Research Article |
Corresponding author: Jacob Maher ( jacob.maher@adelaide.edu.au ) Academic editor: Angela Brandt
© 2023 Jacob Maher, Oliver C. Stringham, Stephanie Moncayo, Lisa Wood, Charlotte R. Lassaline, John Virtue, Phillip Cassey.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Maher J, Stringham OC, Moncayo S, Wood L, Lassaline CR, Virtue J, Cassey P (2023) Weed wide web: characterising illegal online trade of invasive plants in Australia. NeoBiota 87: 45-72. https://doi.org/10.3897/neobiota.87.104472
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Invasive plants seriously impact our environmental, agricultural and forestry assets, and the ornamental plant trade is a major introduction pathway. The variety and extent of the ornamental plant trade is growing in reach and is increasingly facilitated by the internet (i.e., through e-commerce). A lack of surveillance and regulation of e-commerce has resulted in invasive species being widely traded on these platforms. Here, we investigated the extent of illegal trade in invasive plant species in Australia by collecting advertisements found on a popular public e-commerce website. Across a 12-month period we collected a total of 235,162 plant advertisements. From 10,000 of these advertisements (4.25% of total advertisements) we found 155 plant taxa advertised online that were prohibited to trade in at least one Australian State or Territory (12.5% of Australia’s total prohibited plant taxa). We detected 1,415 instances of invasive plants advertised, of which 411 breached local jurisdictional (i.e., State or Territory) laws. Opuntia cacti and invasive aquatic plants were traded in the greatest quantities. A variety of uses for plants prohibited to trade were reported by the sellers, with aquatic uses being the most popular (i.e., water filtering and habitat for aquatic animals). We used generalised linear mixed-effects models to test the effect of prohibiting the sale of invasive plants on the quantity and price of online advertisements. Despite Australia’s strict internal biosecurity regulations, we found that trade prohibitions had no influence on the quantity and price of trade in illegal invasive plants. Given this, and the extent of illegal invasive plants traded, we believe increased monitoring and regulation of online plant trade is warranted. We demonstrate that targeted searches using string matching is an effective tool for detecting e-commerce trade of invasive species. However, to obtain the most optimal outcomes, regulations should be coupled with increased cooperation from e-commerce platforms and public awareness campaigns. Future weed risk assessments should consider online trade as a key factor in the long-distance dispersal and propagule pressure of a plant. Jurisdictions would also benefit from greater alignment on plant trade prohibitions and revision of associated compliance policies.
Aquatic weeds, biosecurity, e-commerce, Opuntia, ornamental plants, prevention, surveillance, web scraping
Invasive plants can cause serious negative impacts to biodiversity, human health, and primary resource industries (
Australia has a highly endemic floral community that has been severely impacted by plant invasions (
To investigate the current invasion risk of e-commerce plant trade within Australia (i.e., internal trade, not international shipments into Australia), we applied web-scraping technology to monitor and record plant trade advertisements on a popular Australian e-commerce website over the course of one year. We investigated five research aims: (i) determine what proportion of plants advertised are prohibited to trade; (ii) determine the quantity and taxonomic composition of declared plants traded; (iii) determine whether current regulations reduce trade quantity or influence the price of declared plants in jurisdictions which prohibit trade versus those that permit trade; (iv) characterise the most frequently traded declared plants; and (v) document advertised plant uses to inform our understanding of the desire for declared plants. We seek to provide a clearer picture of the present risk of e-commerce trade and whether prescriptive laws reduce invasive plant trade. These results will help inform future policy decisions regarding the monitoring and prevention of invasive species occurring in the Australian plant trade.
In order to investigate the trade of invasive plants online, we compiled a list of declared plants in Australia. These declared plants are prohibited from trade under jurisdictional biosecurity legislation because of their current or potential impact as invasive species (
We followed established protocols to select e-commerce websites to monitor for sales of plants (
To collect online advertisement data, we constructed a custom web scraper in Python Programming Language (version 3.8.1;
The data we collected were not immediately ready for analysis because the advertisements from the website were composed of free-form text boxes completed by the users, and thus the taxonomic names could not be automatically retrieved (i.e., no standardization in names). Identification of plants was conducted manually using text and pictures, provided by the seller, which was a time-consuming process. Subsequently, we explored a subset of the advertisements. For our study, we extracted two samples of 5,000 advertisements each. The first sample was a random sample of all plants traded stratified by jurisdiction. For the second sample we utilised natural language processing to focus specifically on detecting declared plants.
The first sample was untargeted; it sampled from all the advertisements we collected and did not intentionally target declared plants. This sample was stratified by jurisdiction with 625 unique advertisements randomly sampled from each jurisdiction, providing 5,000 advertisements in total. We used this dataset to estimate the underlying proportion of declared plant trade in each jurisdiction and to compare the effectiveness of our targeted sampling method.
For the second sample we targeted declared plant advertisements. Our objective was to identify frequently traded declared plants, and capture the composition of declared plants traded. We aimed to capture declared plants traded anywhere in Australia regardless of whether they were advertised in a prohibited jurisdiction. This was to capture the full extent of declared plant trade in Australia. To do this we used string matching to generate a targeted sample aimed at detecting declared plant advertisements (
The number of advertisements collected and sampled from an e-commerce website stratified by jurisdiction. The table provides the number of advertisements from: (i) 12 months of web scraping (Total dataset); (ii) the untargeted sample (Untargeted); (iii) the string-matching for declared plant taxa (Matched); and (iv) the targeted sample (Targeted). The targeted sample was weighted to better capture trade in three jurisdictions with comparatively lower quantities of matched advertisements: Australian Capital Territory, Northern Territory, and Tasmania (* indicates weighted samples). All advertisements that matched search terms for declared plants in these jurisdictions were cleaned. The remaining advertisements were sampled randomly across the remaining jurisdictions to total 5,000 advertisements.
Jurisdiction | Total dataset | Untargeted | Matched | Targeted | |
---|---|---|---|---|---|
Australian Capital Territory (ACT) | 7,362 | 625 | String matching using declared plant search terms → | 420 | *420 |
New South Wales (NSW) | 64,641 | 625 | 3,351 | 1,031 | |
Northern Territory (NT) | 859 | 625 | 66 | *66 | |
Queensland (Qld) | 48,909 | 625 | 2,893 | 948 | |
South Australia (SA) | 21,121 | 625 | 1,073 | 539 | |
Tasmania (Tas.) | 5,991 | 625 | 308 | *308 | |
Victoria (Vic.) | 41,186 | 625 | 2,567 | 921 | |
Western Australia (WA) | 43,625 | 625 | 2,073 | 767 | |
Total | 233,694 | 5,000 | 12,751 | 5,000 |
We cleaned the sampled datasets by identifying the plants in each advertisement using photos and text provided by the seller. Advertisements would often contain multiple species for sale so we recorded each plant species (or lowest taxonomic rank possible) as a separate identification within an advertisement. We recorded the price and quantity for each plant identified, and the location of the advertisement. It is important to note that recorded locations were seller locations and not where a plant may have been transported to after it had been purchased. Predominately, advertisements were for live plants, however we also captured trade of seeds and other propagules. We documented and categorised advertisements that stated uses for plants when specified by sellers (i.e., used for purposes other than as a live ornamental plant, including propagules).
Once we identified the plant taxa in the advertisements, we cross referenced them with our dataset of 1,236 declared plants. We recorded the number of plant taxa identified and how many were declared plants. We used species accumulation curves to assess how well our samples captured the diversity of plant taxa and declared plant taxa traded online. We measured the number of advertisements containing declared plants and identified advertisements that were prohibited (i.e., the advertisement contained a plant that was declared in the jurisdiction where it was advertised). However, multiple declared plant taxa could appear in a single advertisement. To account for this, we also recorded each detection of a declared plant taxon in any single advertisement. To help explain these different types of trade observations an example with term definitions is provided in Fig.
A diagram explaining the terms we used to define the different types of plant trade observations. This example shows two advertisements and two species of declared plant (plants prohibited to trade in a given jurisdiction). The number of observations for each term in this scenario are provided in parentheses. In the ‘prohibited jurisdiction’ there is one advertisement with two plant species, both species are prohibited to trade in this jurisdiction. One of these plant species is sold by itself in the ‘permitted jurisdiction’. In this case we refer to it as a declared plant, but it is permitted to trade in that jurisdiction.
We used generalised linear mixed-effects models to test whether prohibited trade had an effect on the trade quantity and price of declared plants. These models considered declared status as the binary explanatory variable and taxa identity as a random effect (i.e., random intercept). For quantity, we hypothesised fewer declared plants are advertised in jurisdictions that prohibit their trade compared to jurisdictions that permit their trade. We based our rationale on the notion that laws prohibiting trade would reduce the number of advertisements online. For price, we hypothesised that in jurisdictions that prohibit trade, prices for declared plants would be higher compared to jurisdictions that permit trade. Our rationale was that laws prohibiting trade would result in an increased price to offset their risk; i.e., buyers paying a premium for prohibited plants. We measured the performance of the models using Nakagawa and Schielzeth’s conditional R-squared (Rc2) (
We took an additional approach to assess and visualise the difference in quantity and price by exploring the distribution of differences in quantity and price. We calculated the difference of mean quantity and price of each declared plant taxon traded in prohibited jurisdictions compared to permitted jurisdictions (i.e., the mean quantity of taxon A pooled across all prohibited jurisdictions minus the mean quantity of taxon A pooled across all permitted jurisdictions). We used this distribution to determine the degree that prohibited trade affected trade quantity and price, where a distribution centred around zero with low variation suggests little to no influence.
We conducted data analysis and visualisation using the R software environment for statistical and graphical computing (version 4.1.1;
From the 10,000 advertisements we examined (i.e., 5,000 each for the untargeted and targeted samples), we made 13,619 plant identifications (average c. 1.4 identifications per advertisement). We identified 1,777 unique plant taxa (Fig.
Accumulation curves of plant taxa identified from sampling 10,000 online advertisements A accumulation curve of all plant taxa identified. There were 1,777 taxa observed from 10,000 advertisements B accumulation curves of declared plant taxa identified. The red line represents a targeted sample that utilised search terms to locate declared plant advertisements and the blue line represents an untargeted sample that did not use search terms (i.e., random sampling). There were 155 declared taxa identified in 1,415 detections of declared plants.
From the 10,000 advertisements examined, we made 411 prohibited detections (from 374 advertisements) within 1,415 total declared detections (from 1,296 advertisements). From our untargeted sample, we found 59 prohibited advertisements (c. 1%) and 150 total declared advertisements (detection rate of 3%). In comparison, our targeted sample contained 328 prohibited advertisements (c. 7%) and 1,183 total declared advertisements (detection rate of c. 24%) (Fig.
The number of advertisements for declared plants detected on an e-commerce platform over a 12-month period. These detections were made from a sample of 5,000 advertisements that had been matched to search terms for declared plants (i.e., targeted sample) A the number of prohibited declared plant advertisements detected within the jurisdiction (i.e., prohibited in that jurisdiction, refer Fig.
The generalised linear mixed-effects models revealed no statistically significant effect on the quantity and price of declared plants between jurisdictions that prohibited trade and those that did not. The model for quantity had a p-value of 0.58 for the quantity coefficient, with a sample size of 1040, which covered 130 declared taxa (quantity coefficient estimate = -0.000266 ± SE = 0.000479; t = -0.56; Rc2 = 0.32). The model for price had a p-value of 0.13 for the price coefficient, with a sample size of 652, covering 20 declared taxa (price coefficient estimate = -6.25 ± SE = 4.11; t = -1.52; Rc2 = 0.24).
For over 80% (104/130 taxa) of declared taxa analysed, the mean difference in the number of advertisements between prohibited and permitted jurisdictions was less than one advertisement (Fig.
Summary of advertisements for declared plants in Australia’s eight jurisdictions. Results are presented from two samples collected across 12 months of e-commerce activity. The untargeted sample represents a consistent number of plant advertisements sampled for each jurisdiction, based on the location of the seller. The targeted sample is a focused search for advertisements matching declared plant search terms, resulting in a variable number of advertisements sampled for each jurisdiction. The ‘Prohibited’ column indicates the count of advertisements (Ads) containing plants declared within the respective jurisdiction where the advertisement is located. The ‘Total Declared’ presents the number of advertisements (Ads) containing plants declared anywhere in Australia. The percentages (%) are calculated based on these observations and the respective sample sizes, with darker colours for higher relative percentages. The sample sizes represent the total number of advertisements considered in each jurisdiction.
Jurisdiction | Untargeted Sample | Targeted Sample | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Prohibited | Total declared | Sample size | Prohibited | Total declared | Sample size | |||||
Ads % | Ads | % | Ads | % | Ads | % | ||||
Australian Capital Territory (ACT) | 7 | 1.12 | 19 | 3.04 | 625 | 13 | 3.10 | 89 | 21.19 | 420 |
New South Wales (NSW) | 11 | 1.76 | 19 | 3.04 | 625 | 77 | 7.47 | 297 | 28.81 | 1031 |
Northern Territory (NT) | 9 | 1.44 | 32 | 5.12 | 625 | 10 | 15.15 | 17 | 25.76 | 66 |
Queensland (Qld) | 7 | 1.12 | 13 | 2.08 | 625 | 27 | 2.85 | 155 | 16.35 | 948 |
South Australia (SA) | 13 | 2.08 | 21 | 3.36 | 625 | 62 | 11.50 | 139 | 25.79 | 539 |
Tasmania (Tas) | 1 | 0.16 | 10 | 1.60 | 625 | 20 | 6.49 | 78 | 25.32 | 308 |
Victoria (Vic) | 9 | 1.44 | 28 | 4.48 | 625 | 36 | 3.91 | 249 | 27.04 | 921 |
Western Australia (WA) | 2 | 0.32 | 8 | 1.28 | 625 | 83 | 10.82 | 159 | 20.73 | 767 |
Total | 59 | 1.18 | 150 | 3.00 | 5000 | 328 | 6.56 | 1183 | 23.66 | 5000 |
Distribution of the mean difference in the number of advertisements for declared plant taxa between prohibited and permitted jurisdictions. The black curve overlaying the histogram represents the cumulative distribution of mean differences in advertisement quantities. A positive mean difference translates to comparatively more advertisements in prohibited jurisdictions and fewer in permitted jurisdictions. A negative mean difference translates to comparatively more advertisements in permitted jurisdictions and fewer in prohibited jurisdictions. The distribution represents 130 plant taxa and each bar represents one advertisement. We removed taxa that are declared in all jurisdictions and those with fewer than two advertisements in each legality category (i.e., prohibited or permitted) as there was nothing to compare against.
The distribution of plant prices was similar across jurisdictions, typically ranging from $5 to $40 for a potted plant (Australian dollars; AUD) (Suppl. material
The most frequently advertised declared plants were Opuntia cacti and aquatic weeds (Fig.
Invasive plants most frequently advertised on an e-commerce platform during a 12-month period. These plants are prohibited to trade in one or more Australian jurisdictions (i.e., declared plants) A the size of the declared plant photos is approximately scaled by their relative frequency in trade B lists the 10 declared plants that were most frequently advertised in jurisdictions where they are prohibited to trade (i.e., advertised illegally) C lists the 10 most frequently advertised plants declared in any jurisdiction. The superscript numbers next to species names correspond to the plant photos. Photos are sourced from Getty Images and are credited to: (1) Boonsom, (2) TopPhotoImages, (3) Wjarek, (4) Igaguri_1, (5) Reginaldo Bergamo, (6) Jonnyjto, (7) ePhotocorp, (8) Radka Danailova, (9) Belizar73, (10) Membio, (11) Bdspnimage, (12) Paulfjs.
We recorded the following eleven suggested uses for declared plants (Fig.
Thirteen invasive plant taxa prohibited to trade (termed declared plants) that were most frequently advertised with a use. In total, 50 declared plant taxa had uses reported in advertisements. The number of advertisements is stratified by the promoted use for the plant. These uses were reported by traders and were not verified in this study.
Sellers explicitly mentioned uses for plants in only 148 of the 1,296 advertisements of declared plants (c. 11%; 50 taxa). The most advertised use was for aquatic purposes, which encompassed actions such as improving or maintaining water quality and providing habitat for aquatic animals (n = 72). L. laevigatum was the declared plant most often advertised with a use, all of which were for aquatic purposes (Fig.
Ornamental plant trade is the world’s leading pathway for invasive plant introductions and is greatly facilitated by internet e-commerce (
The pace of the ornamental plant trade in Australia is increasing, where 2020 saw a record high number of plant sales in the nursery industry (
In addition to the prohibited trade, declared plants were widely advertised in jurisdictions where they are currently permitted to trade. Just under half of the declared taxa and more than double the number of detections we found were located in the jurisdictions that did not prohibit sale. Some of the most frequently traded declared species are only prohibited to trade in one or two jurisdictions, despite many being known to be invasive in permitted jurisdictions. Some examples of invasive populations in permitted jurisdictions include: Lavandula stoechas in SA (
While more consistent regulations among jurisdictions would provide the legal framework to address invasive plant trade, our results may suggest this is not a cure-all. We found that across declared plant taxa, there was no difference in the quantities of advertisements observed in prohibited and permitted jurisdictions. We also saw no significant effect on price, however our sample size was reduced to 20 declared taxa, making it difficult to draw a meaningful conclusion across all declared taxa traded. It is likely that jurisdictional regulations are reducing the total abundance of declared taxa in Australian plant trade, through compliance from traditional “brick-and-mortar” nurseries. It is important to note that the lack of effect on quantity we saw could be due to the limited size of our sample. Investigations across larger datasets, and across more e-commerce platforms, may reveal different results. However, if trade prohibition is not having an effect on the quantity of online trade, explanations from other plant trade studies may provide an answer. For one, sellers may perceive online trading of declared plants as low risk. This perception may be in part due to limited enforcement of e-commerce due to surveillance and legal challenges (
Given that plant trade is fundamentally human driven, we expected to observe a higher number of advertisements matching search terms and corresponding to declared plants in jurisdictions with larger populations. Consequently, in the targeted sample, we observed this trend with NSW and Vic. having the greatest number of total declared advertisements. Interestingly, NSW and Vic. also had the greatest proportion of total declared advertisements. However, in terms of prohibited advertisements, WA, SA, and NT had the highest proportions in the targeted sample. To explain this, we should consider the plants that jurisdictions have chosen to declare. Regulations are jurisdiction based, therefore differences in declarations arise between jurisdictions. WA declares the greatest number of plant taxa of any Australia jurisdiction (877 plant taxa), more than double that of the next highest jurisdiction. As a result, WA prohibits a larger proportion of Australia’s assemblage of declared plants. Complementary to this is that NT, SA, and WA declare highly traded declared species that other jurisdictions do not. Zantedeschia aethiopica is only declared in SA and WA, Opuntia ficus-indica is only declared in NT and WA, and Gazania spp. are only declared in SA. These species were frequently traded in SA and WA, thus the higher proportions are indicative of the regulations of these jurisdictions. However, NT prohibited advertisements were predominately for aquatic declared plants that are not exclusively declared in the jurisdiction. Evidently this is a popular group of plants traded in the jurisdiction, one that may benefit from targeted management campaigns.
We found that Opuntia cacti and aquatic invasive plants were among the most frequently traded declared plants. This is concerning given the historical extent of Opuntia impact on the Australian environment (
We demonstrated that targeted searches using string matching was a more effective means of detection than random sampling. We took a conservative approach by including common and generic names (e.g., pond plant) alongside scientific names in our effort to detect declared plants. Common and generic names are non-specific and can be shared by many plant species, contributing to a higher rate of false positives. However, we believe this approach is necessary to reduce the chance of missing advertisements for invasive species. Image recognition technology could be employed to further increase detection rate (Di Minin et al. 2019). However, the accuracy of image recognition is dependent on large, pre-identified image datasets and the quality of images provided (
The advertised uses for declared plants revealed some reasons why people desire them, which may complicate their management. We discovered a variety of uses advertised for declared plants, including food, medicine, cosmetics, and decoration (e.g., floral arrangements). However, the most commonly advertised uses fell into the ‘aquatic’ category, uses such as water-conditioning and providing habitat for aquatic pets. Perceived water-conditioning abilities could encourage people to introduce the plant into waterbodies (e.g., ponds and dams), risking dispersal into the surrounding environment. For example, we found E. crassipes traded which has been known to be intentionally introduced into waterbodies to help prevent algal blooms (
We observed the prohibited advertisement of invasive plants online in all Australian jurisdictions. This online trade creates many opportunities for the public to purchase and spread declared invasive plants around the country. As it stands, laws prohibiting the trade of declared plants have not halted prohibited advertisements of declared plants on public e-commerce. We suggest enhancing detection methods of illegal trade using web scraping techniques to improve enforcement. Jurisdictions should also focus on educating the public that certain plants are prohibited to trade while considering the desire that people have for these plants to help promote safe alternatives. Cooperation should be sought from e-commerce websites to prevent instances of illegal trade being facilitated on their platforms. For now, monitoring e-commerce is still needed and we have demonstrated that web-scraping is an effective tool. Data collected from monitoring e-commerce could also be utilised in future weed risk assessments with online availability incorporated as a risk factor. Beyond surveillance, jurisdictions should seek to better align the taxa they choose to regulate as the existing legal disparities could contribute to the persistence of invasive species being distributed within a country. Australia’s biosecurity, and that of other countries and regions, would benefit from more coordinated approaches to controlling the online trade of invasive species.
We, the authors, acknowledge we are all living and working on colonised land. Jacob Maher, Lisa Wood, Charlotte R. Lassaline, and Phillip Cassey live and work on Kaurna land. Oliver C. Stringham and Stephanie Moncayo live and work on Lenape land. John Virtue lives and works on Peramangk land. In all instances, we acknowledge and recognize the longstanding significance of these lands for these nations. The Kaurna, Lenape, and Peramangk people were violently displaced as a result of European settler colonialism yet remain closely connected with these lands and are their rightful stewards. We respect their custodianship of the land, value their past, present, and ongoing connection to the land and their cultural beliefs.
This work was supported by funding from the Australian Research Training Program and the Centre for Invasive Species Solutions (P01-W-003: Biosecurity surveillance of e-commerce and other online platforms for illegal trade in declared plants).
This table details the relevant legislation identifying declared plants in each jurisdiction
Data type: Legislation references
Short-list of invasive plants used for surveying candidate Australian websites
Data type: species list (PDF file)
List of search term exceptions used to remove the majority of false positives in target sample dataset
Data type: Search term exceptions (PDF file)
The price of plants advertised online in Australia from a random sample of 625 advertisements from each jurisdiction
Data type: Boxplot (PDF file)
Distribution of the mean difference in price for declared plant taxa between prohibited and permitted jurisdictions
Data type: image (PDF file)
Total number of detections for invasive plants which are prohibited to trade in at least one Australian jurisdictions
Data type: Table: Species detections (PDF file)
The number of observations for plant taxa prohibited to trade (i.e., declared plants) that were advertised with uses by traders
Data type: table: Species detections with uses (PDF file)