Fragmentation and low density as major conservation challenges for the southernmost populations of the European wildcat.

Knowledge of population dynamics of threatened species in the wild is key to effective conservation actions. However, at present, there are many examples of endangered animals for which their current situation is unknown, and not just in remote areas and less developed countries. We have explored this topic by studying the paradigmatic case of the European wildcat (Felis silvestris silvestris), an endangered small carnivore whose status has been subjectively established on the basis of non-systematic approaches and opportunistic records. Little is known about its demographic situation, prompting the need for information to improve conservation measures. However, the secretive behaviour of felines along with its low density in natural conditions have prevented the gathering of sufficient data. We developed a field sampling strategy for one of the largest populations (Andalusia, South Spain, 87,268 km2), based on a logistically viable systematic non-intrusive survey by camera-trapping. This study offers the first large-scale estimation for any European wildcat population, based on analytical approaches applied on Species Distribution Models. A hierarchical approach based on a Maxent model for distribution estimation was used, along with Generalised Linear Models for density estimation from explicit spatial capture-recapture data. Our results show that the distribution range is smaller and more highly fragmented than previously assumed. The overall estimated density was very low (0.069 ±0.0019 wildcats/km2) and the protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus in demographic terms. Indeed, the most important areas remain unprotected. Our main recommendations are to improve the protected area network and/or vigilance programs in hunting estates, in addition to studying and improving connectivity between the main population patches.

Introduction

Knowledge of the population dynamics of threatened species in the wild is key to effective conservation actions [1, 2]. While this is an obvious idea, at present, there are many examples of endangered animals for which their current situation is unknown, and not only in remote areas and less developed countries. This is the paradigmatic case of the European wildcat (Felis silvestris silvestris), an emblematic feline that has been the target of several studies on its ecology (e.g. [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]) and especially, on its problematic hybridization with domestic cats [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Today, it is assumed to be an endangered taxa in most of the countries in which it lives [28]. It is a legally protected species in Europe, both through the Bern Convention and the European Habitats Directive, which consider the wildcat as “strictly protected”. The available distribution maps show a severely fragmented range, with the main patches in the Iberian Peninsula, France, Germany and the eastern countries of Europe (see a compilation in [28]). However, most of these maps (if not all) have been subjectively built on the basis of non-systematic approaches, using opportunistic records which could be assumed to be unchecked following the detailed phenotypic examination needed for hybridization detection [29, 30, 31]. In fact, these kind of data have traditionally been the sole source of information to define the wildcat's conservation status (see e.g. the case of Spain, where one of the largest populations survives; [32]). Hence, we must ask what is the current situation of the European wildcat populations? More simply, how many “pure” European wildcats are left and how many live under the umbrella of the protected areas network, such as in national parks or other reserves? A paradoxical situation has arisen in which the ecology of the subspecies is well-known, due to ample research on this topic, while very little is known about the species' demographic situation; at the same time, answers to the latter question are critical for the effective conservation of these endangered European wildcats. However, the secretive behaviour of felines along with its low densities in natural conditions [33] have prevented answers to these questions until now. A large-scale conservation strategy for a species must take into account the complexity of density-niche relationships [34] and simultaneously overcome the huge challenges posed by density estimations over large spatial scales (e.g. [35]).

Fortunately, today there is a set of useful methods available to both calculate densities of elusive species [36] and to predict potential distribution and density estimates over large regions based on suitability indexes derived from niche-species models [34]. The use of solid density estimates and robust predictions of suitable areas for the distribution of species allow managers to take well-informed conservation decisions over large spatial scales with reduced field and economic effort. Furthermore, combinations of these methods are also useful to develop long-term monitoring programs, a key element for any strategic conservation programme of an endangered species [37, 38].

For abundance estimations, non-intrusive capture-recapture approaches based on camera-trapping or molecular identification of scats and hairs [39] have proven highly efficient for felines (e.g. [39, 40, 41]), including in the case of the rare European wildcat [12, 42, 43, 44, 45, 46, 47]. On the other hand, during the last decades, species distribution models (hereafter SDMs) have been developed to provide solutions to a wide range of ecological, biogeographical and conservation problems (reviewed in [48]). Statistical methods to calculate distribution ranges and suitability indexes of abundance from partially known occurrence data are available (e.g. [34, 49, 50]). SDMs are especially robust when a good dataset of environmental predictors is available, as has been established for our target species in Germany [51] or Portugal [11] and for other felines elsewhere [52, 53, 54, 55]. Finally, non-intrusive surveys of wildcats have proven to be optimal approaches to estimate the degree of genetic introgression of the domestic cat [43, 46, 47].

The present study aims to evaluate the use of these combined sets of field and analytical methods, to carry out large-scale spatial surveys on the status of European wildcat populations, as a practical example of the demographic diagnosis of felines by non-intrusive surveys that can be useful elsewhere. We have developed a field sampling strategy of one of the largest populations of the species, located in mid-southern Spain in the Andalusia region, where a logistically viable survey was designed to be carried out by practitioners and for long-term monitoring. From the resulting field data, our goals were: (1) to estimate the entire population of wildcats in Andalusia; (2) to estimate domestic (feral) cat abundance in the wild in order to approximate the hybridization problem; and (3) to evaluate the contribution of the protected areas network to European wildcat conservation.

Materials and Methods

Study area

The study was carried out in Andalusia (87,268 km2), southern Spain (Fig 1). Andalusia is a typical Mediterranean area made up of three main regions (Fig 1), from north to south: (1) Sierra Morena, is a low mountain range (altitude 50–1298 m a.s.l.) dominated by well-preserved Mediterranean oak forests (Quercus ilex, Q. suber, Q. faginea) and scrublands (Arbutus unedo, Phylliera altifolia, Cistus ladanifer, Lavandula stoechas), and some pine plantations (Pinus pinea, P. pinaster). The main uses of land are big game (Cervus elaphus, Sus scrofa) and cattle, with a relatively low human density. It is one of the best-preserved areas in Europe, holding the largest population of the endangered Iberian lynx (Lynx pardinus). (2) Guadalquivir River valley, is a low-land region (0–500 m a.s.l.) transformed by cultivation, mainly of olive trees, sunflower and cereal crops and is densely populated by humans. The only remaining patch of wild landscape in this area is the Doñana plain (Fig 1), which is very important for wildlife conservation at an international level. (3) The Sierras Béticas are a complex mountain system (0–3478 m a.s.l.) where the impact of crops (olive and almond trees, cereal) and cattle has led to severe degradation of natural vegetation; the autochthonous forests and scrublands are fragmented (Fig 1), although they hold one of the highest levels of botanical diversity and uniqueness in Europe [56], from moist cork oak forests in the west to arid sub-desert scrubs in the east, with autochthonous boreal relicts of pine forests (P. sylvestris) in the highest mountains. Human density is spatially variable but relatively high on average.

Fig 1

Study area showing the distribution of the camera-trapping blocks (dots) within Andalusia and the distribution range assumed for the European wildcat [94].

Geographic areas considered in the present study: A Sierras Béticas, B Sierra Morena, C Doñana. Block 23 was carried out by Gómez-Chicano et al. [57]; and blocks 24, 25 and 26 by Soto and Palomares [43].

The wildcat is assumed to be well distributed along the Sierra Morena and the Sierras Béticas, a range based on opportunistic unconfirmed records [32]. Twenty-four Natural Parks and two National Parks cover 17.58% of the study area (15.338,88 km2), providing protection to most of Doñana, and patches of the Sierra Morena and Sierras Béticas ranges (Fig 1).

Field surveys

Three types of non-intrusive field surveys have been successfully developed for the wildcat: camera-trapping [44, 45, 46, 47], scat sampling for molecular identification [46] and hair sampling, also for genetics [42]. We used the first two methods, while ongoing field studies by our team are showing a lesser efficiency of hair traps in our study area. Twenty-two survey blocks (Fig 1) were distributed in Sierra Morena (10 blocks) and Sierras Béticas (12 blocks) between 2011 and 2015; this distribution was biased towards eastern Andalusia mainly because the habitats there are much more heterogeneous and representative of the other areas. All sampling blocks were selected first assuming that they were included in the potential habitats of the wildcat (see study area), and second, attempting to represent the main forest and scrubland types of the overall potential range within Andalusia. Third, limitations of access facilities and/or authorizations for private lands were considered.

For each block, we designed a conventional camera-trapping survey, consisting of the installation of 8–12 camera traps in most cases (Table 1) following the procedure described by Gil-Sánchez et al. [47]. Lures were used in all the blocks, both live pigeons in cages (12 blocks) and lynx urine (9 blocks, Table 1). The urine was employed within areas of high risk of robbery and vandalism, because this type of lure makes it easy to camouflage the cameras. Lynx urine was found to be slightly less efficient for wildcats compared to pigeon lures [47], and therefore we assumed lower effects on the comparative results. The cameras were infrared-triggered (DLC Covert™, Leaf River™ model IR-5 and Scout Guard™ model SG565F-BM), with a sampling period for each camera ranging from two to three months. To increase the field data set, we used the results of seven camera-trapping surveys carried out by other teams and data from two published surveys ([43, 57]; see Fig 1).

Table 1

Details of the 26 systematic camera-trapping blocks (see Fig 1 for location of each).

Grey rows: sampling blocks with SCR calculations (N = 11, see details in Table 2); lure: (p) pigeon bait, (o) lynx urine, *data from Simón et al. [58].

#Block	Coordinates	Camera stations (lure)	Camera days	Wildcat				Domestic cat		Hybrid cat		Walked km	Cat scats	Rabbit (latrines/km)
				Individ.	Captures	Cap/100 cam-day	D(ind./100 km2)	individ.	captures	Individ.	captures
1	38.168266–3.959968	14 (p)	1333	4	7	0.52	6.59	0	0	0	0	10.9	0	19.08
2	38.175024–4.010570	16 (p)	924	3	3	0.32	4.68	0	0	0	0	17.3	0	14.68
3	38.229479–4.167551	9 (p)	769	4	14	1.82	6.55	0	0	0	0	9.9	0	5.03
4	38.305422–4.187788	5 (p)	335	2	5	1.49	5.95	0	0	0	0	7.5	0	7.38
5	38.182323–4.156388	5 (p)	336	0	0	0	0	0	0	0	0	5.2	0	0
6	38.491827 -3-264204	12 (p)	998	2	17	1.70	4.90	0	0	0	0	5.4	0	25.9
7	38.471045–3.448069	4 (o)	1288	3	9	0.69	4.49	0	0	0	0	9.2	1	35,76
8	38.162388–3.570727	4 (o)	180	2	16	8.88	19.48	0	0	0	0	14.4	3	34,48
9	38.161735 -3-512425	4 (o)	148	2	2	1.35	5.70	1	1	0	0	-	-	-
10	37.479996–6.658689	10 (o)	180	0	0	0	0	0	0	0	0	-	-	0*
11	37.097731–3,494577	10 (p)	686	1	1	0.14	2.67	0	0	0	0	20.1	2	0
12	36.960573–3.407763	12 (p)	840	2	3	0.35	4.25	2	2	0	0	10.5	0	0
13	37.191936–3.251752	9 (p)	544	1	1	0.18	3.18	0	0	0	0	12.8	0	0
14	37.125307–3.075088	10 (p)	686	1	1	0.14	2.56	0	0	0	0	23.7	0	0,97
15	37.063330–2.995206	11 (p)	763	3	10	1.31	6.11	0	0	0	0	12.8	0	0.078
16	37.084784–2.766284	12 (p)	759	5	37	4.87	10.46	0	0	0	0	12.2	0	0.32
17	37.296489–3.401067	8 (o)	457	1	2	0.43	4.01	0	0	0	0	51.5	0	3.66
18	37.377995–3.417979	16 (o)	450	7	32	7.11	17.55	0	0	0	0	74.1	2	29,29
19	36.997621–3.817692	7 (o)	200	1	1	0.50	4.14	0	0	0	0	24.6	0	13.26
20	37.021747–4.217015	8 (o)	351	0	0	0	0	2	5	0	0	5.0	1	39.5
21	36.499840–5.460368	10 (o)	647	0	0	0	0	0	0	0	0	6.5	0	0.1
22	36.792605–5.397782	12 (o)	1020	0	0	0	0	0	0	0	0	108.7	0	0.05
23	36.242679–5.609176	7 (p)	-	0	0	0	0	0	0	0	0	-	-	0*
24	37.146208–6.551801	124 (p,o)	4329	4	23	0.53	4.19	2	2	3	3	-	-	5.83*
25	37.004890–6.505503	35 (p,o)	1190	0	0	0	0	0	0	0	0	-	-	6.73*
26	36.964384–6.450216	7 (p,o)	242	2	5	2.06	6.99	0	0	0	0			10.33

Table 2

Estimates of relative contributions of the environmental variables to the MaxEnt model.

Variable	Percent contribution	Permutation importance
Frequency of cultivated areas	16.8	17.5
Frequency of oak forests	11.1	3.4
Frequency of urban areas	9.7	17.9
Distance to water bodies	9.5	2
Precipitation (accumulated)	5.8	4.4
Frequency of pine forests	4.6	1.7
Frequency of forest	3.7	1.7
Frequency of eucalyptus plantations	1.9	1.6
NDVI (Normalized Difference Vegetation Index)	1.8	2.6
Frequency of dense scrubland	2.7	1.7
Frequency of pasturelands	4.3	3.6
Elevation	4.3	13.3
Convergence index	0.7	0.6
Hour of sun during winter	1.5	0.6
Medium solar radiation during winter	1.5	0.1
Frequency of olive cultivations	1.5	7.3
Topographic exposure	0.6	1.8
Slope	3.2	1.8
Distance to roads	3.2	4.3
Medium solar radiation during summer	3.2	0.5
Frequency of dispersed scrubland	4	4.2
Frequency of water bodies	2	3.1
Solar radiation during summer	0.2	0.1
South-North gradient	1	2.2
East-West gradient	1	2.1

For most of the sampling blocks (n = 19, Table 1) and simultaneously for the remote camera surveys, walking surveys of scats were designed following the protocols of Anile et al. [46]. However, after an effort of 442.3 walked km (see Table 1) carried out by well-trained personnel (J. M. Gil-Sánchez; J. Herrera-Sánchez), only nine putative wildcat scats were found. Therefore, this method was inefficient for our study area. The walking surveys were used to sample rabbit latrines, as a method to estimate the abundance of this key prey species for wildcat in the Iberian Peninsula [10, 13]. The sampling period lasted from 2011 to 2015.

Identification of cats

We identified each individual as a domestic cat, wildcat or as a hybrid cat after a detailed examination of the coat patterns and the shape of the tail [29, 30, 45, 47]. Seventeen wildcats of our study area were genetically examined in order to detect hybridization with domestic cat [30]; we found that the phenotypic traits of genetically pure individuals of our study area were very constant, and we only considered typical wildcats from camera trapping as “pure” individuals (most of them identical to WC1 in Fig 2 of [30]; see some pictures from camera traps in [47]). Camera traps detected three putative hybrid cats (see Results), which had coat pattern and tail shape were very close to typical wildcats, but had wide white patches on the pelage.

Environmental data

To model the distribution and abundance of the wildcat in the study area, we incorporated 54 environmental descriptors attending to five conceptual groups (climatic, relief, vegetation, water availability and human presence) of environmental variables selected to represent different resources. All the variables were obtained at 40-metre resolution. Previous to the modelling approach, we evaluated Pearson correlations among these selected independent variables to avoid multicolinearity in the models [59]. We chose those with the greatest biological significance for wildcats based on our expertise and on the habitat preferences previously described for this species [7, 11, 13, 51]. We carried out the same exploration, looking for the variables statistically related to rabbit abundance that were evaluated in the systematic blocks. As a result, we obtained a list of 25 uncorrelated environmental predictors (Table 2). The unique climatic variable was cumulative rainfall. To obtain annual averages of rainfall at a 40-metre resolution we applied the climate mapping method proposed by Ninyerola et al. [60], taking as an input the daily records of 1000 weather stations contained in the Andalusian Information Subsystem for Environmental Climatology. Topographic and water availability-related variables were calculated from a 40 meter resolution terrain elevation model provided by the Environmental Information Network of Andalusia (REDIAM, Andalusia Government). The elevation model was then processed through GRASS GIS software (GRASS Development Team, 2009) using R.PARAM.SCALE, R.SLOPE.ASPECT, R.TERRAFLOW, R.SUN and R.RECODE modules. Land cover or land use variables were obtained from the land cover and land use map of Andalusia (SIOSE Andalusia, year 2003, scale 1:25:000). These vector maps were then transformed into raster maps and the distance to target entities were calculated using V.EXTRACT, V.TO.RAST and R.GROW.DISTANCE GRASS GIS modules. Frequencies were also calculated from these rasters using a neighborhood analysis through R.NEIGHBORS GRASS GIS module. The result was the number of pixels with a presence of a given entity within a 1000-metre radius.

Niche models based on presence-only data

To model the distribution of the wildcat in Andalusia, we first selected a dataset of independent samples from camera-trapping. We only used locations separated by at least 1887 metres (n = 68). This distance was the average wildcat home range radius in Iberian Mediterranean ecosystems of the southern Iberian Peninsula [11]. SDMs were performed using MaxEnt (version 3.4.1k; [61, 62]), after checking recommendations by Merow et al. [63] and Yackulic et al. [64]. MaxEnt provides SDMs from presence-only species records and shows good predictive performance when the presence dataset sample is low in comparison to other modelling algorithms, as it was in our case [65]. MaxEnt models were generated, after 500 iterations, with the dataset of 68 presence records. The final result of the MaxEnt model was a continuous map that was transformed into binary using a cutoff point where sensitivity equals specificity. This threshold probability was 0.262. Finally, we removed potential habitat patches of less than 228 hectares, equivalent to the minimum female wildcat home range described within a southern Iberian Mediterranean ecosystem [11].

Our model performance was evaluated using a receiver operating characteristic (ROC) curve. From this curve, the area under the ROC curve value (AUC) is a widely-accepted method to evaluate SDM performance (e.g. [65]). The MaxEnt output was re-evaluated by comparing the predictive map with radio-tracking data (see [51]). We used 370 independent locations of nine resident radio-tagged wildcats (four adult females and five adult males), which were captured within a camera-trapping block (#18, Fig 1) in the Béticas range; radio-tracking periods were March 2003 to September 2004 and November 2017 to February 2019. Following a scientific standardized protocol designed and largely used for our target species [15], animals were captured with box-traps (metal cages of 100 x 50 x 70 cm, porting in our case a wooden roof to prevent from sun or rains), using live house pigeons (Columba sp.) as lure, unavailable to captured carnivores thanks to an isolation cage that prevents injuries. The pigeons were released at the end of the trapping sessions. Box-traps were checked daily after sunrise and before sunset, in order to minimize animal stress and to supply food and water to pigeons. Alternatives capture methods for wildcats (e.g. leg-hold traps or snare traps) have a large risk to fatal injuries and, therefore, were rejected. Following the wildlife laws of Spain (which include any ethic consideration), this research was approved by the regional environmental authorities (Consejería de Medio Ambiente y Ordenación del Territorio, Dpto. Geodiversidad y Biodiversidad, approval number: 201699900550733). Once the cats were captured, they were immobilized by veterinarians using an anesthetic (Xylazine and ketamine hydrochloride) at a dose of 10 mg kg−1. To evaluate the accuracy of MaxEnt, we explored the lineal distance to the nearest predicted patch by pooling it into five categories: inside optimal patch (0 m.), at <250 m, at <500 m, at <1000 m and at >1000 m, carrying out a Chi-square test to evaluate if the observed frequency distribution was different from the null distribution.

Density estimations of wildcats

Only adult or sub-adult individuals were taken into account to avoid seasonal effects. Once the taxonomic status was established, each cat was individually identified following the protocol of Anile et al. [45]. We then carried out density estimation within each sampling block by using spatially explicit capture-recapture (SCR) models, that are thinned spatial point process models used to make inferences about the abundance and distribution of animal activity centres [66, 67]. SCR models allow for inference about population size and density by modelling capture probability as a function of the distance between activity centres and detectors (e.g. camera-traps). The SCR capture probability function typically includes two main parameters: the scale parameter of the half-normal distribution (sigma), which is determined by home range size; and the baseline detection rate, that is the probability of encountering an individual at its activity centre. In order to improve parameter estimates when sample sizes (spatial recaptures) were small [68, 69, 70], we used models in a Bayesian approach sharing among sites sigma and baseline detection parameters. The models were fitted using a script written in Nimble [71, 72] and R [73]. Three parallel Markov chains with 100.000 iterations each (burn-in = 1000 iterations, thinning rate = 1) were run. The Gelman–Rubin statistic, R-hat [74], was used for checking chain convergence, which compares between and within chain variation [75]. R-hat values below 1.1 indicate convergence.

We carried out the SCR calculations for eleven blocks holding more than nine camera stations (Table 1); with them, we carried out a regression analysis between density estimations (individuals /km2) and relative abundance (captures/100 camera-days) with the goal of obtaining a formula for transforming to density the relative abundance of the rest of the blocks [76, 77].

Population size-niche predictor relationships

We used generalised linear models (GLM) where the response variable was the density of wildcats. In these models, we used as explanatory variables the 25 variables cited above (Table 2). These variables were quantified within a 3-km circle centred at the centre of each remote camera-sampling block, resulting in a buffer that included the whole minor convex polygon of every block. We also excluded the four blocks with the highest presence of Iberian lynx (#1, #2, #3 and #9, Table 1; data from our survey) since strong competitive exclusion was expected [58] independently of the environmental descriptors, which hampered the accuracy of the results as we confirmed in early GLM calculations. We carried out a GLM analysis with normal error distribution (confirmed by Kolmogorov-Smirnov tests and q-q plots) and identity link function. For model selection, we first selected explanatory variables with significant associations with the response variable in univariate tests (R correlation). To find the best model explaining wildcat density we used a multi-model selection approach where the importance of variables and the values of estimate coefficients were averaged across similarly supported models [78]. In brief, we evaluated all combinations of predictors and models with different levels of complexity. We selected only the models with AICc values lower than two in relation to the best model (lower value of AICc). We also computed the relative importance of the variables from their Akaike weights (Wi) and the average values of the estimated coefficients and their standard errors [78]. To analyse the model fit, we calculated the R-squared of the final model. We carried out the statistical analyses with R software version 3.4.2 [73] using the package MuMIn [79] for multi-model selection.

Population size estimation and coverage of protected areas

The best GLM model was resampled from a 40-metre resolution raster to a UTM 5x5 km square net (using the spatial analysis extension on ArcMap 10). The 5x5 UTM square is a geographic unit similar in size (25 km2) to the average camera sampling circle (28.2km2), and thus it has a remarkable biological significance for European wildcat spatial ecology (range of territory in southern Mediterranean Iberian Peninsula = 1.70–13.71km2 [11, 15]). We overlapped the 5x5 km square net with the resulting MaxEnt wildcat distribution map of Andalusia, and then removed the 5x5 km squares with less than 10% of potential presence (<2.5 km2), since they did not reach the minimum size for a female wildcat territory (2.28 km2 [11]). The population size N (mean, standard error and 95% of confidence interval) was calculated from the spatial estimate values of density as: N = (∑d/d)*S, with d being the density of each 5x5 km square, d the total number of squares and S the total range size (km2) derived from the MaxEnt presence surface within the 5x5 km squares.

To estimate the wildcat population covered by each National or Natural park (hereafter Natural Protected Areas or NPAs), we carried out the same calculations previously described. For our analyses, we only considered UTM 5x5 with at least 75% of its area included in the NPA.

Results

Distribution range inference

The MaxEnt model shows a very high predictive performance, in that the training AUC was 0.96. Thus, the model can be considered as potentially useful (see ROC curve in S1 Fig). The most important environmental predictor was agricultural lands frequency with a negative response curve, followed by the frequency of oak forests with a positive response, the frequency of urban areas with a negative response, the distance to water bodies with a positive response and altitude with a positive (but partially semi-quadratic) response (Table 2; S2 Fig). The rest of the predictors showed low contribution ranging from 0.1–5.8% of contribution and permutation importance (Table 2; see response curves in S1 Fig and Jackknife test in S3 Fig).

The surface defined by our model shows a potential distribution for the European wildcat in the study area of 8558.73 km2. This area is distributed in 476 patches with an average size of 15.89 km2 (range 2.28–5651.14 km2). Eighty percent of the total area is concentrated in 7.8% of the largest patches and this implies that the majority of the distribution area of the European wildcat in Andalusia is restricted to 37 localities. The distribution model results showed two main populations (Fig 2): the largest one with a continuous distribution at Sierra Morena (4652.87 km2), and another largely fragmented one at Sierras Béticas (3730.61 km2), where the main optimal patches were located in the western mountains. Doñana (175.24 km2) appeared as a secondary and somewhat isolated optimal area, but spatially related to Sierra Morena (Fig 2).

Fig 2

Wildcat potential distribution in the study area modelled with MaxEnt (patches of more than 228 hectares, see text for further details).

A percentage of 48.1 of the radio-tracking locations fell within the predicted range, 24.8% at <250 m, 11.6% at <500 m, 7.0% at <1000 m and 8.1% at >1000 m. The Chi-square test showed that this observed frequency distribution was different from the null distribution (Chi-square = 85.9; P<0.00001). The average lineal distance to the nearest predicted patch was 323.5 m (ES = 40.9 m). Only the home range of one male in 2003–2004 fell outside of the predicted range; this home range was unoccupied during 2018 (J.M. Gil-Sánchez data from intensive camera-trapping).

Density estimations of wild-living cats

Forty-four wildcats were captured on 189 occasions at 19 systematic blocks (Table 1). SECR calculations showed a wide range of densities, from 0.02 to 0.17 wildcats/km2, although low densities were the most frequent (Table 3). We found a significant relationship (R = 0.92, P = 0.0001) between D and the capture rate for the eleven available blocks with D estimation. Therefore, we used this lineal regression formula (wildcats/100 km2 = (1.83*captures/100 camera-days) + 3.23) to estimate D for the rest of the blocks (Table 1). The captures of wild-living domestic cats (both feral cats sensu stricto and roaming house cats) did not allow for density calculations; there were only seven individuals with nine captures (Table 1). They were detected in 14.8% of the sampling blocks. Only four putative hybrids were detected in four blocks, three of them in Doñana (see pictures in [43]) and another in a non-systematic block in Sierra Morena. The capture rates (individuals/100 camera days) were 1.39 for wildcats, 0.098 for domestic cats and 0.029 for putative hybrids (2.04% of apparent hybridization rate).

Table 3

Density estimations (individual/km2) by Bayesian Spatial Explicit Capture Recapture models (block #18 to #14).

See Table 1 for details of each sampling block. λ0 is the baseline detection rate, and σ the parameter of scale from the half-normal distribution, related to the home range.

				Quantiles
# block	mean	sd	CV	2.50%	50%	97.50%
18	0.1755	0.0677	0.39	0.0745	0.1663	0.3383
16	0.1046	0.0437	0.42	0.0438	0.0938	0.2126
1	0.0659	0.0290	0.44	0.0270	0.0595	0.1351
3	0.0655	0.0288	0.44	0.0243	0.0582	0.1359
15	0.0611	0.0301	0.49	0.0226	0.0566	0.1359
6	0.0490	0.0286	0.58	0.0135	0.0404	0.1211
2	0.0468	0.0232	0.50	0.0175	0.0437	0.1048
12	0.0425	0.0252	0.59	0.0109	0.0382	0.1036
17	0.0318	0.0237	0.74	0.0060	0.0240	0.0960
11	0.0267	0.0200	0.75	0.0049	0.0197	0.0789
14	0.0256	0.0191	0.75	0.0049	0.0198	0.0741
λ0	0.0287	0.0068	0.24	0.0182	0.0278	0.0444
σ	1.3828	0.1678	0.12	1.0953	1.3678	1.7511

Population size modelling

We only detected two significant predictors with wildcat density: olive crop cover (R = 0.66, P = 0.002) and precipitation (Rp = -0.49, P = 0.049). Rabbit abundance index (latrines/km) showed a positive relationship with wildcat density (R = 0.54, P = 0.009) and olive crops (R = 0.74, P = 0.0001), and a negative relationship with precipitation (R = -0.39, P = 0.032). The multimodel GLM performed with the two predictors generated a final model including both variables, although olive crop cover showed higher relative importance than precipitation (1 vs 0.32). The multimodel approach laid two equally probable models, one including only olive crop cover and another including the two predictors. The model with only olive crop cover showed a lower AICc value, and higher Akaike Weight (Table 4). Adjusted R-squared values indicated a good fit of the model including both variables, which explained 59.73% of the total variance of wildcat density. Parameter estimates of the full-averaged coefficients of the model can be seen in Table 4.

Table 4

Upper bold line: best selected models in multimodel GLM with the wildcat density as a response variable and olive crop cover and precipitation as predictors.

We show the AICc values, ΔAICc and Akaike weights of each supported model. Lower bold line: model-averaged coefficients from the multimodel GLM with wildcat density as a response variable and olive crop cover and precipitation as predictors. We show parameter estimates and their standard errors, and the Z values.

Model	AICc	ΔAICc	Akaike weight
Olive crop coverOlive crop cover+precipitation	-42.68-41.13	1.54	0.680.32
Predictor	Estimate	SE estimate	Z value
InterceptOlive crop coverPrecipitation	0.0430.0100.00002	0.0320.00270.00004	1.283.470.50

The distribution of the regional wildcat abundance is shown in Fig 3, where three core areas can be observed: central-eastern Sierra Morena, central-western Sierra Morena and north-eastern Sierras Béticas, whereas the rest of the range usually holds low or very low densities. Total estimation was near one thousand individuals, with more than five hundred in Sierra Morena, close to four hundred in Sierras Béticas and less than a dozen in Doñana (see details in Table 5).

Fig 3

GLM-estimated density of the European wildcat in the UTM 5x5 squares with presence predicted by the MaxEnt model.

Table 5

Wildcat population estimations in Andalusia, and percentages of the population under spatial protection by national and natural parks (n: number of 5x5 km squares).

	Wildcat total area (km2)	Wildcat protected area (km2)	Dtotal(indiv./100km2) 95%IC	Dprotected(indiv./100km2) 95%IC	Ntotal95%IC	Nprotegida95%IC	% protected N
Andalusia	7563.33	2530.78	11.3710.50–12.24n = 793	8.817.76–9.87n = 237	860794–926	223196–250	25.9
Sierra Morena range	4479.62	1296.86	11.6510.54–12.76n = 448	11.318.67–11.94n = 100	522472–571	146112–155	27.9
Sierras Béticas range	2850.07	1060.99	11.189.73–12.63n = 323	6.605.25–7.95n = 115	319277–360	7056–84	21.9
Doñana range	233.64	172.92	8.494.02–12.96n = 22	5.355.31–5.40n = 13	209–30	99–9	45

Protected areas cover

The estimated population of wildcats under spatial protection by NPAs is shown in Table 5. Our SDM shows that the potential area of the European wildcat in Andalusia includes 23 of 24 Natural Parks and the two existing National Parks (Fig 2). Despite this, only 33.46% of the potential area is protected, and only eight Natural Parks and one National Park have more than one-third of its total area covered by potential wildcat areas. Overall, 25.9% of the estimated wildcats would be under protection: 16.9% in Sierra Morena, 8.1% in the Sierras Béticas and 1.0% in Doñana, out of the total estimated population.

Discussion

Camera-trapping for large-scale surveys

Our study represents the first large-scale estimation for any European wildcat population, based both on systematic field surveys and analytical approaches applied on SDMs. For this purpose, the use of camera-trapping has proven to be a logistically viable method for these types of surveys designed for the target species, showing: (1) its utility in situations of very low density (see also the case of the tiger Panthera tigris [75, 76]), and (2) its utility for practitioners performing large-scale and long-term monitoring schemes (see the case of the Iberian lynx [80]). These are key advantages over intrusive surveys like radio-tracking, which was previously used for modelling the habitat and distribution of the wildcat in central Europe [51]. Radio-tracking is usually biased towards areas that maximize the chances for captures of individuals to be tagged, and thus is carried out at a priori known areas of good density, as revealed by studies carried out on European wildcats [11, 15, 51]. Our camera-trapping survey has allowed for more randomly distributed surveys, hence covering a wider range of density situations. On the other hand, this non-intrusive survey may offer larger sample sizes in the sense of the number of “captured” individuals, preventing redundancy of data. However, in contrast, camera-trapping offers much less data for each individual and cannot allow accurate estimates of home range areas, movements and spatial use of all elements of the landscape. Moreover, for the wildcat, it may present some limitations for correctly identifying individuals, particularly to determine if they are hybrids. Nonetheless, recent studies show that there is a great concordance between external physical features (such as coat pattern) and genetic identity of wildcats, allowing for reliable identifications [30]. Indeed, the presence of cryptic hybrids is very low in the European wildcat populations, <10% [31, 81]. In any case, we recognise that molecular sampling is a necessary tool to obtain the most precise information on the genetic introgression of the domestic cat. Anyway, camera-trapping represents an optimal method to survey the demographic situation of domestic cats in the wild [47], as the main source of inter-breeding risk.

In fact, camera-trapping is useful for density estimations, a widely acknowledged advantage over other methods [82]. This is true for scat and hair samplings for molecular identification applied to the wildcat as well [42, 46]. However, neither method, especially scat surveys, were useful in our study area. This result could point to severe limitations of scat surveys for European wildcats (see, however, Lozano et al. [83]), again indicating the utility of camera-trapping surveys over large areas.

Distribution and abundance modelling at a large scale

Our results show the reliability of SDMs to infer the distribution and abundance of an endangered and elusive carnivore. Interestingly, we found that environmental variables can impact distribution and abundance in largely different ways (see next section), supporting the use of our hierarchical approach, beginning with a MaxEnt-based distribution map and then applying a GLM-based abundance analysis on the prior distribution map. This analytical procedure improved the accuracy of results: e.g. if only the abundance model is applied on the whole Andalusia region, then a largely unreliable map is obtained since olive trees are one of the main crops in the Guadalquivir Valley, where wildcats are absent (see Study Area). For our study case, MaxEnt allowed us to perform models using presence-only data, since this algorithm can show a solid performance with small data sets [65, 84], as may be the case in the majority of studies on elusive and rare species. Our distribution model shows a high predictive ability following AUC, suggesting that even in limited sample size scenarios, modelling based on presence data was useful to study wildcat distributions at broad scales. However, we recognize that our results must be taken with some caution. First, the radio-tracking data suggest that the MaxEnt output was conservative. Secondly, environmental covariates and individual behaviour responses (e.g. related to baiting strategies) could affect detection probability [64]. Regardless, we were very cautionary and consistent with the sampling design. In fact, both SCR estimations and the relationship of D with relative abundance shows that the positive records were strongly related to the real abundance of wildcats.

The study of abundance patterns at a regional scale based on GLM models also showed robust results in the case of wildcat in Andalusia. A weakness of our approach was that the smaller optimal patches were penalized, whereas the spatial association of some of them could result in more potential territories than predicted. In any case, this was a marginal situation (see Fig 2) that could be assumed to be of negligible impact at our broad scale, although it should be considered in more local studies.

Habitat inferences

It is well known that the European wildcat is associated with forests in central Europe [51] and scrublands in Mediterranean landscapes of the Iberian Peninsula [7, 11, 13, 14, 15]. For the Andalusia region, the crops had a key (and negative) effect on the predicted wildcat presence, as it is a landscape feature dictating the significant fragmentation observed in the wildcat distribution. On the other hand, Mediterranean oak forests (mainly made up of holm oaks) had a key positive effect on the predicted wildcat presence, as a habitat that represents one of the main natural landscapes of the Iberian Peninsula [56]. Andalusian wildcats trend to inhabit patches of oak forests (especially xeric ones) somewhat separated from villages and water courses. The positive selection of the oak forest is not supported by some previous studies on wildcat habitat selection in Mediterranean landscapes, which locally described scrublands as key habitats but not oak forests ([7, 11, 13] but see Oliveira et al. [15]). Nonetheless, the Mediterranean scrublands show a huge diversity and geographic variability. In our study area, most scrubland types in the southern middle of the Iberian Peninsula were available [56]. However, although they may be well represented in the wildcat habitat in Andalusia [14], scrublands had marginal effects in the best predictive model of presence. This could be a result of pooling this complex vegetation into only two types (Table 2), which may not allow differentiation between some types of scrubs selected by wildcats [11, 13] from others avoided by wildcats, such as the hyper-xeric formations of the eastern Andalusian sub-deserts (present study). Scrub–pasture mosaics had a positive effect on wildcats in central Spain [7, 13], and distance to meadows was a key variable for the wildcat prediction models carried out in central Europe [51], showing some positive effects of pasturelands, which we did not find in our study area. This is likely due to the xeric conditions of most of southern Spain.

Regarding wildcat abundance, the only two variables selected by the best model (% of olive tree crops and accumulated annual rainfall) could be related to prey availability. This was confirmed for the case of the olive tree crops, which, although having a lower presence in the habitat of wildcats, they showed a positive relationship to rabbit abundance (see as well Martín-Díaz et al. [14]). During the field walking surveys, we observed that the greater abundances of rabbits were usually associated with mosaics of oak forest/scrublands together with olive tree crops. We detected a negative effect of rainfall on wildcat abundance, being the main reason for the low estimated densities within two of the largest and best conserved patches of Mediterranean forests in Spain: the Alcornocales and Sierra de Grazalema Natural Parks at the western Sierras Béticas, and the Sierra de Cazorla, Segura y las Villas Natural Park at the eastern Sierras Béticas (compare Fig 1 and Fig 2). These two protected areas have the most precipitation for the entire region, along with local areas of western Sierra Morena [56]. The abundance of rabbits is an important variable for the habitat selection models carried out in the Iberian Peninsula [10, 11, 13], where this lagomorph is a key prey for the species [9]. However, for our large-scale survey we found that areas with low rabbit availability were not an exception within the wildcat range (Table 1). Since rodents become a key prey group where rabbits are scarce in southern Spain [8, 85], research on abundance of these preys and its relationships to landscape features is needed to explain the observed negative effect of rainfall.

Implications for conservation and management

Our results show that the distribution range of one of the largest populations of the European wildcat is actually lower than previously assumed (compare Fig 1 and Fig 2). Moreover, the overall average estimated density in the 19 sampling blocks with confirmed wildcats, 0.069 ±0.0019 wildcats/km2, could be evaluated as very low compared to densities reported by camera-trapping elsewhere: 0.28 ±0.1 wildcats/ km2 in Sicilia [45] and 0.22 ±0.06 wildcats / km2 in Turkey [86]. The protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus for short-term conservation in genetic terms (with effective population size N >50 individuals [87]). Indeed, the most important areas are unprotected (Fig 3). Most of the distribution of wildcats is under hunting estates and it is known that the species severely suffers from illegal and legal control of carnivore mammals to protect lesser game [28]. Moreover, large game hunting can produce indirect negative effects on wildcats by reducing the prey base [10]. The situation is worse for the Sierras Béticas, where the predicted distribution range is both more restricted and fragmented, and probably unconnected to the large and continuous population of Sierra Morena. On the other hand, the whole population seems to be little affected by the hybridisation problem (except in Doñana National Park), which was previously reported by local studies [30, 47]. We found very few domestic cats in the sampled blocks, supporting at a large scale the hypothesis that severe ecological barriers may be preventing genetic introgression in Mediterranean mountain ranges [47, 88, 89]. In fact, genetic surveys at the Iberian scale have shown very low levels of domestic cat introgression (see e.g. Oliveira, et al. [23]). Nevertheless, in other sites in the Iberian Peninsula, near farms and villages, a relevant presence of domestic cats has been detected [12, 90].

Taking into account this large-scale diagnosis, we have three major recommendations for the studied population: (1) improve monitoring programs of hunting states at least in the main populations; (2) study and improve the connectivity between Sierra Morena and Sierras Béticas, paying special attention to the internal connectivity within the largely fragmented Sierras Béticas population; (3) review the protection laws, since in Andalusia the wildcat is not listed in any threatened category [91], but its present situation does not appear optimistic: <1000 individuals with N <100 individuals (10% of N [92]) distributed in a very fragmented range. A similar scenario is highly likely for the rest of Spain, where this species is also not included in any threat category [93].

Our approach can be used not only to update information about the European wildcat at large spatial scales, but also to design viable long-term monitoring programs. Both are actions that have yet to be implemented for such endangered European taxa, or for other felines worldwide. The methodological scheme presented and evaluated here for the wildcat can be useful to better design the limits of protected areas elsewhere.

Output of MaxEnt: ROC curve.

(DOCX)

Click here for additional data file.

Output of MaxEnt: Responses of the environmental variables.

(DOCX)

Click here for additional data file.

Output of MaxEnt: Jackknife test.

(DOCX)

Click here for additional data file.

8 Nov 2019

PONE-D-19-27725

Fragmentation and low density as major conservation challenges for the southernmost populations of the European wildcat

PLOS ONE

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Reviewer #1: Andalusian wildcats

This is an important paper documenting a rare and cryptic threatened species that involves a huge amount of field work. It is told badly. The authors make the mistake of using Maxent, a programme that sucks the very soul from scientific results like dementers from the world of Harry Potter. Pages of complex analyses do not substitute for some simple mapping and some thought about how to present the key results.

The authors write (the numbers are mine)

1. Our results show that the distribution range is smaller and more highly fragmented than previously assumed.

2. The overall estimated density was very low (0.069 ±0.0019 wildcats/km 2 )

3. and the protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus in demographic and genetic terms.

4. Indeed, the most important areas remain unprotected.

5. Our main recommendations are to improve the protected area network and/or vigilance programs in hunting estates,

6. in addition to studying and improving connectivity between the main population patches.

Those are interesting results, but it’s far from clear how one goes from figure 1 to figure 2, to figure 3 and these conclusions.

For 1, the authors would need to document previous estimates and previous assertions that the range is continuous. The would also need to convince the reader that the range is fragmented. A quick look at Google Earth reminded me of what I remembered about this area: much of the land below 200m elevations is extensively converted to agriculture.

Doñana, is the important exception. To address (2), it has a low density.

The largest block of potential habitat is Sierra Morena. The authors models suggest the species is widespread there on the basis of intensive sample in the east, (points 1 to 9) and just one sample in the west (10). Some of the highest densities occur there — >0.2 cats per km2, but these predictions are outside the area of systematic sampling. I’d need this to be explained.

This is where Maxent so often fails. The authors fall back on its black magic, when simple GIS mapping would be so much more important. The model predicts that the cat is missing from large areas of this Sierra’s national parks. Why? What’s wrong with the habitat? And how might one map that?

The southern distribution clusters into a southwestern area (points 21-23) and the eastern one that I will call the Sierra Nevada.

The southwestern one predicts very little habitat and estimates very low densities. Why?

The Sierrra Nevadas are well sampled (points 11-20), predicted to be habitat (figure 2). The densities are predicted to be low.

In addition to these core areas is a substantial scatter of small, isolated patches with predicted very high densities of cats (>0.2), running north of points 22, 20 to 18. These are areas that weren’t surveyed. How do we know there are cats there and how to we know what the densities are? Their existence seems to be the key result in justifying the conclusion about fragmentation.

It also seems to be critical in point 4, since Doñana, Morena, Sierra Nevada, and the southwest do have extensive protected areas. The authors have not convinced me that that there are many cats outside these areas.

Finally, points 5 and 6 need specifics. Where exactly would put new protected areas? And where are the hunting estates that one might wish to influence?

In sum, I trust the authors instincts and experience in the conclusions they present. But they should start from them and work backward to justify each one with the simplest analyses possible.

Reviewer #2: Overall this is an excellent paper. The authors SDM approach to this question is robust. Some of their English phrasing needs a bit of work. However, addition of genetic data would make this a much stronger paper and so it is not clear to me why the authors did not collect tissue or blood samples from at least the 9 radio-tagged animals for genetic analysis? And why hair snares were not put out at the camera locations? Even though they state that “Three types of non-intrusive field surveys have been successfully developed for the wildcat: camera-trapping, scat sampling for molecular identification and hair sampling, also for genetics. We used the first two methods, while ongoing field studies by our team are showing a lesser efficiency of hair traps in our study area.” They do not report on any genetic analyses - even if the hair snares are not very efficient they could provide at least some samples for genetic analysis. Also it is curious why they did not put out live traps after identifying animals with the camera traps in order to try and capture these individuals – seems an opportunity lost. As a result they rely on visual determination of hybrids which is sketchy at best. They have no estimates of effective population size or genetic variation and they do not identify or at least to not report numbers of males and females. Therefore they can estimate densities but and population size but this doesn’t translate into how viable these populations are. If they have any genetic data they should include it. Despite the lack of genetic data this is still a very valuable contribution to the literature on European wild cats.

Lines 31-32 numerous grammatical mistakes in the first sentence

“On” should be “of”

Delete the in “the population size”

Population dynamics not just dynamics

Conservation actions not conservation measures

Line 32 delete “huge numbers of examples” and add many examples

Line 49 “… and the protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus in demographic and genetic terms.” You don’t have any genetic data so this is pure conjecture.

Lines 57-58 see comments above

Line 116 hybridization not hybridization

Line 122 capitalize the f in fig.1

Line 132 add “and is densely populated by humans” after cereal crops and delete the next sentence.

Line 133 add “in this area” after wild landscape

Line 163 delete “they are” in front of representative

Line 200 what does “conceptual groups” mean?

Line 251 capitalize fig.

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10 Dec 2019

Response to Reviewers

Journal requirements

When submitting your revision, we need you to address these additional requirements.

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1. In your Methods section, please include a comment about the state of the pigeons used as live bait following this research. Were they euthanized or housed for use in further research? If any animals were sacrificed by the authors, please include the method of euthanasia and describe any efforts that were undertaken to reduce animal suffering.

** Our response: done in L265-266 of “Revised ms”.

2. In your Methods section, please provide additional location information of the study sites, including geographic coordinates for the data set if available.

** Our response: done in second column of Table 1 of “Revised ms”.

3. Thank you for including the following funding information within your acknowledgements section; "The research was partially funded by the Consejería de Medio Ambiente y Ordenación del Territorio through the European Union (FEDER Project) and is part of the Global Change Observatory of Sierra Nevada. J.M.G.-S. was supported by a Prometeo fellowship from the SENESCYT and the national agency for Education and Science of the Government of Ecuador. "

We note that you have provided funding information that is not currently declared in your Funding Statement. However, funding information should not appear in the Acknowledgments section or other areas of your manuscript. We will only publish funding information present in the Funding Statement section of the online submission form.

Please remove any funding-related text from the manuscript and let us know how you would like to update your Funding Statement. Currently, your Funding Statement reads as follows:

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** Our response: done, deleted in the “Revised ms”.

Review Comments to the Author

Reviewer #1: Andalusian wildcats

This is an important paper documenting a rare and cryptic threatened species that involves a huge amount of field work. It is told badly. The authors make the mistake of using Maxent, a programme that sucks the very soul from scientific results like dementers from the world of Harry Potter. Pages of complex analyses do not substitute for some simple mapping and some thought about how to present the key results.

** Our response: We acknowledge the reviewer for thinking that our work is an important paper. However, as we will discuss below, MaxEnt is one of the most prominent methods for species distribution modelling, and it has been proved robust and efficient compared to other statistical methods (GLM, GAM, GARP, Random Forest or Mahalanobis distance). Moreover, MaxEnt appears as key statistical tool in several articles recently published in PLoSONe, please, see some examples listed below, including an article with a feline study case:

Wiese D, Escalante AA, Murphy H, Henry KA, Gutierrez-Velez VH (2019) Integrating environmental and neighborhood factors in MaxEnt modeling to predict species distributions: A case study of Aedes albopictus in southeastern Pennsylvania. PLoS ONE 14(10): e0223821. https://doi.org/10.1371/journal.pone.0223821

Angelieri CCS, Adams-Hosking C, Ferraz KMPMdB, de Souza MP, McAlpine CA (2016) Using Species Distribution Models to Predict Potential Landscape Restoration Effects on Puma Conservation. PLoS ONE 11(1): e0145232. https://doi.org/10.1371/journal.pone.0145232

Barnhart PR, Gillam EH (2016) Understanding Peripheral Bat Populations Using Maximum-Entropy Suitability Modeling. PLoS ONE 11(12): e0152508. https://doi.org/10.1371/journal.pone.0152508

Fourcade Y, Engler JO, Rödder D, Secondi J (2014) Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias. PLoS ONE 9(5): e97122. https://doi.org/10.1371/journal.pone.0097122

The authors write (the numbers are mine)

1. Our results show that the distribution range is smaller and more highly fragmented than previously assumed.

2. The overall estimated density was very low (0.069 ±0.0019 wildcats/km 2 )

3. and the protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus in demographic and genetic terms.

4. Indeed, the most important areas remain unprotected.

5. Our main recommendations are to improve the protected area network and/or vigilance programs in hunting estates,

6. in addition to studying and improving connectivity between the main population patches.

Those are interesting results, but it’s far from clear how one goes from figure 1 to figure 2, to figure 3 and these conclusions.

For 1, the authors would need to document previous estimates and previous assertions that the range is continuous. The would also need to convince the reader that the range is fragmented. A quick look at Google Earth reminded me of what I remembered about this area: much of the land below 200m elevations is extensively converted to agriculture. Doñana, is the important exception.

** Our response: We exactly wrote: (L558-L559 in the first version) “Our results show that the distribution range of one of the largest populations of the European wildcat is actually lower than previously assumed.”

We acknowledge that a reference in the end of this sentence is necessary (ref. [92]) and a call to Fig 1, where we present the IUCN map for the European wildcat. We have added “(compare Fig. 1 and Fig. 2)” in L568-L569 of of “Revised ms”.

To address (2), it has a low density. The largest block of potential habitat is Sierra Morena. The authors models suggest the species is widespread there on the basis of intensive sample in the east, (points 1 to 9) and just one sample in the west (10). Some of the highest densities occur there — >0.2 cats per km2, but these predictions are outside the area of systematic sampling. I’d need this to be explained.

** Our response: we are using SDMs since it is not viable to conduct a complete field survey covering the whole Andalusian region. Therefore, please note that we did not carry out a systematic sampling. In the case of Sierra Morena, this is a homogeneous range (as we expose in “Study Area” section), but in any case our sampling was designed to cover most of it low landscape variability, as we expose in “Field Surveys” section:

L160-L166 in the first version: Twenty-two survey blocks (Fig 1) were distributed in Sierra Morena (10 blocks) and Sierras Béticas (12 blocks) between 2011 and 2015; this distribution was biased towards eastern Andalusia mainly because the habitats there are much more heterogeneous and they are representative of the other areas. All sampling blocks were selected first assuming that they were included in the potential habitats of the wildcat (see study area), and second, attempting to represent the main forest and scrubland types of the overall potential range within Andalusia.

This is where Maxent so often fails. The authors fall back on its black magic, when simple GIS mapping would be so much more important. The model predicts that the cat is missing from large areas of this Sierra’s national parks. Why? What’s wrong with the habitat? And how might one map that?

** Our response: We used MaXent as a tool to produce a spatial prediction of relative suitability of the different regions of Andalusia. The function produced by MaXent was used to predict how suitable one area is for wildcats. Then, as any other predictive mathematical tool, this function can be used to infer if wildcats can be present or not over different spatial points. To calculate number of potential wildcats in each suitable area we used the equation yield by a GLM performed with a subset of locations where wildcat density was known by means of SCR methods (see also Tôrres, N. M., De Marco, P., Santos, T., Silveira, L., de Almeida Jácomo, A. T., & Diniz‐Filho, J. A. (2012). Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics. Diversity and Distributions, 18(6), 615-627). Then, our estimates are the product of a step by step process where common analytical tools such as MaxEnt (for presence only modelling) and GLM (used with density data obtained for SCR methods) were used to estimate number of animals across the space. Indeed, this a very common methodology in conservation science, where predictive modelling has been used to cope with different conservation problems (translocations, efficient sampling of elusive species, effects of climate change or invasive species and some more. e.g. Rodríguez, J. P., Brotons, L., Bustamante, J., & Seoane, J. (2007). The application of predictive modelling of species distribution to biodiversity conservation. Diversity and Distributions, 13(3), 243-251;Guisan, A., Tingley, R., Baumgartner, J. B., Naujokaitis‐Lewis, I., Sutcliffe, P. R., Tulloch, A. I., ... & Martin, T. G. (2013). Predicting species distributions for conservation decisions. Ecology letters, 16(12), 1424-1435). This approach is much better than simple mapping of presence as suggested by referee. Simple mapping did not allows for extrapolation of other potential overlooked populations. It is important to remember that atlas dataset (the usual way to present a species distribution map) is very constrained by where volunteers are present, and even some large populations can be then be overlooked. We understand some people is not very happy with predictive modelling in conservation, but is one of the most usual and powerful analytical tools in modern Conservation Biology (see references above). Another question is if MaXent is the best option to produce a suitability or occurrence map. However, despite criticisms MaXent is one of the most prominent methods for analyzing presence-only data, and it has been proved robust and efficient compared to other statistical methods (GLM, GAM, GARP, Random Forest or Mahalanobis distance, e.g. Elith, J.,Graham, C.H.,Anderson, R.P.etal.(2006).Novel methods improve prediction of species’ distributions from occurrence data.Ecography,29,129–151.;Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists.Diversity and distributions, 17(1), 43-57). For these reasons, we are confident about the usefulness of our approach and statistical tools to predict best areas for wildcats and potential numbers at different regions of Andalusia. Moreover, please, keep in mind that we have gone further by carrying out an independent validation of our MaxEnt maps through radio-tracking data.

The southern distribution clusters into a southwestern area (points 21-23) and the eastern one that I will call the Sierra Nevada. The southwestern one predicts very little habitat and estimates very low densities. Why?

** Our response: Please, go to lines 542-546 of Discussion in the first version: “We detected a negative effect of rainfall on wildcat abundance, being the main reason for the low estimated densities within two of the largest and best conserved patches of Mediterranean forests in Spain: the Alcornocales and Sierra de Grazalema Natural Parks at the western Sierras Béticas”. We think it is well explained here. Moreover, we have consulted to several field biologists and naturalists working within this area and nobody gave us one confirmed record of wildcat. We deleted this sentence in an early version to reduce our ms.

The Sierrra Nevadas are well sampled (points 11-20), predicted to be habitat (figure 2). The densities are predicted to be low. In addition to these core areas is a substantial scatter of small, isolated patches with predicted very high densities of cats (>0.2), running north of points 22, 20 to 18. These are areas that weren’t surveyed. How do we know there are cats there and how to we know what the densities are? Their existence seems to be the key result in justifying the conclusion about fragmentation.

** Our response: we know the presence of wild cats in some of this patches (see the withe dot in between, Fig. 1; and from other data not considered in this ms, as road kills, poaching, sightings…). In any case, this small and scattered patches represent a very marginal contribution to the Betic subpopulation, and huge fragmentation affects to eastern Betic Mountains range, where the main meta-population of the Betic Mountains survives. Thus, the very small patches running north of points 22, 20 to 18 are not really a key result in justifying the conclusion about fragmentation.

It also seems to be critical in point 4, since Doñana, Morena, Sierra Nevada, and the southwest do have extensive protected areas. The authors have not convinced me that that there are many cats outside these areas.

** Our response: Please, see Table 5. Sorry, it is not a matter of opinion.

Finally, points 5 and 6 need specifics. Where exactly would put new protected areas? And where are the hunting estates that one might wish to influence?

** Our response: On the reviewer’s first question, the creation of new protected areas (or increasing the current ones) must be solved by the Environmental authorities. Our study obviously is of great value to them, it is one of our practical application aims for conservation in practice in a short time (it is really urgent for Mediterranean wildcats!!!), but our specific goals fall out this key action, which requires more in depth research including key sociological aspects. On the second question, we provide a map (fig .3) that helps the Environmental authorities to detect the priority hunting estates that one might wish to influence.

In sum, I trust the authors instincts and experience in the conclusions they present. But they should start from them and work backward to justify each one with the simplest analyses possible.

** Our response: We hope that our explanations correctly solve the doubts raised by the reviewer.

Reviewer #2: Overall this is an excellent paper. The authors SDM approach to this question is robust.

** Our response: We sincerely acknowledge the reviewer for thinking that our work is an excellent paper.

Some of their English phrasing needs a bit of work.

** Our response: we have added all the corrections provided below by rev.2. The English was revised by a professional.

However, addition of genetic data would make this a much stronger paper and so it is not clear to me why the authors did not collect tissue or blood samples from at least the 9 radio-tagged animals for genetic analysis?

** Our response: actually we collected samples of all of them, and some of these individuals were genetically analyzed, please see ref [30], where 17 wildcats of our study area genetically analyzed were examined. Indeed, these wildcat sample has been recently analyzed using SNPs, confirming no domestic cat introgression (Mattucci, F., Galaverni, M., Lyons, L.A. et al. Genomic approaches to identify hybrids and estimate admixture times in European wildcat populations. Sci Rep 9, 11612 (2019) doi:10.1038/s41598-019-48002-w). We have added this in the new version (L198-L199).

And why hair snares were not put out at the camera locations? Even though they state that “Three types of non-intrusive field surveys have been successfully developed for the wildcat: camera-trapping, scat sampling for molecular identification and hair sampling, also for genetics. We used the first two methods, while ongoing field studies by our team are showing a lesser efficiency of hair traps in our study area.” They do not report on any genetic analyses - even if the hair snares are not very efficient they could provide at least some samples for genetic analysis.

** Our response: please, see previous response. And, on the other hand, we were not able to collect hair samples since our wildcat rarely rubbed in our baits, which were checked with camera traps.

Also it is curious why they did not put out live traps after identifying animals with the camera traps in order to try and capture these individuals – seems an opportunity lost.

** Our response: it was logistically inviable in our large-scale survey. We only could capture wildcats in block 18, as we explain in the text.

As a result they rely on visual determination of hybrids which is sketchy at best.

** Our response: we detected 50 putative pure individuals (Table 1), following a strict protocol for identification (see ref [30], cited in Methods); the phenotypic traits of genetically pure individuals of our study area are very constant, and we only considered typical wildcats as “pure” (most of them identical to WC1 in fig. 2 of [30]). We did not explain here the identification protocol since it is well explained elsewhere, thus, we put only the references (e.g. references [30] and [47]). Our three detected hybrids where very close to typical wildcats but they had wide white patches in pelage. We have added this in the new version (L199-L204).

They have no estimates of effective population size or genetic variation and they do not identify or at least to not report numbers of males and females. Therefore they can estimate densities but and population size but this doesn’t translate into how viable these populations are. If they have any genetic data they should include it.

** Our response: Sorry, but it was not our goal to run a PVA.

Despite the lack of genetic data this is still a very valuable contribution to the literature on European wild cats.

** Our response: We are very grateful to the reviewer.

Lines 31-32 numerous grammatical mistakes in the first sentence

“On” should be “of”

** Our response: done

Delete the in “the population size”

** Our response: done

Population dynamics not just dynamics

** Our response: done

Conservation actions not conservation measures

** Our response: done

Line 32 delete “huge numbers of examples” and add many examples

** Our response: done

Line 49 “… and the protected areas network seems to be insufficient to cover a significant part of the population or a viable nucleus in demographic and genetic terms.” You don’t have any genetic data so this is pure conjecture.

** Our response: We have delete “genetic” in the new version.

Lines 57-58 see comments above

** Our response: done

Line 116 hybridization not hybridization

** Our response: done

Line 122 capitalize the f in fig.1

** Our response: done

Line 132 add “and is densely populated by humans” after cereal crops and delete the next sentence.

** Our response: done

Line 133 add “in this area” after wild landscape

** Our response: done

Line 163 delete “they are” in front of representative

** Our response: done

Line 200 what does “conceptual groups” mean?

** Our response: climatic, relief, vegetation, water availability and human presence: we have added this in L208-L209 of the new version.

Line 251 capitalize fig.

** Our response: done

Submitted filename: Response to Reviewers.docx

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27 Dec 2019

Fragmentation and low density as major conservation challenges for the southernmost populations of the European wildcat

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9 Jan 2020

PONE-D-19-27725R1

Fragmentation and low density as major conservation challenges for the southernmost populations of the European wildcat

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