Research Article |
Corresponding author: Pablo García-Díaz ( Garcia-DiazP@landcareresearch.co.nz ) Academic editor: Sven Bacher
© 2019 Pablo García-Díaz, Joshua V. Ross, Miquel Vall-llosera, 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:
García-Díaz P, Ross JV, Vall-llosera M, Cassey P (2019) Low detectability of alien reptiles can lead to biosecurity management failure: a case study from Christmas Island (Australia). NeoBiota 45: 75-92. https://doi.org/10.3897/neobiota.45.31009
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When we assume that contemporary management actions will be effective against the global rise of emerging alien species, we can develop management complacency, which leads to potentially disastrous outcomes for native biodiversity. Here, we propose the use of the probability of detection as a metric to assess the feasibility of management actions for alien species. We explore how detectability can influence the management of alien reptiles, a group of emergent alien vertebrates globally. We use a Rapid Biological Assessment method (time-limited transects) to estimate the probability of detection for alien reptiles present on Christmas Island (Australia). Across the five species studied, we found low probabilities of detection and poor explanatory capacity of the individual covariates included in our models. These findings indicate that management options to deal with alien reptiles are limited due to the potential high cost and low efficacy associated with low probabilities of detection. Strict preventive strategies, firmly espousing the principles of adaptiveness and precautionary policies, combined with early detection and biosecurity response activities are needed to address the emergence of alien reptiles. Our research was focussed on alien reptiles on islands, but the rise of new pools of alien species from all taxonomic realms across the world suggests that our conclusions may be applicable more generally. Further research is called for to explore the applicability of our conclusions and recommendations to other taxonomic groups and regions of the world.
Anticipatory policy-making, Christmas Island, preventive decision-making, probability of detection, Rapid Biological Assessment, uncertainty
The global emergence of a new pool of alien species may render existing management actions, ranging from rapid incursion response to the eradication of self-sustaining populations, ineffective (
Successful management of alien species depends on the capacity to anticipate the specific transport pathways, which move alien species, and on adapting management actions to address the associated novel risk. While substantial research effort has been invested in understanding the dynamics of changing pathways (
Rapid Biological Assessments (RBAs) are a widely used tool for conducting biodiversity inventories and monitoring, balancing reliability in sampling biological communities with time and resource constraints to conduct the surveys (
Reptiles are a notable group of emergent alien species responsible for serious environmental impacts in recipient regions worldwide, especially on islands (
We investigated a standardised RBA using the case study of the detection of alien reptiles on Christmas Island (Fig.
Christmas Island is an Australian oceanic territory located in the Indian Ocean, 10°30’S 105°40’E (Fig.
Estimated probabilities of individual detection of five invasive alien reptiles on Christmas Island (Australia) during day time (top panel) and night time surveys (bottom panel). Estimates correspond to the probability of individual detection during a 10-minute survey across the 34 surveying sites. Each dot represents a realisation from 1000 simulations.
We identified 34 survey sites (Fig.
Goodness of fit (Bayesian p-values), summary statistics of the covariates (before standardisation; mean ± standard deviation, and range), and posterior coefficient estimates (mean ± standard error, and 95% Credible Intervals) of the abundance-detection models for five species of alien reptiles on Christmas Island.
Covariate summary statistics (original units) | Common house gecko (Hemidactylus frenatus) | Stump-toed gecko (Gehyra mutilata) | Grass skink (Lygosoma bowringii) | Flowerpot snake (Indotyphlops braminus) | Wolf snake (Lycodon capucinus) | |
Bayesian p-value | 0.51 | 0.49 | 0.44 | 0.29 | 0.31 | |
Estimated mean abundance (across survey sites) | 67.27 ± 27.15 (34.68, 152.85) | 24.19 ± 78.86 (1.50, 265.14) | 1.83 ± 2.25 (0.77, 6.94) | 0.62 ± 0.24 (0.35, 1.24) | 5.43 ± 10.93 (0.53, 32.18) | |
Probability of individual detection (across survey sites) | ||||||
Day | 0.007 ± 0.008 (0.002, 0.03) | 0.001 ± 0.007 (0.00, 0.001) | 0.22 ± 0.15 (0.03, 0.60) | 0.15 ± 0.13 (0.05, 0.53) | 0.02 ± 0.04 (0.00, 0.15) | |
Night | 0.08 ± 0.05 (0.02, 0.21) | 0.11 ± 0.11 (0.005, 0.42) | 0.009 ± 0.02 (0.001, 0.06) | 0.02 ± 0.04 (0.00, 0.53) | 0.03 ± 0.04 (0.003, 0.12) | |
Probability of individual detection: logit model | ||||||
Intercept (day) | -5.31 ± 0.50 (-6.46, -4.50) | -8.61 ± 6.12 (-25.47, -4.83) | -1.30 ± 0.88 (-3.30, 0.16) | -1.67 ± 0.65 (-2.69, -0.40) | -4.01 ± 1.51 (-7.94, 1.92) | |
Intercept (night) | -2.06 ± 0.5 (-3.25, -1.18) | -2.21 ± 1.26 (-5.35, -0.43) | -4.70 ± 1.22 (-7.69, -2.89) | -1.42 ± 0.59 (-2.69, 0.40) | -4.32 ± 1.51 (-8.19, -2.19) | |
Ground temperature (standardised) | Degree Celsius 26.75 ± 3.61 (20.0-48.0) | 0.10 ± 0.10 (-0.11, 0.29) | -0.11 ± 0.56 (-1.64, 0.70) | -0.15 ± 0.19 (-0.55, 0.19) | -0.08 ± 0.34 (-0.92, 0.47) | 0.12 ± 0.34 (-0.56, 0.79) |
Temperature 10cm above ground (standardised) | Degree Celsius 26.40 ± 2.47 (21.0-33.5) | 0.10 ± 0.14 (-0.19, 0.37) | -0.09 ± 0.16 (-1.29, 0.61) | -0.29 ± 0.37 (-1.18, 0.22) | 0.05 ± 0.40 (-0.75, 0.91) | 0.02 ± 0.44 (-0.88, 0.96) |
Temperature 10cm underground (standardised) | Degree Celsius 26.40 ± 2.41 (21.6-33.8) | -0.11 ± 0.09 (-0.29, 0.06) | 0.12 ± 0.28 (-0.34, 0.81) | 0.07 ± 0.33 (-0.64, 0.71) | 0.35 ± 0.42 (-0.26, 1.37) | 0.43 ± 0.41 (-0.18, 1.39) |
Temperature 120cm above ground (standardised) | Degree Celsius 26.25 ± 2.29 (21.40-33.80) | 0.52 ± 0.16 (0.22, 0.84) | 0.13 ± 0.47 (-0.54, 1.39) | 0.50 ± 0.52 (-0.25, 1.73) | 0.02 ± 0.44 (-0.96. 0.89) | -0.26 ± 0.54 (-1.62, 0.49) |
Abundance: Negative Binomial regression | ||||||
Distance to port (standardised) | Metres 1937.0 ± 2876.45 (138.0, 8575.0) | 0.01 ± 0.21 (-0.39, 0.43) | 0.26 ± 0.32 (-0.36, 0.98) | 0.15 ± 0.32 (-0.48, 0.79) | 0.01 ± 0.15 (-0.29, 0.35) | -0.04 ± 0.27 (-0.61, 0.44) |
Cover of trees | Scale 2.00 ± 1.82 (0-5.00) | 0.12 ± 0.15 (-0.17, 0.43) | 0.07 ± 0.27 (-0.40, 0.70) | -0.25 ± 0.26 (-0.78, 0.23) | 0.01 ± 0.12 (-0.21, 0.27) | -0.27 ± 0.26 (-0.91, 0.10) |
Cover of bush | Scale 2.00 ± 1.63 (0.00-5.00) | -0.11 ± 0.18 (-0.47, 0.47) | -0.13 ± 0.30 (-0.79, 0.62) | -0.32 ± 0.27 (-0.88, 0.19) | -0.05 ± 0.14 (-0.40, 0.18) | -0.01 ± 0.21 (-0.43, 0.44) |
Cover of grass | Scale 4.00 ± 1.52 (0.00-5.00) | 0.48 ± 0.12 (0.24, 0.73) | 0.17 ± 0.25 (-0.28, 0.74) | 0.06 ± 0.19 (-0.33, 0.43) | -0.05 ± 0.11 (-0.28, 0.18) | 0.22 ± 0.26 (-0.12, 0.89) |
Cover of rocks and stones | Scale 2.00 ± 0.97 (0.00-4.00) | -0.01 ± 0.24 (-0.48, 0.47) | -0.05 ± 0.35 (-0.80, 0.62) | 0.27 ± 0.34 (-0.32, 1.03) | -0.01 ± 0.15 (-0.33, 0.32) | 0.03 ± 0.27 (-0.48, 0.65) |
Cover of human structures | Scale 3.50 ± 1.74 (0.00-5.00) | 0.69 ± 0.13 (0.46, 0.97) | 0.31 ± 0.24 (-0.07, 0.88) | 0.05 ± 0.21 (-0.31, 0.51) | -0.07 ± 0.12 (-0.35, 0.12) | 0.10 ± 0.19 (-0.26, 0.54) |
Transect length (standardised) | Metres 175.00 ± 72.59 (60.00-384.00) | -0.01 ± 0.20 (-0.40, 0.41) | -0.01 ± 0.31 (-0.64, 0.62) | 0.33 ± 0.37 (-0.24, 1.18) | 0.01 ± 0.16 (-0.31, 0.35) | 0.14 ± 0.26 (-0.28, 0.74) |
Elevation (standardised) | Metres above sea level 194.5 ± 94.25 (10.00, 303.00) | -0.05 ± 0.21 (-0.47, 0.36) | -0.01 ± 0.32 (-0.65, 0.64) | -0.69 ± 0.38 (-1.47. -0.01) | -0.05 ± 0.17 (-0.48, 0.21) | 0.01 ± 0.27 (-0.57. 0.55) |
Overdispersion parameter Θ | 0.97 ± 0.30 (0.54, 1.70) | 0.31 ± 0.25 (0.12, 0.98) | 3.78 ± 15.17 (0.19,47.20) | 23.69 ± 14.70 (1.11, 48.68) | 24.97 ± 14.32 (1.66, 48.75) |
We surveyed for alien reptiles during the dry season, 8th July to 7th August 2015, to minimise disturbances caused by the inclement weather conditions of the wet season. Each of the survey sites was surveyed using the time-limited transect approach; a standardised RBA for reptiles and amphibians (
Summary of the counts (number of observations per each 10-minute survey) of five species of alien reptiles on Christmas Island across the 34 survey sites (mean ± standard deviation, and range). Three repeated surveys were conducted during each day and night time conditions in each survey site. The raw data are available at https://figshare.com/s/e85ac13693bc6272437f
Species | Day surveys | Night surveys |
Common house gecko (Hemidactylus frenatus) | 0.74 ± 1.44 (0.00–7.00) | 6.51 ± 7.84 (0.00–35.00) |
Stump-toed gecko (Gehyra mutilata) | 0.00 ± 0.00 (0.00–0.00) | 0.58 ± 1.45 (0.00–7.00) |
Grass skink (Lygosoma bowringii) | 0.31 ± 0.84 (0.00–5.00) | 0.01 ± 0.10 (0.00–1.00) |
Flowerpot snake (Indotyphlops braminus) | 0.08 ± 0.44 (0.00–2.00) | 0.11 ± 0.31 (0.00–1.00) |
Wolf snake (Lycodon capucinus) | 0.06 ± 0.28 (0.00–2.00) | 0.05 ± 0.26 (0.00–2.00) |
Time-limited transects do not neccesarily require repeated survey occasions (
During each survey occasion, we recorded the number of individuals of each alien reptile species detected, and the temperature (ºC) at four different heights in the habitat (Table
Ndi,z ~ Binominal(Ni,pi,z) (1)
where Ni is the abundance of the species, and pi,z is the probability of individual detection (
where βj (j = 1, …, 8) are the slopes of the model of the mean abundance, λi, in survey site i and Xi is a vector of the eight covariates used for modelling the mean abundance (Table
where αd are the time-of-day specific intercepts (day and night), βdr (r = 1, …, 4) are the slopes of the model of the probability of individual detection, pi,z, during survey occasion z at survey site i, and Ti,z is a vector of the four temperature covariates used for modelling the probability of individual detection (see Table
We employed Bayesian regularisation to construct robust model structures for the abundance and the probability of individual detection as a function of the covariates (
Our Bayesian regularised models were an adequate fit to the count data (Bayesian p-values close to 0.5 and non-skewed Q-Q plots in all cases; see Supplementary Methods), and revealed that the survey time (day vs night) was the main driver of the probability of individual detection across the five alien reptiles (Table
Estimated surveying effort required to detect all 10 individuals of each species of five alien reptiles in two situations and under a best-case detection scenario (i.e., sites surveyed during the time of the day when detection probabilities are higher). Top: surveying effort (minutes) to detect 10 individuals known to be present in one surveying site when only those 10 individuals are present; bottom: surveying effort (hours) to detect 10 individuals distributed at random across 34 surveying sites, where each occupied site harbours one individual, and only ten individuals are present across the 34 surveying sites. We used 10 individuals as an example to showcase and compare detection efforts across different situations. Best-case detection scenarios are night time surveys (common house gecko, stump-toed gecko, and flowerpot snake) and daytime surveys (grass skink and wolf snake). Each dot represents a realisation from 1000 simulations.
All the posterior estimates of the probabilities of individual detection were relatively low, with the upper 95% Credible Interval estimates of daytime surveys of the grass skink and night time surveys of flowerpot snakes being the only values exceeding 0.5 (Table
Common house geckos were the most frequently encountered species, followed by stump-toed geckos during night time surveys and grass skinks during daytime surveys (Table
Our Negative Binomial regressions revealed that common house geckos were more common in sites with higher grass and human structure cover (positive relationships), and that grass skinks tended to become rarer in higher elevation sites (negative relationship with elevation). Across the five alien reptiles, all the covariates had uncertain effects on the estimated alien reptile abundances and probabilities of detection, with wide posterior estimates overlapping zero (Table
The probabilities of detecting individuals using a standardised RBA were consistently low across all the alien reptiles found on Christmas Island. Both the detectability and the abundance of those alien reptiles were difficult to explain given the rather uncertain effects of the covariates tested in our models, even when our surveys explored a representative and variable sample of environmental conditions (Table
It is particularly important to protect island ecosystems, where alien reptiles have produced substantial negative impacts and whose native biodiversity is highly exposed to the threat of alien species (
The compounded effects of low detection probabilities and uncertain effects of covariates will hinder effective measures to manage the emergence of alien reptiles, a conclusion reinforced by previous species-specific research into the management of invasive brown snakes (Boiga irregularis) in Guam (
Framed in this context of overarching uncertainties and high costs, strong preventive policies should be a priority to address the emergent threat of alien reptiles on islands and elsewhere (
Preventive management activities should be complemented with early detection surveys aimed at detecting new populations of alien reptiles promptly (
More broadly, biosecurity regulations, strict quarantine, and early detection activities should be considered within the framework of robust anticipatory policy-making (
The implementation of stringent preventive policies and early detection activities might be more straightforward on oceanic islands, where their remoteness commonly requires all goods and commodities to be imported via shipping and air traffic routes arriving in a small number (usually one) of ports and airports, limiting the number of potential pathways of transport and points of entry into the island (
We have focussed on alien reptiles on islands in this research, but our conclusions are likely applicable to other groups of emergent alien species globally; such as vertebrates in the pet trade (
This work would not have been possible without the support provided by the all the staff of the Christmas Island National Park, the Christmas Island Natural Resource Management Board, and the Department of Agriculture and Water Resources (Indian Ocean). This research was conducted under permit number CIN_2015_5 (Christmas Island National Park, Director of National Parks, Australian Government) and a Licence to undertake research activities on regulated crown land. The research was approved by the University of Adelaide Animal Ethics Committee (S-2014-155). Andrew Robinson, John Measey, and an anonymous reviewer provided constructive feedback that helped improve this manuscript. PG-D was supported by an IPRS/APA scholarship (DET), an Invasive Animals CRC PhD scholarship, and the IA CRC Student Grant. This work was supported by an ARC Discovery Grant (DP140102319) to JVR and PC and ARC Future Fellowships to PC and JVR (FT0991420, FT130100254).
Model evaluation and estimability
Data type: measurement