Corresponding author: Lise Ruffino (
Academic editor: S Bertolino
On large inhabited islands where complete eradication of alien invasive rodents through the use of poison delivery is often not practical or acceptable, mechanical trapping may represent the only viable option to reduce their impact in areas of high biodiversity value. However, the feasibility of sustained rodent control by trapping remains uncertain under realistic operational constraints. This study aimed to assess the effectiveness of non-toxic rat control strategies through a combination of lethal and live-trapping experiments, and scenario modelling, using the example of a remote montane rainforest of New Caledonia. Rat densities, estimated with spatially-explicit capture-recapture models, fluctuated seasonally (9.5–33.6 ind.ha-1). Capture probability (.01–.25) and home range sizes (HR95, .23–.75 ha) varied greatly according to trapping session, age class, sex and species. Controlling rats through the use of lethal trapping allowed maintaining rat densities at ca. 8 ind.ha-1 over a seven-month period in a 5.5-ha montane forest. Simulation models based on field parameter estimates over a 200-ha pilot management area indicated that without any financial and social constraints, trapping grids with the finest mesh sizes achieved cumulative capture probabilities > .90 after 15 trapping days, but were difficult to implement and sustain with the local workforce. We evaluated the costs and effectiveness of alternative trapping strategies taking into account the prevailing set of local constraints, and identified those that were likely to be successful. Scenario modelling, informed by trapping experiments, is a flexible tool for informing the design of sustainable control programs of island-invasive rodent populations, under idiosyncratic local circumstances.
Duron Q, Cornulier T, Vidal E, Bourguet E, Ruffino L (2020) Combining live and lethal trapping to inform the management of alien invasive rodent populations in a tropical montane forest. NeoBiota 63: 101–125.
Human activities, such as agriculture and international trade, modify habitats and disturb the composition, richness and diversity of animal and plant communities (
Among the world’s most pervasive invasive species, rodents (
When rodent control needs to be conducted in the long term, poison delivered as bait is the most cost-effective measure, especially when treated areas are large and dominated by rugged terrain (
In the South Pacific archipelago of New Caledonia, Black and Pacific rats (
Our study aimed at evaluating the effectiveness of mechanical trapping for maintaining rat numbers at low levels using a study site in a remote montane rainforest of New Caledonia as a case study. We combined a capture-mark-recapture (
This study was conducted in a dense evergreen rainforest located between 550 and 950 meters a.s.l. in the wilderness reserve of Mont Panié (
Location of the study area in the Pacific, northern New Caledonia. The study was conducted in Mont Panié montane rainforest on two adjacent 20 × 20 m trapping grids: a capture-mark-recapture (
Rats were live-trapped between September 2014 and September 2015 in the vicinity of the small removal area described below, in order to i) study rat population dynamics within the
Rat removal trapping was performed between May and November 2015 in a 5.5-ha forest area immediately adjacent to the
Rat densities, home ranges and movements were estimated with spatially-explicit capture-recapture (
Our dataset did not allow to test for the effect of individual sessions, rat species and age classes on
Our final model combination allowed for testing the effects of a learned response to trapping, species, age, sex, session as well as group of sessions and group of individuals on
HR95= π × (2.45 ×
HR50= π × (1.18 ×
All the analyses described above were performed with the R package ‘secr’ 4.3.1 (
Rat abundance (Abrat) in the removal area was estimated with the “Zippin removal” method, which assumes closed population within sessions and no heterogeneity in capture probability between individuals (
Dbefore = Abrat / ETA
Dafter = (Abrat – Nremoved) / ETA,
with ETA (effective trapping area: 9 ha) estimated as the size of the removal area (5.5 ha) plus a boundary strip (132.4 m) of the radius of the average
We expected to observe a change in rat population structure in the removal area in response to the removal of a large number of resident individuals (
To detect temporal changes in spatial patterns of captures on the removal trapping grid, for each trapping occasion of each removal session we first calculated the average distance between trapping stations that had captured a rat and the nearest edge of the removal trapping grid (Distrats), and then compared Distrats to the average distance of the 209 traps to the nearest edge of the removal trapping grid (Disttraps = 29.28 m). We further expected that the home range centers of rats dwelling in the
The aim of this modelling exercise was to identify the rat management strategies that would yield the highest probability of rat capture within a single trapping session while being economically viable and socially acceptable in the remote area of the Mont Panié wilderness reserve. We simulated the capture probability of one individual rat for a range of contrasted grids layouts over 200 hectares (10 different layouts; Table
Characteristics of the ten different removal trapping layouts tested in our simulation exercise. These calculations account for local social constraints in the Mont Panié area (i.e. 10 people available for 15 days and willing to work 4 hours a day).
Layout | Dist. Transects | Dist. Traps | Nb. Traps | Nb. Hours | Nb. People | Nb. Splits |
---|---|---|---|---|---|---|
1 | 15 | 15 | 8889 | 316.9 | 79 | 8 |
2 | 25 | 25 | 3200 | 169.6 | 42 | 5 |
3 | 25 | 50 | 1600 | 86.4 | 22 | 3 |
4 | 25 | 75 | 1067 | 58.7 | 15 | 2 |
5 | 25 | 100 | 800 | 44.9 | 11 | 2 |
6 | 50 | 50 | 800 | 78.2 | 20 | 2 |
7 | 50 | 75 | 533 | 53.3 | 13 | 2 |
8 | 50 | 100 | 400 | 40.8 | 10 | 2 |
9 | 75 | 75 | 356 | 51.4 | 13 | 2 |
10 | 75 | 100 | 267 | 39.4 | 10 | 1 |
Abbreviations: Dist. Transects: distance in meters between trapping grid transects; Dist. Traps: distance in meters between traps; Nb. Traps: number of traps used in each trapping layout; Nb. Hours: number of hours required to complete each grid as part of one trapping occasion; Nb. People: number of people required to complete each grid as part of one trapping occasion; Nb. Splits: number of splits required to complete each grid given number of people available.
In the absence of competition, the probability of capture of one rat with home range center at location i by trap j at time t is defined as follows (
where d is the distance between i and j, σ is the scale parameter of the detection function and g0 is the probability of rat capture at trap location j. Parameters
The probability that one rat would be captured by any one of the b traps of a given grid layout over a n-day trapping session (or cumulative probability) is then:
To assess how trapping efficiency varied across grid layouts as the trapping session progresses, we calculated the cumulative rat capture probability against time and project expenditure for each of the ten different grid layouts for one single trapping session. We estimated the average number of hours required to do a complete coverage of each grid over the entire trapping session, accounting for the decline in the number of rats captured as the trapping session progressed. The average time required to check and bait each trap was taken as 37 seconds, estimating that an empty trap that only needs rebaiting would take 30 s, and a trap where a rat had been captured would take 60 s. Based on our own field experience, we considered that it would take 10 minutes to walk 100 m through the rainforest while looking for traps. We constructed our simulation models based on the reasonable assumptions that a maximal number of 10 people would be willing to be away from their own villages for no more than 15 days, and each person would be willing to work 4 hours daily (fieldwork is rough and physically demanding) for a wage of 10 euros per hour. Our model accounted for a non-linear increase in project expenditure as the trapping session progresses, due to some additional helicopter provisioning required every 15 days in this remote part of New Caledonia (provisioning costs for 10 people and 15 days were set to be 6 000 euros). Trapping equipment and grid cutting were not accounted for in the simulations as they would need to be costed separately, for example as initial investments (i.e. before the first trapping session commences) and running costs (i.e. maintenance) over multiple sessions.
Given economic and social constraints (i.e. 10 trappers available for 4 hours/day), some of the 200-ha layouts (i.e. with the finest mesh sizes) could not be completed within one day. In our calculations, we therefore allowed the grids to be trapped as adjacent separate management units (e.g. layout 1 was treated as 8 smaller units of 25 ha), each unit being trapped at a time. This strategy inevitably required extending the overall trapping session by
Code and data for the
The best
Selection of the 10 best spatially explicit capture recapture (
|
σ | N parameters | Log likelihood | AICc | % Weight |
---|---|---|---|---|---|
|
13 | -2046.78 | 4120.64 | 29.81% | |
14 | -2045.91 | 4121.06 | 24.20% | ||
age + session |
|
11 | -2049.55 | 4121.87 | 16.09% |
14 | -2046.77 | 4122.77 | 10.28% | ||
age + sex + session |
|
12 | -2049.18 | 4123.27 | 8.01% |
age + session | 12 | -2049.55 | 4124.01 | 5.51% | |
b + |
15 | -2046.50 | 4124.43 | 4.48% | |
b + species + age + session | 14 | -2049.44 | 4128.12 | 0.7% | |
age + session | sex | 9 | -2060.66 | 4139.84 | 0 |
b + |
11 | -2060.19 | 4143.14 | 0 |
Beta parameter estimates for the best
β |
|
95% |
95% |
|
---|---|---|---|---|
|
-1.98 | 0.34 | -2.65 | -1.31 |
0.69 | 0.33 | 0.05 | 1.33 | |
-0.78 | 0.30 | -1.37 | -0.18 | |
-0.28 | 0.34 | -0.94 | 0.38 | |
-1.57 | 0.36 | -2.27 | -0.86 | |
-0.16 | 0.25 | -0.65 | 0.32 | |
0.22 | 0.26 | -0.28 | 0.72 | |
0.00 | 0.33 | -0.63 | 0.64 | |
-1.33 | 0.41 | -2.13 | -0.53 | |
|
2.70 | 0.10 | 2.50 | 2.90 |
-0.29 | 0.12 | -0.52 | -0.06 | |
0.29 | 0.12 | 0.05 | 0.54 | |
-0.17 | 0.13 | -0.42 | 0.07 |
Estimation of
Group of individuals | σ | HR95 | HR50 | |||
---|---|---|---|---|---|---|
|
|
|
|
|
|
|
|
14.87 | 12.22–18.10 | 0.42 | 0.28–0.62 | 0.09 | 0.06–0.14 |
12.46 | 10.72–14.48 | 0.29 | 0.22–0.40 | 0.07 | 0.05–0.09 | |
19.97 | 17.31–23.04 | 0.75 | 0.57–1.00 | 0.17 | 0.13–0.23 | |
11.05 | 9.75–12.51 | 0.23 | 0.18–0.30 | 0.05 | 0.04–0.68 |
Density of rats (±
While nine trapping days were required to approach a near zero capture rate during removal session 1 (10 rats were captured at day 9 out of 209 traps), this rate was achieved after only two or three trapping days during the subsequent removal sessions. Rat density at the start of our removal experiment (Dbefore: May 2015) was estimated at 32.1 ind.ha-1 (Fig.
Rat population characteristics in both the rat removal and
Date | Trapping method | Number of rats trapped | Re : Rr ratio | Juvenile : Adult ratio | Male : Female ratio | Mean weight Rr ad. (± SD, g) | Mean weight Re ad. (± SD, g) | Proportion of adult males with scrotal sac (%) |
---|---|---|---|---|---|---|---|---|
21–26 May 2015 |
|
74 | 0.24 | 0.35 | 0.95 | 158.51 ± 26.02 | 57.92 ± 5.59 | 2.86 |
28 May–05 June 2015 | Removal | 266 | 0.48 | 0.13 | 0.95 | 168.07 ± 30.04 | 62.24 ± 7.58 | 9.02 |
04–10 July 2015 |
|
36 | 0.50 | 0.42 | 1.00 | 147.00 ± 25.67 | 59.08 ± 7.80 | 22.22 |
11–15 July 2015 | Removal | 59 | 0.51 | 0.03 | 1.03 | 173.11 ± 39.64 | 66.97 ± 11.37 | 75.00 |
02–09 Sept. 2015 |
|
26 | 0.23 | 0 | 1.36 | 173.16 ± 34.34 | 67.83 ± 10.52 | 73.33 |
08–12 Sept. 2015 | Removal | 57 | 0.34 | 0.14 | 0.97 | 175.13 ± 29.98 | 79.82 ± 10.55 | 86.21 |
28 Oct.–01 Nov. 2015 | Removal | 65 | 0.47 | 0.32 | 1.62 | 177.26 ± 27.15 | 85.42 ± 9.41 | 76.32 |
During the last four trapping sessions (May to November 2015), contrasted patterns of rat juvenile proportions were observed between the
In the removal area, average distances of trapped rats to the edge of the trapping grid (Distrats) fluctuated greatly during the four removal sessions. In July, six weeks after removal trapping had been initiated, most rats were captured near the edge of the removal trapping grid (Distrats ± SD = 11.98 ± 10.34 m), whereas in May, September and November, rat captures were distributed more evenly within the removal grid (Distrats = 19.98 ± 7.55 m; 31.81 ± 8.04 m; 22.79 ± 2.24 m, respectively; see also Suppl. material
Estimated barycenters of home range centers (
A total of 27 rats equipped with PIT-tags in the
To simulate scenarios of removal efficiency relative to trapping session duration and project expenditure, we used the average value of parameters
If we were to ignore local social and economic constraints and assume that each trapping grid could be completely covered within one day, two grid layouts would allow achieving a cumulative rat capture probability ≥ .80 after 15 trapping days (Fig.
Cumulative rat capture probability over 15 trap-nights and their associated cumulative costs.
This study demonstrates that mechanical trapping can help maintain rat densities at low levels on a 5.5-ha area despite challenging environmental, logistical and social conditions. Combining live and lethal trapping experiments over 15 months in New Caledonian rainforest habitats has provided us with essential baseline rat biological parameters to inform effective management planning in tropical montane forests. Our cost-effectiveness analysis of trapping efforts also contributes to increase the evidence base that is currently lacking for improving the efficiency of rodent control projects and provides useful practical guidelines to practitioners involved in community-based pest management (
Rat population biology and dynamics on tropical rainforest islands remain less well understood than in other systems. Our
In our study conducted in the Mont Panié wildnerness reserve, black rat home ranges varied between .2 and .7 ha according to age and sex. This is much smaller than what was found in a Hawaiian mesic forest (3.8 ha;
Controlling rats for 5–9 consecutive nights every five to seven weeks over a 5.5-ha area (on a 20 × 20 m trapping grid) allowed reaching an initial 16-fold density decrease (from 32.1 to 2.6 rats.ha-1), followed by a five to fourteen-fold decrease (down to 0.6–1.6 rats.ha-1) after the following sessions, with densities going back to pre-removal levels (ca. 8 rats.ha-1) in-between sessions. This indicates that trapping every five to seven weeks is not sufficient to reach near zero rat densities in the Mont Panié area. In a mesic forest in Hawaii, rat removal efforts deployed over a 26-ha area (25 × 50 m grid with trap spacing of 12.5 m; traps checked daily for two weeks, then every two weeks) allowed maintaining rat numbers ca. 3 times lower (ca. 1 rat/100 trap.night) than the initial pre-removal state, and was shown to enhance the reproduction of an endangered endemic plant
A reduction in rodent abundance may be followed by a rapid reinvasion, induced by enhanced immigration and/or increased breeding and survival of remaining adults and juveniles (
In our study, the level of control efforts applied (5–9 trapping days every 5–7 weeks) appeared to have prevented rapid, complete reinvasion of a 5.5-ha forest area. As our removal trapping only covered the austral winter, it is possible that reinvasion rates would have been higher during the subsequent summer period. We found, however, some evidence of rats travelling from the
Dispersal is commonly observed to be male-biased and principally realized by juveniles in most rodent species, including
Our modelling exercise indicated that it is, in principle, realistic to control invasive rats over moderate-sized areas in challenging environments, using lethal trapping. For example, in the absence of local and economic constraints, a one-shot reduction in rat density of 93% was achievable over 200 ha on a 15 × 15 m grid in a total of 1585 work hours or 5 trapping days (and a 100% reduction in 3169 work hours or 10 trapping days) (see Fig.
With this study, we demonstrate that scenario modelling, informed by trapping experiments, is a flexible tool for informing the design of cost-effective control programs of island-invasive rodent populations, under idiosyncratic local circumstances. Due to rats’ productivity and reinvasion rates, a one shot reduction in density is clearly not enough to produce tangible benefits to native biodiversity. Given the prospect of assessing optimal strategies for a sustainable rat control program, acquiring rat demographic rates (productivity, survival, dispersal movements) will be helpful to explore finer components of trapping design (e.g. number of trapping sessions per season, year and habitat types) and inform on the most cost-effective trapping regime (how often to trap, for how long and where) in the long-term. While our modelling approach was developed for rats in the Mont Panié reserve, it could be easily adapted to other systems and invasive pest species that could be controlled by lethal trapping.
This study was funded by the Northern Province of New Caledonia to REFCOR project (Convention n° 12C240, 14C330 and 15C154). We are grateful to Josepho Bahormal, Hélène De Méringo, Oriana Garcia-Iriarte, Raphaël Gouyet, Matthieu Mativet, Mathilde Méheut, Martin Thibault from IRD for their help in the fieldwork, and the University of Aberdeen for hosting a visit. We thank the team of Dayu Biik NGO, Alain Couhia, Ismaël Farino, Djaèk Folger, Ronald Tein, Aldo Tiempouène, Josine Tiavouane, Silvano Wanguène for field logistics and their help in the rat capture experiments. We also thank Frederic Rigault and Jérémy Anso for their help in preparing maps and figures, Murray Efford for support with the secr analyses, and Pablo Garcia Diaz for providing comments on a draft manuscript.
Density of rats
Statistics
Density of rats (ind.ha-1) according to capture session, species, sex and age. Density was estimated with
Rat abundance and density, rat capture probabilities
Statistics
A) Rat abundance and density (ind.ha-1) before and after rat removal for the four sessions of rat removal trapping. The total number of individuals in the removal area was estimated with the “Zippin removal” method. Densities were estimated based on the rat removal grid size plus a boundary strip of 9 ha. B) Rat capture probabilities (±
Mean distances (± se) of trapped rats from the edge of the removal area during the four trapping sessions
Graphic results
Mean distances (±
Distances (in meters) travelled between rats’ home range centers in the
Statistics
Distances (in meters) travelled between rats’ home range centers in the