Research Article |
Corresponding author: Katherine G. W. Hill ( katherine.hill@adelaide.edu.au ) Academic editor: Emili García-Berthou
© 2020 Katherine G. W. Hill, Kristine E. Nielson, Jonathan J. Tyler, Francesca A. McInerney, Zoe A. Doubleday, Greta J. Frankham, Rebecca N. Johnson, Bronwyn M. Gillanders, Steven Delean, 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:
Hill KGW, Nielson KE, Tyler JJ, McInerney FA, Doubleday ZA, Frankham GJ, Johnson RN, Gillanders BM, Delean S, Cassey P (2020) Pet or pest? Stable isotope methods for determining the provenance of an invasive alien species. NeoBiota 59: 21-37. https://doi.org/10.3897/neobiota.59.53671
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The illegal pet trade facilitates the global dispersal of invasive alien species (IAS), providing opportunities for new pests to establish in novel recipient environments. Despite the increasing threat of IAS to the environment and economy, biosecurity efforts often lack suitable, scientifically-based methods to make effective management decisions, such as identifying an established IAS population from a single incursion event. We present a proof-of-concept for a new application of a stable isotope technique to identify wild and captive histories of an invasive pet species. Twelve red-eared slider turtles (Trachemys scripta elegans) from historic Australian incursions with putative wild, captive and unknown origins were analysed to: (1) present best-practice methods for stable isotope sampling of T. s. elegans incursions; (2) effectively discriminate between wild and captive groups using stable isotope ratios; and (3) present a framework to expand the methodology for use on other IAS species. A sampling method was developed to obtain carbon (δ13C) and nitrogen (δ15N) stable isotope ratios from the keratin layer of the carapace (shells), which are predominantly influenced by dietary material and trophic level respectively. Both δ13C and δ15N exhibited the potential to distinguish between the wild and captive origins of the samples. Power simulations demonstrated that isotope ratios were consistent across the carapace and a minimum of eight individuals were required to effectively discriminate wild and captive groups, reducing overall sampling costs. Statistical classification effectively separated captive and wild groups by δ15N (captive: δ15N‰ ≥ 9.7‰, minimum of 96% accuracy). This study outlines a practical and accessible method for detecting IAS incursions, to potentially provide biosecurity staff and decision-makers with the tools to quickly identify and manage future IAS incursions.
biosecurity, invasive species, pet trade, provenancing, stable isotopes, Trachemys scripta elegans, wildlife trade
Wildlife trade, in particular the legal and illegal pet trade, facilitates the worldwide movements of invasive alien species (IAS), providing novel introduction pathways into new environments (
The relative abundance of stable isotopes within a material is a function of its synthesis and environmental history, which, in the case of vertebrate animals, predominantly relates to their diet (
Stable isotopes are a well-established forensic technique and are a strong candidate for identifying the origin of IAS incursions (
Trachemys scripta elegans (red-eared slider turtles) were selected as a case study to test the efficacy of δ13C and δ15N for biosecurity applications. As one of the world’s top 100 most invasive species, T. s. elegans have the potential to establish and spread in urban and semi-rural areas worldwide (
New methodologies are urgently needed to provide early identification of incursions as distinct from established populations, to allow for quick and effective eradication (
T. s. elegans post-mortem specimens were loaned from the Queensland Museum, the Department of Primary Industries and Regions, South Australia and the Australian Museum Research Institute Herpetology Collection. These animals were collected by state wildlife compliance agencies under their powers to seize animals being kept in contradiction to legislation or found at-large in wild environments. All animals were euthanised as per state and territory biosecurity protocols and stored frozen. The national collection contains seized T. s. elegans incursions from various locations across Australia. Due to the nature of the limited sample collection and the value of biosecurity material, twelve animals from various Australian locations (Fig.
Locations of Australian T. s. elegans incursion samples used in this study. Identified established populations exist in Sydney, New South Wales (
Based on the assumed environmental history of the individual turtles, we assigned the variable “status” and classified individuals as “wild” or “captive”. While the majority of animals used in this study had relatively high confidence of their origin, there remains uncertainty in the status of individuals being correctly assigned by authorities. Therefore, we created an index to determine the percent confidence of correct classification, based on how many secondary characteristics matched the original assessment by authorities, including: (i) proximity to a known established population; (ii) presence of algae or wild features on the carapace; (iii) seized by authorities or surrendered by a member of the public. This provided a confidence scale for selecting the individuals used for a decision model (Suppl. material
Measuring stable isotope ratios from a slow-turnover and inert tissue provides a long-term record of an animal’s environmental history (
Carapaces were washed, removed from the body and freeze-dried to separate partially shed scutes, to exclude water contamination and to ensure only one layer of scute was sampled at a time. Samples on shed scutes were cut using sterile dissecting scissors, while shavings were collected on attached scutes using sterile scalpels. Scute samples were weighed and placed in tin capsules for continuous-flow isotope ratio mass spectrometry (CF-IRMS) using an Elementar elemental analyser coupled to a Nu Horizon mass spectrometer at the University of Adelaide. Standards of glycine, glutamic acid and USGS41 (L-glutamic acid;
To improve the detectable δ13C and δ15N separation between captive and wild groups or to increase effect size differences, the variance of each hierarchal level of sampling (individual > scute > sample) needed to be minimised without oversampling (
Differences in mean isotope ratios amongst individual turtle specimens were evaluated using linear mixed effects models. The values of δ13C and δ15N were fitted independently as response variables, with individual turtles as a fixed effect and scute as a random effect to allow for variation between repeated measurements within a scute. The models explicitly allowed for differences in variation between individuals, because heterogeneity within individuals violated the constant variance assumption of the linear mixed effect models. The effects of sex on δ13C and δ15N were investigated using linear mixed effect models with and without sex as a term and examined significance of dropping different independent variables using a Pearson’s chi-squared test. We were unable to investigate other variables of interest, such as location and climate, due to the broad variety of the small number of representative samples. Instead, these contribute to the between-individual variation.
The overall objective was to evaluate if a decision rule could be developed that allowed wild and captive individuals to be identified, based on their δ13C and δ15N values. To assess this, a classification tree approach was adopted by introducing the status as a response variable. As the data consisted of multiple observations from the same individual turtles, a structured cross-validation approach was used to evaluate the prediction error, with all observations from the same individual included in the ‘hold-out’ set for prediction; and to avoid over-fitting. Individuals with an unknown status were omitted, as well as juvenile turtle W1 due to potential differences in diet between juvenile and adult turtles (
All analyses were conducted in the R software environment for statistical and graphical computing (V 3.5.3;
Power simulations indicated nine samples across two separate scutes on the carapace were sufficient to capture individual variation, while retaining a detectable difference between wild and captive individuals. Variation between scutes of the same layer (primary and secondary) was minimal when compared to variation between individuals. Sampling four individuals per status group (“captive” and “wild” groups, eight individual turtles in total) provided the greatest power at a minimum of 96%. The position of the samples within the scute had no significant effect on δ15N (χ62 = 1.76, p > 0.05) nor δ13C (χ62 = 0.840, p > 0.05).
Status (wild versus captive) was the main factor underlying differences in isotope values. There was evidence for an effect of status on isotopic ratios (χ12 = 4.02, p = 0.0451), but no clear differences between the sexes (χ22 = 3.66, p = 0.160).
Individual turtles had their own unique δ13C and δ15N values and within-individual variation was generally less than between-individual variation (Table
δ15N and δ13C means, standard error (SE) and sample sizes (n) for individual turtles.
Turtle | δ15N mean | δ15N SE | δ13C mean | δ13C SE | n |
---|---|---|---|---|---|
C1 | 13.08 | 0.40 | -21.41 | 0.22 | 25 |
C2 | 10.51 | 0.40 | -22.24 | 0.27 | 26 |
C3 | 10.13 | 0.42 | -21.62 | 0.66 | 18 |
C4 | 10.90 | 0.40 | -19.67 | 0.28 | 16 |
C5 | 12.58 | 0.45 | -18.33 | 0.26 | 15 |
C6 | 13.62 | 0.43 | -19.09 | 0.27 | 18 |
U1 | 8.03 | 0.40 | -22.53 | 0.27 | 18 |
U2 | 12.39 | 0.40 | -18.70 | 0.27 | 18 |
W1 | 7.42 | 0.42 | -20.09 | 0.52 | 23 |
W2 | 6.44 | 0.40 | -27.26 | 0.28 | 24 |
W3 | 8.71 | 0.42 | -25.34 | 0.32 | 26 |
W4 | 9.22 | 0.41 | -22.63 | 0.29 | 16 |
A Confidence in original status assignment based on select characteristics (Suppl. material
The classification tree showed clear differences between captive and wild groups associated with δ15N (Fig.
δ13C and δ15N means and 95% confidence intervals for the shed and retained scute on three individuals: captive C1 and unknowns U1 and U2. Layers are labelled as primary (newest growth; retained on carapace) and secondary (older growth; shed scute). An older scute layer was available on turtle U1, named tertiary (oldest growth; retained scute).
Of the three turtles with shed scutes available, all revealed significant differences between layers in δ13C (C1: F1,31 = 100.4, p < 0.0001; U1: F3,51 = 0.0006, p < 0.0001; U2: F2,34 < 0.0001, p < 0.0001) and δ15N for turtles C1 and U1 (F1,31 = 100.4, p < 0.0001; F2,50 = 82.7, p < 0.0001), but not U2 (F1,34 = 1.771, p = 0.192). However, there was no consistency in the direction of change between layers. Furthermore, the δ15N values for each scute layer remained within the classification range of their assigned status; “wild” and “captive”.
Captive and wild T. s. elegans are effectively differentiated by their δ15N. Sampling scute proved to be a simple method; no specialist equipment was required for collection and samples could be taken anywhere on the scute and across multiple scutes with minimal variation within the individual. Although individuals were dissected for this study, the use of scute shavings is potentially a non-invasive method. This makes the technology accessible for non-specialist practitioners, such as biosecurity or veterinary staff and for samples to be collected and sent to a laboratory for analysis and determination of their origins. Furthermore, the power simulations demonstrated that minimal sampling per individual is required, reducing the overall sampling costs in time, effort and welfare, as well as monetary cost.
As material of high biosecurity risk is inherently difficult to obtain, the availability of T. s. elegans and other reptile IAS is limited, while information surrounding an animal’s history is not always accessible. Wild T. s. elegans specimens are rare in Australia, as at-large populations have only been confirmed in Sydney (
Separation of wild and captive groups used a simple classification tree model, which effectively differentiated wild and captive individuals with minimal misclassification error. As samples with relatively high status confidence were used, this classification tree can be adopted as a set of best-practice methods and model to determine the origins of T. s. elegans individuals found in wild-states. However, further refinement of the model is required, such as including a wider range of locations of samples to improve the discrimination power.
Differences in the δ13C and δ15N composition of scutes from different status groups are likely primarily influenced by different proportions and sources of plant and animal material within a turtle’s diet, as well as varied sources of these food groups (
The δ13C exhibited little power for separating wild and captive groups. As with δ15N, δ13C is influenced by a variety of environmental factors. However, δ13C was identified as the most significant separator for wild and captive juvenile T. s. scripta by
For each turtle, where shed scute was available, the δ15N and δ13C exhibited significant differences between successive active seasons. However, as there was no consistent direction of change in the isotope data, it is unlikely the changes are due to tissue degradation and instead likely reflected temporal variability in the turtle’s diet. The variance in δ15N was sufficiently small to ensure that the specimen remained within the same status group, based on the δ15N‰ ≥ 9.7‰ discrimination value.
It is important to note that the status assignment refers to the confidence that the turtle was wild or captive for the entirety of the scute growth period. The natal origin (birthplace) of the turtle cannot be determined using scute growth alone as scutes are shed yearly (
The exploration of additional biogeochemical tracers may be useful to create a more diverse set of methods and potentially obtain greater evidence of environmental origin. Stable isotopes relating to the animal’s water source such as hydrogen (2H/1H) and oxygen (17O/16O or 18O/16O) may provide useful information on the animal’s geographical origin and have been used in other animal tracking applications (
The values of δ13C and δ15N in scute keratin are effective at filling the requirement for the urgent need for effective forensic techniques to quickly identify the origin of T. s. elegans (red-eared slider turtle) incursions and has promising potential for applications on other high-risk IAS species (
Turtle carcasses were kindly provided through scientific loans by the Queensland Museum (Patrick Couper), Department of Primary Industries and Regions, South Australia (Lindell Andrews) and Australian Museum’s Herpetology Collection (Jodi Rowley and Stephen Mahoney). For sample collection and seizure information, we thank the Primary Industries and Regions SA (Lindell Andrews), the Department of Primary Industry, NSW (Alyssa Trotter, Nathan Cutter), the Elizabeth Macarthur Agricultural Institute (Brendon O’Rourke, Naomi Porter) and Department of Jobs, Precincts and Regions/Agriculture Victoria (Jesse Miller). We are extremely grateful to Mark Rollog for assistance with CF-IRMS data collection, Jennifer Pistevos for her work on pilot analyses and Talia Wittmann for research assistance. This research was supported by the University of Adelaide Environment Institute, by ARC FT110100793 to F.A. McInerney and by Invasive Animals CRC (Project 1L4) and Centre for Invasive Species Solutions (Project PO1-I-002) funding to P. Cassey. An ARC LIEF grant (LE120100054) funded the IRMS used for analyses.
Tables S1.1, S1.2. A detailed description of indexes used for calculating confidence of status of Trachemys scripta elegans individuals
Data type: species data
Table S2.1; Figure S1. Explanation of methods for determining the optimal sampling size and design, using a power analysis on pilot data
Data type: statistical data
Figure S1. Results from a carbon decision tree
Data type: statistical data
Table S4.1. Determining confidence in status assignment
Data type: statistical data