconsistencies in the spatial resolution of the data. Mishra et al.72 show that increasing spatial resolution can result in higher classification rates of land use and land cover classes. The inconsistencies in the spatial resolution of the predictor variables could overinflate the importance of certain predictor variables. However, there is limited research on this as a factor influencing bias.

Conclusion

The predictor variables of annual mean temperature, distance from highway and distance from river were identified as the most important variables predicting the presence of E. curvula in the study area. These predictor variables align with previously identified predictor variables of E. curvula and other invasive plants relating to hydrochory and zoochory6,10. The RF P/A indicates conditions in which the presence of E. curvula is supported based on a range of open-access landscape predictor variables. However, the model’s performance metrics based on extrapolating outside of the data it has been trained on should be interpreted cautiously. As such, the outcomes of our findings should be used as a tool as part of a larger toolbox rather than a rule used to manage E. curvula as part of integrated management strategies. However, the model has inherent limitations in high-resolution accuracy due to the decision to use easily accessible predictor variables with differing spatial resolutions. As more high resolution becomes freely available, further predictive models will utilise this and likely develop more precise and accurate models.

Furthermore, the RF P/A should be tested on data it has not been trained on and compare ground-truth observational data with predictions generated by the RF P/A. Regardless of how accurate predictive models become, community communication is necessary so properties without E. curvula neighbouring properties with E. curvula can act accordingly to put management strategies in place to minimise the risk of further spread, and the outcome from the RF P/A can be utilised to inform those at most risk. The RF P/A produced may aid community members in understanding the possible extent of E. curvula based on their location in the landscape and highlight the areas predicted to be the most susceptible to E. curvula presence. This research highlights the necessity for more detailed mapping of the current distribution and density of E. curvula across the Snowy Monaro to prevent the spreading from high-density E. curvula areas to low-density E. curvula areas. If such a map existed in conjunction with the provided output from the RF P/A, then concentrated survey and management efforts could be focused on the most at-risk areas.