Spatiotemporal dynamics of free-ranging cats in a peri-urban insular protected area

Spatiotemporal dynamics of free-ranging cats in a peri-urban insular protected area | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal dynamics of free-ranging cats in a peri-urban insular protected area Edie Abrahams, Elena J. Soto, Kane Powell, Eduardo Nóbrega, João Nunes, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8086980/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Across most of their nearly global range, free-ranging cats occupy a complex position at the intersection between invasive species and companion animals, making their management particularly challenging. Despite their cultural and emotional value, they have severe impacts on biodiversity, particularly in insular ecosystems where they frequently prey on conservation-sensitive species. We conducted protected area–wide camera trap surveys in 2021 and 2023 to investigate the spatiotemporal dynamics of free-ranging cats on Madeira Island, off northwest Africa. Using data from 588 trap-nights in 2023, we identified 30 individual cats from 276 detections, indicating a population turnover of 76%. Cat density was estimated at 1.5 cats/km 2 − 7% higher than in 2021, corresponding to a population of up to 58 individuals. Cat activity increased over time and with greater open/rocky area cover and cat abundance was negatively associated with rodent activity. Compared to 2021, cat activity was significantly higher at closer proximities to human food resources, while open/rocky areas and rodent activity no longer had a detectable effect. The population of free-ranging cats is increasing within the protected area, and their activity appears less habitat-specific and more dependent on human food subsidies. These findings underscore the importance of revisiting policies that may inadvertently promote cat population growth, and of implementing evidence-based management to reduce their ecological impacts and protect vulnerable native species. camera traps Felis catus invasive species Macaronesia Madeira Island Natura 2000 Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Invasive alien species are recognized as one of the five major direct drivers of global biodiversity loss (IPBES, 2023 ), and free-ranging domestic cats ( Felis catus ) are among the most widespread and impactful of these invaders (Crowley et al., 2020 ; Loss et al., 2022 ). Since their domestication from African wildcats ( Felis sylvestris lybica ) around 9,500 years ago (Twardeck et al., 2017 ), domestic cats have been introduced to most areas inhabited by humans, and their population has expanded to an estimated 600 million individuals throughout their range (Cove, 2023; Lepczyk et al., 2023 ). This near global expansion has greatly increased their ecological influence, as free-ranging cats now prey on more than 2,000 species worldwide, over 16% of which are of conservation concern (Lepczyk et al., 2023 ). The impacts of free-ranging cats are particularly detrimental in insular systems (Doherty et al., 2016 ), where they often become dominant predators. This is further exacerbated by the ecological naïveté of many island species, which, due to prolonged evolutionary isolation, often lose key defences against novel predators, making them especially susceptible to predation by invasive mammals (Azumi et al., 2021; McCreless et al., 2016). To date, cats have been implicated in at least 63 vertebrate extinctions worldwide (Doherty et al., 2016 ). Besides direct predation, these invaders impact native species through competition, disease spread, and broader ecological disruptions (Carrete et al., 2022 ; Rando et al., 2020), which are likely to severely impact the natural abundances of naïve prey. Nevertheless, free-ranging cats have been historically disregarded as agents of ecological change, potentially owing to their liminality as both charismatic pets and wild animals (Crowley et al., 2020 ; Gramza et al., 2016 ). For instance, cats are not listed in the European Commission’s list of Invasive Alien Species of Union Concern (1141/2016/EC). In addition, several countries across Europe have seen recent shifts in cat management which may exacerbate their regional impacts (Carrete et al., 2022 ). On Madeira Island, Portugal, a 2016 decree banned the euthanasia of unowned animals, and current national proposals advocate for increased support to stray cat colonies (ICNF, 2023 ). Population responses to such policies may occur with a time lag, underscoring the need for well-designed, sustained ecological monitoring (Simberloff et al., 2024 ). Free-ranging cats are influenced by both human-provided food sources and environmental characteristics such as vegetation structure. Their densities are typically higher in areas with access to anthropogenic food subsidies than where they rely solely on hunting (Bengsen et al., 2016; Hall et al., 2016; Kays et al., 2020 ). Habitat preferences vary among studies with some reporting greater use of structurally complex habitats (e.g., Hohnen et al., 2016), whereas others find higher hunting success in open or sparsely vegetated areas (e.g., McGregor et al., 2015). Moreover, cats may interact with other invasive species, such as rodents, which can subsidise their populations, eventually enabling them to persist at higher densities (Abrahams et al., 2025 ). Across Madeira Island, both owned and unowned free-ranging cats are common and often occur together (Soto et al., 2023 ). These cats prey upon several threatened species, such as the IUCN Endangered Zino’s Petrel ( Pterodroma madeira ) (Zino et al., 2001 ) and Madeira Pipistrelle ( Pipistrellus maderensis ) (Rocha, 2015). Their diet also includes the endemic Madeiran Wall Lizard ( Teira dugesii ), alongside introduced mammals including house mice ( Mus musculus ), rats ( Rattus spp.), and European rabbits ( Oryctolagus cuniculus ) (Medina et al., 2010 ; Galao et al., 2025 ). In the Ecological Park of Funchal, a peri-urban protected area in Madeira Island, Soto et al. ( 2023 ) observed in 2021 that cats were abundant throughout the reserve, and that their core home ranges were associated with rocky outcrops, likely due to greater availability of shelter and prey such as the Madeira wall lizard ( Teira dugesii ). They were not particularly associated with anthropogenic food sources, suggesting that they were probably relying mostly on wild prey to fulfil their dietary needs. Here, we revisit the camera trap-surveys conducted in 2021 by Soto et al. ( 2023 ) to examine the spatiotemporal ecological dynamics of free-ranging cats within the Ecological Park of Funchal. By monitoring the same sites in 2023, we assessed cat density and their ecological drivers five and seven years, respectively, after legislation banning the euthanasia of unowned cats was enacted. Specifically, we investigated: (1) free-ranging cat density, population size and turnover rate between surveys; (2) the direct effects of environmental drivers and survey year, as well as the indirect effects mediated through rodent activity, on cat activity (number of camera-trap detections) and abundance (number of individuals); and (3) how these relationships changed between the 2021 and 2023 surveys. Given recent legislative changes regarding unowned animal control in Madeira, we predicted an increase in free-ranging cat density over time. Considering possible increases in food provisioning to stray colonies, we further expected cats recorded in 2023 to show greater association with anthropogenic food subsidies. Lastly, if the cat population is expanding spatially, we anticipated lower habitat specificity, reflected in a reduced number of environmental variables - other than human food subsidies – significantly affecting cat activity compared to 2021. Material and Methods Study area Data was collected in the Ecological Park of Funchal, on Madeira Island, Portugal (Fig. 1 ). Madeira is the largest island of the Madeira archipelago, encompassing Madeira, Porto Santo, and the Desertas Islands. The island is an autonomous region of Portugal, situated around 900 km from the mainland. A census in 2023 recorded the island’s population at over 250,000 people, of which ca. 100,000 were living in Funchal (INE, 2023). The island is characterised by a subtropical climate, with mean daytime temperatures between 20 and 26 \(\:^\circ\:\) C, and varying levels of rainfall, accumulating from 2 mm in July to 115 mm in December (Weather Atlas, 2024). The island features an altitudinal progression of distinct vegetation belts, including xerophytic, sclerophyllous flora; the UNESCO-protected Laurisilva forest, and high mountain shrubland (Capelo et al., 2005 ). The Ecological Park of Funchal (7.5 km 2 ) is located North of Funchal capital city, between 470–1818 m asl and was established in 1994. It is an Important Bird Area, a Site of Community Importance, a Special Protection Zone, and is listed in the Natura 2000 Network (Silvestre, 2011 ). It is also a popular tourist destination, attracting many outdoor enthusiasts. Its vertebrate fauna includes over 25 confirmed breeding bird species, including both Madeiran (e.g., Regulus madeirensis and Fringilla maderensis ) and Macaronesian (e.g., Anthus berthelotii ) endemism. Manx shearwaters ( Puffinus puffinus ) are severely depleted, and the first two active nests were discovered in 2005 in the Santa Luzia valley, within the Ecological Park of Funchal (Nunes et al., 2010 ). In addition to free-ranging cats, vertebrate diversity includes three species of bats, one reptile ( Teira dugesii ) and non-native rodents ( Rattus rattus , R. norvegicus and Mus musculus ), rabbits ( Oryctolagus cuniculus ) and ferrets ( Mustela furo ) (Gonçalves et al., 2024 ; Soto et al., 2023 and 2024 ). Cat-human dependency exists on a continuous spectrum, generating ambiguity in exactly when a free-ranging cat becomes “feral”. Here, we refer to “free-ranging cat” as any owned or unowned cat that has some degree of outdoor access (Crowley et al., 2020 ; Farnworth et al., 2010 ). Camera-trapping surveys Camera-trapping took place on two occasions: August-November 2021 and September 2023. The 2023 survey replicated the design of Soto et al. ( 2023 ), who divided the park into eleven 1 km 2 grid cells (Fig. 2 ). The chosen area for each grid reflected average home-range sizes for cats in comparable conditions (López-Jara et al., 2021 ; Zhang et al., 2022 ). Three camera-traps were deployed in each grid, totalling 33 camera locations. As in Soto et al. ( 2023 ), two out of three camera locations were strategically deployed in known cat pathways, whereas the third location was randomly selected using QGIS, Version 3.30.2 (Sherman, 2023 ). Cameras were largely deployed at the same locations in both surveys. However, three camera locations (i.e., locations 3.3, 5.2 and 5.3—see tables S1 & S2) had to be moved between surveys due to changes in the landscape (e.g., landslides and management interventions), that rendered the original sites inaccessible. Cameras were placed at least 300 m apart within each grid cell, elevated 50 cm above the ground, and attached to wooden stakes or suitable tree trunks. The cameras were locked to their attachment points with a braided steel cable lock, and a sign was attached explaining the purpose of the survey to deter theft (Clarin et al., 2014 ). Most of the cameras were from the model Browning Dark OPS Pro X (29 cameras), but additional models included Browning 2020 Patriot (three cameras) and Bushnell Core (one camera). As in Soto et al. ( 2023 ), cameras were set to take three photos when triggered by an infrared motion sensor (at normal sensitivity), with minimum interval between photos and a 30 second interval between photo sets. To avoid false triggers and improve detectability, we cleared obstructing vegetation within a 5 m radius around the camera. Cameras were baited with a mixture of chorizo and oil, identical to the bait used by Soto et al., ( 2023 ). In 2023, all 33 cameras were left simultaneously active in the field, drawing a contrast to Soto et al., (who had a limited number of camera traps and therefore employed a more clustered study design). However, sampling effort was equally distributed across surveys, amounting to 582 and 588 trap-nights in 2021 and 2023, respectively. Camera detections and identification Photos were manually analysed using Timelapse Image Analysis software, version 2.3.0.8 (Greenberg, 2023 ). Following Soto et al. ( 2023 ) and several other camera-trap studies (e.g., Garvey et al., 2022 ; Khan et al., 2023 ), we considered detections to be independent when animals were recorded more than 30 minutes apart, or, in the case of cats, when different individuals were captured (Wearn & Glover-Kapfer, 2019 ). Wherever possible, cats were identified to the individual level from their pelage and morphological features, such as wounds or notched ears, in line with previous studies (Elizondo & Loss, 2016 ; Hohnen et al., 2023 ). Any cat record that could not be identified at the individual level (due to poor photo quality) was excluded from the cat density or abundance estimates. Although we considered kittens in both density and abundance estimates, this likely did not constitute recruitment during the survey nor violate model assumptions of a closed population. Kittens are altricial for the first three weeks of life, so would not be able to roam on their own, as all kittens in our images were observed to be doing (Spotte, 2014 ). Previous studies excluding kittens from density estimates had a longer duration (several months to years), with more cause to assume that a kitten could be born and reared during the sampling period (Juhasz et al., 2022 ). Explanatory variables In both 2021 and 2023, we measured several abiotic and biotic variables that were hypothesised to influence cat abundance and activity (Table S6), including the cover of (1) open/rocky area, (2) native shrubland, (3) non-native shrubland, (4) native trees, and (5) non-native trees. Each of these metrics were calculated for a circular area with a 300 m radius around each camera trap using orthophotos from the Regional Directorate for Spatial Planning in Madeira (IRIG, 2018 ). We also measured the (6) distance to a water source ( water-dist ), described as the two main water courses running through the park: the Ribeira das Cales and the Ribeira de Santa Luzia. This was done using QGIS, with GIS layers supplied by coordinating authorities of the protected area (IRIG, 2018 ). We recorded the (7) altitude and (8) slope at each camera trap location using a digital elevation model with a 10 m resolution (IRIG, 2018 ). Relating to human influences on cat ecology, we measured the (10) distance to urban area ( urb-dist ) and (11) distance to human resource subsidies ( food-dist ), defined as any area where cats could access anthropogenically sourced food (e.g., picnic benches, rubbish bins, rental accommodations, an astronomy observatory, and restaurants). These two variables were measured using the same orthophotos (IRIG, 2018 ). We also considered the activity of rodents given by the number of camera-trap detections of either Mus musculus , Rattus rattus or Rattus norvegicus . Data analysis Cat population density and turnover Efficiency of cat capture was calculated as the number of cat detections for all cameras divided by the total number of trap-nights and multiplied by 100. We calculated turnover rate for the cat population with the same formula used by Dul’a et al. (2021): ( number of individuals recorded in 2021 but not in 2023 / total number of individuals recorded in 2021 ) \(\:\times\:\) 100 (Dul’a et al., 2021). As per Soto et al. ( 2023 ), cat density (number of cats/km 2 ) was estimated for the 2023 survey using Spatially Explicit Capture–Recapture (SECR) models. This approach allows to incorporate a spatial aspect to traditional statistical capture-recapture models by including trap locations, and therefore, the location of each detection (Broekhuis & Gopalaswamy, 2016 ). Capture probability is dependent on the distance of the animal from the detector (Borchers, 2012 ), allowing for a non-uniform distribution of sampling effort across a study area (Efford & Fewster, 2013 ). Individuals are assumed to move randomly around the sampling area, whereby the sum of their activity centres constitutes the estimate of density (Green et al., 2020 ). SECR models were fitted using the ‘secr’ R package (Efford, 2023a ). We made a data frame of capture records for cats with the trap ID, session (survey), occasion of detection and cat ID. To generate a capture histogram, we combined this capture record with an array of cameras, detailing trap ID and their XY coordinates, including usage (reflecting recording effort). The ‘count’ detector function was used, as our data consisted of a vector of counts per detector. We assumed that cats in the Ecological Park followed a homogenous Poisson spatial distribution throughout the sampling period. When fitting SECR models to this data, we selected the Halfnormal (HN) detection function for the data, as it reached a prompt plateau from a plot of effective sampling area (ESA) and ensured comparability with the 2021 survey (Figs. S2 & S3). To represent the area in which the target species could potentially occupy, we used two different approaches and ran SECR models with two different habitat mask areas. For the best suitability to our 2023 data and study design, we applied the suggest.buffer() function to our capture histogram, specifying a radius for the habitat mask around detectors (Efford, 2023b ). This buffer value was tested for suitability from the ESA plot. With this method, we selected a 1700 m-buffer around each camera, resulting in a habitat mask area of 39.24 km 2 . However, to be more comparable with the 2021 study, we also created SECR models with a 1200 m-buffer and a 26.01 km 2 habitat mask area, as was used by Soto et al. ( 2023 ). SECR density estimates tend to be robust against a range of study designs and detection probabilities (Efford & Fewster, 2013 ), but the estimated population size of cats in the study area is heavily influenced by the area of the habitat mask, so we presented estimates using both masks in acknowledgment of this fact. Using the SECR models, we also calculated Mean Maximum Distance Moved (MMDM), which was the maximum average distance moved by an individual between detections, and the spatial scale of movement, which reflected how far an animal moved within their home range during the sampling period. Finally, we estimated free-ranging cat population size by multiplying cat density by the habitat mask area. Direct and indirect drivers of cat activity and abundance We investigated the direct drivers of cat activity and abundance, and indirect drivers as mediated through rodent activity using Piecewise Structural Equation Models (SEMs) from the “piecewiseSEM” package from R (Lefcheck, 2023 ). SEMs comprise pathways representing hypothesized causal relationships between variables, so can be used to quantify indirect effects that may be overlooked by any single model. Piecewise SEMs further allow analyses to be carried out for smaller sample sizes (Lefcheck, 2016 ). We generated one piecewise SEM for cat activity and another for cat abundance. Prior to running the Piecewise SEMs, we applied a z-score standardization to the variables regarding cover of open/rocky area, native and non-native shrubland and trees, and run a Principal Component Analysis (PCA) with those variables. The scores from the PC1 were retained as an explanatory variable named “ vegetation simplification ” (describing a gradient from tree to shrubland cover, Fig. S5) as a predictor variable, reducing habitat dimensionality (e.g., Ferreira et al., 2017 ). We then examined inter-variable correlation and removed variables that were highly correlated (r > 0.70) from our analysis. In this case, both altitude and slope were highly correlated with urb-dist , as the city of Funchal is located at the base of a valley, so neither altitude nor slope were included in our models. Each SEM included two Generalized Linear Mixed Models (GLMMs): one relating rodent activity with open/rocky area, food-dist, urb-dist, water-dist and vegetation simplification, and another relating cat activity/abundance with open/rocky area, food-dist, urb-dist, water-dist , vegetation simplification and year of sampling. A random variable regarding the grid identity was retained to account for the spatially nested placement of the camera-traps within the grids. We applied Shipley’s test of directed separation to ensure no important relationships were missing from the SEM basis set (Shipley, 2000 ) and evaluated models by reporting standardised estimates of relationships and R 2 for response variables. Indirect effects of environmental variables on cats as mediated through rodent activity were obtained by multiplying the corresponding standardised estimates (Lefcheck, 2016 ). Cat activity drivers in 2021 and 2023 Given that the year of sampling was an important predictor of cat activity, we further examined the relationship between cat activity and the above-mentioned environmental variables (i.e., rodent activity, open/rocky area, food-dist , urb-dist , water-dist and vegetation simplification) separately for each of the sampling years and compared whether cat activity was still being affected by the same factors. Cat activity was modelled using GLMMs. We generated four GLMMs: namely, cat activity and abundance in 2021 and in 2023. These models consider both fixed effects (environmental covariables), and random effects (variation among grids) that may influence cat abundance/activity. We considered 11 environmental covariables (detailed above). We also included an offset accounting for the number of camera trap-nights (log 10 x ) which was further excluded from the analysis as it did not provide any additional explanatory power. We applied the “dredge” function to GLMMs to evaluate all combinations of fixed effects, followed by “model.avg” to help tackle model selection uncertainty. Both functions are found within the “MuMIn” R package (Barton, 2023 ). The residuals of the final models were evaluated with the “DHARMA” package (Hartig, 2022 ; Figs. S7 and S8). All data analyses carried out in R version 4.3.2 (R Studio Team, 2023). Results In 2023, we obtained 1227 independent detections, mostly from rats (566 detections, 46.1% of the total) and cats (276 detections, 22.5%). Cat capture efficiency was 46.9 detections per 100 trap-nights. A total of 30 individual cats were recognised, marking a 20% increase in sampled individuals from 2021. Photographed cats could not be identified in 12 occasions (Table S1 ). The spatial distribution of cat activity and abundance shifted between 2021 and 2023 (Fig. 2 ). In 2021, most cat activity occurred in the most southern portion of the park (Fig. 2 a), whereas in 2023 it was mostly concentrated in the central grids (Fig. 2 b). In 2023, cat activity per grid ranged from 0 to 48 records (mean \(\:\pm\:\:\) SD = 24; \(\:\pm\:\:\) 14.0), with grid 5 showing the highest number of records (n = 48) from seven individuals (Fig. 2 b, d). Activity per camera ranged from 0 to 24 records (8 \(\:\pm\:\:\) 6.9). Cat abundance was also more concentrated in the central grids in 2023, relative to 2021 (Fig. 2 c and d), peaking in grid 8 where 12 cats were recorded (44 detections). Ultimately, grid 8 accounted for 40% of the individuals . The most recorded cat was observed in 33 instances across grids 6, 8 and 9, and the second most recorded cat was observed in 23 instances across grids 5, 6, 7, 8 and 9. Mean Maximum Distance Moved (MMDM) is the average maximum distance between detections of an individual, and was recorded at 1084.5 m. Free-ranging cat density, population size and turnover rate between surveys In 2023, the maximum detection probability at each camera (g0) was 0.22 (95% CI: 0.21–0.23), compared to 0.17 (0.13–0.24) in 2021. The spatial scale of movement (σ) increased from 387 m in 2021 (342.23–439.14) to 426 m (408.53–444.44) in 2023. The estimated population density of free-ranging cats was 1.5 cats/km 2 (95% CI: 1.0–2.1), representing a 7% (0.1 cats/km 2 ) increase from 2021 (1.4 cats/km 2 ; 95% CI: 0.94–2.1; Table S7). Estimated population size within the surveyed area varied with the specified buffer around each detector. From the model using a 1700 m-buffer around each detector, the estimated population size rose from 36 cats in 2021 (25–55) to 58 cats (39–82) in 2023. From the model with a 1200 m-buffer as used by Soto et al. ( 2023 ), the estimated population size rose from 36 cats in 2021 (25–55) to 39 cats in 2023 (27–55) (Tables S7 & S8). Six individuals were detected in both sampling years (Table S3), representing a population turnover rate of 76%. Notably, one individual had lost an eye between surveys (Fig. 2 c, d). Camera-trap images also revealed five instances of trophic interaction, including one cat preying on a pregnant rat (Fig. S2), and a mother-offspring pair (Fig. 2 b). Direct and indirect environmental drivers of cat activity and abundance Cat activity directly increased with time (β = 0.26, p < 0.01) and with the proportion of open/rocky area (β = 0.29, < 0.05; Fig. 3 a). Rodent activity - negatively influenced by vegetation simplification (β = − 0.49, p = 0.11) and distance to water (β = − 0.34, p = 0.16)— had a negative effect on cat activity (β = − 0.69, < 0.01), while vegetation simplification and distance to water had a positive indirect effect on the cat activity (β = 0.34 and 0.24, respectively; Fig. 3 a). Cat abundance was only negatively affected by rodent activity (β = − 0.39, p < 0.05; Fig. 3 b; Tables S12 & S13). Changes in cat activity drivers between 2021 and 2023 Between surveys, the effect of distance from human food resources on cat activity shifted from positive (β = 0.26, p < 0.05) in 2021 to negative (β = -0.43, p < 0.05) in 2023, while previous effects of open/rocky areas (β = 0.26, p < 0.05) and rodent activity (β = -0.33, p < 0.05) were no longer evident (Fig. 4 ). Discussion Mounting evidence highlights the detrimental impacts of free-ranging cats on natural ecosystems (Loss et al., 2022 ; Szentivanyi et al., 2023 ), yet data on their spatial ecology – and especially their temporal dynamics – remain rare. Here, we uncovered an increase in the estimated cat population size within the Ecological Park of Funchal from 36 in 2021 to 58 individuals in 2023. This represents a 7% rise in density, from 1.4 to 1.5 cats/km 2 . Over this period, cat activity also increased. Notably, the effect of distance to human food resources on cat activity shifted from positive in 2021 to negative in 2023, while open/rocky areas and rodent activity ceased to exert significant influence. These results suggest that the free-ranging cat population is becoming increasingly associated with anthropogenic food sources and exhibiting reduced habitat specificity. These results have implications for the management of free-ranging cats in protected areas, as actions that supplement cat activity may be in violation of several nature conservation laws regarding invasive species control, management of protected areas, and protection of threatened natives species (Trouwborst et al., 2020 ). Considering the larger habitat mask area as the most representative for our 2023 study, our results indicate a population of up to 58 individual cats in this protected area. Irrespective of the chosen habitat mask area, our results correspond to a density increase of 0.1 cats/km 2 over two years. Despite inherent limitations in contextualizing these estimates, our findings align with previous studies in protected areas, including 1.5 cats/km 2 in a Spanish Natura 2000 site (Lázaro et al., 2024 ), 0.7-1.0 cats/km 2 in the World Heritage Area of Auckland Island, New Zealand (Glen, 2022 ), and 2.65 cats/km 2 in the Solomon Islands (Lavery et al., 2020 ). In our study area, dietary analyses combining DNA metabarcoding and morphological scat examination revealed that a single cat can consume over 90 lizards and 100 passerines per year (see Table 2 in Galao et al., 2025 ). If the dietary composition remained consistent, and for estimates for the best suited masks for each of the datasets, the observed increase from 36 cats in 2021 to 58 cats in 2023 could have resulted in the additional predation of > 2,000 reptiles and > 2,200 passerines annually (adding to an estimated > 11,000 native prey per year, for the estimated mean cat population). To our knowledge, this study also provides the first estimate of free-ranging cat population turnover in an island system − 76% within two years. High turnover rates may indicate elevated abandonment, fecundity, or mortality rates (Dul’a et al., 2021). Anecdotal evidence of at least four kittens during the 2023 survey supports high reproductive rates, consistent with other studies estimating 1.0 and 1.6 litters/year for feral and semi-feral cats, respectively (Schmidt et al., 2007 ). Additionally, free-ranging cats tend to have shorter lifespans due to exposure to environmental extremes, intraspecific aggression, and disease (Lepczyk et al., 2022 ; Loyd & DeVore, 2010 ), and their dynamics can be strongly influenced by human pet ownership patterns (McDonald et al., 2023). The observed turnover likely reflects a combination of these factors. High turnover can lead to changes in population structure, which in turn affects the robustness of abundance estimates (Harmsen et al., 2017 ). Yet, such demographic instability is not unique to our study population; comparable levels of individual replacement have been reported in other felid populations, including Carpathian lynx (46.3% turnover in five years, Dul’a et al., 2021); jaguars (63.6% turnover in 14 years, Harmsen et al., 2017 ), and Geoffroy’s cat (89% turnover in 2 years, Pereira et al., 2011 ). These findings suggest that high turnover may be a common feature of carnivore populations - being them composed of wild, native species, or non-native invasives. Nonetheless, in an insular system where cats face no interspecific predation, a turnover of 76% within two years likely reflects elevated mortality, suggesting poor welfare among free-ranging cats. Road traffic accidents are a major cause of mortality in free-ranging cats (McDonald et al., 2017 ), and fatalities linked to vehicle collisions are often observed in – and around – our study area. This situation poses risks not only to the cats themselves but also to humans, who may be involved in road accidents caused by the presence of free-ranging cats (e.g., collision with other vehicles, while trying to avoid a collision with a cat). Our SEM analysis suggests that the cover of open or rocky areas - habitats where lizards are commonly found (Pacheco, 2008 ) - positively influenced cat activity, whereas rodent activity was negatively associated with both the activity and abundance of free-ranging cats. This pattern indicates that while cats may prefer habitats rich in lizards, they are less frequently active in areas of high rodent activity. Despite their substantial consumption of rodents (Galao et al., 2025 ), this finding suggests that cats may contribute little to rodent suppression across the park. Lastly, our results also reveal shifts in environmental associations over time, with cats exhibiting stronger links to anthropogenic food sources in 2023. Combined with the high turnover rate, this may reflect a shift toward a greater proportion of stray (human-subsidized) cats relative to feral individuals (Bonnaud et al., 2011 ; Lepczyk & Calver, 2022 ; Medina et al., 2016 ). Importantly, even cats benefiting from human-subsidized food capture and consume wild prey (Woods et al., 2003 ; Cecchetti et al., 2021 ). For instance, in Australia, ca. 40% of stray cats consumed wildlife, including conservation-sensitive endemic species (Crawford et al., 2020 ). Conclusion Overall, our findings suggest that in the aftermath of a decree banning the euthanasia of unowned animals, the numbers of free-ranging cats inhabiting the protected area home to our study are potentially expanding. This situation suggests that current management practices may not meet obligations from the EU Nature Directives, which require that the free roaming of cats is “ forbidden and effectively prevented ” (Trouwborst & Somsen, 2020 ). The increasingly positive associations between cat activity and anthropogenic food sources likely reflect increased provisioning of stray cats, a trend that may exacerbate ecological impacts by supporting greater cat densities (Herrera et al., 2022 ; Tennent & Downs, 2008 ). Furthermore, the observed high individual turnover likely reflects elevated mortality, fecundity, or rates of abandonment raising concerns about the welfare of these animals in wild environments. The political landscape regarding cat management can be as dynamic as populations themselves (Crowley et al., 2017 ). However, effective management decisions must be grounded in robust scientific evidence. The results of this study provide a compelling case study of a protected area experiencing ongoing ecological pressures from free-ranging cats - a threat that appears to be intensifying relative to 2021. To mitigate the impacts of this invasive species and meet conservation obligations, authorities must translate such evidence into targeted, science-based management interventions. Declarations Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study was funded by the Department of Biology, University of Oxford. Author Contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Edie Abrahams, Elena J. Soto, Kane Powell, Eduardo Nόbrega, and Ricardo Rocha. Data analysis was performed by Edie Abrahams. The first draft of the manuscript was written by Edie Abrahams and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. 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Oryx 35(2):128–136. https://doi.org/10.1046/j.1365-3008.2001.00165.x Supplementary Files AbrahamsetalSupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 14 Dec, 2025 Reviewers invited by journal 05 Dec, 2025 Editor invited by journal 22 Nov, 2025 Editor assigned by journal 15 Nov, 2025 First submitted to journal 13 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8086980","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555846121,"identity":"6528a7c8-f71b-462f-b3bd-2663c0dc4204","order_by":0,"name":"Edie Abrahams","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Edie","middleName":"","lastName":"Abrahams","suffix":""},{"id":555846122,"identity":"64d63e84-e3e6-4b0c-9839-edc930ea2fe4","order_by":1,"name":"Elena J. Soto","email":"","orcid":"","institution":"MARE-Madeira: Centro de Ciencias do Mar e do Ambiente Polo da Madeira","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"J.","lastName":"Soto","suffix":""},{"id":555846123,"identity":"3768d4e8-4aa8-417b-8026-e6ce8738968e","order_by":2,"name":"Kane Powell","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Kane","middleName":"","lastName":"Powell","suffix":""},{"id":555846124,"identity":"9be2722e-c6c1-4117-aee6-ab9108f5d5a5","order_by":3,"name":"Eduardo Nóbrega","email":"","orcid":"","institution":"Câmara Municipal do Funchal: Funchal","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Nóbrega","suffix":""},{"id":555846125,"identity":"ced2b06b-95a4-4afd-bed8-e45c7c0cc4ce","order_by":4,"name":"João Nunes","email":"","orcid":"","institution":"Município do Funchal: Funchal","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"","lastName":"Nunes","suffix":""},{"id":555846126,"identity":"d54aa7ef-633e-4008-adb6-6e9e0ce40a9c","order_by":5,"name":"Ana Filipa Palmeirim","email":"","orcid":"","institution":"Université Libre de Bruxelles: Universite Libre de Bruxelles","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Filipa","lastName":"Palmeirim","suffix":""},{"id":555846127,"identity":"db779a9b-6c27-4a99-ac54-535405d7b49b","order_by":6,"name":"Ricardo Rocha","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2757-7347","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Rocha","suffix":""}],"badges":[],"createdAt":"2025-11-11 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16:23:20","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213202,"visible":true,"origin":"","legend":"","description":"","filename":"BINVD25008450structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/24100b8afb82aaeb2d767b11.xml"},{"id":97813436,"identity":"b30c13c2-8bcf-4a8a-b9c6-f81d6dcb04b7","added_by":"auto","created_at":"2025-12-09 16:23:19","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":229375,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/c98df3ce990daf54ef4ce055.html"},{"id":97813392,"identity":"a5ea18af-fd2d-4fd4-b68a-ce53f45ee776","added_by":"auto","created_at":"2025-12-09 16:23:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1741914,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the location of the Ecological Park of Funchal in Madeira Island, Portugal. Randomly allocated camera locations are shown as white dots; strategically chosen camera locations as black dots. Grid cells are numbered in the top corners.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/7c2ec79345598779c5d934bd.png"},{"id":97813401,"identity":"64c9dd5a-1cdb-4ffc-aa29-c8c95dfa6105","added_by":"auto","created_at":"2025-12-09 16:23:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1699038,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the total number of cat detections (cat activity; a and b) and total number of cat individuals detected (cat abundance; c and d), per grid, for 2021 and 2023. The only known location for Manx shearwater nests on the island is indicated by a blue triangle. Images of some of the sampled cats are included, with the grid the images were captured in.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/0528e42c021cb928c925c7e1.png"},{"id":97813403,"identity":"19c1dfc7-28da-471d-bce8-11556998e271","added_by":"auto","created_at":"2025-12-09 16:23:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":727888,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Structural Equation Models for cat activity (a) and cat abundance (b), with data from both surveys. Positive relationships are shown as solid arrows and negative relationships as dashed arrows, coloured blue for covariables affecting cat activity/abundance, and green for those affecting rodent activity. Standardized coefficients (β) are provided next to arrows, with asterisks indicating the level of significance (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). R\u003csup\u003e2\u003c/sup\u003e for endogenous variables are indicated. From left to right, environmental covariables are as follows: open/rocky area cover, \u003cem\u003efood-dist\u003c/em\u003e, \u003cem\u003eurb-dist,\u003c/em\u003e \u003cem\u003ewater-dist\u003c/em\u003e, vegetation simplification, and year of sampling.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/bcd1f7d5065d26c6d3b00a7a.png"},{"id":97813387,"identity":"f5b2ff71-8b7c-4c9e-9903-910e31dfea6f","added_by":"auto","created_at":"2025-12-09 16:23:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156946,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates of averaged Generalised Linear Mixed Models and their 95% confidence intervals for the response variable cat activity in 2021 (a) and 2023 (b). Positive significant coefficients are shown in blue, negative significant coefficients in orange, and non-significant coefficients in black. From top to bottom, environmental covariables are as \u0026nbsp;follows: \u003cem\u003efood-dist\u003c/em\u003e, open/rocky area cover, \u003cem\u003eurb-dist\u003c/em\u003e, vegetation simplification, and \u0026nbsp;rodent activity. \u003cem\u003ewater-dist \u003c/em\u003eis also included in (c) and (d).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/41f77b97c1ed4d9a8c900847.png"},{"id":97898362,"identity":"e4314e74-9946-4e97-ae08-ec31ce8f71aa","added_by":"auto","created_at":"2025-12-10 15:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4787094,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/909b67c6-3afb-4f68-992b-bf36360ae7a3.pdf"},{"id":97813400,"identity":"9e30e8f0-17f4-449a-a68c-3ebb2c83e103","added_by":"auto","created_at":"2025-12-09 16:23:18","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1392479,"visible":true,"origin":"","legend":"","description":"","filename":"AbrahamsetalSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8086980/v1/654b30850c732e5d1c727d30.docx"}],"financialInterests":"","formattedTitle":"Spatiotemporal dynamics of free-ranging cats in a peri-urban insular protected area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInvasive alien species are recognized as one of the five major direct drivers of global biodiversity loss (IPBES, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and free-ranging domestic cats (\u003cem\u003eFelis catus\u003c/em\u003e) are among the most widespread and impactful of these invaders (Crowley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Loss et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Since their domestication from African wildcats (\u003cem\u003eFelis sylvestris lybica\u003c/em\u003e) around 9,500 years ago (Twardeck et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), domestic cats have been introduced to most areas inhabited by humans, and their population has expanded to an estimated 600\u0026nbsp;million individuals throughout their range (Cove, 2023; Lepczyk et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This near global expansion has greatly increased their ecological influence, as free-ranging cats now prey on more than 2,000 species worldwide, over 16% of which are of conservation concern (Lepczyk et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe impacts of free-ranging cats are particularly detrimental in insular systems (Doherty et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), where they often become dominant predators. This is further exacerbated by the \u003cem\u003eecological na\u0026iuml;vet\u0026eacute;\u003c/em\u003e of many island species, which, due to prolonged evolutionary isolation, often lose key defences against novel predators, making them especially susceptible to predation by invasive mammals (Azumi et al., 2021; McCreless et al., 2016). To date, cats have been implicated in at least 63 vertebrate extinctions worldwide (Doherty et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBesides direct predation, these invaders impact native species through competition, disease spread, and broader ecological disruptions (Carrete et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rando et al., 2020), which are likely to severely impact the natural abundances of na\u0026iuml;ve prey.\u003c/p\u003e\u003cp\u003eNevertheless, free-ranging cats have been historically disregarded as agents of ecological change, potentially owing to their liminality as both charismatic pets and wild animals (Crowley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gramza et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For instance, cats are not listed in the European Commission\u0026rsquo;s list of Invasive Alien Species of Union Concern (1141/2016/EC). In addition, several countries across Europe have seen recent shifts in cat management which may exacerbate their regional impacts (Carrete et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On Madeira Island, Portugal, a 2016 decree banned the euthanasia of unowned animals, and current national proposals advocate for increased support to stray cat colonies (ICNF, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Population responses to such policies may occur with a time lag, underscoring the need for well-designed, sustained ecological monitoring (Simberloff et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFree-ranging cats are influenced by both human-provided food sources and environmental characteristics such as vegetation structure. Their densities are typically higher in areas with access to anthropogenic food subsidies than where they rely solely on hunting (Bengsen et al., 2016; Hall et al., 2016; Kays et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Habitat preferences vary among studies with some reporting greater use of structurally complex habitats (e.g., Hohnen et al., 2016), whereas others find higher hunting success in open or sparsely vegetated areas (e.g., McGregor et al., 2015). Moreover, cats may interact with other invasive species, such as rodents, which can subsidise their populations, eventually enabling them to persist at higher densities (Abrahams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAcross Madeira Island, both owned and unowned free-ranging cats are common and often occur together (Soto et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These cats prey upon several threatened species, such as the IUCN Endangered Zino\u0026rsquo;s Petrel (\u003cem\u003ePterodroma madeira\u003c/em\u003e) (Zino et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Madeira Pipistrelle (\u003cem\u003ePipistrellus maderensis\u003c/em\u003e) (Rocha, 2015). Their diet also includes the endemic Madeiran Wall Lizard (\u003cem\u003eTeira dugesii\u003c/em\u003e), alongside introduced mammals including house mice (\u003cem\u003eMus musculus\u003c/em\u003e), rats (\u003cem\u003eRattus\u003c/em\u003e spp.), and European rabbits (\u003cem\u003eOryctolagus cuniculus\u003c/em\u003e) (Medina et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Galao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the Ecological Park of Funchal, a peri-urban protected area in Madeira Island, Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed in 2021 that cats were abundant throughout the reserve, and that their core home ranges were associated with rocky outcrops, likely due to greater availability of shelter and prey such as the Madeira wall lizard (\u003cem\u003eTeira dugesii\u003c/em\u003e). They were not particularly associated with anthropogenic food sources, suggesting that they were probably relying mostly on wild prey to fulfil their dietary needs.\u003c/p\u003e\u003cp\u003eHere, we revisit the camera trap-surveys conducted in 2021 by Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to examine the spatiotemporal ecological dynamics of free-ranging cats within the Ecological Park of Funchal. By monitoring the same sites in 2023, we assessed cat density and their ecological drivers five and seven years, respectively, after legislation banning the euthanasia of unowned cats was enacted. Specifically, we investigated: (1) free-ranging cat density, population size and turnover rate between surveys; (2) the direct effects of environmental drivers and survey year, as well as the indirect effects mediated through rodent activity, on cat activity (number of camera-trap detections) and abundance (number of individuals); and (3) how these relationships changed between the 2021 and 2023 surveys. Given recent legislative changes regarding unowned animal control in Madeira, we predicted an increase in free-ranging cat density over time. Considering possible increases in food provisioning to stray colonies, we further expected cats recorded in 2023 to show greater association with anthropogenic food subsidies. Lastly, if the cat population is expanding spatially, we anticipated lower habitat specificity, reflected in a reduced number of environmental variables - other than human food subsidies \u0026ndash; significantly affecting cat activity compared to 2021.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eData was collected in the Ecological Park of Funchal, on Madeira Island, Portugal (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Madeira is the largest island of the Madeira archipelago, encompassing Madeira, Porto Santo, and the Desertas Islands. The island is an autonomous region of Portugal, situated around 900 km from the mainland. A census in 2023 recorded the island\u0026rsquo;s population at over 250,000 people, of which ca. 100,000 were living in Funchal (INE, 2023). The island is characterised by a subtropical climate, with mean daytime temperatures between 20 and 26\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:^\\circ\\:\\)\u003c/span\u003e\u003c/span\u003eC, and varying levels of rainfall, accumulating from 2 mm in July to 115 mm in December (Weather Atlas, 2024). The island features an altitudinal progression of distinct vegetation belts, including xerophytic, sclerophyllous flora; the UNESCO-protected Laurisilva forest, and high mountain shrubland (Capelo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Ecological Park of Funchal (7.5 km\u003csup\u003e2\u003c/sup\u003e) is located North of Funchal capital city, between 470\u0026ndash;1818 m asl and was established in 1994. It is an Important Bird Area, a Site of Community Importance, a Special Protection Zone, and is listed in the Natura 2000 Network (Silvestre, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). It is also a popular tourist destination, attracting many outdoor enthusiasts. Its vertebrate fauna includes over 25 confirmed breeding bird species, including both Madeiran (e.g., \u003cem\u003eRegulus madeirensis\u003c/em\u003e and \u003cem\u003eFringilla maderensis\u003c/em\u003e) and Macaronesian (e.g., \u003cem\u003eAnthus berthelotii\u003c/em\u003e) endemism. Manx shearwaters (\u003cem\u003ePuffinus puffinus\u003c/em\u003e) are severely depleted, and the first two active nests were discovered in 2005 in the Santa Luzia valley, within the Ecological Park of Funchal (Nunes et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition to free-ranging cats, vertebrate diversity includes three species of bats, one reptile (\u003cem\u003eTeira dugesii\u003c/em\u003e) and non-native rodents (\u003cem\u003eRattus rattus\u003c/em\u003e, \u003cem\u003eR. norvegicus\u003c/em\u003e and \u003cem\u003eMus musculus\u003c/em\u003e), rabbits (\u003cem\u003eOryctolagus cuniculus\u003c/em\u003e) and ferrets (\u003cem\u003eMustela furo\u003c/em\u003e) (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soto et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e and \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cat-human dependency exists on a continuous spectrum, generating ambiguity in exactly when a free-ranging cat becomes \u0026ldquo;feral\u0026rdquo;. Here, we refer to \u0026ldquo;free-ranging cat\u0026rdquo; as any owned or unowned cat that has some degree of outdoor access (Crowley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Farnworth et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCamera-trapping surveys\u003c/h3\u003e\n\u003cp\u003eCamera-trapping took place on two occasions: August-November 2021 and September 2023. The 2023 survey replicated the design of Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who divided the park into eleven 1 km\u003csup\u003e2\u003c/sup\u003e grid cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The chosen area for each grid reflected average home-range sizes for cats in comparable conditions (L\u0026oacute;pez-Jara et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Three camera-traps were deployed in each grid, totalling 33 camera locations. As in Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), two out of three camera locations were strategically deployed in known cat pathways, whereas the third location was randomly selected using QGIS, Version 3.30.2 (Sherman, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cameras were largely deployed at the same locations in both surveys. However, three camera locations (i.e., locations 3.3, 5.2 and 5.3\u0026mdash;see tables S1 \u0026amp; S2) had to be moved between surveys due to changes in the landscape (e.g., landslides and management interventions), that rendered the original sites inaccessible. Cameras were placed at least 300 m apart within each grid cell, elevated 50 cm above the ground, and attached to wooden stakes or suitable tree trunks. The cameras were locked to their attachment points with a braided steel cable lock, and a sign was attached explaining the purpose of the survey to deter theft (Clarin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Most of the cameras were from the model Browning Dark OPS Pro X (29 cameras), but additional models included Browning 2020 Patriot (three cameras) and Bushnell Core (one camera). As in Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), cameras were set to take three photos when triggered by an infrared motion sensor (at normal sensitivity), with minimum interval between photos and a 30 second interval between photo sets. To avoid false triggers and improve detectability, we cleared obstructing vegetation within a 5 m radius around the camera. Cameras were baited with a mixture of chorizo and oil, identical to the bait used by Soto et al., (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In 2023, all 33 cameras were left simultaneously active in the field, drawing a contrast to Soto et al., (who had a limited number of camera traps and therefore employed a more clustered study design). However, sampling effort was equally distributed across surveys, amounting to 582 and 588 trap-nights in 2021 and 2023, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCamera detections and identification\u003c/h3\u003e\n\u003cp\u003ePhotos were manually analysed using Timelapse Image Analysis software, version 2.3.0.8 (Greenberg, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Following Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and several other camera-trap studies (e.g., Garvey et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we considered detections to be independent when animals were recorded more than 30 minutes apart, or, in the case of cats, when different individuals were captured (Wearn \u0026amp; Glover-Kapfer, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWherever possible, cats were identified to the individual level from their pelage and morphological features, such as wounds or notched ears, in line with previous studies (Elizondo \u0026amp; Loss, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hohnen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Any cat record that could not be identified at the individual level (due to poor photo quality) was excluded from the cat density or abundance estimates. Although we considered kittens in both density and abundance estimates, this likely did not constitute recruitment during the survey nor violate model assumptions of a closed population. Kittens are altricial for the first three weeks of life, so would not be able to roam on their own, as all kittens in our images were observed to be doing (Spotte, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous studies excluding kittens from density estimates had a longer duration (several months to years), with more cause to assume that a kitten could be born and reared during the sampling period (Juhasz et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eExplanatory variables\u003c/h3\u003e\n\u003cp\u003eIn both 2021 and 2023, we measured several abiotic and biotic variables that were hypothesised to influence cat abundance and activity (Table S6), including the cover of (1) open/rocky area, (2) native shrubland, (3) non-native shrubland, (4) native trees, and (5) non-native trees. Each of these metrics were calculated for a circular area with a 300 m radius around each camera trap using orthophotos from the Regional Directorate for Spatial Planning in Madeira (IRIG, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We also measured the (6) distance to a water source (\u003cem\u003ewater-dist\u003c/em\u003e), described as the two main water courses running through the park: the Ribeira das Cales and the Ribeira de Santa Luzia. This was done using QGIS, with GIS layers supplied by coordinating authorities of the protected area (IRIG, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We recorded the (7) altitude and (8) slope at each camera trap location using a digital elevation model with a 10 m resolution (IRIG, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Relating to human influences on cat ecology, we measured the (10) distance to urban area (\u003cem\u003eurb-dist\u003c/em\u003e) and (11) distance to human resource subsidies (\u003cem\u003efood-dist\u003c/em\u003e), defined as any area where cats could access anthropogenically sourced food (e.g., picnic benches, rubbish bins, rental accommodations, an astronomy observatory, and restaurants). These two variables were measured using the same orthophotos (IRIG, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We also considered the activity of rodents given by the number of camera-trap detections of either \u003cem\u003eMus musculus\u003c/em\u003e, \u003cem\u003eRattus rattus\u003c/em\u003e or \u003cem\u003eRattus norvegicus\u003c/em\u003e.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003eCat population density and turnover\u003c/h2\u003e\u003cp\u003eEfficiency of cat capture was calculated as the number of cat detections for all cameras divided by the total number of trap-nights and multiplied by 100. We calculated turnover rate for the cat population with the same formula used by Dul\u0026rsquo;a et al. (2021): (\u003cem\u003enumber of individuals recorded in 2021 but not in 2023\u003c/em\u003e/\u003cem\u003etotal number of individuals recorded in 2021\u003c/em\u003e) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e 100 (Dul\u0026rsquo;a et al., 2021).\u003c/p\u003e\u003cp\u003eAs per Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), cat density (number of cats/km\u003csup\u003e2\u003c/sup\u003e) was estimated for the 2023 survey using Spatially Explicit Capture\u0026ndash;Recapture (SECR) models. This approach allows to incorporate a spatial aspect to traditional statistical capture-recapture models by including trap locations, and therefore, the location of each detection (Broekhuis \u0026amp; Gopalaswamy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Capture probability is dependent on the distance of the animal from the detector (Borchers, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), allowing for a non-uniform distribution of sampling effort across a study area (Efford \u0026amp; Fewster, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Individuals are assumed to move randomly around the sampling area, whereby the sum of their activity centres constitutes the estimate of density (Green et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SECR models were fitted using the \u0026lsquo;secr\u0026rsquo; R package (Efford, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). We made a data frame of capture records for cats with the trap ID, session (survey), occasion of detection and cat ID. To generate a capture histogram, we combined this capture record with an array of cameras, detailing trap ID and their XY coordinates, including usage (reflecting recording effort). The \u0026lsquo;count\u0026rsquo; detector function was used, as our data consisted of a vector of counts per detector. We assumed that cats in the Ecological Park followed a homogenous Poisson spatial distribution throughout the sampling period. When fitting SECR models to this data, we selected the Halfnormal (HN) detection function for the data, as it reached a prompt plateau from a plot of effective sampling area (ESA) and ensured comparability with the 2021 survey (Figs. S2 \u0026amp; S3).\u003c/p\u003e\u003cp\u003eTo represent the area in which the target species could potentially occupy, we used two different approaches and ran SECR models with two different habitat mask areas. For the best suitability to our 2023 data and study design, we applied the suggest.buffer() function to our capture histogram, specifying a radius for the habitat mask around detectors (Efford, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). This buffer value was tested for suitability from the ESA plot. With this method, we selected a 1700 m-buffer around each camera, resulting in a habitat mask area of 39.24 km\u003csup\u003e2\u003c/sup\u003e. However, to be more comparable with the 2021 study, we also created SECR models with a 1200 m-buffer and a 26.01 km\u003csup\u003e2\u003c/sup\u003e habitat mask area, as was used by Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). SECR density estimates tend to be robust against a range of study designs and detection probabilities (Efford \u0026amp; Fewster, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), but the estimated population size of cats in the study area is heavily influenced by the area of the habitat mask, so we presented estimates using both masks in acknowledgment of this fact.\u003c/p\u003e\u003cp\u003eUsing the SECR models, we also calculated Mean Maximum Distance Moved (MMDM), which was the maximum average distance moved by an individual between detections, and the spatial scale of movement, which reflected how far an animal moved within their home range during the sampling period. Finally, we estimated free-ranging cat population size by multiplying cat density by the habitat mask area.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eDirect and indirect drivers of cat activity and abundance\u003c/h3\u003e\n\u003cp\u003eWe investigated the direct drivers of cat activity and abundance, and indirect drivers as mediated through rodent activity using Piecewise Structural Equation Models (SEMs) from the \u0026ldquo;piecewiseSEM\u0026rdquo; package from R (Lefcheck, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). SEMs comprise pathways representing hypothesized causal relationships between variables, so can be used to quantify indirect effects that may be overlooked by any single model. Piecewise SEMs further allow analyses to be carried out for smaller sample sizes (Lefcheck, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We generated one piecewise SEM for cat activity and another for cat abundance.\u003c/p\u003e\u003cp\u003ePrior to running the Piecewise SEMs, we applied a z-score standardization to the variables regarding cover of open/rocky area, native and non-native shrubland and trees, and run a Principal Component Analysis (PCA) with those variables. The scores from the PC1 were retained as an explanatory variable named \u0026ldquo;\u003cem\u003evegetation simplification\u003c/em\u003e\u0026rdquo; (describing a gradient from tree to shrubland cover, Fig. S5) as a predictor variable, reducing habitat dimensionality (e.g., Ferreira et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We then examined inter-variable correlation and removed variables that were highly correlated (r\u0026thinsp;\u0026gt;\u0026thinsp;0.70) from our analysis. In this case, both altitude and slope were highly correlated with \u003cem\u003eurb-dist\u003c/em\u003e, as the city of Funchal is located at the base of a valley, so neither altitude nor slope were included in our models.\u003c/p\u003e\u003cp\u003eEach SEM included two Generalized Linear Mixed Models (GLMMs): one relating rodent activity with open/rocky area, \u003cem\u003efood-dist, urb-dist, water-dist\u003c/em\u003e and vegetation simplification, and another relating cat activity/abundance with open/rocky area, \u003cem\u003efood-dist, urb-dist, water-dist\u003c/em\u003e, vegetation simplification and year of sampling. A random variable regarding the grid identity was retained to account for the spatially nested placement of the camera-traps within the grids. We applied Shipley\u0026rsquo;s test of directed separation to ensure no important relationships were missing from the SEM basis set (Shipley, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and evaluated models by reporting standardised estimates of relationships and R\u003csup\u003e2\u003c/sup\u003e for response variables. Indirect effects of environmental variables on cats as mediated through rodent activity were obtained by multiplying the corresponding standardised estimates (Lefcheck, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCat activity drivers in 2021 and 2023\u003c/h3\u003e\n\u003cp\u003eGiven that the year of sampling was an important predictor of cat activity, we further examined the relationship between cat activity and the above-mentioned environmental variables (i.e., rodent activity, open/rocky area, \u003cem\u003efood-dist\u003c/em\u003e, \u003cem\u003eurb-dist\u003c/em\u003e, \u003cem\u003ewater-dist\u003c/em\u003e and vegetation simplification) separately for each of the sampling years and compared whether cat activity was still being affected by the same factors. Cat activity was modelled using GLMMs. We generated four GLMMs: namely, cat activity and abundance in 2021 and in 2023. These models consider both fixed effects (environmental covariables), and random effects (variation among grids) that may influence cat abundance/activity. We considered 11 environmental covariables (detailed above). We also included an offset accounting for the number of camera trap-nights (log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003ex\u003c/em\u003e) which was further excluded from the analysis as it did not provide any additional explanatory power. We applied the \u0026ldquo;dredge\u0026rdquo; function to GLMMs to evaluate all combinations of fixed effects, followed by \u0026ldquo;model.avg\u0026rdquo; to help tackle model selection uncertainty. Both functions are found within the \u0026ldquo;MuMIn\u0026rdquo; R package (Barton, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The residuals of the final models were evaluated with the \u0026ldquo;DHARMA\u0026rdquo; package (Hartig, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Figs. S7 and S8). All data analyses carried out in R version 4.3.2 (R Studio Team, 2023).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn 2023, we obtained 1227 independent detections, mostly from rats (566 detections, 46.1% of the total) and cats (276 detections, 22.5%). Cat capture efficiency was 46.9 detections per 100 trap-nights. A total of 30 individual cats were recognised, marking a 20% increase in sampled individuals from 2021. Photographed cats could not be identified in 12 occasions (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe spatial distribution of cat activity and abundance shifted between 2021 and 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In 2021, most cat activity occurred in the most southern portion of the park (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), whereas in 2023 it was mostly concentrated in the central grids (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In 2023, cat activity per grid ranged from 0 to 48 records (mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\:\\)\u003c/span\u003e\u003c/span\u003eSD = 24; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\:\\)\u003c/span\u003e\u003c/span\u003e14.0), with grid 5 showing the highest number of records (n = 48) from seven individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, d). Activity per camera ranged from 0 to 24 records (8 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\:\\)\u003c/span\u003e\u003c/span\u003e6.9). Cat abundance was also more concentrated in the central grids in 2023, relative to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and d), peaking in grid 8 where 12 cats were recorded (44 detections). Ultimately, grid 8 accounted for 40% of the individuals .\u003c/p\u003e\u003cp\u003eThe most recorded cat was observed in 33 instances across grids 6, 8 and 9, and the second most recorded cat was observed in 23 instances across grids 5, 6, 7, 8 and 9. Mean Maximum Distance Moved (MMDM) is the average maximum distance between detections of an individual, and was recorded at 1084.5 m.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eFree-ranging cat density, population size and turnover rate between surveys\u003c/h2\u003e\u003cp\u003eIn 2023, the maximum detection probability at each camera (g0) was 0.22 (95% CI: 0.21\u0026ndash;0.23), compared to 0.17 (0.13\u0026ndash;0.24) in 2021. The spatial scale of movement (σ) increased from 387 m in 2021 (342.23\u0026ndash;439.14) to 426 m (408.53\u0026ndash;444.44) in 2023. The estimated population density of free-ranging cats was 1.5 cats/km\u003csup\u003e2\u003c/sup\u003e (95% CI: 1.0\u0026ndash;2.1), representing a 7% (0.1 cats/km\u003csup\u003e2\u003c/sup\u003e) increase from 2021 (1.4 cats/km\u003csup\u003e2\u003c/sup\u003e; 95% CI: 0.94\u0026ndash;2.1; Table S7). Estimated population size within the surveyed area varied with the specified buffer around each detector. From the model using a 1700 m-buffer around each detector, the estimated population size rose from 36 cats in 2021 (25\u0026ndash;55) to 58 cats (39\u0026ndash;82) in 2023. From the model with a 1200 m-buffer as used by Soto et al. (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the estimated population size rose from 36 cats in 2021 (25\u0026ndash;55) to 39 cats in 2023 (27\u0026ndash;55) (Tables S7 \u0026amp; S8).\u003c/p\u003e\u003cp\u003eSix individuals were detected in both sampling years (Table S3), representing a population turnover rate of 76%. Notably, one individual had lost an eye between surveys (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, d). Camera-trap images also revealed five instances of trophic interaction, including one cat preying on a pregnant rat (Fig. S2), and a mother-offspring pair (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003cem\u003eDirect and indirect environmental drivers of cat activity and abundance\u003c/em\u003e\u003c/p\u003e\u003cp\u003eCat activity directly increased with time (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and with the proportion of open/rocky area (β\u0026thinsp;=\u0026thinsp;0.29, \u0026lt; 0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Rodent activity - negatively influenced by vegetation simplification (β = \u0026minus;\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;0.11) and distance to water (β = \u0026minus;\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.16)\u0026mdash; had a negative effect on cat activity (β = \u0026minus;\u0026thinsp;0.69, \u0026lt; 0.01), while vegetation simplification and distance to water had a positive indirect effect on the cat activity (β\u0026thinsp;=\u0026thinsp;0.34 and 0.24, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Cat abundance was only negatively affected by rodent activity (β = \u0026minus;\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; Tables S12 \u0026amp; S13).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eChanges in cat activity drivers between 2021 and 2023\u003c/h2\u003e\u003cp\u003eBetween surveys, the effect of distance from human food resources on cat activity shifted from positive (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in 2021 to negative (β = -0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in 2023, while previous effects of open/rocky areas (β\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and rodent activity (β = -0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were no longer evident (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMounting evidence highlights the detrimental impacts of free-ranging cats on natural ecosystems (Loss et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Szentivanyi et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), yet data on their spatial ecology \u0026ndash; and especially their temporal dynamics \u0026ndash; remain rare. Here, we uncovered an increase in the estimated cat population size within the Ecological Park of Funchal from 36 in 2021 to 58 individuals in 2023. This represents a 7% rise in density, from 1.4 to 1.5 cats/km\u003csup\u003e2\u003c/sup\u003e. Over this period, cat activity also increased. Notably, the effect of distance to human food resources on cat activity shifted from positive in 2021 to negative in 2023, while open/rocky areas and rodent activity ceased to exert significant influence. These results suggest that the free-ranging cat population is becoming increasingly associated with anthropogenic food sources and exhibiting reduced habitat specificity. These results have implications for the management of free-ranging cats in protected areas, as actions that supplement cat activity may be in violation of several nature conservation laws regarding invasive species control, management of protected areas, and protection of threatened natives species (Trouwborst et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsidering the larger habitat mask area as the most representative for our 2023 study, our results indicate a population of up to 58 individual cats in this protected area. Irrespective of the chosen habitat mask area, our results correspond to a density increase of 0.1 cats/km\u003csup\u003e2\u003c/sup\u003e over two years. Despite inherent limitations in contextualizing these estimates, our findings align with previous studies in protected areas, including 1.5 cats/km\u003csup\u003e2\u003c/sup\u003e in a Spanish Natura 2000 site (L\u0026aacute;zaro et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), 0.7-1.0 cats/km\u003csup\u003e2\u003c/sup\u003e in the World Heritage Area of Auckland Island, New Zealand (Glen, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and 2.65 cats/km\u003csup\u003e2\u003c/sup\u003e in the Solomon Islands (Lavery et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our study area, dietary analyses combining DNA metabarcoding and morphological scat examination revealed that a single cat can consume over 90 lizards and 100 passerines per year (see Table\u0026nbsp;2 in Galao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). If the dietary composition remained consistent, and for estimates for the best suited masks for each of the datasets, the observed increase from 36 cats in 2021 to 58 cats in 2023 could have resulted in the additional predation of \u0026gt;\u0026thinsp;2,000 reptiles and \u0026gt;\u0026thinsp;2,200 passerines annually (adding to an estimated\u0026thinsp;\u0026gt;\u0026thinsp;11,000 native prey per year, for the estimated mean cat population). To our knowledge, this study also provides the first estimate of free-ranging cat population turnover in an island system \u0026minus;\u0026thinsp;76% within two years. High turnover rates may indicate elevated abandonment, fecundity, or mortality rates (Dul\u0026rsquo;a et al., 2021). Anecdotal evidence of at least four kittens during the 2023 survey supports high reproductive rates, consistent with other studies estimating 1.0 and 1.6 litters/year for feral and semi-feral cats, respectively (Schmidt et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, free-ranging cats tend to have shorter lifespans due to exposure to environmental extremes, intraspecific aggression, and disease (Lepczyk et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Loyd \u0026amp; DeVore, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and their dynamics can be strongly influenced by human pet ownership patterns (McDonald et al., 2023). The observed turnover likely reflects a combination of these factors. High turnover can lead to changes in population structure, which in turn affects the robustness of abundance estimates (Harmsen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Yet, such demographic instability is not unique to our study population; comparable levels of individual replacement have been reported in other felid populations, including Carpathian lynx (46.3% turnover in five years, Dul\u0026rsquo;a et al., 2021); jaguars (63.6% turnover in 14 years, Harmsen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Geoffroy\u0026rsquo;s cat (89% turnover in 2 years, Pereira et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These findings suggest that high turnover may be a common feature of carnivore populations - being them composed of wild, native species, or non-native invasives. Nonetheless, in an insular system where cats face no interspecific predation, a turnover of 76% within two years likely reflects elevated mortality, suggesting poor welfare among free-ranging cats. Road traffic accidents are a major cause of mortality in free-ranging cats (McDonald et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and fatalities linked to vehicle collisions are often observed in \u0026ndash; and around \u0026ndash; our study area. This situation poses risks not only to the cats themselves but also to humans, who may be involved in road accidents caused by the presence of free-ranging cats (e.g., collision with other vehicles, while trying to avoid a collision with a cat).\u003c/p\u003e\u003cp\u003eOur SEM analysis suggests that the cover of open or rocky areas - habitats where lizards are commonly found (Pacheco, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) - positively influenced cat activity, whereas rodent activity was negatively associated with both the activity and abundance of free-ranging cats. This pattern indicates that while cats may prefer habitats rich in lizards, they are less frequently active in areas of high rodent activity. Despite their substantial consumption of rodents (Galao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), this finding suggests that cats may contribute little to rodent suppression across the park.\u003c/p\u003e\u003cp\u003eLastly, our results also reveal shifts in environmental associations over time, with cats exhibiting stronger links to anthropogenic food sources in 2023. Combined with the high turnover rate, this may reflect a shift toward a greater proportion of stray (human-subsidized) cats relative to feral individuals (Bonnaud et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Lepczyk \u0026amp; Calver, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Medina et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Importantly, even cats benefiting from human-subsidized food capture and consume wild prey (Woods et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Cecchetti et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, in Australia, ca. 40% of stray cats consumed wildlife, including conservation-sensitive endemic species (Crawford et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, our findings suggest that in the aftermath of a decree banning the euthanasia of unowned animals, the numbers of free-ranging cats inhabiting the protected area home to our study are potentially expanding. This situation suggests that current management practices may not meet obligations from the EU Nature Directives, which require that the free roaming of cats is \u0026ldquo;\u003cem\u003eforbidden and effectively prevented\u003c/em\u003e\u0026rdquo; (Trouwborst \u0026amp; Somsen, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The increasingly positive associations between cat activity and anthropogenic food sources likely reflect increased provisioning of stray cats, a trend that may exacerbate ecological impacts by supporting greater cat densities (Herrera et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tennent \u0026amp; Downs, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Furthermore, the observed high individual turnover likely reflects elevated mortality, fecundity, or rates of abandonment raising concerns about the welfare of these animals in wild environments.\u003c/p\u003e\u003cp\u003eThe political landscape regarding cat management can be as dynamic as populations themselves (Crowley et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, effective management decisions must be grounded in robust scientific evidence. The results of this study provide a compelling case study of a protected area experiencing ongoing ecological pressures from free-ranging cats - a threat that appears to be intensifying relative to 2021. To mitigate the impacts of this invasive species and meet conservation obligations, authorities must translate such evidence into targeted, science-based management interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the Department of Biology, University of Oxford.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by Edie Abrahams, Elena J. Soto, Kane Powell, Eduardo Nόbrega, and Ricardo Rocha. Data analysis was performed by Edie Abrahams. The first draft of the manuscript was written by Edie Abrahams and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the staff at the Ecological Park of Funchal for their assistance during fieldwork.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrahams E, Soto EJ, Powell K, Nunes J, Palmeirim AF, Rocha R (2025) Diel activity of free-ranging cats and their mammalian and avian prey on the oceanic Madeira Island, Macaronesia. 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Oryx 35(2):128\u0026ndash;136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-3008.2001.00165.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-3008.2001.00165.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"camera traps, Felis catus, invasive species, Macaronesia, Madeira Island, Natura 2000","lastPublishedDoi":"10.21203/rs.3.rs-8086980/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8086980/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcross most of their nearly global range, free-ranging cats occupy a complex position at the intersection between invasive species and companion animals, making their management particularly challenging. Despite their cultural and emotional value, they have severe impacts on biodiversity, particularly in insular ecosystems where they frequently prey on conservation-sensitive species. We conducted protected area\u0026ndash;wide camera trap surveys in 2021 and 2023 to investigate the spatiotemporal dynamics of free-ranging cats on Madeira Island, off northwest Africa. Using data from 588 trap-nights in 2023, we identified 30 individual cats from 276 detections, indicating a population turnover of 76%. Cat density was estimated at 1.5 cats/km\u003csup\u003e2\u003c/sup\u003e \u0026minus;\u0026thinsp;7% higher than in 2021, corresponding to a population of up to 58 individuals. Cat activity increased over time and with greater open/rocky area cover and cat abundance was negatively associated with rodent activity. Compared to 2021, cat activity was significantly higher at closer proximities to human food resources, while open/rocky areas and rodent activity no longer had a detectable effect. The population of free-ranging cats is increasing within the protected area, and their activity appears less habitat-specific and more dependent on human food subsidies. These findings underscore the importance of revisiting policies that may inadvertently promote cat population growth, and of implementing evidence-based management to reduce their ecological impacts and protect vulnerable native species.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal dynamics of free-ranging cats in a peri-urban insular protected area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 16:23:11","doi":"10.21203/rs.3.rs-8086980/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-15T03:01:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-05T13:52:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Biological Invasions","date":"2025-11-22T22:26:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-15T07:32:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Invasions","date":"2025-11-14T04:08:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-invasions","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binv","sideBox":"Learn more about [Biological Invasions](https://www.springer.com/journal/10530)","snPcode":"10530","submissionUrl":"https://submission.nature.com/new-submission/10530/3","title":"Biological Invasions","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2714d5ab-bf1a-4e03-a3d9-d1fbb2125fbc","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T16:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 16:23:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8086980","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8086980","identity":"rs-8086980","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}