Rapid Recolonisation of Feral Cats Following Intensive Culling in a Semi-Isolated Context

Abstract

Invasive feral cats threaten biodiversity at a global scale. Mitigating feral cat impacts and reducing their populations has therefore become a global conservation priority, especially on islands housing high endemic biodiversity. The New Caledonian archipelago is a biodiversity hotspot showing outstanding terrestrial species richness and endemism. Feral cats prey upon at least 44 of its native vertebrate species, 20 of which are IUCN Red-listed threatened species. To test the feasibility and efficiency of culling, intensive culling was conducted in a peninsula of New Caledonia (25.6 km²) identified as a priority site for feral cat management. Live-trapping over 38 days on a 10.6 km² area extirpated 36 adult cats, an estimated 44% of the population. However, three months after culling, all indicators derived from camera-trapping (e.g., abundance, minimum number of individuals and densities) suggest a return to pre-culling levels. Compensatory immigration appears to explain this unexpectedly rapid population recovery in a semi-isolated context. Since culling success does not guarantee a long-term effect, complementary methods like fencing and innovative automated traps need to be used, in accordance with predation thresholds identified through modelling, to preserve island biodiversity. Testing general assumptions on cat management, this article contributes important insights into a challenging conservation issue for islands and biodiversity hotspots worldwide.

Full text

Rapid recolonisation of feral cats following intensive
culling in a semi-isolated context
Pauline Palmas1,2,3, Raphaël Gouyet1, Malik Oedin1,4, Alexandre Millon5,
Jean-Jérôme Cassan6, Jenny Kowi6, Elsa Bonnaud2, Eric Vidal1,7
1Institut Méditerranéen de Biodiversité et d’Ecologie marine et continentale (IMBE), Aix Marseille Université,
CNRS, IRD, Avignon Université, Centre IRD de Nouméa, BPA5, 98848, Nouméa cedex, New Caledonia,
France 2Ecologie Systématique Evolution, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay,
91400, Orsay, France 3Univ. Polynesie Francaise, Ifremer, Ilm, Ird, Eio Umr 241, Tahiti, French Polynesia
4Institut Agronomique Néo-Calédonien (IAC), Equipe ARBOREAL (AgricultuRe BiOdiveRsité Et vALori-
sation) BP73, Païta, New Caledonia, France 5Institut Méditerranéen de Biodiversité et d’Écologie marine
et continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, Europôle de l’Arbois,
BP 80, 13545, Aix-en-Provence, France 6Direction du Développement Economique et de l’Environnement
(DDEE), Koohnê (Koné), Province Nord, New Caledonia, France 7UMR ENTROPIE (IRD-Université de la
Réunion-CNRS), Laboratoire d’Excellence Labex-CORAIL, Institut de Recherche pour le Développement, BP
A5, 98848, Nouméa Cedex, New Caledonia, France
Corresponding author: Pauline Palmas (pauline.palmas@ird.fr, palmas.pauline@gmail.com)
Academic editor: J. Jeschke |Received 26 August 2020|Accepted 26 November 2020|Published 29 December 2020
Citation: Palmas P, Gouyet R, Oedin M, Millon A, Cassan J-J, Kowi J, Bonnaud E, Vidal E (2020) Rapid
recolonisation of feral cats following intensive culling in a semi-isolated context. NeoBiota 63: 177–200. https://doi.
org/10.3897/neobiota.63.58005
Abstract
Invasive feral cats threaten biodiversity at a global scale. Mitigating feral cat impacts and reducing their
populations has therefore become a global conservation priority, especially on islands housing high en-
demic biodiversity. e New Caledonian archipelago is a biodiversity hotspot showing outstanding ter-
restrial species richness and endemism. Feral cats prey upon at least 44 of its native vertebrate species, 20
of which are IUCN Red-listed threatened species. To test the feasibility and eciency of culling, intensive
culling was conducted in a peninsula of New Caledonia (25.6 km²) identied as a priority site for feral cat
management. Live-trapping over 38 days on a 10.6 km² area extirpated 36 adult cats, an estimated 44%
of the population. However, three months after culling, all indicators derived from camera-trapping (e.g.,
NeoBiota 63: 177–200 (2020)
doi: 10.3897/neobiota.63.58005
https://neobiota.pensoft.net
Copyright Pauline Palmas et al. 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.
RESEARCH ARTICLE
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A peer-reviewed open-access journal
NeoBiota

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
178
abundance, minimum number of individuals and densities) suggest a return to pre-culling levels. Com-
pensatory immigration appears to explain this unexpectedly rapid population recovery in a semi-isolated
context. Since culling success does not guarantee a long-term eect, complementary methods like fenc-
ing and innovative automated traps need to be used, in accordance with predation thresholds identied
through modelling, to preserve island biodiversity. Testing general assumptions on cat management, this
article contributes important insights into a challenging conservation issue for islands and biodiversity
hotspots worldwide.
Keywords
Camera trap monitoring, invasive predator, invasive species control, live-trapping, SECR analysis
Introduction
Feral cats are among the most harmful invasive predators for insular native fauna (Bon-
naud et al. 2011; Medina et al. 2011; Bellard et al. 2016; Doherty et al. 2016). ey
threaten more than 430 vertebrate species, including mammals, birds and reptiles, and
are implicated in the recent extinction of 63 species (40 bird, 21 mammal and 2 rep-
tile species), i.e. 26% of recent terrestrial vertebrate extinctions since AD 1500 (Do-
herty et al. 2016; Palmas et al. 2017). Mitigating feral cat impacts and reducing their
populations has therefore become a global conservation priority (Doherty et al. 2017),
especially on islands housing high endemic biodiversity (Nogales et al. 2013). Feral cat
eradications have been successfully conducted on islands worldwide, generally resulting
in clear conservation benets for many island mammals, birds and reptiles (e.g. Camp-
bell et al. 2011; Jones et al. 2016). However, although recent management actions
succeeded in eradicating cats from small and medium-sized islands (up to 29,000ha
– Marion, Bester et al. 2002 and up to 63,000 ha – Dirk Hartog – Algar et al. 2020)
including fenced enclosures, to date feral cat eradications remain largely unfeasible on
the largest islands, particularly when inhabited (Nogales et al. 2004; Campbell et al.
2011; Oppel et al. 2011; DIISE 2020), and even harder to achieve in mainland areas.
If eradication is not feasible, population control – i.e. local limitation of predator
abundance by culling or other measures – could constitute an alternative management
strategy (Doherty et al. 2017). As for any “open” populations though, cats present a
high risk of re-invasion since they can move rapidly and over long distances (Schmidt
et al. 2007; Moseby and Hill 2011; Leo et al. 2016; McGregor et al. 2017): a typical
response to spatially restricted culling is compensatory immigration from surrounding
source populations (e.g. Lieury et al. 2015; Millon et al. 2019). Population control
may thus entail a continuous removal of individuals (Lazenby et al. 2015). is is
generally not a sustainable management strategy given the usually limited resources
and time available for such conservation programmes (e.g. Doherty and Ritchie 2017;
Venning et al. 2020). Most studies that found feral cat culling to be eective and with
a lasting impact on the cat population were examining either intensive and sustained

Eect of control on feral cat population 179
management eorts (Algar and Burrows 2004) or situations where populations are
relatively closed (e.g. peninsulas and fenced areas, Short et al. 1997; Moseby and Read
2006). Our study area, a peninsula, was chosen for its potential to act as a population
lter and limit immigration from surrounding populations (like Heirisson Prong in
Short et al. 2002, and the Tasman Peninsula in Lazenby et al. 2015).
Camera trapping and a spatially explicit capture-recapture approach (hereafter,
SECR) are novel and eective tools that are increasingly used to estimate occupancy
rates, abundances and densities for feral cats in natural areas. ey provide relevant
information for conservation practitioners (such as recolonisation rate, spatial distribu-
tion of cats) and allow for testing the eciency of culling as a management technique
(Robley et al. 2010; Bengsen et al. 2012; Lazenby et al. 2015; McGregor et al. 2015).
Surprisingly little is known about the speed with which a treated area is recolonised by
cats. is is a crucial parameter for managers to estimate how long the positive eect of
their control operations is lasting, so as to determine how frequently these have to be
repeated in order to maintain invasive predators at a low density (Denny and Dickman
2010; Leo et al. 2018). e rate of re-invasion probably depends on the abundance
of cats outside the treated area, the degree of connectivity of the treated area with the
untreated peripheral areas and the intensity of removal of individuals during culling.
Nor is there adequate data on the magnitude of control (i.e. the number of individuals
or percentage of a population to remove) required to successfully reduce the invasive
predators’ population and impacts (e.g. Reddiex et al. 2006; Kapos et al. 2009; Denny
and Dickman 2010; Walsh et al. 2012). Modelling studies can estimate optimal re-
moval rates (e.g. Lohr et al. 2013), but proper modelling requires information on
numerous parameters like the biology and distribution of both managed and sympa-
tric species, or population sizes (Leo et al. 2018). is would enable to determine the
viability of prey populations in the face of predation under dierent conditions and
management programmes (e.g. King and Powell 2011).
We report herein a short but intensive feral cat culling operation conducted at
Pindaï peninsula (New Caledonia), which is a priority conservation area for seabirds
(it hosts a large colony of Wedge-tailed shearwaters, Ardenna pacica) (Spaggiari et al.
2007). It is a case study of how ecient and durable the eects of such short intensive
operations are, taking advantage of the peninsula’s setting and simulating the typical
resources currently available to local managers of natural areas (DDEE – Province
Nord, New Caledonia).
Our specic aims were to (i) assess feral cat abundance and density, (ii) test a live-
trapping protocol and its success in controlling feral cats, (iii) test the durability of the
culling eect on feral cat abundance and densities, and (iv) derive guidance for adap-
tive and eective management.
While a compensatory eect from immigration was expected, we hypothesised
that the lower connectivity between treated and untreated areas at this peninsular tip
would limit cat re-colonisation as observed in dierent studies conducted in peninsulas
or fenced areas (Short et al. 1997; Read and Bowen 2001).

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
180
Materials and methods
Study site
e New Caledonia main island (“Grande Terre”) is an old continental island located
in the Pacic Ocean (Grandcolas et al. 2008). With an area of 16,372 km2, it houses
three main natural habitats: Dry forest, Humid forest and Maquis mosaic. e New
Caledonian biodiversity hotspot shows outstanding terrestrial species richness and en-
demism rates (Myers et al. 2000; Mittermeier et al. 2011).
Since their introduction around 1860 (Beauvais et al. 2006), cats have invaded
the New Caledonian archipelago, from seashore habitats to the highest altitude for-
est (1,628 m). A recent study showed that feral cats preyed upon at least 44 native
vertebrate species, 20 of which are IUCN Red-listed threatened species (Palmas et al.
2017). As a result, the feral cat has been listed among the ve priority invasive species
for future management in New Caledonia. e Pindaï peninsula (Northern Province)
has been identied as a priority site for feral cat management, part of a move to address
conservation issues in natural areas through expert management.
e Pindaï Peninsula (21°19.40'S, 164°57.50'E; Fig. 1), with an area of 25.6 km²,
is between 2.45 km and 3.24 km wide and a maximum 7 km long. It has a low
(<15m) canopy and mean annual rainfall of less than 1,100 mm (Jaré et al. 1993).
It is covered in dry forest composed of a mosaic of sclerophyllous and mesic forests on
Figure 1. Location of the Pindaï Peninsula and sampling design; camera trap stations (cross, n = 77),
live-trap positions (circle, n = 32), seabird colony (grey area), roads and trails (grey lines).

Eect of control on feral cat population 181
sedimentary and metamorphic rocks (Gillespie and Jare 2003; Isnard et al. 2016).
Secondary successional sclerophyllous forests dominate this peninsula with Acacia
spirorbis and Leucana leucocephala formations, and there is a large remnant of closed
sclerophyllous forest to the East and South. To implement our culling campaign, we
specically chose the southern part of the peninsula because (i) it houses the largest
Wedge-tailed shearwater colony of Grande Terre, the mainland of New Caledonia,
with about 10,000–15,000 breeding pairs present from mid-October (adult arrival) to
the end of May (juvenile edging) (Table 1; Fig. 1) (Spaggiari and Barre 2003; BirdLife
International 2016); (ii) the peninsula narrows (2.45 km) in the middle, providing
lower connectivity between treated and untreated areas ; and (iii) it aords an area of
10.6 km2 for intensive treatment, using the available human and material resources
(i.e. local managers).
Camera trapping design
40 camera traps (three were stolen during the study period) were deployed along
paths and unsealed roads according to a systematic grid covering the study area
(10.6km2). is grid was constructed on GIS (QGis 2.2.0), and was overlaid on
an aerial photograph of the Peninsula to maximise homogeneity of camera trap dis-
tribution. Automated digital cameras with ash (7), infrared ash (2), black light
(31) (CuddebackAmbush 1170, Cuddeback Attack IR 1156, Moultrie M1100i, re-
spectively) were used. To ensure homogeneous detection probabilities throughout a
camera trapping session, no baits or lures were used. Cameras were set up at a height
of between 30 and 100 cm (to cover cat body height), directed towards the track
preferentially used by cats (Turner and Bateson 2014; Recio et al. 2015), and were
checked to conrm that the camera’s shutter was triggered (Wang and Macdonald
2009; Nichols et al. 2017). ere was an interval of ten seconds between trigger
events, with three images captured in each of them, to maximise cat identication
and to reduce the risk of fuzzy pictures.
Camera trapping was conducted for 30 successive days in both sessions (Table2).
A capture event was dened as all photographs of unique individuals within a 30-
min time period (Di Bitetti et al. 2006; Farris et al. 2015). A sampling occasion was
considered as one day (24 h) (Otis et al. 1978; Wang and MacDonald 2009). Camera
traps were inspected at least once every two weeks to check battery system charge and
Table 1. Control schedule using live-traps and camera trapping according to Wedge-tailed shearwater
breeding periods. Dash indicate inter-periods.
Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.
Wedge-tailed shearwater
presence (P.) and breeding
periods
P. P.
Hatching
P. P. Juv.
Fledging
–P.
Adult
arrival
P. P.
Laying
Camera trapping – 908 trap-days – 1181 trap-days –
Feral cat control by
live-traps
– 1200 trap-days –

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
182
to download data from memory cards. At the end of each trapping period, the cameras
were retrieved and the images downloaded. e trapping eort was obtained by mul-
tiplying the number of traps by the number of active capture days over the considered
periods (Table 1). Capture per unit eort (camera trapping sampling occasion) was
calculated by dividing the numbers of trapped cats per 100 trap-days.
Feral cat trapping and culling
Cat trapping and culling were carried out for 38 days over 3.5 months (2–3 working
days per week) during the dry cold season (between mid-May and July 2015, austral
winter) in collaboration with wildlife rangers. In predator trapping, food availability
in the targeted site may be decisive for control eciency (i.e., baited traps may be
more attractive when few alternative food resources are available) (Algar et al. 2013;
Rocamora and Henriette 2015). erefore, feral cat trapping and culling were car-
ried out during the dry cold season, when resources are scarcer (i.e., before seabird
arrival, a low activity period for squamates and invertebrates and probably the lowest
rodent abundance).
Table 2. Model selection results for density estimation (SECR) using four habitat masks (ZE; study
area, ZE_AV; using MDMM pre-culling, ZE_AP; using MDMM post-culling and ZE_moy; using mean
MDMM pre- and post-culling). Models are based on Akaike’s information criterion corrected for small
sample sizes (AICc). Delta AICc is the dierence in AIC values between each model and the model with
the lowest AIC. AICcwt is the model weight.
Model
N°
Model name Model Detection function No.
Par
LogLik AICc delta
AICc
AICcwt
M1 #secr_dfn15_ZE_Buer_AP λ(0)~1 σ~1 z~1 hazard hazard rate 3 -1853.106 3712.798 0 0.5325
M2 #secr_dfn1_ZE_Buer_AP g0~1 σ ~1 z~1 hazard rate 3 -1853.236 3713.058 0.26 0.4675
M3 #secr_dfn15_ZE_Buer_Moy λ(0)~1 σ ~1 z~1 hazard hazard rate 3 -1864.527 3735.64 22.842 0
M4 #secr_dfn1_ZE_Buer_Moy g0~1 σ ~1 z~1 hazard rate 3 -1864.62 3735.826 23.028 0
M5 #secr_dfn15_ZE_Buer_AV λ(0)~1 σ ~1 z~1 hazard hazard rate 3 -1874.757 3756.1 43.302 0
M6 #secr_dfn1_ZE_Buer_AV g0~1 σ ~1 z~1 hazard rate 3 -1874.792 3756.169 43.371 0
M7 #secr_dfn1_ZE g0~1 σ ~1 z~1 hazard rate 3 -1884.633 3775.851 63.053 0
M8 #secr_dfn15_ZE λ(0)~1 σ ~1 z~1 hazard hazard rate 3 -1884.694 3775.973 63.175 0
M9 #secr_dfn2_ZE_Buer_AP g0~1 σ ~1 exponential 2 -1887.627 3779.54 66.742 0
M10 #secr_dfn16_ZE_Buer_AP λ(0)~1 σ ~1 hazard exponential 2 -1889.41 3783.105 70.307 0
M11 #secr_dfn2_ZE_Buer_Moy g0~1 σ ~1 exponential 2 -1897.213 3798.711 85.913 0
M12 #secr_dfn16_ZE_Buer_Moy λ(0)~1 σ ~1 hazard exponential 2 -1898.902 3802.091 89.293 0
M13 #secr_dfn2_ZE_Buer_AV g0~1 σ ~1 exponential 2 -1906.91 3818.106 105.308 0
M14 #secr_dfn16_ZE_Buer_AV λ(0)~1 σ ~1 hazard exponential 2 -1908.556 3821.397 108.599 0
M15 #secr_dfn2_ZE g0~1 σ ~1 exponential 2 -1920.357 3844.999 132.201 0
M16 #secr_dfn16_ZE λ(0)~1 σ ~1 hazard exponential 2 -1921.938 3848.162 135.364 0
M17 #secr_dfn0_ZE_Buer_AP g0~1 σ ~1 halfnormal 2 -1942.385 3889.055 176.257 0
M18 #secr_dfn14_ZE_Buer_AP λ(0)~1 σ ~1 hazard halfnormal 2 -1942.945 3890.175 177.377 0
M19 #secr_dfn0_ZE_Buer_Moy g0~1 σ ~1 halfnormal 2 -1946.147 3896.58 183.782 0
M20 #secr_dfn14_ZE_Buer_Moy λ(0)~1 σ ~1 hazard halfnormal 2 -1946.684 3897.653 184.855 0
M21 #secr_dfn0_ZE_Buer_AV g0~1 σ ~1 halfnormal 2 -1952.44 3909.165 196.367 0
M22 #secr_dfn14_ZE_Buer_AV λ(0)~1 σ ~1 hazard halfnormal 2 -1952.966 3910.217 197.419 0
M23 #secr_dfn0_ZE g0~1 σ ~1 halfnormal 2 -1963.612 3931.509 218.711 0
M24 #secr_dfn14_ZE λ(0)~1 σ ~1 hazard halfnormal 2 -1964.072 3932.429 219.631 0

Eect of control on feral cat population 183
Live traps (2 WIRETAINERS models, CatTrap and PossumTrap; 32 traps in to-
tal, 17 and 15 respectively of each model) were deployed across the 10.6 km2 covered
(Fig.1). e trapping density rate (3 traps per km2) was comparable to that of similar
studies (e.g. Algar et al. 2010; Lazenby et al. 2015). Traps were deployed near paths
and unsealed roads used by cats (Turner and Bateson 2014; Recio et al. 2015; Palmas
et al. 2017). ey were hidden in vegetation and out of direct public sight. Feral cats
were live-trapped during both day and night, since our study site does not house non-
target native species liable to be caught by this type of trap (Desmoulins and Barré
2005). Traps were checked and baited with oiled sh (tinned sardines) twice a day
(Peters et al. 2011).
Trapped cats were euthanised by an accredited veterinarian using rst a light anaes-
thetic via intramuscular injection of Tiletamine/Zolazepam (10 mg kg-1 body-weight),
followed by an intracardiac injection of Pentobarbital 500 mg/cat. e cats were han-
dled in compliance with the directives of the Department of Conservation’s Animal
Ethics Committee, and the traps were used in accordance with New Caledonian regu-
lations (Northern Province Environmental Code, New Caledonia).
Data analyses
Camera trapping was used to calculate three complementary indicators of population
abundance and density pre- and post-culling: (i) a general index of feral cat activity
(GI), (ii) the minimum number of feral cats present in the study area (MKTBA), and
(iii) feral cat absolute density (SECR).
e general index (GI) allowed us to estimate feral cat activity over the study area
by measuring the mean of virtual camera capture events per station and per sampling
occasion. is index follows the equation of Engeman (2005):
11
11
sj
d
ij
ji
GI x
d sj
==
=∑∑ ,
with d = the day, s = the station, and xij the number of captures at the ith station on
occasion jth.
To compare the GI calculated before and after culling, we used bilateral mean
comparison: t-test with Welch approximation for unequal variance.
Camera-trapped cats were identied based on distinct natural markings (Karanth
and Nichols 1998; Bengsen et al. 2012). First, adult cats were classied by coat colour
and patterns on left or right anks. en morphological criteria were used: number,
shape, dimension and position of stripes, bands and spots on the trunk and limbs;
number and shape of rings on the tail; body signs such as scars or other distinctive
traits; and sex (observation of the genital area or female with cubs). Pictures from each
session were sorted into folders, one for each potential individual (McGregor et al.

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
184
2015). All identication folders were checked twice, by two dierent operators, for any
inconsistencies requiring the pictures to be reassigned. e folders were then reviewed
by another operator for validation.
Culled cats were identied using the same morphological criteria from the pictures
of both anks to (i) identify cats camera trapped during the pre-culling session and (ii)
match right- and left-ank pictures of the same individual from the pre-culling pictures.
e minimum number of feral cats known to be alive (MKTBA, Lazenby et al.
2015) was calculated as the total number of individuals identied from one side (left
or right side of all cat pictures). is ensured the identication of a maximum of in-
dividual cats. Since uniformly black cats are very dicult to identify individually, we
assumed that our number of dierent black individuals was an underestimation.
Spatially explicit capture-recapture models were applied to capture-mark-recapture
data to provide population density estimations (Eord et al. 2015). is allows not
to use the study area calculation as a density reference (a major bias) and gives greater
exibility in study design (Eord et al. 2009). SECR models require that: (i) every
animal has a non-zero probability of encountering a camera trap station during the
sampling period (Karanth and Nichols 1998), (ii) the location and density of stations
ensure that any feral cats (adult) can be photographed from at least two camera trap
stations (Foster and Harmsen 2012; McGregor et al. 2015), and (iii) sampling design
maximises capture probabilities (Burnham et al 1987). SECR estimations also require
encounter histories for density calculations (Eord et al. 2015; McGregor et al. 2015).
Here, such histories were built separately for pre- and post-culling sessions by divid-
ing each of them into a series of 25 and 35 days, respectively (one sampling occasion
corresponding to 24 h). is involved identifying each cat as observed or not, with
the location of the camera trap. Cat density was estimated using the ‘secr’ library in R
(Eord 2020). To avoid bias linked to low condence in identication of black cats,
the latter were excluded from the analyses (McGregor et al. 2015). Excluding black
cats from SECR analyses reduced photo capture events by 13.05%, while black cats
accounted for 11.1% of total culled cats.
e sampled population was assumed to be demographically closed during each
camera trap session, based on the fact that (i) kittens were not considered in the analyses
(Otis et al. 1978; McGregor et al. 2015 who used a 3–6 week survey period and SECR
analysis for closed populations), (ii) there was a very low probability of mortality over
the period considered, as this site houses no cat predators and is infrequently used by hu-
mans. e spatial-history capture matrix for camera trapping data was then constructed
by linking each capture of each individual with the respective coordinates of the camera
station and j-occasion, which covered 24 h. Trap detector type ‘count’ was chosen for the
SECR analysis (allowing for multiple detections of the same individual within the same
occasion, and including the two camera trapping sessions within the same analysis).
We evaluated six dierent spatial detection functions (half-normal, hazard half-
normal, hazard rate, hazard hazardrate, hazard exponential, exponential), using two
dierent functions for the distribution of home range centres: (i) a Poisson point

Eect of control on feral cat population 185
process (Borchers and Eord 2008) and (ii) a binomial point process (Royle et al.
2009). We created four habitat masks using (i) the Mean Maximum Distance Moved
(MDMM), the average maximum distance between detections of each individual
(Otis et al. 1978), and (ii) the function SECR which excludes areas inaccessible to cats
(open water) (Oppel et al. 2012). is yielded twenty-four dierent candidate models
using all combinations of detection functions and masks. Root Pooled Spatial Variance
(RPSV) was used to measure the dispersion of the sites where individual animals were
detected, pooled over individuals (Calhoun and Casby 1958; Slade and Swihart 1983;
Eord 2011). Mean home ranges pre- and post-culling were calculated using MDMM
estimations (O’Connell et al. 2010).
SECR models were compared using delta-corrected Akaike Information Criterion
(AICc) values and selected using the weighted AIC (AICwt) of each model (Burnham
and Anderson 2002).
We then compared home range at individual level between the two sessions. Home
range was calculated per individual using a Minimum Convex Polygon estimator
(MCP 95%) and the “sf” package (Pebesma 2018), and compared using mean com-
parison analysis after checking that variance is homogeneous. Individuals with more
than three dots from three dierent detectors out of alignment were kept. Generalized
Linear Models (GLM) were run to test the eect of period on home range size. A
Gaussian distribution and ‘weights’ option were used.
Residual homoscedasticity and normality were assessed via Q-Q plots and Shapiro-
Wilk tests. All statistical analyses were conducted with R 3.0.3 software (R Core Team
2014), using ‘‘ade4’’ (Chessel et al. 2004), “pROC” (Fawcett 2006) “plyr” (Wickham
2011), ‘‘varComp’’ (Qu et al. 2013), ‘‘maptools’’ (Bivand and Lewin-Koh 2013) and
‘‘GISTools’’ (Brunsdon and Chen 2014) packages. For all analyses, signicant relation-
ships were inferred at α = 0.05.
Results
Camera trapping
ere were 908 camera trap-days in the pre-culling session and 1181 camera trap-days
in the post-culling session. ese yielded 473 feral cat detections from 51 of the 77
stations for pre-culling and 514 feral cat detections from 35 of the 40 stations for post-
culling (Fig. 2). e camera trapping rates for the pre- and post-culling sessions were
50 and 43 detections/100 trap-days, respectively. Feral cat camera trapping rates varied
spatially between pre- and post-culling sessions (Fig. 2).
Camera trapping yielded 416 feral cat pictures showing identiable cats (209 left-
anked and 207 right-anked). Pictures of cats’ left ank, matched with the corre-
sponding right ank, were used for the pre- and post-culling camera trap analyses
MKTBA and SECR.

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
186
ere was at least one uniformly black individual in the pre-culling session and two
in the post-culling session, one of which was distinguished by distinctive damage to
its tail. Uniformly coloured (here black) cats’ pictures were not included in the SECR.
Live-trapping
A total of 36 cats were trapped and culled during the campaign (26 females, 10 males),
with a trapping eort of 1200 trap-days representing a capture per unit eort of 3
trapped cats / 100 trap-days. Females comprised 72.2% of all captured cats. e trap-
ping campaign culled 44% of the feral cats previously identied by the pre-culling
camera trap survey.
Culling effect on cat indices and density
e General Index (GI ± S. E) did not dier signicantly between pre- and post-cull-
ing sessions (t = 1.28, df = 37, p-value = 0.21), with respectively 0.50 ± 0.24 and 0.43
± 0.15 virtual capture per sampling occasion per station (Suppl. material 3: Fig. S3).
A total of 40 dierent cats (MKTBA) were identied over the whole study pe-
riod, with 25 and 23 dierent individuals from pre- and post-culling camera trap
Figure 2. Variation in number of camera trapping events (black circles) and number of cats individually
identied at camera trap stations pre- (a) and post- (b) culling. e sizes of black circles are proportional
to the number of camera-trap capture events per sampling occasion. Camera trap stations; temporary
locations (white stars), permanent locations (white points).

Eect of control on feral cat population 187
sessions, respectively. Eight individuals (29%) were identied during both pre- and
post-culling periods.
Of the twenty-four models tested (Table 2), model M1 (parameters: “hazard haz-
ard rate” function, a probability function of λ(d) and mask « ZE+Buer S2 ») and
model M2 (parameters: “hazard rate” function, a probability function of g(d) and
mask « ZE+Buer S2 ») gave the best estimation of cat densities. Model M1 showed a
ΔAICc = 0 and AICwt = 0.53, and Model M2 showed a ΔAICc = 0.26 and AICwt =
0.47 (Table 2). ese two models yielded very similar parameter values (λ(0), g(0), σ,
z) and densities (Table 3).
Estimated feral cat densities (D ± S. E.) were 1.60 ± 0.33 adult cats/ km2 pre-
culling and 1.38 ± 0.30 adult cats/ km2 post-culling. e movements and home range
of feral cat populations did change following culling. Root Pooled Spatial Variance
(RPSV) was higher post-culling, with 752.2 m pre-culling and 878.9 m post-culling.
e mean home range estimation using MDMM was more than twice as high post-
culling (0.95 km² pre-culling and 2.21 km² post-culling). Mean home range (95%
MCP) did not dier signicantly between sessions, but appeared slightly higher post-
culling (0.784 ± 0.338 km² pre-culling and 0.827 ± 0.351 km² post-culling). Before
culling, the highest numbers both of detections and of identications of individual
cats were in the South of the Peninsula, around the seabird colony. After culling, the
highest numbers of detections were in the North-West of the study area and the high-
est number of individually identied cats in the North-West and North-East (Fig. 2).
Discussion and conclusion
e camera trapping method provided adequate cat detection, enabling us to estimate,
for the rst time, accurate cat densities in New Caledonia. It also provided an eective
way to monitor variations in feral cat abundance, as in previous studies (e.g. Comer et
al. 2018). Moreover, this trapping design enabled us to live-trap cats with a success rate
within, or even slightly above, the range of other studies using wire cage traps (Algar
et al. 2010; McGregor et al. 2015; Lazenby et al. 2015). is short but intense culling
of resident feral cats proved to be eective in rapidly reducing the target population.
Table 3. Mean Maximum Distance Moved (MDMM), the average maximum distance between detec-
tions of each individual (km2) and feral cat density estimations (number of individuals per km2) pre- and
post-culling of feral cat populations. Results are given for the best SECR models; Model 1 (M1) and
Model 2 (M2) according to AIC criteria.
Model Session MDMM (km²) Density ± S. E (cat.
km-2)
Inf. limit 95% Sup. limit 95%
M1 Pre-culling 11.00 1.601 ± 0.327 1.077 2.380
Post-culling 16.68 1.379 ± 0.301 0.903 2.105
M2 Pre-culling 11.00 1.600 ± 0.327 1.077 2.379
Post-culling 16.68 1.378 ± 0.300 0.903 2.104

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
188
However, three months later, the dierent cat population indicators calculated post-
culling showed little dierence from those calculated pre-culling. Our culling cam-
paign simulating the resource eort that might currently be expected from local natu-
ral site managers failed to reduce the feral cat population over the mid-term. Despite
the favourable peninsula setting, this cat population recovered through recolonisation
faster than expected. e natural geography of the site, a semi-isolated peninsula, did
not limit connectivity between the treated and untreated feral cat sub-populations.
Camera trap monitoring: advantages and consistency of the three indicators
Camera trapping at our study site resulted in a high level of feral cat detection, simi-
lar to or even higher than in studies using either un-baited or baited camera trap-
ping methods. e high level of detection, and the high number of individual cats
identied from at least two dierent stations, met the two requirements for accurate
SECR calculations (Eord et al. 2015; McGregor et al. 2015). In addition, camera
trap capture probabilities were optimised in this study by positioning camera trap
stations close to open roads and tracks. us, we were able to almost systematically
observe pictures of the stripe patterns on cat legs, which are considered to be suitable
for individual identication (Bengsen et al. 2012). However, more pictures of cats’
two anks could be obtained by using paired cameras at each camera station (Karanth
and Nichols 1998; McGregor et al. 2015), which would further improve cat iden-
tication. Moreover, all undistinguishable black cats were excluded from MKTBA
and SECR analysis. Future studies could usefully attempt to incorporate uniformly
coloured cats in analysis when they represent a signicant proportion of the popula-
tion, for example by using robust home range data based on a sample of GPS-tracked
animals (e.g. Bengsen et al. 2011). Our camera trapping method provided an eective
way to monitor variations in feral cat abundance, and the consistency of its estima-
tion calculated with GI, MKTBA and densities via SECR should prove widely useful.
e GI could be used to monitor changes in the feral cat population as an alternative
to SECR estimations, which require more time and can be used to respond to more
specic research questions (Bengsen et al. 2012; Legge et al. 2017). However, conclu-
sions are often based on relative abundance indices, and this kind of index does not
consider important parameters such as variable detection (Sollman et al. 2013). Since
relative abundance indices do not systematically reect dierences in density (Sollman
et al. 2013), a valuable avenue for future research would be to compare these dierent
indices. In particular, we recommend that in areas of interest to managers, the rst
step should be to calculate all of the dierent indices (GI, MKTBA, densities). Sec-
ond, the relationship between GI and the other indices should be determined; if GI is
suciently reliable and in line with the densities estimated by SECR, only GI should
be used. For this reason, we advocate hand-in-hand collaboration between researchers
and managers from project set-up to evaluation of management results, especially in
such remote areas (Meyer et al. 2018).

Eect of control on feral cat population 189
Effect of culling on cat abundance/density over time
ree months after the end of the culling campaign that eliminated 36 cats over
10.6km2, no meaningful dierences in the relative abundance and density of feral cats
were observed in response to culling, whatever the indicator of population size consid-
ered. e abundance index (GI) indicated a similar cat presence in the peninsula, the
minimum number of individuals (MKTBA) decreased by only 8%, and estimated feral
cat densities (SECR) were similar between the two sessions. No lasting eect of culling
eort was therefore observed, despite the intensity of trapping and of traps deployed.
e recovery of the feral cat population is probably attributable to the immigra-
tion of new individuals rather than to a demographically-dependent process, as cat
detections were mainly recorded in the North of the peninsula during the post-culling
session. Culling operations could have removed dominant individuals whose extirpa-
tion enhanced the permeability of the population to young individuals. In fact, the
abundance and distribution of feral cats are partly controlled by territorial behaviour
and social interactions (Goltz et al. 2008). Removing dominant individuals could in-
crease numbers, particularly of sub-adults (e.g. Lazenby et al. 2015) presenting lower
home-range delity than adults and still seeking and delimiting their home ranges
(McGregor et al. 2014). e probable attractiveness of the tip of this peninsula, with
its large shearwater colony, could explain the rapid recolonisation of the culled area and
the changes observed in activity patterns.
Post-culling, estimated home range and RPSV (Root Pooled Spatial Variance)
increased by approximately 132% and 16.8% respectively. We also observed a
trend towards a higher home-range Minimum Complex Polygon (MCP). Taken
together, these ndings may indicate that the cats recolonising the peninsula are
largely young males travelling long distances in search of a territory (Algar et al.
2013; McGregor et al. 2014). ese results could also support the hypothesis that
the remaining cats may increase their range post-culling, having to move farther
to access mates. Male territories are primarily determined by access to females,
whereas female territories are primarily determined by prey availability and dis-
tribution of other females (Liberg et al. 2000; Turner and Bateson 2014). For this
reason, the cats increasing their range in our study are more likely to be males, since
we removed more females. e female-biased sex ratio of culled feral cats prob-
ably reects a trapping bias due to dierences between male and female behaviour
(females may seek food resources more actively due to reproductive costs, “sex-
bias” on trap attractiveness may also be linked to trapping method), rather than a
disproportionate number of females (Molsher 2001; Short and Turner 2005; Algar
et al. 2014). If future studies show a female-biased sex ratio, however, this would
suggest faster population growth than with a non- or male-biased sex ratio (Short
and Turner 2005). In any case, trapping more females could signicantly contrib-
ute to controlling cat population dynamics, which suggests that trap attractiveness
to females might be worth investigating.

Pauline Palmas et al. / NeoBiota 63: 177–200 (2020)
190
Culling may provide a greater access to resources for the remaining local cats, thus
promoting juvenile survival, although this would probably be more pronounced at a
larger temporal scale. Since we only measured density across one season, we are unable
to identify possible season-related or breeding-related changes in cat density.
While recovery or even increases in populations due to compensatory demograph-
ic response have been documented for numerous species, in contrast to our study, these
were observed following low-level culling (Sinclair et al. 2006; Lazenby et al. 2015).
Fortunately, most studies report a post-culling reduction in feral cat numbers, al-
though often after an intensive and sustained control eort (Algar and Burrows 2004)
or in situations where populations show limited population ows (e.g. peninsulas and
fenced areas, Short et al. 1997; Moseby and Read 2006).
Local and general implications for feral cat management
Camera trapping yields data on pre-culling population density, key information for
scientists and managers who aim to control invasive predators. We provide here the
rst feral cat density estimates from New Caledonia. At our study site, feral cat density
was estimated to be relatively high compared to many places in Australia (Bengsen
et al. 2012; McGregor et al. 2015; Hohnen et al. 2020) and on two Salomon islands
(Lavery et al. 2020). However, it is lower than at other locations: one Salomon island
(Lavery et al. 2020), Great Britain (Langham and Porter 1991), Europe (Liberg 1980),
New Zealand (Macdonald et al. 1987), United States (Warner 1985), and highly mod-
ied landscapes in Australia (Legge et al. 2017). According to the model by Legge et al.
(2017), the feral cat density at Pindaï Peninsula (1.6 cats/ km²) is higher than expected
(0.5–1 cat/ km²). is unexpected density illustrates the importance of specically
evaluating animal densities at each site before management actions start, especially
given that New Caledonia tends to use base data from Australia. e higher density
found here and the rapid return to initial densities argue for increasing the intensity
and/or duration of trapping, which we calculated based on mean densities found in
the literature.
As we co-conducted an intense but short culling eort, our trapping success is
similar to that reported in comparable studies using wire cage traps (Algar et al. 2010;
Lazenby et al. 2015; McGregor et al. 2015). e culling of 44% of camera-trapped
feral cats is within or slightly below the range of other studies (e.g. 65% for Kangaroo
Island in Bengsen et al. (2012), 44% and 56% for the Mount Field and Tasman Pen-
insula sites in Lazenby et al. (2015)). is culling eort can therefore be concluded to
have been eective, but should be implemented longer (i.e. continuously) if possible,
using more cage traps and at peninsula scale. Our ndings support the view that lethal
control in unfenced areas needs to be intense and continuous to reduce populations
of resident animals, and immigration from the perimeter of core conservation areas
needs to be limited (Veitch 1985; Norbury et al. 1998; Short et al. 1997; Edwards et al.
2001; Campbell et al. 2011; McCarthy et al. 2013). is applies even when recolonisa-
tion seems low due to the natural geography of the site, like a peninsula. Intense lethal
control could be implemented during the presence of Wedge-tailed shearwaters in the

Eect of control on feral cat population 191
Pindaï peninsula colony, but their long breeding cycle (from October to May) makes
this type of annual control costly and labour-intensive. Moreover, it is likely to result
in large numbers of trap-shy feral cats (Parkes et al. 2014). We also recommend acting
on a larger spatial scale, i.e. on the scale of the whole peninsula, which is rather wide
and short compared to other peninsulas (e.g. Heirisson Prong in Short et al. 2002 and
Tasman Peninsula in Lazenby et al. 2015).
For several years, innovative technical solutions have been sought to optimise
the management of feral cats. ese include both baiting and trapping strategies, as
well as the development of ecient baits (e.g. Eradicat and Curiosity baits) and of
automated traps that specically recognise and poison feral cats (Algar et al. 2011;
Johnston et al. 2011; Fisher et al. 2015; Fancourt et al. 2019; Read et al. 2019; Mo-
seby et al. 2020). Other highly innovative genetic, cellular or behavioural methods
are also being developed and oer promise for controlling feral cats in the future
(Kinnear 2018; Moro et al. 2018). An interesting physiological and behavioural
method called “Toxic Trojan prey”, based on making the prey of feral cats speci-
cally toxic to them, could be considered for feral cat control on our study site (Read
et al. 2016).
Guard dogs could also be trained to protect wildlife and to prevent predation by
feral cats on the Wedge-tailed shearwaters’ breeding colony, as reported in two cases
in South-West Victoria involving little penguins Eudyptula minor and gannets Morus
serrator (van Bommel et al. 2010; Doherty et al. 2016). Exclusion fencing, widely
used in Australia and New Zealand to protect biodiversity (Long and Robley 2004;
Woinarski et al. 2014), might be another eective way to limit the recolonisation
process that is particularly protable and ecient in the peninsular context (Young et
al. 2018; Tanentzap and Lloyd 2017). Last but not least, modelling approaches can
provide numerical estimates of parameter values (e.g. predation rate) beyond which
the prey population will decrease and/or cannot be sustained (Keitt et al. 2002; Peck
et al. 2008; Bonnaud et al. 2009). Knowing such threshold values would support and
greatly improve future management decisions.
Acknowledgements
is study was funded by Province Nord (Contracts N°14C330, 15C331). We are very
grateful to Corentin Chaillon, Agathe Gerard, Mathieu Mathivet, Edouard Bourguet and
Province Nord landowners for providing support in eldwork. We are also very grateful
to the veterinary sta of Koné, particularly Henri Lamaignère and Yann Charpentier, for
handling the feral cats. We thank Marjorie Sweetko for English language editing.
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Supplementary material 1
Figure S1
Authors: Pauline Palmas, Raphaël Gouyet, Malik Oedin, Alexandre Millon, Jean-Jé-
rôme Cassan, Jenny Kowi, Elsa Bonnaud, Eric Vidal
Data type: gure
Explanation note: Box plot home range MCP pre post.
Copyright notice: is dataset is made available under the Open Database License
(http://opendatacommons.org/licenses/odbl/1.0/). e Open Database License
(ODbL) is a license agreement intended to allow users to freely share, modify, and
use this Dataset while maintaining this same freedom for others, provided that the
original source and author(s) are credited.
Link: https://doi.org/10.3897/neobiota.63.58005.suppl1
Supplementary material 2
Figure S2
Authors: Pauline Palmas, Raphaël Gouyet, Malik Oedin, Alexandre Millon, Jean-Jé-
rôme Cassan, Jenny Kowi, Elsa Bonnaud, Eric Vidal
Data type: gure
Explanation note: Accu curve preculling.
Copyright notice: is dataset is made available under the Open Database License
(http://opendatacommons.org/licenses/odbl/1.0/). e Open Database License
(ODbL) is a license agreement intended to allow users to freely share, modify, and
use this Dataset while maintaining this same freedom for others, provided that the
original source and author(s) are credited.
Link: https://doi.org/10.3897/neobiota.63.58005.suppl2
Supplementary material 3
Figure S3
Authors: Pauline Palmas, Raphaël Gouyet, Malik Oedin, Alexandre Millon, Jean-Jé-
rôme Cassan, Jenny Kowi, Elsa Bonnaud, Eric Vidal
Data type: gure
Explanation note: Accu curve livetrapping.
Copyright notice: is dataset is made available under the Open Database License
(http://opendatacommons.org/licenses/odbl/1.0/). e Open Database License
(ODbL) is a license agreement intended to allow users to freely share, modify, and
use this Dataset while maintaining this same freedom for others, provided that the
original source and author(s) are credited.
Link: https://doi.org/10.3897/neobiota.63.58005.suppl3