Drivers of the Australian native pet trade: The role of species traits, socioeconomic attributes and regulatory systems

1. The pet trade is a major driver of both biodiversity loss and the introduction of invasive alien species. Building a comprehensive understanding of the pet trade would improve prediction of conservation and biosecurity threats, with the aim to prevent further negative impacts. 2. We used South Australia’s native wildlife permit reporting system as a data-rich example of a vertebrate pet market, spanning 590 distinct taxa across 105 fami - lies of terrestrial vertebrates (mammals, reptiles, birds and amphibians). Using a piecewise structural equation modelling


| INTRODUC TI ON
The trade of non-domesticated pets is a major source of threats to global biodiversity conservation and environmental biosecurity (Gippet & Bertelsmeier, 2021;Ribeiro et al., 2019).While pet keeping can improve human mental and physical well-being (Pasmans et al., 2017;Peng & Broom, 2021), and pets can be traded as commodities that support businesses (Andersson et al., 2021), market demand for pets can also drive the unsustainable harvest of wild populations, which is of particular concern for threatened species (Altherr & Lameter, 2020;Marshall et al., 2020).International transport and the subsequent release or escape of pets also lead to higher rates of alien species introductions and their subsequent establishment (Lockwood et al., 2019).There is clear incentive to predict and mitigate negative trade-based impacts, which should first be underpinned by a thorough understanding of pet trade patterns and drivers.
The dynamic nature of pet demand is leading to an ever-increasing number of species being exploited, including species with no prior history of trade (Altherr & Lameter, 2020;Marshall et al., 2020).
However, to effectively anticipate negative impacts before they occur, an understanding of the drivers of trade and the motivations for keeping and trading wildlife is needed (Mohanty & Measey, 2019).
Current examples of such an approach include modelling trade as a function of species traits (Stringham & Lockwood, 2018;Tedds et al., 2020), profiling pet owners (Alves et al., 2019), characterising owner desire for specific pets (Measey et al., 2019;Siriwat et al., 2019;Toomes et al., 2020) and analysing pet ownership from a sociological perspective (Hergovich et al., 2011).While providing valuable insight into pet ownership behaviour and species' desirability, existing research has seldom considered the effects of regulatory systems, pet attributes and owner socioeconomic metrics on trade dynamics using a common data source, nor have multiple aspects of trade (e.g.pet keeping, breeding and trading) been investigated concurrently.
Here, we seek to identify relationships between nondomesticated species involved in the pet trade, the extent to which their trade is regulated, and the attributes of both the animal (at the species level) and the trade participant (i.e.owner/breeder/sellers).
To date, investigation of these relationships has been hindered by a lack of unbiased data (i.e.biased by taxonomy or detection) related to trade dynamics, owner attributes and species traits in a combined context.For example, documentation of legal trade may be incomplete or contaminated by deliberate mislabelling (Janssen & Leupen, 2019), and few legal markets have any formal documentation process to track trade dynamics (Marshall et al., 2020).
Fortunately, examples of legal trade, where detailed regulatory systems are implemented and thorough permit information is documented, provide a valuable context in which to study these dynamics (e.g.Elwin et al., 2020).We define pets herein as animals traded or possessed for reasons of companionship or ornament, and exclude animals used in cultural ceremonies, as gifts or status symbols, or used in recreational hunting (Phelps et al., 2016).
We analysed the relationships between non-domesticated pets, pet owners and trade dynamics (i.e.pet keeping, breeding, selling and escapes) using South Australia's domestic vertebrate permit system as a data-rich example of high-diversity trade at the resolution of individual trade participants.Critically, we provide a unique set of analyses of the drivers of pet trade across multiple stages (from trade to escape), levels of regulation, diversity of taxa (105 families, 590 species/subspecies of terrestrial vertebrates) and Australian socioeconomic metrics.Moreover, we used direct measures of pet keeping and trading quantities, rather than proxies such as market price or presence/absence records, which may not accurately reflect rate of trade for all taxa (e.g.Vall-llosera & Su, 2019).Using a structural equation modelling (SEM) framework, we identified a network of interrelationships and predict the effects of pet attributes and owner demographics on trade dynamics.

| Study context
In the Australian State of South Australia, the Department for Environment and Water (DEW) categorises all native terrestrial vertebrates (i.e.amphibians, birds, mammals and reptiles) into four tiered levels of increasing protection for wildlife keeping and trade: (i) Unprotected, (ii) Exempt, (iii) Basic and (iv) Specialist.The possession of wildlife in the Basic and Specialist categories requires respective permits, and reflects differences in animal husbandry and keeping requirements, as well as a species' IUCN threat status (Department for Environment and Water, 2018).Unique permits are required for different taxa, which are categorised at either the species or subspecies level by DEW.Specialist taxa are considered fully protected, as the possession, breeding or trade of any number of individuals of said taxa is permit-regulated, whereas Basic species are only partially regulated (e.g. one individual may be possessed without a permit).Highly popular pets that require little keeping experience, such as cockatiels (Nymphicus hollandicus), are in the Unprotected and Exempt permit categories and therefore do not require a permit to trade.There is a low proportion of Exempt and Unprotected taxa in our dataset (6.44% and 1.19%, respectively; see Appendix S1.1 for full descriptions of permit categories).Alien taxa (including highly popular domesticated animals such as cats, dogs, rabbits and nonnative rodents), which are not regulated under a common licencing framework in Australia, are not included.
biosecurity, exotic pets, invasive alien species, pet trade, threatened species, trade dynamics, wildlife trade We obtained all DEW Basic and Specialist permits from January 1, 2015 to June 30, 2017 (n = 37,461 unique records pertaining to live animals).For each unique permit, the dataset included: (a) the species/subspecies held; (b) South Australian suburb of captivity; (c) total number of individuals possessed; as well as the total reported (d) births; (e) deaths; (f) escapes and (g) sales per taxa over the entire monitored period.The permit data were de-identified to ensure no further personally identifiable information was accessed.
We summarised variables of interest (total possession, sales, births and escapes) across each unique permit-holder suburb and across each taxon prior to analysis of socioeconomic attributes and species traits, respectively.In total, there were 590 native taxa (573 species and 17 subspecies; hereafter referred to as 'species') held across 592 suburbs (out of 1891) in South Australia.We excluded permits from eight suburbs known to contain zoos or wildlife parks, prior to all analysis, because there is no distinction in the DEW permit data between zoo or wildlife park permits and private keeping permits.Our research did not work directly with animals; therefore, the research did not require ethics approval.

| Explanatory variables
We selected a set of species attributes and socioeconomic variables to test for relationships with possession, breeding, trade and escapes of captive wildlife (hereafter referred to as trade dynamics).
We selected the following species attributes based on availability of data, existing peer-reviewed evidence of relationships with trade dynamics and our own hypothesised relationships (Table 1): adult mass, threatened status, annual fecundity, endemic status and extent of occurrence (see Appendix S1.4 for trait data sources and Appendix S1.5A for specified relationships).We also collated data for maximum longevity and age at sexual maturity, but omitted them from our analysis, as data were missing for over 50% of species.We recorded whether a species is subject to full trade regulation (i.e.whether permits are required for possession, breeding and trade of any number of individual animals; 'Regulatory status' hereafter).We standardised scientific names according to the Catalogue of Life annual checklist (Roskov et al., 2019) and recorded the International Union for the Conservation of Nature (IUCN) conservation status of each species, using a precautionary approach where a species' IUCN status was superseded by the highest State-wise threat rating of any State in Australia (Atlas of Living Australia, 2019).We used Global Biodiversity Information Facility occurrence data to verify whether species are endemic to Australia, disregarding populations outside of Australia if they are introduced (GBIF, 2019).
We aggregated permit suburbs at the Australian Statistical Area 2 (SA2) level, and gathered the following SA2-resolution metrics from the Australian Bureau of Statistics ( 2019): population density, median household income, mean number of household inhabitants, median age, unemployment rate, education rate, proportion of Australian citizens and proportion of households with dependents (see Appendix S1.5B for specified relationships).We used socioeconomic variable annual means from 2011 to 2017, which was most concurrent with the permit data.When a single suburb spanned multiple SA2 regions, we used the mean values of all socioeconomic variables (Table 2) in those regions.(Lefcheck, 2016;Shipley, 2013).This process is outlined fully in Appendix S1.6.

| Structural equation modelling
We generated separate SEMs to model species attributes (including whether a species was fully regulated) and socioeconomic variable relationships, with the quantity of possession, breeding, sales and escapes included as response variables in all SEMs.While this approach implies that attributes of pets and pet-keepers influence trade dynamics independently, which may not be fully representative of the system, preliminary analysis indicated this approach was necessary to avoid overfitting and over-parameterising the SEM, thus losing information due to lack of model convergence.
Specifically, separate species attribute SEMs were generated for birds (297 species, 65 families and 18,707 permits; Bird SEM hereafter) and reptiles (224 species, 16 families and 15,063 permits; Reptile SEM hereafter) to determine whether different variable relationships exist among these taxa.Due to the relative paucity of mammalian (60 species, 21 families and 3,664 permits) and amphibian (nine species, three families and 27 permits) permit data, we did not generate SEMs specific to these taxa.The relationships between socioeconomic variables and trade dynamics were modelled in a single SEM using permit data for all taxa (590 species, 105 families and 37,461 permits; Socioeconomic SEM hereafter).We did not partition socioeconomic data by taxonomic class because the resulting sample sizes and zero inflation are unlikely to be adequate for suitable model fit.Thus, in total, we constructed three separate SEMs: (a) species attributes for reptiles, (b) species attributes for bird and (c) socioeconomic variables for all four vertebrate classes.
All data analyses were conducted in the R software version 3.4.4(R Core Development Team, 2019) and we used the PiecewiseSEM package to generate and evaluate SEMs (Lefcheck, 2016).Our choice of statistical distribution and model type for each SEM path is outlined in Appendix S1.2.All explanatory variables were investigated for collinearity prior to their final inclusion in the initial SEM path diagram using a variance inflation factor test in the car package (Fox & Weisberg, 2018).If collinearity was detected, variables with the least explanatory power were excluded from the SEM (as determined by AIC comparison; Shipley, 2013).Model fit was reported using trigamma pseudo-R 2 for the Bird and Reptile SEMs, and Nagelkerke's pseudo-R 2 for the Socioeconomic SEM.Relative variable importance (RVI) scores were calculated for each explanatory variable to evaluate the relative strength of each variable relationship with a given response (summing to one for each response) (Lindeman & Merenda, 1980).Reptiles that are kept in larger quantities have higher annual fecundity (RVI = 0.732), are not fully regulated (RVI = 0.171) and were sold in larger quantities (possession and sales covaried; R 2 = 0.24).
Escapes (R 2 = 0.376) occurred more for regions that kept a greater quantity of pets (RVI = 0.952).

| DISCUSS ION
Here we demonstrate the role of a trade regulatory system as a key determinant of trade dynamics, alongside species attributes and (to a lesser degree) socioeconomic metrics.Specifically, whether a species was fully regulated (reflective of greater husbandry demands, and with higher associated costs/difficulty of acquiring a permit) had a significant impact on the quantity of pet keeping, breeding, trading and escapes, even after accounting for all other species-level attributes.Although this process explains only a proportion of the overall variation in pet keeping, our result indicates that South Australia's wildlife permit system is, in part, influencing trade dynamics, and may provide a valuable regulatory tool to shift trade away from undesirable or otherwise detrimental aspects of the trade.For example, while we reported 1,126 escapes in total, our findings also indicate that escapes would have been higher in the absence of regulation (Figure 3).
Our results supported our prediction that bird and reptile trade dynamics would have idiosyncratic relationships with species attributes.The most prominent difference we detected was the lack of direct significant effects of species attributes on bird keeping and trading, such as the effect of adult mass that was present for reptiles.
Additionally, endemic reptiles were sold in greater quantities, yet endemism had no impact on the trade of birds (see Appendix S4).It is possible that bird trade dynamics are driven by more specific attributes for which data were not available on a sufficiently broad scale for our analysis.Examples include bird temperament (i.e.aggressiveness) or attractiveness (Vall-llosera & Cassey, 2017).Our results did support species-attribute relationships reported in previous reports of pet trade dynamics.The significant positive effect of adult mass on reptile escapes and the positive (though non-significant) effect of annual fecundity on reptile and bird escapes are concurrent with trends in the U.S. vertebrate trade (Stringham & Lockwood, 2018) and the South African alien reptile trade (Van Wilgen et al., 2010).
The prevalence of the aforementioned relationships across multiple countries with different strategies for managing trade (e.g.complete prohibition of alien reptiles in Australia contrasted with South African prohibition of native reptiles) suggests that they may represent ubiquitous drivers of trade dynamics.
Other known attribute-dynamic relationships only partially support, or even contrast with, our findings.For example, birds traded in Taiwan tend to be smaller in size (Su et al., 2014), and a study of both native and alien cagebird trade in Australia found that larger birds had higher market value, which is negatively correlated with abundance (Vall-llosera & Cassey, 2017).These discrepancies may be due to differing dynamics between markets with different use types (e.g.companion versus ceremonial animals), as well as between native and alien pet markets.It is also noteworthy that Vall-llosera and Cassey (2017) used proxies for trade quantity (i.e.price), whereas we analysed trade abundance directly.
We found no direct relationship between household income and trade dynamics.While our results are correlative and do not necessarily represent causal relationships, the lack of an income-trade relationship suggests that associated costs are not a limiting factor for the acquisition of pets in Australia.There are many known benefits of pet ownership (e.g.Smith, 2012) that are likely evaluated alongside the economic costs by prospective pet keepers.We also found that population density and unemployment rate had very low   2021) suggest that wealth inequality (i.e.relative wealth), rather than absolute wealth, is a driver for both the harvesting and consumption of wildlife, including live pets.
Australia's comparably low rates of income inequality and high absolute wealth per capita (Ortiz & Cummins, 2011) may partially explain the lack of any universal trade-income relationship across nations with idiosyncratic cultural and socioeconomic backgrounds.
While our findings contribute to a correlative understanding of pet trade dynamics, they also have key biosecurity implications.
For both reptile and birds, more animals escaped when possessed in higher quantities (also observed in zoos, see Cassey & Hogg, 2015).Therefore, it appears that pet popularity (i.e.possession) is partly proportional to propagule pressure, a major determinant of alien population establishment (Cassey et al., 2018).Moreover, we found that escaped pets had higher annual fecundity, which is a trait associated with successful establishment of new populations (Allen et al., 2017;Howeth et al., 2016).It is possible that high pet fecundity itself contributes to the probability of release from captivity, especially considering the increased space and financial requirements of resultant offspring (Lockwood et al., 2019).DEW have a process of inquiry when unusually high instances of deaths/escapes are reported by permit holders during annual data collection.It is therefore possible that escapes were underreported by permit holders if they perceive that escapes might negatively influence their eligibility to retain permits.The desire for alien pets in Australia is substantially biased towards threatened species (Toomes et al. 2020), and increased trade demand for threatened species is a known component of international wildlife trade (Courchamp et al., 2006;Holden & McDonald-Madden, 2017).Yet, we found no such preference towards threatened species in the keeping of native species in South Australia.Furthermore, this finding is even more meaningful considering our data source omitted some of the most common nonthreatened pets that would undeniably be kept in high quantities (as well as an undocumented number of instances where pet keepers possess only one individual of a Basic species).This finding implies that the potential conservation threat posed by permit-regulated pet trade, as a driver of unsustainable harvest of wild populations, is minimal.However, it is important to note that our analysis did not quantify the rate of permit non-compliance; rapid changes in trade demand can lead to population declines even for species that were not previously threatened or known to be traded (Marshall et al., 2020;Nijman et al., 2019); and incidents of illegal wild harvest of reptiles have been documented in Australia (e.g.Heinrich et al. ( 2021)).Our study system may be unique in that desire for threatened species does not appear to directly translate into acquisition, at least through legal means.

| Recommendations and conclusions
We conclude that the use of a permit system with multiple tiers of We recommend that contemporary tiered permit systems, such as those used to regulate the alien bird trade in Victoria, Australia (Woolnough et al., 2020), be used to regulate a greater diversity of native and alien taxa elsewhere.However, different jurisdictions are likely to vary in their willingness to adopt such regulatory systems (e.g.Harris et al., 2019).As such, we recommend that relevant government agencies evaluate both their own enforcement capacity and the anticipated level of compliance from affected hobbyists and businesses, before proceeding with implementation.We recommend exploring the relevance of our findings to broader market contexts such as illegal/unregulated trade, and trade across broader cultures and use-types, given the future availability of suitable data.
There are a number of species, particularly alien cagebirds and ornamental fish, that are not consistently regulated in Australia despite posing clear environmental threats (e.g.DAWE ( 2021)).
Prohibition of the trade of these species is not feasible due to the large number of domestic keepers and breeders already in operation (Vall-llosera et al., 2017).However, implementation of a permit system, with ease of acquisition and trade of pets proportional to the associated risks, may lead to long-term positive shifts in trade participation if sufficient resources are allocated for enforcement.We also recommend the development of consistent criteria across Australian State/Territory jurisdictions, which currently use disjointed regulatory systems, to select which category a species should be allocated based on animal welfare, conservation and biosecurity risks.
Such criteria could incorporate information from pre-existing data sources or assessment tools, such as animal husbandry requirements (e.g.Warwick et al., 2018), threatened status, life-history traits, invasion risk (e.g.Environment and Invasives Committee 2019), rate of e-commerce trade (Stringham et al., 2020) and the presence/absence of a species in Australian seizure records (Heinrich et al., 2021;Toomes et al., 2019).
In summary, our findings highlight the roles and relative contributions of regulatory systems, as well as owner and (particularly) species-level attributes to wildlife trade dynamics, many of which follow previously observed patterns.By contributing to a growing understanding of these relationships, our research can help predict the susceptibility of species to high rates of trade or high escape probability based on life-history traits, regulatory control and owner demographics.We hope to encourage a wider implementation of trade regulatory systems by emphasising the key role of fully enforced permits in de-incentivising undesirable trade practices.

F
I G U R E 1 Total number of pet permits (a, b) and number of pet permits per person (c, d) for each SA2 in South Australia (a, c) and greater Adelaide (b, d) explanatory power and are therefore likely to have less influence on trade dynamics compared to species traits and trade regulation.By contrast, pet keepers in other pet markets are known to either have higher disposable incomes compared with people who do not keep pets(Alves et al., 2019), or associate pet ownership with wealthy status(Reuter et al., 2018).Norconk et al. (2020) and Bennett   et al. ( While our analysis pertains to species native to Australia, many Australian species are nonetheless held in domestic captivity outside of their native range and therefore still pose biosecurity risks via the potential establishment of the so-called 'domestic alien' species (e.g.Australian King parrots [Alisterus scapularis] are not native to South Australia yet escape in large abundances; Figure2c).Not only is there a risk that new populations will establish to the detriment of native ecosystems(Lockwood et al., 2019), but released pets, regardless of whether they are outside their native range or whether they successfully establish, pose a biosecurity risk through the potential transmission of pet-borne pathogens and parasites (e.g.Norval et al. 2020).Additional risks include the release on nonnative subspecies or captive breeding morphs that may hybridise with native populations (e.g.Fox & Hogan, 2020).
Species trait variables (i.e.model covariates) and corresponding units.See Appendix S1.4 for further details Not threatened' is defined as Near Threatened, Least Concerned or Data Deficient.If species had a State-specific conservation status that differed from their IUCN status, the most severe (i.e.threatened) status was used To investigate the complex network of relationships between and among trade dynamics, species attributes, socioeconomic variables and trade regulation, we used a structural equation modelling (SEM) approach.SEMs unite multiple statistical models, typically linear regressions or 'paths', to model multivariate direct and indirect relationships TA B L E 1 State-wise Conservation Status condensed into a binary outcome: 'Threatened' is defined as IUCN Endangered, Critically Endangered or Vulnerable.'2 Extent of occurrence, calculated from BirdLife International and IUCN spatial distribution data, using the 'EOO.computing'function in the ConR package (Dauby et al., 2017) with R software version 3.4.4(R Core Development Team, 2019) 513 Regulatory status Binary category (Specialist/ non-specialist) Whether a species has 'Specialist' South Australian permit status 590 by partitioning variable correlation between exogenous (explanatory) and endogenous (response) variables (Grace, 2006).Since some of our variable relationships were non-Gaussian distributed.(e.g.Poisson, binomial), we adopted the piecewise SEM approach developed by Lefcheck (2016).This approach constructs SEM path diagrams (i.e.networks of variable relationships) based on multiple local estimations of Generalised Linear Model coefficients and can incorporate both models with non-Gaussian distributed relationships and spatial lag models (see Appendix S1.2 for full details of SEM model choice).We generated initial path diagrams in a threefold repeatable process, which we based on existing Piecewise SEM literature: (a) collate a priori known univariate relationships; (b) validate relationships with model selection and (c) construct initial multivariate path diagram from selected models (see Appendix S1.3 for further details).To test whether missing paths should be included or current paths should be omitted (i.e. to select a final path diagram), we used directional tests of separation (d-Sep) to add or remove paths based on the probability of two variables being independent, conditional on the existing causal relationships specified Socioeconomic variables (available for 163 suburbs) and corresponding units.See.Australian Bureau of Statistics (2019) for further details (55.6%), 201 species of bird (67.7%), 14 species of mammal (23.3%) and 180 species of reptile (80.4%).A high proportion of mammal (43.3%) and bird (12.5%) taxa were classified as Endangered by the IUCN, and a total of 116 taxa classified as threatened by the IUCN were kept as pets (n = 41,672 animals).All other taxa were Near Threatened (n = 73) or Data Deficient (n = 2).Most TA B L E 2 13652664, 2022, 5, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.14138by University Of Aberdeen, Wiley Online Library on [16/12/2022].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License E 2 Model predictions for native reptile possession quantity against (a) adult mass and (b) annual fecundity (see Appendix S1.1 for details of model choice).Shaded areas represent 95% confidence intervals derived from parametric bootstrapping and points represent raw data.Raw data points with possession quantity above 2000 are not displayed (N = 3; Intellagama lesuerii, Morelia spilota, pogona vitticeps).(c) Raw number of escaped birds for fully regulated (i.e.specialist) and not fully regulated (i.e.non-specialist) birds.Species with more than 30 total escapes are labelled.While emus are known to be bred for commercial purposes in Australia, 91.9% of permit holders in our dataset possessed 10 or fewer individuals 13652664, 2022, 5, Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.14138by University Of Aberdeen, Wiley Online Library on [16/12/2022].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Downloaded from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2664.14138by University Of Aberdeen, Wiley Online Library on [16/12/2022].See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions)on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License regulation has, in part, shaped patterns of pet keeping in the South Australian pet trade.Such regulatory systems can be used to effectively manage the trade of species that are (a) threatened by trade activities such as demand-induced harvesting; (b) aggressive, dangerous or otherwise difficult to ethically house in captivity; (c) traded outside their native range; and (d) known to be a biosecurity risk such as potential invasive species or vector for high-risk pathogens.