Research Article |
Corresponding author: Staci M. Amburgey ( staci.m.amburgey@gmail.com ) Corresponding author: Adam J. Knox ( adamjknox@gmail.com ) Academic editor: Sandro Bertolino
© 2021 Staci M. Amburgey, Amy A. Yackel Adams, Beth Gardner, Bjorn Lardner, Adam J. Knox, Sarah J. Converse.
This is an open access article distributed under the terms of the CC0 Public Domain Dedication.
Citation:
Amburgey SM, Yackel Adams AA, Gardner B, Lardner B, Knox AJ, Converse SJ (2021) Tools for increasing visual encounter probabilities for invasive species removal: a case study of brown treesnakes. NeoBiota 70: 107-122. https://doi.org/10.3897/neobiota.70.71379
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Early detection and rapid response (EDRR) are essential to identifying and decisively responding to the introduction or spread of an invasive species, thus avoiding population establishment and improving the probability of achieving eradication. However, detection can be challenging at the onset of a species invasion as low population densities can reduce the likelihood of detection and conceal the true extent of the situation until the species is well established. This is doubly challenging if the invading species displays cryptic behavior or is nocturnal, thus further limiting opportunities for its discovery. Survey methods that maximize a searcher’s ability to detect an incipient population are therefore critical for successful EDRR. Brown treesnakes (Boiga irregularis) on Guåhan are a classic cautionary example of the dangers of not detecting an invasion early on, and the risk of their introduction to other islands within the Marianas, Hawai’i and beyond remains. Nocturnal visual surveys are known to detect brown treesnakes of all sizes and are the primary detection tool used by the Brown Treesnake Rapid Response Team, but detection probability remains low in complex forest habitats. As such, we investigated the use of two potential enhancements to nocturnal visual surveys – a live mouse lure and spray scent attractant – that may create hotspots of increased detection probability during surveys. We found that, while brown treesnake detection probabilities were low for all surveys, visual surveys conducted on transects with live mouse lures resulted in detection probabilities that were 1.3 times higher than on transects without live mouse lures. Conversely, the spray scent attractant did not increase the probability of detecting brown treesnakes compared to transects without scent, and in fact had detection probabilities that were 0.66 times lower, though the reasons for this phenomenon are unclear. Unlike scent attractants, live mouse lures likely provide both visual and olfactory cues that attract brown treesnakes to transects and thus provide more opportunities to detect and capture them. These enhancements were trialed on Guåhan, where prey populations are depressed. It remains unclear whether live mouse lures will be as effective for EDRR applications in prey-rich settings.
Detection probability, early detection, Guam, lure, rapid response, spatial capture-recapture
In invasive species management, the ability to quickly detect and decisively respond to the introduction or spread of an invasive species is often cited as key to the efficacy and success of eradication (i.e., early detection and rapid response or EDRR;
The brown treesnake (Boiga irregularis) provides a classic example of the dangers posed by a species characterized by a low detection probability that, in combination with belated concern, resulted in a delayed response to its establishment on Guåhan (in the CHamoru language, known in English as Guam) (
To respond to these reports, the U.S. Geological Survey (
Though visual surveys can be effective in detecting individuals, detection probabilities of brown treesnakes tend to be quite low overall, due to the snakes’ use of complex habitat, cryptic behavior and coloration, and nocturnal activity patterns. While direct comparison is challenging as effort level is not easily translatable between methods, detection probabilities of p̂ < 0.15 (i.e., probability that an individual snake in the effective survey area is encountered on a given night) have been reported for typical surveys using both searching and trapping methods (
As a potential tool to maximize detection probabilities during visual surveys, we investigated the use of two potential attractants for use in EDRR: 1) snake traps that contain a protected live mouse lure and 2) a scented spray applied to surveyed transects. Mouse lures can be detected by brown treesnakes from up to 20 m away (
We conducted two field experiments within the Closed Population (CP), a 5-ha (50,000 m2) fenced area on Andersen Air Force Base in the north of Guåhan. The fence, consisting of a 1.5-m tall, galvanized mesh and chain link wall, had a bulge on both sides about 1.2 m above ground level that eliminated immigration and emigration of snakes in the study area. This fence was also bounded by a 0.5-m concrete footer and vegetation was removed 2 m to either side of the fence to provide a study population of brown treesnakes for investigation of management and population estimation methodologies (
Teams of two observers conducted night-time surveys. Snakes in the CP were part of an ongoing, multi-year (starting in 2004) capture-mark-recapture (CMR) study using unique ventral scale clip patterns and internal passive integrated transponder (PIT) tags. When searchers found a snake, they attempted to scan it without handling to avoid disturbing the individual. If a PIT tag could not be remotely scanned, searchers captured snakes and further checked for a mark or PIT tag or gave a unique mark and PIT tag to previously unmarked animals. In traditional CMR, searchers avoid disturbing traps to avoid deterring animals from being captured; however, as many animals were already marked in the CP and the objective of these surveys was to test the efficacy of EDRR tools, searchers instead focused on checking these areas for snakes.
Because all data were analyzed using a framework that assumes demographic closure of the population (i.e., no immigration, emigration, births, or deaths), we truncated the data for both projects to a two-month timespan. During this two-month period, while demographic closure cannot be guaranteed, there was a low probability of new individuals entering (i.e., being born into or found for the first time) the surveyed population or existing individuals dying. The CP was closed to emigration and immigration due to the two-way barrier surrounding the entire study area.
Searchers conducted 25 surveys between February 1 and March 31, 2015. During these surveys, transects either had no traps or live mouse lures (henceforth, no lures) placed on them or had snake traps with live mouse lures (henceforth, lures) placed at all 13 grid-cell markers on a transect. Eleven to 13 transects were surveyed every evening with four to five of these transects having lures. Lures were rotated to new locations every one to three weeks (Fig.
Weekly catch per unit effort (CPUE) of brown treesnakes along transects A without and with traps with mouse lures and B without scent, with fresh scent (applied that day), or with old scent (applied the day before). A Overall, average CPUE of snakes was 32% higher along transects with mouse lures than transects without such lures. Vertical lines group traps deployed at similar locations (Week 1 and 8, Week 2–4 and 9, and Weeks 5–7 denote same trap locations). B Overall, average CPUE of snakes was 45% and 12% higher along transects that were not sprayed as compared to those with fresh scent and old scent respectively. Asterisks indicate that no spraying occurred in week 4.
Searchers conducted 32 surveys between November 1 and December 30, 2016. During these surveys, transects were either unsprayed (henceforth, no scent) or sprayed either in the early evening before the night-time survey (fresh scent) or the previous day (old scent). We distinguished these latter two groups from each other to account for a potential lingering effect of scent. The scent consisted of a mixture of 500 ml fish fertilizer (Alaska Fish Fertilizer) and 14.74 L of water and was sprayed along the entire length of a transect on the ground (1–1.5 feet above the surface) over the course of four minutes to ensure a consistent application rate. The mixture was emitted in a flat, constant spray that resulted in little drift and even application, requiring a little under 14.74 L for three transects-worth. This scent mixture was selected from a pilot study that also tested beef blood and canned tuna mixtures (B. Lardner & A. Knox, pers. comm.), with the fish fertilizer eliciting the highest level of brown treesnake activity (as quantified by number of times animals entered and investigated an area with the scent applied). Brown treesnakes are scavengers that will consume a variety of carrion (
On most evenings of the study, nine transects were sprayed with scent and continued to be sprayed daily for three days in a row. The other 18 transects were surveyed but no spraying occurred. On the fourth day, no new spraying occurred but all transects were surveyed. After this 4-day surveying bout, a three-day break occurred after which nine new transects were sprayed (Fig.
We calculated weekly catch per unit effort (CPUE) for each project, measured as the total number of snakes caught divided by the total transect distance (km) walked during surveys each week. This metric is a commonly reported way of capturing the benefit to cost (in time) ratio of an action. We calculated this at the temporal scale of a week to better match the time frame that treatments were implemented on a transect before being rotated to a new location and to also summarize if there were any accumulated benefits to using these treatments. However, CPUE does not lend itself to statistical testing of differences, requiring further analysis of capture data.
We also analyzed the individual capture data using a spatially explicit capture mark-recapture (SCR or SECR) model (
We ran one model for each project where we allowed the baseline encounter rate (λSTATUSjk) to vary by the lure or scent of each grid cell j at the time k. For both analyses, we assumed a half-normal detection function such that
(1)
where ||si – xj|| is the squared Euclidean distance between each activity center (si) and grid cell (xj). For the first analysis, the status of a grid cell could take three forms: 1) inactive (i.e., not surveyed that evening), 2) active and without a lure (λnolure), or 3) active and with a lure (λlure). For the second analysis, the status of a grid cell could take four forms: 1) inactive, 2) active and without scent (λnoscent), 3) active and with fresh scent (λfreshscent), or 4) active and with old scent (λoldscent).
We used a data augmentation approach to estimate the number of individuals present in the study area but not detected during the study (
We fit both models using a data augmentation value of M = 250 and vague priors where si~Uniform[S], λSTATUS~Uniform (0,1), ψ~Uniform (0,1), and σ~Uniform (0,50). We ran all models using three parallel chains comprised of 1,000 adaptation iterations followed by 2,000 iterations and no burn-in or thinning. Model convergence was determined by visual inspection of traceplots and Gelman Rubin statistics (Ȓ ≤ 1.01;
We also calculated the probability (% of total Markov chain Monte Carlo iterations) per project that the encounter probability when using each attractant was greater or less than the encounter probability without the use of that attractant. We also calculated the mean difference between the encounter probabilities (e.g., λlure – λnolure).
Using values estimated from the data that impact the way searchers detect snakes (λSTATUS and s), we simulated data to better understand the way each attractant could impact the probability of detecting snakes on a given night. For a single snake with an activity center s in the very center of the study area, we simulated a single evening survey where the entire study area (the same dimensions of CP; 50,000 m2) was uniformly subjected to each of the different attractants or not (e.g., every grid cell contained a lure or not). We estimated the encounter probability at each grid cell in the study area and calculated the probability that the individual would be detected at least once in the study area when using that attractant (or lack thereof). We fit all models in JAGS (
During this study, we captured 100 unique individuals, with snakes being caught an average of 1.9 times (range: 1–5 times) and 3–14 snakes being caught every evening. The mean snout-vent length (SVL) of captured snakes was 918.91 mm (min = 566, max = 1205). Weekly CPUE was often higher on transects with live-mouse lures present (Fig.
Encounter probabilities of snakes in grid cells with lures was generally higher (λlure = 4.26e-3 [95% credible interval {CI} = 2.98e-3, 5.82e-3]) than those in grid cells without lures (λnolure = 3.25e-3 [2.33e-3, 4.37e-3]), though 95% CIs overlapped (Fig.
In this study, we captured 96 unique individuals, with snakes being caught an average of 2.5 times (range: 1–8 times) and 2–18 snakes being caught every evening. The mean SVL of captured snakes was 950.21 mm (min = 462, max = 1203.75). Weekly CPUE was highest on transects without any scent sprayed (Fig.
Encounter probabilities of snakes on transects that were unsprayed (λnoscent = 1.46e-3 [1.14e-3, 1.83e-3]) or sprayed the day before (λoldscent = 1.47e-3 [1.14e-3, 1.83e-3]) were higher than for snakes on transects with fresh scent (λfreshscent = 0.97e-3 [0.67e-3, 1.33e-3]), though again 95% CIs overlapped (Fig.
Using the estimates from the live-mouse lure component, we found that the probability of detecting a single individual on a single night (when searching every grid cell) in a study area entirely lacking a lure was 0.66 (0.55, 0.77) but increased to 0.76 (0.64, 0.86) with lures placed at every grid cell. Using estimates from the sprayed scent project, we found that the probability of detecting a single individual on a single night (when searching every grid cell) in a study area entirely lacking scent or with older scent was 0.39 (0.32, 0.47) or 0.39 (0.26, 0.53), respectively. The lowest probability of detection, 0.28 (0.21, 0.37), was in a study area with fresh scent.
For EDRR, the probability of detecting an incipient population dictates how rapid a management response can be assessed and implemented. We tested the utility of pairing visual surveys with attractants (i.e., lures and scent) to increase the probability that searchers would encounter brown treesnakes during a rapid response effort. The CI of estimates overlapped likely due to imprecision caused by small sample sizes and limited recaptures (Fig.
When considering the efficacy of different attractants, a live-mouse lure provides both an olfactory and visual cue to brown treesnakes (
Our estimated abundances and densities for both projects are consistent with other studies on this population (
In a novel environment with high prey densities, a snake’s activity status would more often be in a “fed” vs. “foraging” state and the efficacy of a lure could be limited due to an abundance of alternate prey options (
Previous work in EDRR has highlighted the use of supplemental data types and attractants as a means to ensure detection of incipient populations that can cause massive, ecosystem-wide damage (
We profoundly thank the biologists who helped collect these data, specifically P. Barnhart, A. Collins, V. Deem, F. Erickson, M. Hogan, E. Holldorf, T. Hinkle, J. Kaseman, M. Nafus, A. Narzynski, C. Robinson, G. St. Aubin, T. Tadevosyan, M. Viernes. We also thank R. Reed, J. Savidge, S. Siers, A. Collins, T. Tadevosyan, and L. Bonewell for project support. We also thank Andersen Air Force Base for granting field access to the study site and Joint Region Marianas for providing support for this project. Snake and mouse handling were conducted as per protocols of the U.S. Geological Survey (FORT IACUC 2013-13) and Colorado State University (IACUC-15-5892A) Institutional Animal Care and Use Committees. The Office of Insular Affairs and U.S. Geological Survey Invasive Species Program provided funding. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. All code and data to run these analyses are available at https://github.com/amburgey/Browntreesnake_ATTRACTANTS.
R code to fit the spatial capture-recapture model in JAGS
Data type: model code
Explanation note: R code to fit the spatial capture-recapture model in JAGS. Code example is for the mouse lure project but was similar to that used for the spray scent project. Simulated data are included for reference as to the structure and form of data input into the model.
Code used to simulate detection probabilities
Data type: model code
Explanation note: Code used to simulate detection probabilities and observations of a single snake in the study area based on estimated parameters (from JAGS model, saved as “out”). Example code shows calculations for mouse lure predictions but is similar to that used for spray scent predictions. By using all the samples in the posterior, we estimated uncertainty.