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Research Article
Application of genetic and Spatially Explicit Capture-Recapture analyses to design adaptive feral cat control in a large inhabited island
expand article infoClaire-Cécile Juhasz, Naïs Avargues, Laurence Humeau§, David Ringler|, Patrick Pinet, Clémence Hollinger, Richard Beaulieu#, Lucie Faulquier, Arthur Choeur, Sophie Bureau, Denis Da Silva§, Jérôme Dubos, Yahaia Soulaimana-Mattoir, Matthieu Le Corre
‡ UMR ENTROPIE, Saint Denis, France
§ Université de La Réunion, Saint Denis, France
| Kiore Services, La Castillerie, France
¶ Parc National de La Réunion, La Plaine des Palmistes, France
# Association pour la Valorisation de l'Entre deux Monde, La Plaine des Cafres, France
Open Access

Abstract

Faunas of oceanic islands have a high proportion of endemic species which contribute to the uniqueness of island communities. Island species are particularly naïve and vulnerable to alien predators, such as cats (Felis catus). On large, inhabited islands, where the complete eradication of feral cat populations is not considered feasible, control represents the best management option to lower their detrimental effects on native fauna. The first objective of our study was to investigate population genetics of feral cats of Réunion Island. The second objective was to understand the space use of feral cats established near the breeding colonies of the two endemic and endangered seabirds of Réunion Island, the Barau’s Petrel (Pterodroma baraui) and the Mascarene Petrel (Pseudobulweria aterrima). We evaluated genetic diversity, population structure and gene flow amongst six groups of feral cats located at a maximum of 10 km from known petrel colonies. We also analysed the behaviour and space use of one of these feral cat groups using camera-trap data and Spatially Explicit Capture-Recapture (SECR) models. Genetic analyses revealed that feral cats were structured in three genetic clusters explained mostly by the island topography. Two clusters were observed at five sampled sites, suggesting high connectivity amongst these sites. The last cluster was found in only one site, suggesting high isolation. This site was a remote mountain area located in the vicinity of one of the main Barau’s Petrel colonies. The behavioural study was conducted on this isolated feral cat population. Mark recapture analysis suggested that feral cats were present at low density and had large home ranges, which is probably explained by reduced food availability. Finally, we make several recommendations for refining feral cat management programmes on inhabited islands.

Keywords

camera trapping, endemic seabird conservation, Felis catus, genetic tools, invasive species control, oceanic island, SECR model

Introduction

One third of the terrestrial biodiversity hot-spots includes oceanic islands and most of them are in the tropics (Myers et al. 2000). Oceanic islands are characterised by a high proportion of endemic species (Carlquist 1974; Myers et al. 2000; Kier et al. 2009) contributing to the uniqueness of island communities (see Burlakova et al. 2011). Insular species are particularly naive and vulnerable to the introduction of exotic predators (Moors and Atkinson 1984; Medina et al. 2011; Legge et al. 2017), which are known to be the main drivers of species extinction and biodiversity loss on islands (Moors and Atkinson 1984; Courchamp et al. 2003; Leclerc et al. 2018; Russell and Kueffer 2019). Domestic cats (Felis catus) have established feral populations on many islands worldwide (hereafter referred to as feral cats; Nogales et al. 2013). They present a high invasive ability (Van Aarde 1986) and are one of the most damaging species introduced on islands (Fitzgerald 1988; Courchamp et al. 1999b; 2003, Medina et al. 2011; Nogales et al. 2013; Jones et al. 2016). This generalist and opportunist predator has caused numerous extinctions of insular species and particularly of endemic vertebrates (Nogales et al. 2004; Nogales and Medina 2009; Doherty et al. 2016). There is an urgent need to counteract the biodiversity loss due to feral cat predation by optimising methods to eradicate or regulate this invasive predator (Myers et al. 2000; Kier et al. 2009; Burlakova et al. 2011).

Feral cat eradications, which consist of a complete and definitive removal of the feral cats, have been frequently conducted on islands (see Medina et al. 2011). However, their implementation on large, inhabited islands remains challenging. The main difficulties to eradicate feral cats from large inhabited islands are low social acceptability, inappropriate legislation, lack of long-term political commitment, important financial cost and reduced technical feasibility of such large-scale operations (Oppel et al. 2010; Glen et al. 2013; Russell et al. 2018). The situation is even more complicated by the presence of domestic cats which can be accidentally culled and which permanently supplement the feral cat population through breeding (Choeur et al. 2022). One alternative to eradication is long-term control of feral cats in key areas, in order to maintain the population below a threshold that results in a low and acceptable impact on biodiversity (Doherty et al. 2017; Palmas et al. 2020). However, in most cases, controlled areas are not isolated from nearby uncontrolled areas and are continuously re-invaded by cats (Lazenby et al. 2015; Palmas et al. 2020). The re-invasion rate depends on various factors, such as the density of cats in uncontrolled nearby areas, the connectivity between controlled and uncontrolled areas and the dispersive behaviour of the cats (Palmas et al. 2020; Choeur et al. 2022). When cat control is implemented in key areas, there is a strong need to understand the individual dispersion (Pulliam 1988; Hanski 1999) and space use at global and local scales to estimate the rate of re-invasion and to optimise the cost-effectiveness of control campaigns (Palmas et al. 2020).

Population genetics is an efficient tool for informing the management of invasive mammalian species (Browett et al. 2020). Genetic-based techniques can be used to identify the origin of the invaders, to trace the invasion pathways and to appropriately target a population of manageable size with low re-colonisation risk (Robertson and Gemmell 2004; Abdelkrim et al. 2005; Rollins et al. 2006, 2009). This information can be used to design the best strategy for successful control campaign. For instance, introduced feral cats on the main island of the Kerguelen Archipelago are now well established over the entire island, suggesting that a complete eradication would be extremely difficult (Simberloff 2003; Pontier et al. 2005; Barbraud et al. 2021). However genetic analyses highlighted limited connections between sites, indicating that local control may have long-term benefits (Pontier et al. 2005). On the island of Hawai’i, the genetics of feral cat populations indicated high genetic diversity, population expansion and weak, but significant structure amongst three sites (Hansen et al. 2007). These results indicated that the most isolated site could be targeted for control (Hansen et al. 2007). On Christmas Island (Indian Ocean), no genetic structure was detected amongst feral cat populations, suggesting high connectivity and higher risk of re-invasion after local control. This indicates that, in this case, feral cats of the entire island should be removed or simultaneously controlled (Koch et al. 2020).

Camera trapping and Spatially Explicit Capture-Recapture (SECR) are effective tools to understand species behaviour and spatial ecology (Bridges and Noss 2011; Rovero et al. 2013; Royle 2015). SECR models are hierarchical models that account for both the spatial organisation and movement of individuals in relation to the placement and effectiveness of the detection devices (Kane et al. 2015). This method provides key information for designing effective feral cat control and for optimising management techniques (Robley et al. 2010; Lazenby et al. 2015; McGregor et al. 2015). Palmas et al. (2020) tested the feasibility and efficiency of an intensive control of feral cats in a semi-isolated peninsula in New Caledonia. SECR modelling suggested that feral cats recolonised the controlled area in three months, recovering to the same density as the one determined before the culling (Palmas et al. 2020). Recolonisation by feral cats was faster than expected despite the favourable geographical situation of the peninsula. In such cases, genetic studies may offer a strong benefit to management actions by highlighting the connectivity between controlled and uncontrolled populations.

Réunion Island (21°00'S, 55°39'E) is a large (2512 km2), young (about two million years) inhabited (861,200 people in 2021) volcanic island of the Mascarene Archipelago, western Indian Ocean. The topography is extremely rough with a maximum elevation of 3,071 m a.s.l., several summits above 2,500 m and three massive eroded calderas surrounded by huge vertical cliffs (of 1 km high). This topography has generated an important ecological heterogeneity (Strasberg et al. 2005). Réunion Island is part of the biodiversity hotspot of Madagascar and surrounding islands (Myers et al. 2000; Roberts et al. 2002; Mittermeier et al. 2005; Kier et al. 2009). Since the colonisation of the Island by humans in the late 1600s, this biodiversity has been strongly impacted by habitat loss, unregulated hunting and invasive species. A total of 57% (17 of the 30 species) of the native vertebrates of the Island are now extinct (Gargominy et al. 2020). One of the most problematic alien predators is the feral cat. This species was introduced in 1702 (Cheke and Hume 2010) and is known to prey upon several endemic species including the endangered Barau’s Petrel (Pterodroma baraui; Faulquier et al. 2009) and the critically endangered Mascarene Petrel (Pseudobulweria aterrima; Riethmuller et al. 2012).

The objectives of our study were to estimate the genetic connectivity and space use of feral cat populations near the breeding colonies of the two endemic petrels. Based on 10 polymorphic microsatellite markers, we evaluated genetic diversity, population structure and gene flow amongst six groups of feral cats located at a maximum of 10 km from known petrel colonies. We used feral cat capture-mark-recapture (CMR) data and SECR models to estimate the density and the home range of feral cats near the well-studied Barau’s Petrel breeding colony of Grand Bénare. We determined their density and general activity (movements and detection probabilities) in relation to the types of habitat used (trail vs. vegetation cover). Finally, genetic data and behavioural data were combined to propose an adapted feral cat control strategy.

Materials and methods

The endemic petrels of Réunion Island

The population size of Barau’s Petrel is estimated between 10,000 and 30,000 breeding pairs (Virion et al. 2020). The first breeding colony was discovered in 1995 (Probst 1995; Probst et al. 2000). Several breeding sites have been discovered since then and two of them are monitored annually: Bras des Etangs (west side of Piton des Neiges, 2,400 m a.s.l.) and Grand Bénare (2,600 m a.s.l.). The breeding habitat is characterised by steep cliffs between 2,200 and 2,800 m a.s.l. mainly covered by endemic shrubs, such as Erica reunionensis, Stoebe passerinoides and Sophora denudata (Cadet 1977; Probst et al. 2000; Strasberg et al. 2005). Barau’s Petrels breed seasonally between September and April. They completely leave their colonies after breeding. Feral cats have been reported at all known colonies. Faulquier et al. (2009) showed that feral cats established at Barau’s Petrel colonies prey intensively upon adults, inducing a strong negative impact on populations, as this long-lived species is particularly sensitive to any additive mortality on adults (Russell et al. 2009; Dumont et al. 2010). Rats and mice are also present at Barau’s Petrel colonies, at low density, probably because these altitudinal habitats are suboptimal for these species (authors’ unpubl. data).

The population size of Mascarene Petrel is estimated at 250 breeding pairs (Attie et al. 1997). Two breeding colonies were discovered in 2016 and 2017 (Virion et al. 2020; Juhasz et al., in press) and have been monitored annually since. Burrows are located on steep cliffs from 650 to 1,200 m a.s.l. where the habitat is dominated by shrubs, such as the indigenous Olea lancea and the endemic Monimia rotundifolia (Juhasz et al., in press). The Mascarene Petrel breeds seasonally between August and March. They completely leave their colonies after breeding. Preliminary studies conducted at the breeding colonies suggest that predation by exotic mammals (rats and cats) and habitat loss constitute the main threats for this critically-endangered seabird species (Juhasz et al. in press; authors’ unpubl. data).

Genetic sampling of feral cats

We sampled feral cats from July 2015 to December 2016 at six sites. Five of these sites are included in the National Park of Réunion Island and located in native mountain forests (Fig. 1A, B). The site of Cilaos is in the southernmost massive caldera of the Piton des Neiges volcano (called “Cirque de Cilaos”). The site of Les Makes is located 9 km to the southwest of Cilaos. Dimitile and Grand Bassin are located respectively at 6 and 8 km to the southeast of Cilaos. These four sites are located between 1,200 and 1,400 m a.s.l. The fifth site, named Maïdo, is a volcanic plateau sloping to the west and located 9 km to the northwest of Cilaos. Maïdo lies between 1,500 and 2,850 m a.s.l. and is characterised by subalpine shrubland. The sixth study site, Grande Anse, is a coastal peri-urban area located 20 km to the south of all other sites at 0 to 110 m a.s.l. (Fig. 1B). All sampled sites are located at less than 10 km from a breeding colony of Barau’s Petrel or Mascarene Petrel (Fig. 1B) and four of them (Cilaos, Dimitile, Grand Bassin and Maïdo) are located less than 3 km from the nearest petrel colony.

Figure 1. 

Maps illustrating A the locality of Réunion Island B genetic sampling sites, in 2015–2016 and C camera trapping sites in 2016. Each colour of dots corresponds to a different geographical area. Area codes and sample sizes are indicated in parentheses. The grey area corresponds to the National Park and the orange areas correspond to the presence of Barau’s and Mascarene Petrels. Triangles correspond to the localities of camera traps on trails (yellow triangles) and under vegetation cover (black triangles). The black lines show the trails.

Cats were live-trapped with Tomahawk cage traps baited with sardines and brought to the veterinary clinic for sanitary inspection. The veterinarian checked for individual pit-tags and tattoos to identify potential owned cats. The behavioural profile of the cat was then evaluated to estimate the possibility to adopt it. If adoption was impossible because the cat was too wild, the cat was euthanised after the legal guard period of four days. The euthanasia was made by intra-venial injection of pentobarbital. Kidney tissue samples were collected from each euthanised cat and stored in 70% ethanol at - 80 °C until laboratory analysis. The protocol was approved by the CYROI institutional ethical committee, certified by the French Ministry of Higher Education and Research (NoAPAFIS#6916-20151 00213267087 v.6). A total of 158 feral cats were trapped including 87 males, 67 females and 4 indeterminate (Table 1). None of them was identified as an owned or adoptable cat.

Table 1.

Estimates ± standard errors of genetic diversity for 10 microsatellite loci of feral cats (total Nsample = 158 individuals) in six geographical areas in Réunion Island, 2015–2016.

Area Code Nsample N AL PA AR HO HE FIS
Maïdo MAI 23 22.80±0.20 5.20±0.53 1 4.68±0.45 a 0.58±0.05 0.58±0.04 -0.02±0.03
Makes MAK 31 30.30±0.37 7.20±0.47 4 6.11±0.35 b 0.65±0.04 0.70±0.02 0.06±0.04
Cilaos CIL 22 21.60±0.31 6.90±0.41 3 6.35±0.38 b 0.68±0.03 0.70±0.03 0.01±0.03
Dimitile DIM 46 45.30±0.40 6.90±0.53 4 5.49±0.36 ab 0.68±0.04 0.66±0.04 -0.05±0.04
Grand Bassin GB 16 15.70±0.15 6.00±0.47 3 5.90±0.45 ab 0.58±0.06 0.65±0.04 0.08±0.05
Grande Anse GA 20 19.80±0.13 6.50±0.27 2 6.20±0.24 b 0.74±0.03 0.71±0.03 -0.07±0.03

Microsatellite genotyping

Total DNA was extracted from a small piece of kidney tissue using the QIAmp Blood and Tissue kit (Qiagen, Hilden, Germany). Genotyping was conducted for 10 polymorphic microsatellite loci (described in Menotti-Raymond and O’Brien 1995 for Fca43 and Fca96, Menotti-Raymond et al. 1999 for Fca31, Fca69, Fca76, Fca173, Fca275, Fca441 and Fca531 and Menotti-Raymond et al. 2003 for Fca1027) on DNA extracts from 158 individuals. A 3-primer PCR approach, using a M13 tail for the forward primer, was used for microsatellite loci amplification following Schuelke (2000). Four different dyes (6-FAM, PET, VIC, NED) were used for the universal M13 forward primer to enable fragment analysis multiplexing. Simple PCR amplifications were performed using a GeneAmp PCR System 9700 (Applied Biosystems, Waltham, Massachusetts, USA) in 10 μl reaction volume containing 5 μl of MasterMix Applied 2× (Applied Biosystems, Waltham, Massachusetts, USA), 0.3 μl of the forward primer with M13 5’-tail (1 μM), 0.3 μl of the reverse primer (10 μM), 0.3 μl of dyes (10 μM), 2.1 μl of sterile deionised water and 2 μl of genomic DNA (20–40 ng/μl). PCR amplifications were carried out under the following conditions: an initial denaturing step at 95 °C for 5 min, followed by 40 cycles of 95 °C for 30 sec, 56 °C for 30 sec and 72 °C for 30 sec and a final elongation at 72 °C for 20 min. Up to four different simplex PCR plates, each with a different dye, were mixed and PCR product sizes were determined, using a 3730XL DNA analyser (Applied Biosystems, Waltham, Massachusetts, USA) at the Gentyane platform (Clermont-Ferrand, France) and were sized with LIZ(500) standard using GeneMapper (Applied Biosystems, Waltham, Massachusetts, USA).

Genetic diversity

Evidence of null alleles, large-allele dropout and stutter peaks in all microsatellites was examined using MicroChecker 2.2.3 (Van Oosterhout et al. 2004). Each locus-pair combination was tested for linkage disequilibrium with GenePop 4.7.5 (Rousset 2008). The P-values were corrected using the Benjamini and Yekutieli (2001) method for multiple comparisons (Narum 2006). The mean observed number of alleles per locus (AL) and the number of private alleles per area (AP) were computed using GenAlEx 6.5 (Peakall and Smouse 2012). Allelic richness (AR; El Mousadik and Petit 1996), adjusted for discrepancies in sample size by incorporating a rarefaction method as implemented using FSTAT 2.9.3 (Goudet 2001), was used to make comparisons of the mean number of alleles amongst areas. The means of allelic richness amongst areas were compared using pairwise Wilcoxon’s signed rank tests with Bonferroni correction. Observed heterozygosity (HO), unbiased expected heterozygosity estimated according to Nei (1978) (HE) and Wright’s F-statistics (FIS) according to the method of Weir and Cockerham (1984) were calculated for all and each population using GenAlEx 6.5 (Peakall and Smouse 2012). Deviations from Hardy-Weinberg equilibrium (HWE) were tested for each of the six areas using the package pegas 0.13 (Paradis 2010) using R 3.2.0 (R Core Team 2021), with the exact test based on 103 Monte Carlo permutations.

Genetic differentiation and structuring

Assignment tests, based on multi-locus microsatellite genotypes, were evaluated using two different clustering approaches. First, we used a Bayesian genotype clustering procedure in STRUCTURE 2.3.3 (Pritchard et al. 2000). The admixture model was used with the LOCPRIOR setting, which considers sample location and allows structure to be detected when genetic structure is weak or when the number of loci is small (< 20; Hubisz et al. 2009). The r-index was also used to determine the relevance of the sampling location (LOCPRIOR), with low values of r indicating that sampling locations are informative to the overall model (Falush et al. 2003). Correlated allele frequencies were assumed (Pritchard et al. 2000). For each value (1–10) of the number of independent genetic clusters (K), we ran 106 iterations 10 times (after a burn-in of 105 steps). For choosing the optimal number of clusters, two criteria were used; the log likelihood given K (L(K); Pritchard et al. 2000) and the second-order rate of change of mean log-likelihood (∆K; Evanno et al. 2005). Both criteria were calculated using STRUCTURE HARVESTER online Web server (Earl and vonHoldt 2012). CLUMPAK software (Kopelman et al. 2015) was used to find the optimal individual alignments of replicated cluster analyses and to visualise the results.

Population structure was also explored by integrating spatial coordinates of samples using a Bayesian model executed in a Markov Chain Monte Carlo, as implemented in the R package Geneland 4.0.5 (Guillot et al. 2005, 2008). The geographical information was used to detect spatial delineation of genetic discontinuities, where the number of area units is treated as an unknown parameter. We ran the MCMC ten times independently to verify the consistency of the results. We used K from 1 to 10, 105 iterations with 100 burn-in generations, an uncertainty attached to spatial coordinates fixed to 200 m (i.e. the precision of our sample locations) and the maximum number of nuclei in the Poisson–Voronoi tessellation fixed to 300. The analysis was run with correlated allele frequency models, true spatial and no null allele models. Finally, all runs were examined for consistency.

Genetic differentiation amongst all pairs of areas was assessed by calculating pairwise FST values following Meirmans (2006) and pairwise Nei’s G’ST distances (Nei 1978). Statistical significance was tested by 104 permutations of genotypes amongst areas under Bonferroni’s correction, using GenoDive 3.04 (Meirmans and Van Tienderen 2004).

Pairwise genetic and geographic distances amongst sampling locations were used to test patterns of isolation by distance (IBD) using a Mantel test (Mantel 1967). We used the scaleGen function in adegenet 2.1.3 package (Jombart 2008) to calculate the Euclidean genetic distances amongst samples. Euclidian geographical distances between each pair of samples were calculated. The significance of the correlation coefficient between sample pairs was estimated using a Mantel test with 10,000 randomisations in R. In addition, we repeated IBD analyses using only the subset of natural areas to investigate the effect of geographic distance of Grande Anse peri-urban area from other areas, which might disproportionately contribute to IBD patterns.

Estimates of recent gene flow

To determine possible source populations that could be targeted for control (Rollins et al. 2006), we estimated recent migration rates amongst areas using two methods. First, we used Bayesian assignment tests with BIMr 1.0 (Bayesian Inference of Migration rates, Faubet and Gaggiotti 2008). BIMr infers the proportion of recent immigrants in a population from their genotypes and calculates corresponding asymmetrical migration rates amongst pairs of populations. BIMr determines recent migration even amongst weakly-differentiated populations (i.e. FST > 0.01) with unequal sample sizes (Faubet and Gaggiotti 2008). Due to overlapping generations in feral cats, BIMr estimates were interpreted as a relative index of recent gene flow rather than a precise estimate of gene flow in the previous generation. A burn-in period of 20,000 iterations followed by 105 iterations for each run was used. The default values were used for all other parameter settings. Migration rate estimates were obtained by choosing the run with lowest Bayesian deviance, measured by the assignment values (Dassign; Faubet and Gaggiotti 2008). Posterior mean and mode migration rates and 95% high density predictive interval (HDPI) were estimated using the package HDInterval (Kruschke 2011) in R.

Estimates of recent migration rates and approximate 95% confidence interval (CI) were also explored by the Bayesian approach as implemented in BayesAss 3.0.4 (Wilson and Rannala 2003). Five runs were first performed by changing the number of seeds (s = 10, 100, 500, 750 and 1000) to obtain a suitable convergence. The number of iterations was 106, of which 105 were burn-in and the sampling frequency was 100. Mixing parameters were 0.6 for allele frequencies, 0.9 for inbreeding coefficients and 0.5 for migration rates. The final run consisted of the same mixing parameters and 100 numbers of seeds.

Spatially explicit capture-recapture study

CMR data of feral cats were obtained during a single season of camera trap survey at Maïdo (Fig. 1C). This site was selected for: (i) the presence of scats and direct observations of feral cats, (ii) its proximity to a monitored Barau’s Petrel colony (about 5 km) where feral cat predation is known to occur (Faulquier et al. 2009), (iii) the proximity of trails which were supposed to maximise the feral cat detection probability (Meek et al. 2014; McGregor et al. 2015) and (iv) the technical feasibility. We deployed and geo-referenced with GPS (Garmin 64 s; 5 m accuracy) 20 camera-traps (9 Scoutguard-MG882K-12mHD, 10 Bushnell Trophy Cam HD and 1 Reconyx HC600 Hyperfire) from 17 February to 25 April 2016 (68 days). Camera-traps were first placed along trails (17 Feb – 23 March), then under vegetation cover (23 March – 25 April). This study period encompassed the second half of the chick rearing and the beginning of the fledging period of Barau’s Petrels. The mean distance (± sd) between cameras was 2,114.0 ± 1,273.0 m (min = 49 m, max = 5,626 m). Neither bait nor lure was used, to maintain homogeneous detection probabilities. Devices were set using a high-sensitivity trigger to capture three images per event at rapid-fire interval (0.13 s), with no delay between trigger events, to maximise feral cat identification. During the first week, half of the capture stations were equipped with two cameras placed on the opposing side of trails to capture both flanks of passing animals. Each observed feral cat was identified, based on natural marking such as spots, stripes and ocelli on both sides when possible (Bengsen et al. 2012; McGregor et al. 2015; Lavery et al. 2020; Palmas et al. 2020). A sampling occasion lasted one day (24 h, hereafter named “trap-night”; Otis et al. 1978; Wang and Macdonald 2009). For each sampling occasion, individuals were photographed (“captured”), identified (“marked”) and “released”. All feral cats previously identified and re-sighted were considered as a recapture. A capture event was defined as all pictures of unique individuals within a 30-min time period (Di Bitetti et al. 2006). Cameras were checked every 10 days to download data from memory cards and replace batteries. No feral cats were captured for the genetic study in the CMR study area during and 6 months before the behavioural study. However, from September to December 2015, four feral cats were captured at 5.9 to 7.8 km from the nearest camera trap.

The trapping effort (in trap-nights) was calculated by adding for each camera the number of days where each camera was active over the study period. The capture efficiency (in number of capture events/100 trap-nights) was calculated by dividing the number of feral cat capture events for all cameras divided by the total trapping effort and multiplied by 100 (Palmas et al. 2020).

Since we designed a short study period (to avoid emigration, immigration or mortality) and we did not consider kitten pictures in the dataset (to avoid recruitment), we applied SECR models that require a demographically closed population. These models assume no emigration or immigration, no mortality and no recruitment during each trap session (Otis et al. 1978; Efford 2004). The matrix of spatially explicit histories of capture-recaptures was constructed for each feral cat by linking each capture of each individual with the coordinates of the camera and with the occasion. Each camera was associated with a spatial covariate (trail vs. under vegetation) to check if trap location affects the detection probability. Data analyses were performed using the SECR package (Borchers and Efford 2008; Efford et al. 2009) in R 4.0.3. First, we estimated the mean maximum distance moved (MMDM) by the individuals between captures. Then, we implemented SECR models. The trap detector type « count » was chosen for the analysis, allowing more than one detection per animal. A habitat mask was used with a buffer width of 3,000 m around each camera-trap (determined with the SECR package; Efford 2021), but excluding the deep cliffs considered as inaccessible for feral cats. This resulted in a sampling area of 60.55 km2. We assumed that home ranges of feral cats were distributed following a homogeneous spatial Poisson process during the trapping period (Efford 2004; Borchers and Efford 2008; Efford et al. 2009). The half-normal detection function was selected as the most appropriate for our models. This detection function is defined by two parameters: the animal detection probability considering that the camera-trap is located at its home range centre (g0) and the movement parameter, i.e. the distance scale of the detection function (σ). Models were used to investigate the effects of camera locations (on trail vs. under vegetation) on g0 and σ. Model performances were compared using the difference in Akaike Information Criterion modified for small sample size (AICc). Each model presenting a ΔAICc < 2 was considered a competing best model. Finally, based on the estimates of the best model, we determined: (1) the site-specific population density D; in number of cats/km2 and (2) the home range (HR95) and core area (HR50) in km2 of feral cats (see Ringler et al. 2014).

Results

Genetic diversity

No null alleles, large-allele dropout nor stutter peaks were detected for the 10 microsatellite loci. The percentage of missing data was 1.58%. Linkage disequilibrium amongst loci was detected for four of the 45 loci pairs (P < 0.05), but no significant linkage disequilibrium was observed amongst any of the loci after the Benjamini and Yekutieli (2001) correction for multiple tests, suggesting that all loci were independent. The mean allelic richness (AR), based on a minimum sample size of 15 individuals, ranged from 4.7 (Maïdo) to 6.4 (Cilaos) alleles per locus and was relatively similar amongst areas, except for the less variable Maïdo area (Table 1). All areas contained one (Maïdo) to four (Makes, Dimitile) private alleles (Table 1). Observed heterozygosity for Maïdo (HO = 0.58) was similar to Grand Bassin, but it was lower than all other areas (Ho range 0.65–0.74; Table 1). Deviations from HWE were not significant for all areas (all Ps > 0.05). The raw microsatellite genotypes of the 158 individual feral cats are available in the supporting information (Appendix 1).

Genetic structuration and gene flow

Clustering of microsatellite genotypes using STRUCTURE analysis indicated that mean values of the r-index used to determine the relevance of the sampling location in the clustering analysis was low (0.31 ± 0.14), suggesting that sampling locations are informative to the model. Analysis clearly showed that the best-supported model contained three genetic clusters (maximum value of Evanno’s likelihood at K = 3, maximum value of L(K) and minimum standard deviation of L(K) at K = 3, Appendix 2). The first genetic cluster was almost exclusively detected for samples from Maïdo (91% of the genetic pool from Maïdo samples). The second cluster was detected amongst samples from Dimitile (77% of Dimitile’s samples), Grand Bassin (38%) and Cilaos (25%). The third cluster was shared between Makes and Grand Bassin (more than 90% of samples), then Cilaos and Grand Bassin (about 70% of samples) and finally Dimitile (about 18% of samples; Fig. 2, Appendix 2).

Analysis using Geneland yielded a modal number of populations with a higher proportion of three putative genetic groups (K = 3; Appendix 3: Fig. A2F). The run with the highest average posterior density was selected. Sampled feral cats were clustered into five groups. Two inferred groups (part of Cilaos and Grande Anse) had very low posterior probabilities (Appendix 3: Fig. A2D and E, respectively) and the areas of these groups were already represented in groups with strong posterior probabilities (Appendix 3: Fig. A2A–C). The last three putative groups roughly corresponded to the areas defined using the topography of Réunion Island (Appendix 3: Fig. A2A–C); Maïdo, Dimitile, and the other areas, and corresponded to the results obtained using non-spatial analysis with STRUCTURE.

Figure 2. 

Distribution of microsatellite clusters based on Bayesian clustering analysis using STRUCTURE (pies) and Geneland (coloured areas) and map of the migratory pathways suggested by BIMr and BayesAss (black arrows, the thickness is proportional to the amount of gene flow) of the feral cats (N = 158 individuals) in Réunion Island, 2015-2016. Area codes: MAI for Maïdo, CIL for Cilaos, MAK for Makes, GB for Grand Bassin, DIM for Dimitile and GA for Grande Anse.

Pairwise FST values ranged between 0.011 and 0.149 with a global FST of 0.026 (P < 0.001). The highest values were for the comparison of Maïdo to the other areas. Nei distances showed the same pattern. For both indexes, 8 of the 10 loci showed p-values less than 0.001. Based on the two differentiation index values, three groups were distinguishable: (1) Maïdo, (2) Dimitile and (3) all other areas, suggesting an isolation of Maïdo particularly and Dimitile to a lesser extent, as previously suggested by the clustering analysis (Appendix 4).

Genetic distance amongst individuals showed no significant relationship with geographic distance either at the global scales (Mantel r-test, P = 0.189) or after excluding Grande Anse (Mantel r-test, P = 0.385).

Recent mean migration rates determined using BIMr ranged from nearly zero amongst most pairs of areas to nearly 0.05 between Grand Bassin and Dimitile (Appendix 5). Based on non-overlapping 95% HPDIs, we only recorded significant asymmetric dispersal between Dimitile and Cilaos, with clearly highest migration from Dimitile to Cilaos (Appendix 5). Globally similar results were obtained using BayesAss, suggesting asymmetric dispersal between these two areas. Moreover, all the mean values of recent migration rate were clearly higher compared to those from BIMr and six values had a confidence interval not including zero (Appendix 5), suggesting the migratory pathways presented in Fig. 2.

Camera trapping and SECR results

During the camera-trapping survey, we collected 41,905 pictures including 376 (0.9%) pictures of feral cats. All photographed feral cats were identified and included in the study. Ten individuals were identified. There was no picture capturing more than one feral cat simultaneously (see details in Table 2).

Table 2.

Summary of the results obtained from both camera trapping sessions of feral cats, 2016, Réunion Island. For each session, the trapping effort corresponds to the product of the number of occasions per session and the number of active cameras. The capture efficiency is the number of detections divided by 100 trap-days.

Location Period (days) Date Number of cameras Trapping effort (trap-day) Number of pictures Number of cat pictures (%) Total number of cats Total number of recaptures Capture efficiency (detections/100 trap-days)
Trail 35 17 Feb – 23 Mar 20 550 21,524 114 (0.5%) 6 34 7
Vegetation 33 23 Mar – 25 Apr 20 532 20,381 262 (1.3%) 8 12 4
68 17 Feb – 25 Apr 20 1082 41,905 376 (0.9%) 10 50 5.5

In total, we obtained 60 feral cat detections between 17 February and 25 April 2016, corresponding to 10 captures and 50 recaptures for 10 individuals. For cameras on trails, we obtained six captures and 34 recaptures of five individuals at 14 of the 20 cameras. When camera traps were placed under vegetation, eight feral cats were first detected (including four feral cats previously identified on trails) and five of them were recaptured (12 detections) at eight of the 20 cameras (Table 2; Appendix 6). The global trapping effort was 1,082 trap-nights with a capture efficiency of 5.5 detections/100 trap-nights. The mean maximum distance moved (MMDM ± se) by feral cat was 1,926 ± 589 m. We tested the effect of camera types and the linear time trend over occasions on the detection probability g0. No effect was detected.

The model with the greatest support was the null model (Table 3). This model had a maximum detection probability at each camera trap (g0) of 0.06 (95% CI [0.03; 0.09]) and a spatial scale of movement (σ) of 971 m (95% CI [791; 1,193]; Appendix 7). The estimated population density of feral cats was 0.25 feral cats/km2 (95%CI [0.12; 0.47]). The mean home range was estimated at 15.0 km2 (HR95) with a core area of 2.5 km2 (HR50).

Table 3.

Model selection testing the spatial effect of camera trap location (on trail vs. under vegetation) on the detection parameters (g0 and σ) on Réunion Island, 2016. D is the density, g0 is for the probability of feral cat detection at the home range centre and σ is the scale parameter of the detection function. Models are ranked by their AICc values. The best model (ΔAICc < 2) is in bold.

D g0 σ Npar AICc ΔAICc W (%)
1. ~ 1 ~ 1 ~ 1 3 639.25 0.00 76
2. ~ 1 ~ location ~ 1 5 641.60 2.35 24
3. ~ 1 ~ 1 ~ location 5 649.70 10.45 0
4. ~ 1 ~ location ~ location 7 680.84 41.59 0

Discussion and conclusion

Genetic diversity, structure and gene flow in feral cat populations

The genetic diversity of feral cats on Réunion Island is similar to that observed on cat populations recently introduced on other islands (Kerguelen, Pontier et al. 2005; Hawai’i, Hansen et al. 2007; Christmas & Cocos Island, Spencer et al. 2016). It is also similar to the diversity observed in non-insular contexts, in isolated populations with low dispersal rates (France, Say et al. 2003; Australia, Cowen et al. 2019). It is assumed that most feral cats of Réunion Island are descendants of cats introduced from France (Dreux 1990). Interestingly the genetic diversity of feral cats on Réunion Island is lower than that observed in Europe (Pierpaoli et al. 2003), which may be explained by a founding effect leading to a genetic drift as expected in such isolated contexts (Slatkin and Excoffier 2012; Bélouard et al. 2019).

Microsatellite analysis and Bayesian clustering analysis suggested significant structuring amongst studied populations. Genetic structure was strong compared to populations of Hawai’i (three sampled sites, FST < 0.05; Hansen et al. 2007) and the Kerguelen Archipelago (four sampled sites, FST ≤ 0.09; Pontier et al. 2005). We found three genetic clusters amongst which, one was observed only at our highest sample site, Maïdo. This suggests very low gene flow between this site and other lower populations. The isolation of Maïdo was also supported by a lower allelic richness compared to other areas, as expected in isolated populations (Frankham 1996; Peter and Slatkin 2015). This pattern is probably due to the very rough topography of the island. Indeed, Maïdo is separated from other sites by a vertical cliff of more than 1 km, which probably represents a geographical obstacle for feral cat dispersal.

The second and third genetic clusters were detected in the five other areas (Dimitile, Cilaos, Grand Bassin, Makes, Grande Anse, Fig. 2). Although differentiation indexes indicated an isolation of Dimitile, feral cats sampled in this area were mostly assigned to a genetic group that was also detected in the four other areas. The low FST and Nei distance estimates, coupled with a lack of isolation by distance, suggest that Grande Anse, Makes, Cilaos and Grand Bassin areas were weakly isolated from each other, despite large geographical distances between Grande Anse and the others (minimum of 20 km). This result suggests either or both natural and human-mediated dispersal of feral cats amongst these areas. The human-mediated dispersal hypothesis is reinforced by the lack of genetic isolation through geographical distances, which would be expected if a progressive colonisation process occurred amongst neighbouring sites (Kimura and Weiss 1964; Slatkin 1993).

Density and home range of feral cats

Comparing home range of feral cats from the literature is challenging because of the large diversity of the methods used, ranging from GPS tracking to SECR modelling (Jones and Coman 1982; Nogales et al. 2004; Bengsen et al. 2012; McGregor et al. 2015). However, our results suggest that feral cats at Maïdo are present at low density (0.25 feral cat/km2) with large home ranges (15 km2). To our knowledge, the only cases where feral cats live in such low densities in the tropics are also in mountainous habitats (Hawai’i, Smucker et al. 2000; Goltz et al. 2008). This suggests that some bottom-up limitation due to low densities of prey are occurring in these extreme habitats, resulting in a low carrying capacity for feral cats (see Liberg et al. 2000; see Bengsen et al. 2016). Furthermore, in the case of feral cats living at seasonal seabird breeding colonies (which is the case of both petrel species), the carrying capacity of their habitats fluctuate in relation to the phenology of the petrels.

This space use strongly contrasts to that observed in a low altitude area of Réunion Island transformed by human activities. A recent study has shown that, at sea level, cat density can reach 27 ± 2 cats/km2 (Choeur 2021), with an average home range of 0.12 km2 (Choeur et al. 2022). This peri-urban habitat is characterised by extremely abundant food resources for cats including anthropogenic food wastes, supplemental feeding and introduced prey such as mice, rats and lizards.

Management implications and perspectives

The combined results of the genetic and behavioural studies of feral cats indicate that, in mountainous habitats of Réunion Island, such as Maïdo, harbouring Barau’s Petrel colonies, cats are likely to be isolated and at low density. This is favourable for long term feral cat control. The genetic isolation implies there might be a low re-colonisation rate from surrounding cat populations (e.g. Lieury et al. 2015; Millon et al. 2019; Palmas et al. 2020).

In terms of feral cat control optimisation, the CMR study produced results that can be used to design the operations. First, in order to increase the number of feral cats exposed to control devices while minimising the number of devices, we propose to use the estimated sigma to optimise the spatial arrangement of the trapping grid (Goltz et al. 2008; Bengsen et al. 2012). In our case, with such an arrangement, each trap should be set every 950 to 1,000 m. This method minimises the number of cages to deploy (which minimises the human effort) while maximising the chance of a cat encountering at least one cage in its home range. Second, we suggest deploying traps near trails as the maximum detection probability has no variation between trail and vegetation cover for this site. This grid design presents the advantage of reducing the workload and the time spent in the field by facilitating the access and maintenance of the traps. Of course, this recommendation is limited to habitats that have trails nearby. This design of device deployment can also be used after an intensive cat control to deploy camera traps for early detection of any re-invasive cats.

In addition to these recommendations, we propose trapping operations be conducted before the breeding season of Barau’s Petrels (i.e. in austral winter, July and August, which correspond to the period when food availability for cats is the lowest, because of the absence of petrels). We also recommend the implementation of an early detection protocol, based on a network of camera traps to detect any re-invasion by cats and to respond with appropriate control actions.

For other sites located at lower altitudes, the absence of genetic isolation indicates strong connectivity between feral cat populations and, thus, a high risk of re-invasion after a feral cat control. Other strategies should be adopted to prevent or limit feral cat impact: 1) permanent feral cat control at colonies and in their vicinity and 2) predator-proof fences around bird colonies (Smith et al. 2020). However, the technical feasibility and financial costs of such operations may limit their implementation on the Island.

Feral cats are known to also prey upon other introduced mammals, such as rats or mice (Faulquier et al. 2009). Thus, in theory, a control of cats may result in the release of these prey, which in turn may impact birds, the so-called “mesopredator release effect” (Courchamp et al. 1999a). However, for such a release to occur, rat or mice populations must be controlled by feral cat predation (top-down control) rather than by their resources (bottom-up control; Courchamp et al. 1999a; Russell et al. 2009; Dumont et al. 2010). In the tropical context, it has been shown that rat and mice populations are controlled mostly by their resources through rain seasonality (Russell et al. 2011), which reduces the risk of a meso-predator release in case of feral cat control (Russell et al. 2011; Ringler et al. 2015). Furthermore, for long-lived animals, like seabirds, the population growth rate is more sensitive to change in adult survival than in breeding success (Le Corre 2008; Russell et al. 2009; Dumont et al. 2010). Feral cats prey upon adults and fledglings, whereas rats prey exclusively on eggs or chicks (Faulquier et al. 2009; authors’ comm. pers.). Thus, even if rat population were released as a consequence of cat control, this would have less impact on the population growth of petrels than the impact of cats. Thus, we recommend to implement cat control at petrel colonies wherever possible.

Another more general recommendation would be to improve the public awareness and sensitisation at the scale of the entire island to stop human-mediated displacement of cats, to stop abandonment of domestic cats or kitten in the wild and to sterilise as many domestic cats as possible (Dias et al. 2017; Russell et al. 2018; Choeur et al. 2022).

Acknowledgements

This study was funded by the SMAC programme, Seabird Multidisciplinary Applied Research for Conservation, co-funded by the European Union (ERDF) and the Région Réunion. This study is a production of the European project LIFE + Petrels (grant number: LIFE13 BIO/FR/000075) co-led by the National Park of Réunion Island, the Réunion University, the Société d’Etudes Ornithologiques de La Réunion (SEOR) and the Office National de la Chasse et de la Faune Sauvage, with financial support from the European Union, the Direction de l’Environnement l’Aménagement et du Logement (DEAL) and the Conseil Départemental of Réunion Island. We thank Ségolène Praud for her help in the lab.

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Appendix 1

Table A1.

Raw microsatellite genotypes for 158 individuals of feral cats from six geographical areas, in Réunion Island, 2015–2016. Area codes are described in Fig; 1.

Samples Area 531 531 173 173 43 43 1027 1027 76 76 441 441 96 96 69 69 275 275 31 31 X Y
LP0004 CIL 137 143 137 145 148 148 258 258 152 152 174 182 226 230 110 124 156 156 251 253 339151 7664054
LP0005 CIL 143 145 145 147 148 150 258 258 152 152 174 182 236 244 110 110 156 158 235 255 338905 7664691
LP0008 CIL 137 141 137 137 136 148 258 262 150 152 174 182 226 226 110 126 0 0 235 251 338905 7664691
LP0009 CIL 133 133 137 137 148 150 262 262 154 154 170 182 238 244 110 128 156 160 235 235 342891 7664514
LP0012 CIL 133 133 137 145 148 148 256 258 152 152 174 178 242 244 110 126 156 156 235 251 342962 7664490
LP0013 CIL 133 137 141 149 148 148 258 258 150 154 174 182 230 244 110 126 156 162 235 239 342912 7664572
LP0014 CIL 137 139 137 139 148 148 258 258 150 152 174 182 226 226 110 126 156 156 251 251 339151 7664054
LP0016 CIL 145 145 125 131 140 150 250 258 152 154 174 186 226 230 110 126 156 156 255 255 341653 7663152
LP0019 CIL 133 133 137 149 148 150 258 262 154 154 174 182 242 244 110 126 156 156 235 239 342891 7664514
LP0020 CIL 143 147 137 145 148 148 258 268 138 152 174 182 226 226 110 110 154 156 235 235 338669 7662701
LP0040 CIL 133 137 139 149 148 148 258 262 150 154 182 182 0 0 110 110 156 156 235 239 342891 7664514
LP0041 CIL 143 143 141 141 148 148 258 258 152 154 178 182 236 244 110 118 156 158 235 235 343834 7664954
LP0089 CIL 133 143 125 125 138 148 258 268 148 152 174 174 226 238 110 128 156 164 243 255 343614 7660602
LP0092 CIL 133 147 137 145 148 150 258 262 154 154 178 182 226 226 124 124 0 0 235 243 341651,884 7663464,38
LP0094 CIL 133 147 137 143 150 150 258 262 138 154 174 174 226 238 118 124 156 164 249 255 341651,884 7663464,38
LP0120 CIL 133 143 143 145 138 148 258 258 150 152 174 182 232 244 110 128 0 0 251 253 343614 7660602
LP0125 CIL 137 143 137 147 148 150 258 258 144 154 178 182 234 244 118 126 156 156 235 235 343614 7660602
LP0146 CIL 133 133 143 147 148 148 262 268 148 152 182 194 226 232 124 126 156 158 235 255 341826 7660378
LP0150 CIL 143 143 137 145 148 150 256 256 148 152 174 186 232 244 126 126 162 162 235 253 342568 7660656
LP0153 CIL 143 143 145 145 142 150 258 258 124 152 174 178 226 244 110 110 156 158 235 253 341874 7660450
LP0181 CIL 133 143 143 145 140 148 258 262 144 152 182 182 226 230 110 126 152 156 235 255 342917 7663413
LP0182 CIL 143 143 125 141 148 154 258 258 152 152 178 186 226 226 110 126 156 170 243 249 340204,556 7658931,34
LP0006 DIM 133 133 141 145 148 148 256 262 152 154 186 186 226 226 110 126 156 158 235 235 343194 7656559
LP0007 DIM 133 143 125 145 148 148 258 258 152 154 174 174 226 244 110 122 156 162 235 255 344251 7656100
LP0010 DIM 137 145 145 145 148 150 262 262 152 152 174 182 226 226 110 126 162 162 251 255 342242 7656286
LP0011 DIM 137 143 145 145 148 150 258 258 152 152 186 186 232 238 124 126 156 158 235 251 345322,17 7657642,09
LP0028 DIM 133 143 143 145 148 150 256 258 152 152 174 174 226 236 110 126 156 158 235 253 344056,112 7658441,64
LP0029 DIM 137 147 145 145 148 152 258 260 152 152 174 174 226 226 110 126 156 158 235 255 344251 7656100
LP0032 DIM 143 143 141 141 150 152 258 258 152 152 178 182 226 232 110 126 156 156 235 249 343844 7655518
LP0034 DIM 137 143 145 147 138 148 256 258 152 152 174 178 226 236 126 126 156 156 235 255 343974 7655595
LP0036 DIM 137 143 137 145 148 148 258 258 152 152 182 186 232 236 124 126 156 158 235 253 345322,17 7657642,09
LP0046 DIM 133 137 125 145 148 148 258 258 152 154 174 186 232 236 110 128 156 162 235 253 345311,993 7660541,02
LP0052 DIM 143 143 125 145 148 150 258 258 152 154 174 182 226 244 110 122 156 158 235 235 344025 7656863
LP0053 DIM 133 143 125 145 148 148 256 258 152 154 174 182 226 230 122 124 162 170 235 237 344251 7656100
LP0056 DIM 139 145 137 137 140 150 258 262 152 152 178 178 232 232 110 124 156 160 235 235 343953 7655725
LP0057 DIM 143 145 143 145 148 148 258 258 152 154 174 178 226 236 124 128 156 158 235 253 343660 7656552
LP0058 DIM 147 147 145 145 148 150 256 258 0 0 174 178 236 238 114 118 156 158 243 251 344001 7655866
LP0060 DIM 133 143 145 145 148 150 256 258 152 152 174 174 0 0 124 126 158 170 235 253 344109 7656689
LP0072 DIM 145 145 145 145 148 150 258 262 152 154 182 182 236 244 110 110 0 0 235 255 344551,001 7657867
LP0074 DIM 143 145 145 145 148 148 256 258 152 152 174 174 238 240 110 124 156 158 251 251 345312,053 7660387,19
LP0075 DIM 137 145 145 145 148 150 256 258 152 154 182 186 232 236 124 128 156 158 235 253 344064,189 7658444,27
LP0076 DIM 137 143 145 145 148 150 258 260 152 154 178 186 226 236 114 126 156 158 235 251 344553,72 7657861,89
LP0085 DIM 133 143 139 145 148 150 258 262 138 152 174 178 242 244 126 130 156 160 235 255 345452,189 7660727,29
LP0086 DIM 143 143 137 137 140 148 258 260 152 152 174 178 226 236 110 110 156 162 235 235 344056 7653594
LP0087 DIM 137 145 125 147 148 148 260 262 152 152 174 174 226 226 120 122 156 162 255 255 344056 7653594
LP0088 DIM 137 143 141 147 148 152 258 258 0 0 174 178 226 232 124 126 158 158 235 235 344086 7653441
LP0096 DIM 133 143 139 145 148 148 258 262 152 152 170 186 226 236 118 120 156 158 235 235 344064,189 7658444,27
LP0103 DIM 133 145 125 145 148 148 256 258 152 152 178 186 234 236 110 124 158 162 235 235 343762 7654114
LP0109 DIM 143 145 145 145 148 148 258 258 152 152 174 182 242 244 110 110 158 170 235 251 343711 7652783
LP0110 DIM 137 137 141 143 148 152 258 260 152 152 174 174 226 226 124 126 162 162 235 255 344401 7653628
LP0114 DIM 137 147 145 145 138 148 260 262 152 152 174 174 226 226 110 126 158 162 235 255 343662 7653716
LP0115 DIM 133 137 143 147 138 148 258 268 138 152 174 182 226 236 124 128 156 162 251 255 344169 7653841
LP0117 DIM 143 143 139 147 148 150 258 262 152 152 178 182 232 236 110 110 156 158 249 257 344400 7653782
LP0119 DIM 143 145 145 147 148 150 258 258 152 154 186 186 226 238 124 126 156 158 235 253 343946 7654171
LP0122 DIM 143 143 145 145 148 150 258 260 152 152 174 182 226 244 124 128 156 158 235 253 343488 7654539
LP0123 DIM 143 143 141 141 148 152 258 258 152 152 178 178 226 236 110 126 158 162 235 249 343745 7654812
LP0124 DIM 137 145 145 147 148 150 258 260 152 152 174 178 232 236 110 124 156 158 235 235 344169 7653841
LP0133 DIM 133 143 145 145 148 152 258 262 150 152 178 182 226 244 110 124 162 170 235 235 343745 7654812
LP0138 DIM 137 145 145 145 148 148 256 258 152 152 174 174 232 238 110 110 154 156 235 235 344400 7653782
LP0144 DIM 143 143 125 125 148 150 256 258 0 0 174 174 226 244 110 122 156 162 235 235 343776 7654707
LP0147 DIM 143 143 145 145 148 148 258 258 152 152 174 182 226 244 110 118 162 170 235 253 343711 7652783
LP0151 DIM 143 143 141 145 148 148 246 262 152 154 174 182 226 226 110 110 156 158 251 253 343660 7655007
LP0158 DIM 137 145 145 145 148 150 258 258 0 0 174 186 232 238 124 126 156 158 235 251 344901,611 7657760,99
LP0164 DIM 143 145 145 145 150 154 262 268 130 150 182 182 226 234 124 126 156 158 255 255 343899 7654209
LP0165 DIM 143 145 141 147 148 150 246 258 138 152 174 182 202 232 124 126 156 156 235 243 344056 7653594
LP0175 DIM 137 143 145 147 148 148 256 260 150 152 178 182 236 238 110 124 156 158 0 0 343776 7654707
LP0177 DIM 143 145 141 147 148 150 258 258 150 152 174 182 226 236 110 114 156 192 243 249 344905,252 7657584
LP0179 DIM 143 147 141 145 148 148 258 258 150 154 174 174 226 238 114 124 156 158 243 251 343427 7655879
LP0042 GA 143 143 145 147 148 148 256 258 148 152 178 182 226 232 124 124 156 156 235 243 350858,198 7636083,08
LP0043 GA 133 139 145 147 148 148 246 262 150 154 174 174 226 226 122 124 158 158 235 255 350858,198 7636083,08
LP0045 GA 133 133 141 145 148 148 258 262 138 152 174 178 230 244 124 126 162 166 235 255 350858,198 7636083,08
LP0047 GA 143 145 145 145 148 150 258 262 146 150 178 178 226 230 120 128 156 156 235 255 350858,198 7636083,08
LP0048 GA 137 143 141 143 138 150 258 258 152 152 182 186 230 238 110 126 156 162 235 251 350858,198 7636083,08
LP0049 GA 139 143 143 143 140 142 254 258 132 150 174 178 226 240 124 126 154 158 235 243 350858,198 7636083,08
LP0050 GA 143 143 145 147 148 150 258 258 152 154 182 182 0 0 126 126 156 158 235 235 350858,198 7636083,08
LP0064 GA 143 145 143 147 140 154 258 258 146 152 174 178 226 226 120 124 156 156 249 251 350858,198 7636083,08
LP0065 GA 143 143 125 145 142 148 256 258 152 152 178 178 232 236 124 126 156 156 243 249 350858,198 7636083,08
LP0067 GA 143 147 143 145 138 148 254 268 152 152 162 178 228 230 110 110 156 158 235 245 350858,198 7636083,08
LP0070 GA 133 143 125 141 138 150 258 258 152 154 0 0 226 236 118 120 156 162 235 245 350858,198 7636083,08
LP0071 GA 133 143 141 145 148 148 258 262 152 152 174 182 236 244 110 126 156 166 235 245 350858,198 7636083,08
LP0073 GA 133 143 125 145 148 154 258 262 152 152 178 178 226 236 110 124 156 162 239 243 350858,198 7636083,08
LP0078 GA 133 143 125 137 138 146 246 262 152 152 162 178 226 226 124 126 134 156 235 255 350858,198 7636083,08
LP0079 GA 133 143 125 145 142 148 258 268 152 152 178 182 226 226 110 124 156 156 239 243 350858,198 7636083,08
LP0080 GA 133 143 125 137 146 150 258 262 152 152 162 182 226 236 118 126 156 156 235 235 350858,198 7636083,08
LP0081 GA 143 143 143 147 138 150 254 258 148 152 178 178 230 232 120 128 156 158 235 255 350858,198 7636083,08
LP0082 GA 143 147 125 147 140 146 258 258 152 154 174 178 226 232 124 126 156 166 235 243 350858,198 7636083,08
LP0083 GA 143 145 137 145 148 148 258 258 146 152 178 182 236 236 110 126 156 162 235 255 350858,198 7636083,08
LP0084 GA 143 143 137 141 138 150 246 258 152 154 178 182 226 226 120 126 134 156 235 235 350858,198 7636083,08
LP0015 GB 143 147 137 145 148 150 256 258 152 154 178 182 226 244 110 124 164 168 235 235 348970 7658171
LP0022 GB 143 147 137 147 148 148 258 266 152 152 174 182 236 236 110 116 152 156 235 253 349484 7658622
LP0030 GB 143 143 137 145 154 154 258 258 152 152 174 174 226 244 110 110 156 156 235 251 347824 7653382
LP0051 GB 141 143 125 145 142 142 256 262 150 152 174 182 226 236 124 128 156 156 253 255 349880 7659100
LP0059 GB 143 147 147 147 148 148 256 258 152 152 162 178 236 236 110 110 156 158 235 253 349720 7658792
LP0062 GB 145 147 141 145 148 150 258 258 152 152 174 174 242 244 110 120 156 158 251 251 349608 7658645
LP0063 GB 143 145 125 147 148 148 256 258 152 152 182 182 236 244 110 126 158 162 235 245 349483 7658437
LP0111 GB 143 143 137 137 140 148 258 258 152 152 174 186 226 236 114 126 156 156 253 255 347935,24 7658920,11
LP0112 GB 145 145 131 137 148 150 258 258 152 152 174 182 236 244 110 126 156 156 235 255 349481 7658623
LP0142 GB 143 143 145 147 148 148 258 266 152 152 174 178 236 236 110 114 0 0 253 255 347932 7661950
LP0145 GB 143 145 137 147 148 148 258 258 152 152 174 174 226 244 110 110 156 156 235 255 349883 7659101
LP0148 GB 143 143 137 145 150 150 258 258 146 152 174 182 226 226 110 110 156 156 235 255 348429 7661450
LP0167 GB 133 143 145 145 0 0 0 0 150 150 178 182 232 234 114 126 156 156 235 255 348116 7655939
LP0168 GB 133 143 125 145 144 148 258 258 150 152 174 174 226 230 110 110 158 162 235 243 350872 7658655
LP0169 GB 133 145 131 141 148 150 258 258 152 152 174 174 226 246 110 110 156 158 235 255 349045 7660365
LP0183 GB 139 143 145 145 138 150 256 262 150 150 178 178 202 234 124 124 156 156 235 255 349824,913 7658843,05
LP0027 MAI 133 143 145 145 144 148 258 258 152 154 174 182 230 236 110 120 156 162 235 255 333677,367 7665497,52
LP0037 MAI 133 143 137 145 148 148 258 258 152 152 178 178 226 244 120 124 134 156 235 255 332819,755 7667694,89
LP0044 MAI 143 143 145 145 148 148 258 262 152 152 182 182 236 236 118 124 156 158 235 255 336242,024 7664056
LP0054 MAI 133 133 137 145 142 148 250 260 148 152 178 178 226 230 112 118 156 158 235 255 334019,61 7658585,33
LP0068 MAI 133 133 145 145 140 148 250 258 152 154 182 182 230 236 118 124 158 158 235 235 336340,044 7663521,99
LP0069 MAI 133 143 145 145 148 148 258 258 152 152 174 178 230 236 110 118 156 156 235 235 336330,619 7663582,43
LP0093 MAI 133 143 137 145 148 148 250 258 152 154 178 182 226 230 114 128 156 156 235 235 330867,51 7669268,36
LP0104 MAI 143 143 145 147 140 150 250 258 138 152 174 182 226 236 110 128 156 158 243 255 330737,89 7668030,97
LP0105 MAI 133 133 137 147 148 148 258 258 152 154 174 178 226 244 110 110 156 162 235 235 330404,71 7669412,77
LP0106 MAI 133 133 137 137 148 152 250 260 152 152 178 178 228 230 110 124 156 156 235 235 333532 7662357
LP0107 MAI 143 143 145 145 148 148 258 258 152 152 178 178 226 244 120 128 134 156 235 235 331878 7668510
LP0113 MAI 133 143 137 145 148 148 258 258 152 152 178 182 230 244 110 124 156 162 235 235 333532 7662357
LP0126 MAI 133 133 137 137 148 148 258 258 152 152 182 182 226 230 110 124 156 156 235 235 333532 7662357
LP0131 MAI 133 143 137 145 144 148 258 262 150 154 178 182 230 244 120 124 162 162 235 235 330867,51 7669268,36
LP0132 MAI 133 143 135 145 144 148 256 258 0 0 178 182 226 226 110 120 156 162 235 235 330537,86 7670690,27
LP0134 MAI 143 143 135 145 148 148 258 262 0 0 182 182 226 244 110 124 156 158 235 255 333532 7662357
LP0136 MAI 133 143 145 147 148 148 258 260 152 152 174 174 226 236 110 110 156 156 235 253 329934,94 7667151,6
LP0137 MAI 133 143 145 145 152 152 258 258 152 154 174 178 226 236 110 120 156 158 235 255 330668,26 7667098,23
LP0143 MAI 133 143 137 145 150 152 250 258 152 152 178 182 226 236 110 110 156 156 235 235 333609 7665097
LP0152 MAI 133 133 137 145 144 148 258 262 130 154 182 182 226 230 110 110 156 162 235 235 333532 7662357
LP0154 MAI 133 143 145 145 140 148 246 262 152 152 182 182 234 236 110 126 156 156 235 235 336242 7664056
LP0173 MAI 137 143 135 145 148 148 256 258 152 152 178 182 228 230 110 110 156 160 235 235 329454,482 7666797
LP0185 MAI 133 143 135 145 148 148 260 260 144 152 178 182 228 234 128 128 156 158 235 235 330668,259 7667098,23
LP0017 MAK 143 143 127 139 148 150 258 258 152 152 174 182 226 236 110 110 156 158 235 255 336631 7657360
LP0018 MAK 133 133 127 147 146 148 258 258 152 154 178 182 230 232 112 128 156 158 235 243 337083 7656762
LP0021 MAK 143 143 145 145 148 148 258 258 150 152 174 182 230 230 124 126 156 158 255 255 337476 7654771
LP0023 MAK 143 147 145 145 148 150 256 258 132 132 162 170 236 236 110 110 156 158 235 251 336649 7657420
LP0031 MAK 143 143 137 147 148 150 258 258 150 150 178 186 226 238 110 126 156 168 239 255 335467 7658028
LP0033 MAK 133 147 145 145 140 148 258 268 152 152 182 182 226 232 110 110 156 156 235 235 336325 7657861
LP0035 MAK 133 145 125 131 138 148 256 262 150 152 182 182 226 226 110 124 154 164 243 249 336325 7657861
LP0038 MAK 133 143 139 145 148 148 258 262 150 154 178 182 226 232 110 124 156 168 235 243 337056 7656624
LP0039 MAK 143 143 137 145 138 142 256 262 138 154 174 182 0 0 110 128 156 156 235 235 335342 7658063
LP0055 MAK 143 143 145 147 148 150 258 268 152 152 174 182 226 236 110 124 0 0 235 243 334516,79 7657580,56
LP0061 MAK 143 143 137 147 148 148 258 258 148 152 172 186 226 226 110 110 156 162 243 255 336881,726 7655700,6
LP0066 MAK 143 143 137 147 138 148 258 258 150 152 172 178 226 226 110 126 156 162 243 251 337776,677 7656129,04
LP0091 MAK 143 143 137 145 138 150 258 258 150 152 178 182 226 238 126 126 156 168 239 251 334840,713 7657807,07
LP0095 MAK 133 143 131 141 148 148 262 262 132 154 174 182 234 236 118 124 134 156 235 251 336879,698 7656881,21
LP0097 MAK 143 143 145 145 142 142 246 262 152 152 182 182 232 232 124 126 156 156 235 255 335364 7658061
LP0098 MAK 133 143 131 145 148 148 258 262 150 154 174 174 226 236 110 124 156 156 235 255 336879,698 7656881,21
LP0099 MAK 133 133 127 137 148 148 246 256 150 154 178 182 226 232 114 128 156 162 235 235 337446 7656509
LP0100 MAK 133 133 137 141 138 148 256 262 130 152 174 182 224 236 110 110 134 134 235 235 337758 7656424
LP0101 MAK 139 147 127 141 148 150 256 262 152 154 174 178 0 0 110 110 0 0 235 251 336694 7655803
LP0102 MAK 133 147 137 141 148 148 262 262 152 154 178 182 230 232 110 124 156 158 235 251 335104 7658028
LP0108 MAK 133 133 137 137 138 148 258 258 152 152 162 178 236 236 110 126 134 168 235 235 336970,406 7657053,94
LP0116 MAK 143 145 141 149 148 150 258 262 152 152 172 174 202 238 124 124 158 164 247 255 337784 7656044
LP0118 MAK 143 143 137 139 150 150 258 262 152 152 174 178 226 226 110 114 156 156 235 255 337890 7655551
LP0121 MAK 133 139 137 147 146 148 256 258 152 152 178 182 232 232 110 110 0 0 235 243 337402 7655051
LP0128 MAK 133 143 127 145 148 150 258 258 152 154 178 182 226 230 110 120 156 156 235 243 334840,713 7657807,07
LP0130 MAK 139 143 143 145 148 154 258 258 150 152 174 186 226 236 110 110 164 164 235 255 337758 7656424
LP0139 MAK 133 143 145 147 142 142 258 258 150 152 162 182 226 226 126 128 156 156 235 255 337568 7656070
LP0140 MAK 143 145 125 125 148 150 258 262 0 0 174 174 226 226 110 110 158 158 243 251 337758 7656424
LP0141 MAK 133 143 131 141 148 148 262 262 0 0 174 182 226 226 110 124 156 156 251 255 336970,406 7657053,94
LP0149 MAK 143 143 137 145 148 150 246 258 132 152 174 182 226 226 110 118 156 156 251 255 336970,406 7657053,94
LP0166 MAK 143 145 125 137 148 148 246 268 150 152 182 186 226 234 110 110 156 156 235 253 337069 7656738

Appendix 2

Figure A1. 

Cluster analysis of 158 feral cats from six geographical areas, on Réunion Island, 2015–2016 A detection of the number of genetic clusters K using the log-likelihood mean values L(K) (black circles; ± standard deviation) and ΔK statistic (black triangles; Evanno et al. 2005) as derived from STRUCTURE with K ranging from 1 to 10 with each value obtained by averaging the posterior probabilities over 10 independent runs B proportional membership probability to a given genetic cluster. Colours correspond to genetic clusters. Area codes are detailed in Fig. 1.

Appendix 3

Figure A2. 

Spatial distribution of each group defined by Geneland for sampled feral cats (n = 158), on Réunion Island, 2015–2016. Black dots represent sample locations A, B, C, D and E are maps of posterior probability to belong to each group. Clusters are indicated by areas with different intensities of colour. Probability of population membership increases as shading intensity decreases (values of probability are indicated on each contour line) F shows the mode map of the posterior probability to belong to each group (see Table 1 for area codes). Unit of axis is metre.

Appendix 4

Table A2.

Pairwise FST (above diagonal) and Nei distance estimates (below diagonal) for 6 areas where feral cats were sampled (n = 158) on Réunion Island, 2015–2016. Area codes are described in Fig. 1.

Area/Area MAI MAK CIL DIM GB GA
MAI 0.120*** 0.132*** 0.145*** 0.149*** 0.136***
MAK 0.043*** 0.011 NS 0.096*** 0.036 NS 0.049 NS
CIL 0.047*** 0.003NS 0.100*** 0.055 NS 0.093 NS
DIM 0.055*** 0.030*** 0.032*** 0.056 NS 0.096***
GB 0.057*** 0.012 NS 0.018 NS 0.019 NS 0.097 NS
GA 0.048*** 0.015 NS 0.028* 0.030*** 0.031 NS

Appendix 5

Table A3.

Posterior mean and mode migration rates over the last generations amongst the six geographical groups of sampled feral cats on Réunion Island, 2015–2016. 95% high density predictive interval (HDPI) estimated by software BIMR and means of the posterior distributions of the migration rate (with 95% confidence intervals) using BayesAss are indicated. Asymmetric immigration is shown in bold text. Means values using BayesAss with a confident interval not including zero are in italic. Area codes are described in Fig. 1.

From-Into BIMr BayesAss
mean, mode HDPI 95CI mean 95CI
CIL-CIL 1, 1 [1;1] 0.681 [0.653;0.709]
CIL-DIM 6.90e-09, 1.05e-08 [2.41e-13;1.79e-08] 0.016 [-0.006;0.037]
CIL-GA 1.27e-08, 1.32e-08 [4.27e-13;3.35e-08] 0.014 [-0.012;0.040]
CIL-GB 2.70e-08, 2.94e-08 [4.56e-13;8.12e-08] 0.015 [-0.013;0.044]
CIL-MAI 4.60e-09, 1.13e-08 [3.37e-13;1.23e-08] 0.012 [-0.011;0.035]
CIL-MAK 3.19e-09, 5.80e-09 [1.09e-13;8.41e-09] 0.012 [-0.011;0.036]
DIM-CIL 0.020, 0.101 [2.08e-12;0.126] 0.186 [0.098;0.275]
DIM-DIM 0.903, 1 [0.50;1] 0.913 [0.859;0.967]
DIM-GA 1.29e-08, 1.21e-08 [1.06e-12;3.40e-08] 0.088 [-0.005;0.179]
DIM-GB 2.67e-08, 2.91e-08 [1.56e-13;8.02e-08] 0.203 [0.130;0.276]
DIM-MAI 4.66e-09, 9.99e-10 [1.98e-13;1.23e-08] 0.048 [-0.017;0.113]
DIM-MAK 3.17e-09, 3.73e-09 [3.47e-13;8.36e-09] 0.133 [0.015;0.250]
GA-CIL 6.80e-09, 2.28e-09 [3.96e-13;1.79e-08] 0.012 [-0.011;0.035]
GA-DIM 0.009, 1.16e-08 [1.23e-12;0.065] 0.007 [-0.007;0.022]
GA-GA 1, 1 [1;1] 0.680 [0.655;0.705]
GA-GB 2.67e-08, 1.42e-08 [5.34e-13;8.00e-08] 0.015 [-0.013;0.044]
GA-MAI 4.58e-09, 1.11e-08 [9.69e-14;1.21e-08] 0.012 [-0.011;0.035]
GA-MAK 3.20e-09, 1.24e-09 [1.09e-13;8.36e-09] 0.011 [-0.011;0.032]
GB-CIL 6.73e-09, 1.31e-08 [9.26e-13;1.79e-08] 0.012 [-0.011;0.034]
GB-DIM 0.047, 0.176 [1.18e-12;0.291] 0.007 [-0.006;0.020]
GB-GA 1.28e-08, 1.03e-08 [2.21e-13;3.38e-08] 0.013 [-0.012;0.038]
GB-GB 1, 1 [1;1] 0.682 [0.653;0.711]
GB-MAI 4.59e-09, 2.31e-09 [3.24e-13;1.22e-08] 0.012 [-0.010;0.033]
GB-MAK 3.20e-09, 1.58e-09 [1.52e-13;8.43e-09] 0.010 [-0.008;0.027]
MAI-CIL 6.78e-09, 2.40e-09 [4.07e-14;1.77e-08] 0.075 [-0.007;0.157]
MAI-DIM 0.008, 1.83e-08 [3.09e-12;0.051] 0.024 [-0.012;0.059]
MAI-GA 1.27e-08, 1.01e-08 [9.34e-13;3.36e-08] 0.054 [-0.023;0.130]
MAI-GB 2.64e-08, 1.23e-08 [2.92e-12;8.00e-08] 0.019 [-0.016;0.055]
MAI-MAI 1, 1 [1;1] 0.894 [0.818;0.970]
MAI-MAK 3.17e-09, 1.87e-09 [1.81e-13;8.33e-09] 0.089 [0.011;0.166]
MAK-CIL 6.72e-09, 2.31e-09 [6.59e-14;1.77e-08] 0.036 [-0.024;0.091]
MAK-DIM 0.013, 1.23e-08 [3.97e-13;0.085] 0.034 [-0.006;0.073]
MAK-GA 1.29e-08, 1.22e-08 [6.76e-13;3.423e-08] 0.152 [0.047;0.257]
MAK-GB 2.63e-08, 1.34e-08 [2.83e-12;7.89e-08] 0.065 [0.004;7.0.126]
MAK-MAI 4.63e-09, 3.98e-09 [9.623e-14;1.23e-08] 0.022 [-0.016;0.060]
MAK-MAK [9.623e-14;1.23e-08] [9.623e-14;1.23e-08] 0.747 [0.619;0.875]

Appendix 6

Figure A3. 

Plot of detection histories of feral cat over the detector maps during the study period, in Réunion Island, 2015-2016. Red crosses are for camera-trap locations and each dot represents a capture event (one colour per individual).

Appendix 7

Figure A4. 

Variation of the detection probability with the distance of the home range centre. The dark grey mark is for the value of the spatial scale of the movement parameter for a half-normal detection function.

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