{"paper_id":"05dae3c8-bbde-4428-a765-78d58663364e","body_text":"1\n1 Human subsidies facilitate hyperpredation of Mediterranean \n2 island wildlife by outdoor cats\n3\n4 Jeffrey R. Ferrer1*, John Vandermeer2&, Collin J. Richter1 & Erin R. Baldwin1&, Johannes \n5 Foufopoulos1&\n6\n7 1School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan, \n8 United States of America\n9 2Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, \n10 Michigan, United States of America \n11\n12 *Corresponding Author\n13 Jeffreyferrer93@gmail.com (JRF)\n14\n15 & Collin J. Richter, John Vandermeer, and Erin R. Baldwin contributed equally to this work\n16\n17\n18\n19\n20\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n2\n21 Abstract\n22 Domestic cats (Felis catus) are avid wildlife predators and one of the most harmful invasive \n23 species. The lethal impacts of an introduced predator such as cats on wildlife can be further \n24 exacerbated by the introduction of an additional abundant non-native prey species capable of \n25 supporting an exceptionally dense predator population, a phenomenon known as hyperpredation. \n26 A special case of hyperpredation involves human food subsidies, when an invasive predator \n27 impacting native wildlife is supported not by another invasive taxon, but by human-derived food \n28 sources. To test whether access to anthropogenic food subsidies by cats is causing hyperpredation, \n29 mark-recapture methodology was used to measure twelve cat populations experiencing a gradient \n30 of human subsidies on the Mediterranean island of Naxos, Greece. Line-transect surveys were \n31 conducted at each site to measure reptile population abundance across this gradient, and other \n32 factors including human density and distance of reptiles from villages were considered as well. \n33 Strong evidence was found that the population size of cats is a direct result of the density of \n34 anthropogenic food available to them, and a corresponding decline in the size of reptile populations \n35 as cat density increased was observed. It was also shown that as cat populations exceed the \n36 available supply of human food, the negative effects of cat density on reptile populations are \n37 exacerbated. These results demonstrate that access to anthropogenic subsidies has allowed cat \n38 populations to expand to exceptional levels, driving hyperpredation on island wildlife.\n39 Introduction\n40 Invasive species are one of the greatest threats to biodiversity worldwide [1-3]. Invasive \n41 species have been implicated in over 50% of all modern extinctions whose causes are known, and \n42 in about 20% of total cases, invasive species have been identified as the sole driver of extinction \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n3\n43 [4-5]. One of the most harmful groups of invasive species are mammals, who currently threaten \n44 more IUCN listed critically endangered terrestrial vertebrate species than any other group, an \n45 impact driven primarily by black rats (Rattus rattus) and domestic cats (Felis catus) [6].\n46 Cats have been linked to ≥ 63 vertebrate extinctions [7] and are recognized as one of the \n47 most harmful invasive species due to their global distribution and devastating impacts on wildlife \n48 [8]. In the United States alone, it has been estimated that cats kill billions of birds, mammals, and \n49 reptiles each year, with mortality driven primarily by unowned free-roaming cats, rather than pets \n50 [9]. In addition to direct predation, cats can have many other negative effects on wildlife such as \n51 influencing prey behavior by increasing prey wariness and decreasing foraging time [10-11], \n52 transmission of diseases such as rabies, toxoplasmosis, cutaneous larval migrans, tularemia, and \n53 plague [12], and loss of genetic integrity in native species such as the Eurasian wildcat (Felis \n54 silvestris silvestris) through hybridization [13]. \n55 The damaging effects of cats are particularly prevalent on islands [14-15]. Despite making \n56 up only 5.3% of the Earth’s landmass, 61% of all known extinctions have occurred on islands, and \n57 invasive species have been cited as the most frequent cause of insular species extinctions [16]. \n58 Island communities are disproportionately affected by invasive mammalian predators, and \n59 especially cats, where a general lack of native predators typically results in down-regulated \n60 antipredator responses, rendering island wildlife relatively tame and easy to capture [17-19]. Cats \n61 have emerged as the causal factor in at least 33 extinctions of insular vertebrate species [9], and \n62 because they have already been introduced to most suitable islands worldwide [14], they represent \n63 a threat to numerous additional island endemics [10]. \n64 The impacts of an introduced predator on native prey can be exacerbated by the \n65 introduction of an additional non-native prey species, a phenomenon known as hyperpredation \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n4\n66 [20-23]. When this occurs, a rapidly reproducing non-native prey species supports an unusually \n67 dense population of predators. This can have severe consequences for the native prey species, \n68 which may be decimated by the resulting increase in predators. For example, the extinction of the \n69 endemic Macquarie Island Parakeet (Cyanoramphus novaezelandiae erythrotis) is thought to have \n70 occurred following hyperpredation by cats. Although cats were introduced onto Macquarie Island \n71 within ten years of its discovery in 1810, the native parakeet withstood predation pressure and \n72 remained numerous there until about 1880. In 1879, European rabbits (Oryctolagus cuniculus) \n73 were released onto the island and reproduced rapidly. Within a few years, cat populations \n74 persisting primarily on rabbits expanded rapidly and opportunistically consumed parakeets. The \n75 parakeet was last definitively seen in 1890 [18, 20, 24]. Hyperpredation has since been recorded \n76 in several systems [25-28], and understanding how to predict and manage it will only increase in \n77 importance as the spread of invasive species is expected to increase with the expansion of global \n78 trade networks [29].\n79 A related situation can occur when an invasive predator, impacting native wildlife, is \n80 supported not by another invasive taxon, but by human-derived food sources [30] (Fig 1). In many \n81 urban environments, densities of predators increase simply through ease of access to anthropogenic \n82 resources like trash and hand-outs [31-33]. For example, while cats are typically solitary and \n83 territorial hunters, the presence of large quantities of a stable food resource can alter their behavior \n84 by reducing their home-range size and territoriality, increasing tolerance of home-range overlap, \n85 and leading to the formation of cat colonies [34-35]. While densities of cats can vary greatly, \n86 Liberg et al. (2000) [31] found that densities above 100 cats/km 2 are only found in urban areas \n87 with a constant source of anthropogenic food supplementation. These unnaturally high densities \n88 of cats can potentially result in a unique form of hyperpredation where the introduced ‘prey’ that \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n5\n89 is supplementing the predator population is simply anthropogenically derived food. As cats have \n90 been shown to have high fidelity to a single feeding site [34], it is expected that the number of \n91 feeding sites available to cats should be correlated with the size of the cat population. However, \n92 this has yet to be demonstrated, and the extent to which increased food supplementation affects \n93 greater impacts on wildlife remains unclear as for example, more subsidies may simply result in a \n94 switch away from wildlife to anthropogenic sources.\n95 Fig 1. Conceptual diagram showing how the addition of anthropogenic food supplementation \n96 into a predatory-prey relationship can result in hyperpredation on wildlife. \n97 Cats were first introduced to the Mediterranean islands thousands of years ago [36-38] and \n98 are playing an important predator role in both urban and rural Mediterranean ecosystems [39-40] \n99 where they have been implicated in significant wildlife mortality [11,41,42]. The goals of this \n100 study were to establish which factors support cat populations in a representative Mediterranean \n101 ecosystem (the island of Naxos in the Aegean Sea, Greece), and to determine how cats are affecting \n102 resident wildlife populations while considering other factors that may shape a possible relationship \n103 between cats and wildlife. \n104 Methods used to answer these questions included, 1) obtaining population and density \n105 estimates of twelve discrete cat populations experiencing a range of food supplementation, 2) \n106 quantifying the amount of food supplementation available to each cat population, 3) surveying \n107 reptile communities in the natural habitat in the immediate vicinity of each of these locations, and \n108 4) providing visual support through the development of heat maps showing the spatial distribution \n109 of cats. \n110 By investigating these population level relationships along a gradient of food \n111 supplementation within a representative island system, this study is taking a novel and fine-grained \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n6\n112 approach that will allow a quantitative demonstration of the connection between human food \n113 subsidies and decreases in wildlife in Mediterranean wildlife.\n114 Methods\n115 Study Site\n116 Field work was conducted during the months of May and June 2024 on the Aegean Island \n117 of Naxos, Greece. Naxos (430km2) is the largest island in the Cyclades archipelago with a typical \n118 Mediterranean climate consisting of warm dry summers, and mild moist winters. The landscape \n119 of Naxos is characterized by xeric phrygana, a type of thorny summer-deciduous Mediterranean \n120 cushion scrub community dominated by Sarcopoterium spinosum and Genista acanthoclada, as \n121 well as high evergreen maquis dominated by Quercus coccifera and Juniperus turbinata. The wide \n122 distribution of these communities is consistent with a long history of disturbance and degradation \n123 of the original oak forest habitats through grazing and agricultural activities on the island [43]. \n124 Areas near villages consist of a mosaic of these communities mixed with irrigated gardens, \n125 grainfields, as well as olive groves.\n126 Cycladic wildlife is dominated by reptiles which, by virtue of their life history, are \n127 particularly well suited for the Mediterranean climate [44]. Naxos is home to 13 reptile species, \n128 including two introduced species (Hemidactylus turcicus and Chalcides ocellatus). While reptiles \n129 are overall common, communities on the island are dominated by the Aegean wall lizard (Podarcis \n130 erhardii), hereafter “wall lizard”, which can easily be observed. Both wall lizards and other reptile \n131 taxa occur in particularly high densities along the drystone walls and terraces which act as focal \n132 points of reptile activity, and are a ubiquitous feature of the Mediterranean landscape [45-46]. \n133 Reptile populations in the region are likely shaped by a combination of both bottom-up, as well as \n134 top-down effects. Among lizard predators, snakes are probably the most important [47]. The only \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n7\n135 other mammalian predator besides cats are stone martens (Martes foina), although because of their \n136 strictly nocturnal activity pattern, they do not likely encounter the broadly heliothermic reptiles. \n137 Because of this high aggregate biomass, reptiles are a particularly important component of \n138 Cycladic ecosystems and constitute an ideal system to investigate the impacts of cats on local \n139 species communities. \n140 Naxos contains over 30 towns and villages that range in population size from being \n141 essentially abandoned, to the capital of Chora (with ca. 6000 inhabitants) [48]. Naxian settlements \n142 have traditionally been built in a very compact manner, facilitating defense against pirate raids \n143 [49]. They typically encompass a dense network of stone-lined footpaths that allow access to every \n144 part of the village. Household refuse is collected in select sites located at the edge of a settlement \n145 where garbage truck access is possible, creating distinct hot spots of waste accumulation. At the \n146 same time, there is a clear border and very sharp transition from the last outer row of houses to the \n147 surrounding agricultural matrix where resident wildlife occurs.\n148 Cats can be found in large numbers in nearly every town on Naxos. At the same time, \n149 because of Naxos’ arid landscape, cats are present but rare in the remote countryside, especially \n150 more than 1-2km away from human habitations [11]. They appear to be highly dependent on the \n151 shelter of human settlements where they readily consume nutritionally dense cat food provided by \n152 residents as well as garbage from open dumpsters (S1 Appendix). These conditions are \n153 representative of many coastal areas around the Mediterranean Basin [40]. Within villages, cats \n154 exist across a spectrum of associations with humans, ranging from fully domesticated and fed to \n155 completely ignored and even persecuted (personal observation). Even when affiliated with a \n156 household, cats on Aegean islands are traditionally kept as outdoor pets and are allowed to roam \n157 freely. Because they are typically fed in a yard or in the street, and because of the open nature and \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n8\n158 dense path network of island villages, it is possible to obtain a good estimate of the extent of food \n159 supplementation in each village. \n160 This study focuses on 12 representative villages selected randomly as study sites (Fig 2, \n161 S1 Table) while ensuring that the following criteria were met: 1) presence of a discrete village \n162 edge and a sufficiently dense network of village paths allowing easy access and high visibility to \n163 the entire settlement, and 2) the presence of dry-stone walls extending radially away from the \n164 village into the surrounding habitats to facilitate reptile surveys. Two of these rock walls ( ≥ 200m \n165 in length) were randomly selected for surveys for each village, except for one smaller village that \n166 contained a single wall. \n167 Fig 2. Map of the study area. Survey locations are indicated by black stars. Naxos, Greece and \n168 surrounding islets shown in main map. Entire country outlined in the inset, with a square around \n169 Naxos for reference)  Map created in ArcGIS Pro v3.4 (2024). Produced with custom ESRI \n170 National Geographic Basemap (2025) [50-51].\n171 Reptile surveys\n172 Reptile population abundance for each village were quantified through standardized \n173 surveys. Each survey was conducted along 200-meter transects parallel to drystone walls, which \n174 have been shown to act as foci for reptile activity in the region [11,52]. All surveys were completed \n175 during the months of May and June 2024 during peak reptile activity time (8am – 12pm), and \n176 under favorable weather conditions [53]. Weather metrics (temperature, wind speed, relative \n177 humidity, cloud cover) were collected at the beginning and end of each survey (Kestrel 5000 \n178 Environmental Meter, 2024, Nielsen-Kellerman Company, Boothwyn, Pennsylvania). Surveys \n179 began at the edge of a village and were conducted by slowly walking along the length of the \n180 drystone wall. Species name and distance (m) from the origin were recorded (Garmin, 2024, \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n9\n181 GPSMAP® 65s Handheld GPS device, Olathe, Kansas) for all reptiles observed on the wall or on \n182 the ground within 1 meter from the wall (using ‘unknown’ if no species ID was possible). Each \n183 wall received four surveys spread over the duration of the field season.\n184 Cat surveys\n185 Cat surveys were conducted for three consecutive nights within each village. Surveys were \n186 performed in the evening during the time of peak cat activity (5pm – 8pm) and under appropriate \n187 weather conditions [54]. Surveys were conducted by walking along every path in a village once, \n188 and taking a picture and recording the GPS location of every cat observed. Survey tracks were \n189 recorded and uploaded into ArcGis Pro v3.4 [50] to confirm comprehensive coverage of each \n190 village. Great individual variability in terms of cat color, size, condition, and unique markings \n191 allowed cats to be identified to the level of individual [55]. Each cat was given a unique identifier \n192 code, and on the second and third survey nights, cats were designated as new individuals or \n193 recaptures from previous nights. Cat population size in each village was calculated using the \n194 Schnabel Index, a method used to estimate population size in mark-recapture studies when \n195 multiple sampling events are conducted [56-57]. Kittens were also recorded in the surveys but \n196 were excluded from the population size calculation due to their secretive nature making \n197 comprehensive identification difficult. \n198 Additional field data\n199 During the first cat survey conducted at each village, the locations of all food available to \n200 cats in the form of either pet food dishes or communal garbage dumpsters were recorded. The \n201 aggregate number of food dishes and dumpsters were combined into a number of ‘food stations.’ \n202 Human population data for every community on the island were provided by the municipality of \n203 Naxos and confirmed through a review of the Greek National Statistical Agency. The area of each \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n10\n204 village (ha) was quantified in ArcGIS Pro v.3.4 by manually digitizing the perimeter of each \n205 village. Larger villages have more cats, as well as humans and feeding sites, necessitating the need \n206 to establish a correction for community size. To correct for area effects, cat population, number of \n207 food stations, and human population were divided by the village surface area.\n208 Statistical Analyses\n209 Statistical analyses were performed in R version 4.4.2 [58] with the following the lme4 \n210 [59] and AICcmodavg [60] packages with the significance threshold set at 0.05.\n211 Cat density\n212 Ordinary least squares regression (OLS) was used to first explore univariate associations \n213 between cat density, human density, and food station density and then to develop more complex \n214 models that included combinations of these predictors and their interactions. Corrected Akaike \n215 information criterion (AICc) [61] was used to determine the best fit model.\n216 Reptile populations\n217 A cat ‘satiation index’ was calculated for each village as the residuals of the regression of \n218 cat density against food density. Positive residuals were interpreted as cats being in a food deficit, \n219 and therefore hungrier, while negative residuals were interpreted as cats being in a food surplus \n220 and more satiated. The residual values were rescaled so that the most satiated (index = 0) had a \n221 value of 0, and higher positive values indicated lower satiation, or increased hunger. This index \n222 was used as a model parameter to investigate whether level of cat satiation influenced reptile \n223 populations. \n224 To analyze the overall effect of distance from a village on reptile populations, all reptile \n225 observations were combined, and the total number of reptiles observed at each 1-meter interval on \n226 the transects were calculated. These data met the assumptions of a linear regression, with a linear \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n11\n227 relationship between count and distance, normally distributed residuals, and homoscedasticity. \n228 Therefore, OLS was performed to analyze the association between distance and reptile counts for \n229 the combined dataset. Surveys for the village that only had one rock wall were given twice as much \n230 weight to prevent reptile counts for this village to be smaller due to reduced sampling effort. To \n231 further test whether cat density influenced the effect of distance on reptile counts, the number of \n232 reptiles observed at each 1-meter interval were summed within each village, and a Poisson \n233 regression was run for each village with reptile counts regressed against distance. The coefficient \n234 for distance was exponentiated to obtain a slope, and the slopes were regressed against cat density \n235 to investigate the association between cat density and the change in reptile counts with distance.\n236 The association between reptile counts and cat density, cat satiation, and distance were \n237 analyzed in two separate analyses. For analysis one, reptile counts in each village were grouped \n238 into eight 25-meter distance bins. Then, through Poisson generalized linear mixed effect models \n239 (GLMM), with village ID included as a random effect, combinations of model parameters were \n240 explored to determine the best fit model, with AICc criteria used to determine model fit. An \n241 interaction term between cat density and satiation index was included in model exploration as well. \n242 For analysis two, reptile counts were analyzed at the survey level, through Poisson generalized \n243 linear mixed effect models (GLMM) with village ID included as a random effect. These models \n244 were used to analyze the association between the number of reptiles observed in each survey and \n245 cat density and cat satiation index across all villages without distance. An interaction term between \n246 cat density and satiation index was explored in this analysis as well. \n247 Results\n248 Cat density analysis\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n12\n249 Cat densities in all villages were higher than what is normal for the species under rural or \n250 natural conditions (Fig 3, S2 Appendix) [31], except for one village with no cats that also had no \n251 additional food supplementation or permanent human residents. \n252 Cat density was significantly positively associated with both predictor variables when analyzed \n253 independently- food density (adjusted R2 = 0.66, p = 0.009, n = 12, OLS, Fig 3), and human density \n254 (adjusted R 2 = 0.45, p = 0.001, n = 12, OLS, Fig 4). When multiple predictor variables were \n255 included in model selection, two models showed nearly equal fit for predicting cat density. The \n256 first model (Model A) included only food density as a predictor (AICc = 65.54, AICcWt = 0.67, \n257 Log Likelihood = -28.27, Table 1) and the second model (Model B) included both food density \n258 and human density (AICc = 67.47, AICcWt = 0.26, Log Likelihood = -26.88, Table 1). Food \n259 density was significantly positively associated with cat density in both models (adjusted R 2 = \n260 0.657, p = 0.0001 for Model A and adjusted R 2 =  0.698, p = 0.014 for Model B, Multiple \n261 Regression, Table 2), while human density was not significant in the model that included it. When \n262 the type of feeding supplementation (garbage bins and food dishes) was separated, cat density was \n263 significantly positively associated with both density of garbage bins (adjusted R2 = 0.28, p = 0.044, \n264 OLS) and density of feeding dishes (adjusted R2 = 0.73, p  = 0.0002, OLS).\n265 Fig 3. Ordinary least squares regression showing association between cat density and feeding \n266 density (p = 0.0008, adjusted R2 = 0.66). Shaded region represents 95% confidence interval.\n267 Fig 4. Ordinary least squares regression showing association between cat density and human \n268 density (p = 0.01, adjusted R2 = 0.45). Shaded region represents 95% confidence interval.\n269\n270\n271\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n13\n     \nPredictors AICc ΔAICc AICcWt. Log \nLikelihood\nCat Density\n~food density 65.54 0.00 0.67 -28.27\n~human density + food density 67.47 1.93 0.26 -26.88\n~human density 71.2 5.66 0.04 -31.10\n~ human density + food density + interaction 72.16 6.62 0.02 -26.08\nnull model 75.87 10.33 0.00 -35.27\n272\n273 Table 1: Model selection for factors predicting density of cats, Models are ranked in ascending \n274 order by corrected Akaike corrected information criteria (AICc). ΔAICc, AICc weight, and Log \n275 Likelihood are provided for each model. Bold selections show models with near equal fit. Model \n276 variables include food density, human density, and an interaction between food.\n \n Statistic Estimate Std. \nError t-value p-value\nModel A.\nIntercept 0.443 1.534 0.289 0.779\nFood Density 3.624 0.771 4.702 0.0001*\nModel B.\nIntercept -0.675 1.6`4 -0.418 0.779\nHuman Density 0.068 0.045 1.532 0.160\n Food Density 2.769 0.914 3.031 0.014*\n277\n278 Table 2: Results for cat density analysis using multiple linear regression showing two models with \n279 similar fit for predicting cat density. Asterisks indicate significant predictors (p < 0.05). Estimates, \n280 standard errors, and z-values are also shown. Model A adjusted R 2 = 0.657. Model B adjusted R2 \n281 =  0.698.\n282 Reptile population analysis \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n14\n283 A significant positive correlation was found between reptile counts and distance from the \n284 village edge, indicating that reptile numbers declined closer to human habitations and cats \n285 (Pearson’s R = .2176, p = 0.002, OLS, n = 200, Fig 5). \n286 Fig 5. Ordinary least squares regression showing association between the number of reptiles \n287 observed at each 1-meter interval distance from village for the entire study (p = 0.002, adjusted R2 \n288 = 0.43). Shaded region represents 95% confidence interval.\n289 For analysis one, when variables with the binned dataset were examined individually, \n290 Poisson GLMM’s showed that both cat density (marginal  R 2 = 0.33, conditional R 2 = 0.82, p = \n291 0.007, n = 96, Poisson GLMM), and satiation index (marginal R2 = 0.38, conditional R2 = 0.82,  p \n292 = 0.002, n = 96, Poisson GLMM) were significantly negatively associated with reptile counts. \n293 When models that included different combinations of predictor variables and their interactions \n294 were compared, the model that best fit the data included all of the predictor variables; distance (p \n295 = 0.0003), cat density (p = 0.053), satiation index (p = 0.212), as well as the interaction between \n296 cat density and satiation index (p  = 0.001) (marginal R 2 = 0.67, conditional R 2 = 0.83, AICc = \n297 569.11 and AICcWt = 0.87, Tables 3 & 4, compared to next best model AICc = 574.91, and \n298 AICcWt = 0.05,). Hence, distance and the interaction term were found to be significant, while cat \n299 density and satiation index were not. \n300 For analysis two, when variables were examined individually, cat density (marginal R 2 = \n301 0.33, conditional R2 = 0.83, p = 0.008, n = 96, Poisson GLMM, Fig 6) and cat satiation (marginal \n302 R2 = 0.39, conditional R 2 = 0.82, p = 0.002, n = 96, Poisson GLMM, Fig 7) were significantly \n303 negatively associated with the number of reptiles observed in a survey . The full model that best \n304 fit the data included cat density (p = 0.047) cat satiation (p = 0.218), and the interaction between \n305 cat density and cat satiation (p = 0.001) (marginal R2 = 0.67, conditional R2 = 0.83, AICc = 686.08 \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n15\n306 and AICcWt = 0.89, Tables 3 & 5, compared to the next best model AICc = 692.09 and AICWt = \n307 0.04, Table 3). In this model, cat satiation was not significant, cat density was significant, and there \n308 was a significant negative interaction between cat density and cat satiation.\n309 Fig 6. Poisson generalized linear model showing association between reptile counts per survey \n310 and cat density. Shaded region represents 95% confidence interval for fixed effects,  p = 2e-16, \n311 null deviance = 544.02  on 95  degrees of freedom, residual deviance = 462.78 on 94 degrees of \n312 freedom. \n313 Fig 7. Poisson generalized linear model showing association between reptile counts per survey \n314 and satiation index. Shaded region represents 95% confidence interval for fixed effects, p = 2e-16, \n315 null deviance = 544.02 on 95 degrees of freedom, residual deviance = 440.90 on 94 degrees of \n316 freedom. \n317\n318\n319\n320\n321\n322\n323\n324\n325\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n16\n      \n \nReptile Count ~ AICc ΔAICc AICcWt. Log \nLikelihood\nA Reptile Analysis 1\n~ distance + cat density + cat satiation \n+ interaction 569.11 0.00 0.87 -278.09\n~ distance + cat satiation 574.91 5.79 0.05 -283.23\n~ distance + cat density + cat satiation 575.04 5.93 0.04 -282.19\n~ cat density + distance 576.16 7.05 0.03 -283.86\n~ cat density + cat satiation + interaction 579.63 10.51 0.00 -284.48\n~ distance 579.83 10.71 0.00 -286.78\n~ cat satiation 585.52 16.4 0.00 -289.63\n~ cat density + cat satiation 585.61 16.49 0.00 -288.58\n~ cat density 586.77 17.66 0.00 -290.26\nnull model 590.48 21.37 0.00 -293.18\nB Reptile Analysis 2\n~ cat density + cat satiation + \ninteraction 686.08 0.00 0.89 -337.71\n~ cat satiation 692.09 6.01 0.04 -342.91\n~ cat density + cat satiation 692.24 6.16 0.04 -341.90\n~ cat density 693.59 7.51 0.02 -343.66\n null model 697.27 11.2 0.00 -346.57\n326\n327 Table 3: Model selection for factors predicting number of reptile observations from two different \n328 analyses. Model A shows results from reptile analysis 1 and Model B shows results from reptile \n329 analysis 2. Models are ranked in ascending order by corrected Akaike corrected information \n330 criteria (AICc). ΔAICc, AICc weight, and Log Likelihood are provided for each model. Model \n331 variables include distance (model A only), cat density, cat satiation, and an interaction between \n332 cat distance and cat satiation.\n333 Heat maps\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n17\n334 Heat maps showing the spatial distribution of cats, dumpsters, and food dishes were created \n335 for each village in ArcGIS Pro v3.4 to provide visual supplementation to these results. Heat maps \n336 include every cat observation across all three surveys in each village. These heat maps clearly \n337 show that cats form high density aggregations near human-derived food resources. Due to large \n338 cat population size, these results were best visually observed in the village of Vivlos (Fig 8), where \n339 the highest cat density regions (red regions) are all associated with a dumpster or a food dish, while \n340 none of the lowest cat density areas (blue regions) had a detectable source of human-derived food \n341 within them. Results from heat maps from other villages with smaller populations were consistent \n342 with these findings.\n343 Fig 8. Heat map showing the spatial distribution of cats (n = 171 cat observations) in relationship \n344 to human subsidies in a typical Naxian village (Vivlos). Black dots show all cat observations made \n345 during three nights of surveys. Cats typically stayed in close proximity to sources of food, whether \n346 deeding dishes or dumpsters Map created in ArcGIS Pro v3.4. Produced with ESRIi World Image \n347 WGS1983 World Image Base Map (2025) [62].\n348 Discussion\n349 Cats pose an increasingly pressing ecological problem across most island ecosystems \n350 globally [14-15], yet managers and local decisionmakers still have a poor understanding of the \n351 magnitude of the impacts, as well as the causative factors. By surveying outdoor cats along a \n352 gradient of increasing food supplementation at twelve discrete sites within a single representative \n353 Mediterranean ecosystem, this study demonstrated that cat population size is the direct product of \n354 the quantity of human-derived food resources available to them. It was found that food resources \n355 come primarily in the form of food scraps or dry cat food placed by local residents in their yards \n356 or village paths. Equally important are the communal dumpster bins that are usually open and \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n18\n357 sought out by groups of stray cats. These results demonstrate that cat densities in villages are \n358 correlated with the density of both these resources. Beyond density of food resources, a separate \n359 and distinct effect of human population on cat density was detected, potentially reflecting \n360 additional benefits in the form of shelter and medical care some cats derive from a subset of each \n361 community’s inhabitants. While previous studies have shown that cats will congregate and form \n362 high density colonies around easily accessible food resources [34,63,64], these results further \n363 demonstrate that cats are not just simply reorganizing themselves spatially around these resources \n364 (Figs 4 & 10), but rather that population level cat density readily rises to match bottom-up human \n365 supplementation effects.\n366 These analyses assume that the cat population within each village constitute a discrete unit. \n367 The existing literature indicates that while the home range size of cats varies widely [64-65], home \n368 range decreases with increasing productivity of the landscape [66]. The high density of food dishes \n369 and garbage sites within each village in this study (Fig 8) indicates that sufficient resources are \n370 available to collapse home ranges and congregate cats within the individual village. The cat \n371 surveys, conducted over three consecutive nights demonstrate high level of philopatry of \n372 individual cats with most animals not moving more than a few dozen meters from day to day. The \n373 assumption of closed village populations was further supported by a conspicuous absence of cats \n374 away from human settlements (personal observation). These observations agree with past research \n375 [11] and indicate that cats are not roaming the landscape of Naxos or travelling between villages \n376 in significant numbers, but rather remain close to their within-village food sources. Consequently, \n377 increases in cat density within a village are the result of local legacies rather than the result of \n378 extensive inter-village movement.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n19\n379 Is it possible that the association between cats and food dishes is simply the result of \n380 increased provisioning by compassionate locals in response to rising cat populations, rather than \n381 the other way around? While some of this cannot be completely ruled out, field observations \n382 suggest instead that feeding intended for a local house pet ends up inadvertently supporting other \n383 stray cats and hence promotes population increases. Furthermore, cat populations closely reflect \n384 the density of garbage dumpsters, the number of which certainly is not determined by cat density. \n385 In summary, the existing evidence suggests that intentional or unintentional supplementation of \n386 cats drives population numbers rather than vice versa.\n387 We found that cat population density is negatively correlated with resident reptile \n388 population abundance, indicating substantial levels of predation on lizards and snakes. This \n389 corroborates regular but ad hoc observations during the study of cats consuming wildlife (S1 \n390 Appendix). While data collection in this study focused on reptiles, parallel trends are known to \n391 occur within birds as well [40].\n392 This study also reveals that while cat impacts on local wildlife are both pervasive and \n393 density-dependent, they are also modulated by the degree of cat satiation. Because cat populations \n394 do diverge somewhat from what would be predicted based on available resources, the residuals of \n395 this relationship were used as a metric of the relative resources available per cat capita, essentially \n396 obtaining an index of over/under provisioning (termed cat satiation). The results indicate that not \n397 only is the extent to which cat satiation is proportional to decreases in wildlife populations \n398 independent from cat density (Fig 7), but also that the interaction between cat density and satiation \n399 is particularly important. Hence, cat numbers are more negatively associated with wildlife \n400 populations when satiation indices are high, i.e. cats are hungry. This is consistent with a scenario \n401 where cats preferentially consume human-derived resources and switch to preying local wildlife \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n20\n402 when these are not sufficient. Despite the strong evidence of direct cat impact on reptiles through \n403 predation, a different explanatory process cannot be completely excluded. In this scenario, higher \n404 cat density and reduced provisioning results in reptiles that are more wary, more cryptic, and less \n405 active, therefore reducing detectability during surveys.\n406 A clear linear rise in reptile abundance with increasing distance from a village edge was \n407 found (Fig 5). Reptile counts increased on average by ~40% from the immediate edge of the village \n408 to 200 meters away from a village, suggesting that proximity to villages was detrimental to reptiles. \n409 While this pattern is consistent with increased cat-induced mortality close to villages, there was \n410 no relationship between cat density and how rapidly reptile numbers rose with increasing distance \n411 from a village. Hence, the steepness of this decline in reptile abundance when approaching a \n412 village was not significantly related to cat density or cat satiation, perhaps because of insufficient \n413 sample sizes or non-linear relationships. While the possibility that cats do not influence the effect \n414 of distance is not being ruled out, alternative causes may be that 1) as lizards are predated by cats, \n415 the high density of highly active lizards means that the new spaces created by cat predation are \n416 readily filled by a lizard from further away and 2) the increase in reptiles away from villages could \n417 be attributed to other factors such as habitat suitability increasing with greater distance from human \n418 settlements.\n419 Because anthropogenic food subsidies are critical for cat population growth, and increasing \n420 cat population size has proportional negative impacts on resident wildlife population sizes, this \n421 study strongly indicates that human subsidies are facilitating hyperpredation of wildlife on Naxos. \n422 As a result, in many Mediterranean regions, human supplementation drives the impacts of an \n423 artificially elevated cat population on resident wildlife.\n424 Management recommendations\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n21\n425 Public opinion regarding free-ranging cats on the Aegean islands varies greatly, with some \n426 residents considering them pests while others taking care of them through regular feeding (personal \n427 observation). On Naxos and other islands, cats are increasingly becoming a source of amusement \n428 for tourists, and cat-themed merchandise can be found on sale in many retail stores. Local \n429 businesses have also capitalized on the tourist’s affection for cats by selling bags of cat food to \n430 allow for a petting zoo-like experience with the local strays. Over the last several years, there has \n431 been a pronounced shift in public opinion regarding cat welfare away from the past laissez-faire \n432 attitude toward a more compassionate stance. As a matter of fact, it is the attitude of support and \n433 consequent feeding that has resulted in the present-day ballooning of cat populations on Aegean \n434 islands. Concurrently, there is a growing realization that cat overpopulation is a problem, primarily \n435 due to animal welfare rather than wildlife conservation reasons, and cat population control \n436 programs are increasingly discussed.\n437 This study has shown that increasing cat densities translate directly into higher mortality \n438 for reptiles, and that the negative impacts of cats is strongest when human food becomes limiting, \n439 and cats are forced to rely more on wildlife for nutrition. These results may be interpreted as \n440 providing conflicting management suggestions. While in the short term, overprovisioning of cats \n441 reduces predation of wildlife, in the long term this will result in accelerated reproduction and \n442 expanding cat populations. This conflict between short-term benefits versus long-term detriments \n443 can best be navigated through a carefully balanced approach. If cat populations are to be managed \n444 as to reduce wildlife mortality, several steps need to be taken including expansion of existing trap-\n445 neuter-release programs, as well as reduction in supplemental feeding of unsterilized cats to reduce \n446 reproductive potential. More specifically, proposed recommendations include 1) improving waste \n447 management policies to decrease accessibility of this resource to cats, 2) increasing trap-neuter \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n22\n448 release efforts, and 3) providing local education about the deleterious impacts of cats on wildlife \n449 and encouraging both residents and tourists to only feed sterilized cats. This last point is \n450 significant, as keeping only sterilized cats fed will decrease their dependence on wildlife while \n451 also not contributing to cat population growth. The reduced access to human-derived food by \n452 unsterilized cats through recommendations 1 and 3 may cause them to consume wildlife more in \n453 the short term, but the loss of easily available human food resources should also reduce their \n454 reproductive output. While controversial, it is worth mentioning that humane cat culling is an \n455 option as well, as it has been shown to be highly effective, especially on islands [67]. Additionally, \n456 the implementation of cat management needs to be followed by close environmental monitoring \n457 to assess unintended consequences such as mesopredator release of rats following cat population \n458 declines [22].\n459 Conclusion and future research directions\n460 As global biodiversity continues to be lost at an alarming rate [68], the importance of \n461 understanding the mechanisms driving species extinction is increasing. The conclusions of this \n462 study should be applicable to numerous regions in the Mediterranean Basin, at least those where \n463 cats impact small- and medium- bodied vertebrates. Further research efforts need to center on \n464 investigating how the extent of human subsidies influences that distance cats will roam from a \n465 village, and stable-isotope analysis of scat can help elucidate how the ratio of anthropogenic and \n466 natural food sources vary along this gradient. Lastly, this study focused on the high tourism \n467 summer period when large numbers of visitors come to the island. Extending the study to the \n468 winter months when less food supplementation is available can help to understand the true impact \n469 on resident wildlife.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n23\n470 Acknowledgements\n471 We would like to thank Father George Palamaris for providing us with housing in the \n472 Venetian castle of Naxos while conducting this research, and to Mayor Dimitris Lianos for being \n473 supportive of the research the Foufopoulos lab conducts in Greece. 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Wildlife \n634 Research. 1982; 9(3): 409–420. \n635 66. Bengsen AJ, Algar D, Ballard G, Buckmaster T, Comer S, Fleming PJS, et al. Feral cat \n636 home-range size varies predictably with landscape productivity and population density. \n637 Journal of Zoology. 2016; 298(2): 112-120. \n638 67. Nogales M, Martín A, Tershy BR, Donlan CJ, Veitch D, Puerta N, et al. A Review of \n639 Feral Cat Eradication on Islands. Conservation Biology. 2004; 18(2): 310-319. \n640 68. Wilson EO. Threats to Biodiversity. Scientific American. 1989; 261(3): 108-117.\n641 S1 Table. Summary table showing data used in this study. Area and density columns are in \n642 hectares, total food density column is equal to the summation of dish density and dumpster \n643 density, the reptile count column shows the average and standard deviation of total reptile \n644 counts observed at the corresponding village across all surveys.\n645 S1 Appendix. A. Outdoor cats line a set of dumpsters in Chalkio. B. Outdoor cats feed out of \n646 a feeding dish in Vivlos. C. Cat predates a Eurasian Collared Dove in Chora. D. Cat predates \n647 a wall lizard in Moni.\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n31\n648 S2 Appendix. Photo taken in Glynado demonstrating the exceptional densities cats can \n649 achieve in urban environments.\n650\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint \n\n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted July 31, 2025. ; https://doi.org/10.1101/2025.07.27.667076doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}