Urbanization drives patterns of resource partitioning among coyotes

Urbanization drives patterns of resource partitioning among coyotes | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 March 2026 V1 Latest version Share on Urbanization drives patterns of resource partitioning among coyotes Authors : Samantha Kreling 0000-0002-6298-2041 [email protected] , Ellen Reese , Olivia Cavalluzzi , Alexandra Renouard , Yasmine Hentati 0000-0003-3531-6956 , Robert Long 0000-0003-4004-9573 , and Laura Prugh 0000-0001-9045-3107 Authors Info & Affiliations https://doi.org/10.22541/au.177243864.48139834/v1 233 views 109 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 1. Niche partitioning and resource specialization can lead to spatial heterogeneity in trophic dynamics, but little is known about how these dynamics may change across landscapes of varying complexity. 2. Urban ecosystems are characterized by a wider array of both natural and anthropogenic food sources, which can increase predator densities compared to exurban and wildland areas. Urbanization could thus intensify intraspecific competition and facilitate individual specialization, or relax intraspecific competition if resources are not limited, leading to more generalized diets. 3. To investigate how urbanization affects dietary niche breadth and individual specialization, we used fecal metabarcoding to analyze diets of 50 coyotes (Canis latrans) across three landscapes in Washington, USA: the Seattle metropolitan area, Bainbridge Island, and two wildland study sites. 4. Population level dietary richness was similar between urban and island coyotes (x̄=56 and 52 taxa, respectively), but considerably lower for wildland coyotes (x̄=35 taxa). Despite variation in population-level richness, the diversity of individual diets was surprisingly consistent across regions. Urban coyotes had limited individual specialization, whereas wildland coyotes exhibited the highest individual specialization. 5. Specialization and dietary diversity may be constrained by home range size and the diversity of prey types available to an individual coyote, highlighting the importance of landscape and prey heterogeneity in urban landscapes. These findings suggest that urbanization can relax intraspecific competition among predators, whereas individual dietary specialization may have a greater influence on predator-prey dynamics and human-wildlife conflict in more natural ecosystems with less diverse resources. Introduction Niche partitioning and resource specialization are key mechanisms by which species reduce inter and intraspecific competition (Alley 1982, Bolnick et al. 2003). The ways in which resources are partitioned among competing individuals can define the success of the population, especially when population density is high or resources are limited (Bolnick et al. 2003). For example, individuals that specialize in singular or limited food types often experience higher foraging success, leading to reduced energy expenditure and higher reproductive success (Bolnick et al. 2003, van den Bosch et al. 2019). While much theoretical work on niche partitioning has been conducted, empirical data is still lacking especially in novel ecosystems like urban areas (Bolnick et al. 2003). Urban regions can support high densities of human- and disturbance-tolerant wildlife species, such as mesocarnivores (Fischer et al. 2012), because cities are generally characterized by diverse, abundant food resources (Smith et al. 2018. Gámez et al. 2022). The higher diversity of food sources may allow more individuals to specialize in different diets while the higher density of mesocarnivores may increase intra and interspecific competition, facilitating strong resource partitioning. However, the degree to which anthropogenic influences alter the resource specialization is not well understood and may be highly context dependent (Newsome et al. 2015, Sévêque et al. 2020, Manlick & Newsome 2021). Understanding how these coexistence mechanisms are affected by urbanization is increasingly important as urbanization accelerates worldwide (Sévêque et al. 2020). In urban areas where the density of mesocarnivores is inflated due to increased food availability (Fischer et al. 2012, Šálek et al. 2014), opposing strategies for coexistence could emerge (Sévêque et al. 2020). While population dietary niche breadth is expected to be wider in urban areas due to increased resource variety, individuals may specialize on certain resources to decrease intraspecific competition. For example, coyotes ( Canis latrans ) in Chicago, Illinois had greater dietary variation among individuals than coyotes in surrounding exurban regions (Newsome et al. 2015). Alternatively, if resources are plentiful enough that the increased density of mesocarnivores does not sufficiently limit the population through resource competition, coyotes may consume the most abundant items within their home ranges and have low degrees of individual specialization. Indeed, Larson et al. (2020) found reduced individual variation in urban coyotes and wide niche breadth in suburban areas of Los Angeles, California compared to nearby wildland areas. In wildland areas with a lower diversity of available resources, individuals should theoretically have more generalized diets and reduced levels of resource partitioning. However, inferences regarding effects of urbanization on niche partitioning and individual specialization have been limited by traditional methods of scat analysis that identify diet items based on identification of undigested remains. Advances in genomic methods such as fecal metabarcoding allow for high-resolution dietary construction that is especially advantageous in urban areas where carnivores may consume foods that are difficult or impossible to identify using traditional methods. Traditional methods generally rely on hard-object and undigested components that are not likely to be left behind by highly processed human foods (De Barba et al. 2013, Forin-Wiart et al. 2018). Coyotes ( Canis latrans ) are one of the most well-studied carnivore species in North America and function as an important apex predator in urban ecosystems (Gallo et al. 2019). Coyotes are classic dietary generalists at the population level, consuming a wide variety of plants and animals across a range of urbanization (Murray et al. 2015, Poessel et al. 2017a, Sugden et al. 2021, Jensen et al. 2022). However, it is unclear if this population-level generalism arises because individuals are generalists, or because individuals specialize on different dietary items across the population (Figure 1). Coyotes in urban regions tend to have higher diet diversity compared to non-urban coyotes because of access to a larger number of native or non-native prey, ornamental plants, and human food (Murray et al. 2015, Sugden et al. 2021). However, understanding how dietary diversity and population density contribute to individual specialization remains poor, with conflicting results from prior studies (Newsome et al. 2015, Larson et al. 2020). Understanding how urbanization affects individual specialization of carnivores has important management implications because conflicts related to diet may arise from a handful of “problem” individuals rather than population-wide consumption patterns. For instance, sheep depredation in northern California was found to be almost entirely carried out by breeding male coyotes rather than entire family groups or transients (Blejwas et al. 2006). To facilitate human-coyote coexistence, it is imperative to understand whether individuals specialize on different items, and how the degree of specialization varies across landscape contexts. Our objective was to determine how urbanization affects the niche breadth and individual specialization of coyotes. We used fecal metabarcoding to generate a comprehensive diet dataset among three distinctive landscape contexts in Washington, USA. Our samples come from the Seattle metropolitan area, an offshore exurban island, and two wildland study sites in northern Washington. If urbanization intensifies intraspecific competition, we expect coyotes to have reduced niche breadths and increased individual specialization compared with wildland regions. If urbanization relaxes competition, we expect coyotes to have wider niche breadths and less individual specialization in urban areas. Figure 1: Conceptual figure depicting population-level dietary generalism coming from high inter-individual variation and individual specialization (left) versus individual generalism with low inter-individual variation (right). Study Region Non-invasive scat collection was conducted in three separate landscape types within the state of Washington. Our urban samples were collected from the Seattle metropolitan area, Washington (~1200 km 2 ; Figure 2). This area is home to over 4 million people and is the 15 th largest metropolitan area in the United States (US Census Bureau 2022 ). The metropolitan area is a mix of natural forest fragments, dominated by Douglas fir ( Pseudotsuga menziesii ), western red cedar ( Thuja plicata ), and western hemlock ( Tsuga heterophylla ), as well as suburban and highly urbanized regions. Our island data comes from Bainbridge Island, an exurban island just off the coast of Seattle within the Puget Sound (~72 km 2 ) with forests dominated by the same tree species as the Seattle metropolitan areas. Both the Seattle metropolitan area and Bainbridge Island experience maritime climates. Our wildland samples were collected as part of the Washington Predator Prey Project (https://predatorpreyproject.weebly.com/) in Okanagan (OK) and Northeastern Washington (NE). These wildland areas (sampling area covered ~3,000 km 2 each) were also dominated by Douglas fir forests but included a number of other primary tree species such as western red cedar, ponderosa pine ( Pinus ponderosa ), western larch ( Larix occidentalis ), and grand firs ( Abies grandis ). Most of these wildland study areas consisted of natural land cover, with 3.5% agricultural cover and 1.1% developed areas (Ganz et al. 2024). Human density was 4.5 people/km2 across both wildland study areas (OK: 4.1 people/km2; NE: 4.9 people/km2; Malesis et al. 2024). We analyzed our wildland areas together as they shared similar landscape characteristics. Figure 2: On the top, a map of the Northern portion of the state of Washington state with all study areas outlined in black and a depiction of where the inset is within Washington state, and the United States. Waterways are highlighted in blue. The gradient from yellow to brown represents the percent of impervious surface (NLCD 2019) across the region depicted. In the middle, an inset of the Bainbridge Island and Seattle metropolitan study areas. Major cities are indicated on both maps. Scat Collection Scats were collected with a Ziploc bag and then placed within a Whirl-Pak bag and frozen at -80°C after removal from the environment until they were thawed to be swabbed for DNA extractions. Scats were collected in the Seattle metropolitan area 2021-2023, with emphasis on months May – October when precipitation was lower and samples were more likely to yield higher quality DNA. In 2021, green spaces within the city of Seattle were repeatedly sampled on an approximately 2 week basis as reported in Kreling et al. (2024). Scats in the Okanagan and Northeast study regions were semi-systematically collected with dedicated scat transects walked or driven once per month during summer 2018-2020 and winter 2020-2021 as part of the Washington Predator Prey Project. DNA Extraction & Microsatellite Genotyping All laboratory steps, except for metabarcoding sequencing and DNA fragment analysis, were conducted in the School of Environmental and Forest Sciences (SEFS) Genetics Lab at the University of Washington. To extract DNA, scat samples were thawed and divided into four sections. We swabbed the outer surface of each scat, focusing on the ends where epithelial cells are most likely to be present, to collect DNA for individual coyote genotyping. Using the same swab, we then sampled each internal section to gather DNA from ingested items. Each scat was swabbed with a flocked swab moistened with phosphate-buffered saline solution. DNA extraction followed a modified version of the QIAmp DNA Investigator Kit protocol (Qiagen, Hilden, Germany). Scats collected from wildland study areas for the Washington Predator Prey Project were screened for predator ID using a mitochondrial species ID panel, allowing coyote scats to be distinguished from other sympatric carnivore species (see Ganz et al. 2023 for details). This panel was initially applied to scats collected from urban areas, but we determined that the collection rate of non-coyote scats from urban areas was much lower than in the wildland study areas, and that it was more cost-effective to screen out non-carnivore scats at the later microsatellite genotyping and metabarcoding steps. For individual coyote identification, we amplified DNA from the samples at 10 microsatellite loci and 2 sex ID loci using two multiplexed PCR panels. To minimize genotyping errors, PCR was replicated three times for each sample, following Prugh et al. (2005). The first panel amplified nuclear microsatellite loci FH2001, FH2010, FH2054, FH2088, and FH2328. The second panel amplified CXX2235, FH2096, FH2137, FH2140, FH2159, and two additional loci on the X and Y chromosomes for sex determination (DBX6, DBY7; Prugh et al., 2005; Seddon, 2005). Both panels were processed under identical cycling conditions using touchdown-PCR: an initial 15-minute denaturation at 95°C, followed by 10 touchdown cycles at 94°C for 30 seconds, 68°C decreasing by 1°C per cycle for 30 seconds, and 72°C for 45 seconds. This was followed by 30 cycles at 94°C for 30 seconds, 58°C for 30 seconds, and 72°C for 45 seconds, with a final extension at 60°C for 15 minutes. After amplification, the PCR plates were frozen and sent to Yale’s Keck DNA Sequencing Core for fragment analysis using an Applied Biosystems 3130 Series Genetic Analyzer via capillary electrophoresis. Allele sizes were quantified using GeneMapper (Curie-Fraser & Shah, 2010), and consensus genotypes were created from the replicates following Prugh et al. (2005) and imported into GeneAlEx in Excel (Peakall & Smouse, 2006). Genotypes were compared with those from domestic dog reference samples in Structure (Pritchard et al. 2000) to filter out dog scats or samples contaminated by dogs (e.g., urination in urban areas). Library Preparation & Sequencing To analyze coyote diet, we performed PCR amplifications using a multiplex of two primer pairs: 12SV5 for vertebrates (Riaz et al., 2011) and trnL g/h for plants (Taberlet et al., 2007). Each sample was processed in triplicate. The PCR products were cleaned using size-selecting SPRI beads, quantified with a Qubit DNA quantifier, normalized, indexed with sample-specific indices, and pooled following established protocols. The pooled library was then submitted to the Northwest Genomics Center for sequencing on an Illumina NexSeq platform. Bioinformatics Upon receiving the raw sequence files, we decompressed them using the ‘tar’ command in the terminal. Using CLC Workbench for the first two runs, we merged reads from the same samples across the four sequencing lanes. These merged files were exported as forward and reverse reads in fasta format for each sample and replicate. For the third run, we created a custom Bash script to merge our reads from across sequencing lanes. FastQC (Andrews, 2010) was employed to assess the quality of the raw reads, and MultiQC (Ewels et al., 2016) was used to summarize the results across all samples. After confirming acceptable quality metrics, we used cutadapt to trim primers and overhangs from both forward and reverse reads, followed by another round of FastQC and MultiQC to ensure successful trimming. Trimmed reads were then processed through the ‘DADA2’ package in R (Callahan et al., 2016). We plotted the quality profiles of a subset of reads to ensure normal distribution. Reads were filtered and trimmed using DADA2’s ‘filterAndTrim’ function, with reads having expected errors greater than EE = 2 being discarded. The ‘learnErrors’ function was used to assess error rates, and the filtered reads were denoised using the ‘dada’ command, which eliminates erroneous reads to reconstruct the true community composition. Forward and reverse reads were merged using ‘mergePairs’. A sequence table was generated using the ‘makeSequenceTable’ function, and chimeras were removed with the ‘removeBimeraDenovo’ command. The resulting sequence table was converted to a matrix and saved as a CSV file. NCBI Blast Given the large number of ornamental and non-native species in urban areas, creating a comprehensive custom reference database was not practical. The fasta files were uploaded to NCBI’s Blastn tool (Madden, 2002) for manual review of sequence matches. We identified matches to the lowest taxonomic unit when we had high confidence based on high ‘Percent Identity’ (above 95%) and high ‘Query Cover’ scores (above 90%), and when the species were likely to be found in our study area. For unlikely species, we either classified them at the genus level or assigned a closely related species known to exist in the area. For instance, reads matching European beaver ( Castor fiber ) were identified as American beaver ( Castor canadensis ). When sequences matched multiple species, the lowest common taxonomic unit was recorded. We also documented instances where no sequences matched the NCBI nucleotide database. Additional Read Filtering We excluded samples that failed to amplify in at least two PCR replicates or had fewer than 100 total reads. For each sample, we retained only prey items that were detected in at least two out of three PCR replicates and averaged the number of reads for each prey item across those replicates. To account for contamination, we used extraction blanks and PCR negatives, averaging the reads from these controls and subtracting them from the total reads for each sequence in the respective samples. Additionally, we removed any human ( Homo sapiens ) reads from the dataset. We then calculated the total number of reads per sample, excluding both contamination and canid reads. Following standard practices, we considered only the reads representing at least 1% of the total for plants and 0.05% for vertebrates, after excluding canid and contamination reads (Caspi et al. 2025). Furthermore, we excluded scats that had more non-canid carnivore reads than canid reads, because the species of origin typically has the highest read count. Thus, these scats likely originated from a different carnivore species as opposed to being cases where coyotes consumed the other carnivore. In cases where coyotes consumed these carnivores, the scat should have more canid reads than reads of the other carnivore. Consumption of dogs by coyotes could not be identified, however, because the 12S metabarcoding region cannot distinguish among these canids. Taxonomic Resolution Plant taxonomic identifications were limited to those we were confident were from diet items rather than environmental contamination, such as pollen or substrate contamination. Plant taxa were identified at the genus level when possible, and otherwise were identified at the family level. For corn, trnL cannot specify species, but when Zea and closely related grass taxa were the closest match in NCBI Blast, we counted these reads as Zea . Other Poaceae reads that did not have Zea as a top match were discarded as contamination unless representing another genus that likely represented a purposefully consumed food item (e.g., Avena, Hordeum ). Vertebrates were largely identified to species but were represented at the genus level when it was not possible to differentiate between species (e.g., shrews, voles). We ensured that no samples were categorized as containing a species or genus and a higher level classification (genus or family) that could represent the same food item. Niche Breadth & Individual Specialization To control for the effect of sample size on diet diversity, we first created species accumulation curves for two diversity metrics using the ‘iNEXT’ package in R (Chao et al. 2014, Hsieh et al. 2020). Based on these curves, we set a minimum cutoff of 8 scats per individual for inclusion in analyses (Figure 3). We then used resampling to draw 8 random samples from each coyote with more than 8 scats. Resampling was repeated 1,000 times, and the average frequency of occurrence values across all dietary items for each study area were recorded. We used frequency of occurrence instead of relative read abundance to allow the direct incorporation of dietary information across plant and vertebrate primers. In addition, frequency of occurrence and relative read abundance values were found to be highly correlated in a study of Pacific fisher ( Pekania pennanti ) diets in Washington (Shively et al. 2025). Figure 3: Species accumulation curves for all individuals identified with more than 1 scat sample against Hill-Shannon Diversity for all three study regions. Each line represents an individual. Where the line is solid is the observed value, the dashed component is interpolated by the ‘iNEXT’ package in R. A vertical black bar is drawn at the 8-scat cutoff for each graph. Next, we calculated species richness and Shannon diversity across the diet of individual coyotes using the ‘estimateD’ function from the ‘iNEXT’ package in R based on presence/absence of dietary items in each coyotes’ scat samples. This function rarefies to the lowest sample size ( n =8) to account for differences in diversity related to uneven sampling among individuals. To determine how urbanization affected population-level niche breadth, we calculated Levin’s niche breadth index ( B ) using the rarified frequency of occurrence dataset for each study area and the formula \(B=\frac{1}{\Sigma p_{i}^{2}}\) where p i is the proportion of resources used (Levin 1968). To meet assumptions of this index, we standardized the data so that the frequency of occurrence of diet items summed to one for each individual coyote prior to calculating B . We used Wilcoxon Signed Rank tests to test for differences in individual niche breadth among the different study areas (Wilcoxon 1945). We additionally calculated rarified species richness across all study areas, resampling to the lowest number of samples per area ( n = 106 in Bainbridge) and resampling 1,000 times. To assess individual specialization, we computed a Jaccard dissimilarity matrix using the ‘vegdist’ function from the ‘vegan’ package (Oksanen et al., 2022). We then used the ‘Eindex’ function from the ‘RInSp’ package in R (Zaccarelli et al., 2013) to calculate a jackknife cross-validated estimate of the E index for individual diet specialization and mean dietary overlap among individuals within each population. These indices are derived from the DIETA1 software and based on Araújo et al. (2008). The E index measures the degree of individual variation in diet, ranging from 0 (all individuals have identical diets) to 1 (each individual has a unique diet). Mean overlap (O mean ) also ranges from 0 to 1, with 0 indicating no dietary overlap across the population and 1 representing complete overlap across the population. We then used a permutation-based multivariate analysis of variance (PERMANOVA) on the dissimilarity matrices to determine if diet varied among individuals and among our study areas. PERMANOVA’s were conducted in R using the ‘adonis2’ function from the ‘vegan’ package. To account for unequal sampling sizes between individuals, we ran 1000 separate PERMANOVA’s drawing 8 samples from each individual and averaged across the results. Results Sampling Of 1,220 scats collected in the Seattle metropolitan area, 952 were deemed of coyote origin and successfully sequenced for diet. Of these, 827 were successfully genotyped ( n = 147 individuals). On average there were 5.59 scats collected per individual ( sd = 6.42, range = 1-36), and 35 individuals had at least 8 scat samples (x̄ = 14.57, sd = 7.57). Of 145 Bainbridge Island scats collected, 106 were successfully sequenced for diet, and 99 were assigned to one of 26 coyotes (x = 3.96 scats per individual, sd = 3.17, range = 1-12 scats). Five individuals had at least 8 scat samples (x̄ = 9.4, sd = 1.67). Of 175 scats from our two wildland study areas, 146 were successfully sequenced for diet ( n = 106 from the Okanagan and 35 from the Northeast). Of these, 105 were successfully genotyped ( n =15 individuals), with an average of 9.6 scat samples collected per individual ( sd = 4.14, range = 1-18). Ten individuals (7 from OK, 3 from NE) had at least 8 samples assigned to them (x̄ = 11.8 scat samples/individual, sd = 2.82). Dietary Diversity & Niche Breadth In the Seattle metropolitan area, we identified 23 vertebrate families (40 species; 10 additional genera) and 14 plant families consumed with frequency of occurrence values ≥ 0.01 (Figure 4, Table S1 & S2). In the Bainbridge Island study area, we identified 23 vertebrate families (22 species, 4 additional genera) and 9 plant families. Lastly, for the wildland study areas we identified only 14 vertebrate families (13 species, 4 additional genera) and 10 plant families across all of our scat samples. Rarified taxa richness across all samples was similar between the Seattle metropolitan area (x̄ = 55.79, 95% CI: 54.90-56.68) and Bainbridge Island (x = 52.00), while it was much lower in the wildland area (x̄ = 35.07, 95% CI: 34.75-35.39). Figure 4: Bar graphs of frequency of occurrence of different taxonomic families identified across all coyote scats for each study region. The top dietary items are depicted with silhouettes to the right of the bars. Vertebrate prey are shown in light brown, while vegetative foods are depicted in dark green. Despite the differences in population-level species richness, diet diversity per individual coyote was remarkably similar among the three study areas. For the Seattle metropolitan area coyotes, Hill-Shannon diversity was 9.19 ( sd = 2.91) and Hill-species richness was 11.56 ( sd = 3.23). Similarly, Hill-Shannon diversity was 9.15 ( sd = 1.71) and Hill-species richness was 11.99 ( sd = 2.85) for Bainbridge Island diets. Lastly, for wildland coyotes Hill-Shannon diversity was 7.96 ( sd = 3.01) and Hill-species richness was 9.28 ( sd = 2.98). Population-wide Levin’s niche breadth index was also similar among all three study areas, but widest by a slight margin for the Seattle metropolitan area coyotes (x̄ = 8.19, sd = 3.05), followed by the wildland coyotes (x̄ = 7.99, sd = 2.80), and lastly the Bainbridge Island coyotes (x̄ = 7.51, sd = 1.15). Individual Specialization The degree of dietary overlap varied by study region (Figure 5). The Seattle metropolitan area coyotes had lower overlap across individuals (O mean = 0.43) than Bainbridge Island coyotes (O mean = 0.55), but higher overlap than wildland coyotes (O mean = 0.34). Thus, individuals on Bainbridge Island had more similar diets across the population than the other two study areas. PERMANOVAs for each study area revealed significant differences in diet among individuals within each study area (Seattle metropolitan area: R ID 2 = 0.33, p = 0.001; Bainbridge Island: R ID 2 = 0.22, p = 0.010; Wildland: R ID 2 = 0.29, p = 0.001). Based on these PERMANOVAs, 22-33% of diet variation within each study area was explained by individual coyote ID. The E index of specialization for the Seattle metropolitan area (E = 0.57, Var = 2.22 x 10 -7 ) was between the values of the Bainbridge Island coyotes (E = 0.45, Var = 5.43x10 -5 ) and wildland coyotes (E = 0.65, Var = 2.74x10 -5 ). This indicates the highest degree of individual specialization was found in the wildland study areas and lowest in the Bainbridge Island study area, consistent with the population-wide overlap values showing highest dietary overlap among the Bainbridge Island coyotes and lowest overlap among wildland coyotes. Figure 5: Bar graph of number of taxonomic families identified, average E-index of specialization, average mean network overlap (O mean ), average Levin’s niche breadth, and average Hill-Shannon richness results across all three study areas. E-index and mean network overlap (O mean ) range between 0 and 1, while the other metrics range between 0 and 100. Seattle metropolitan area results are displayed in red, Bainbridge Island in blue, and the wildland areas in yellow. Discussion Coyotes have arguably been one of the most successful species in the Anthropocene, doubling their historic range to occupy virtually every ecosystem available across North America, including metropolitan areas (Hody & Kays 2018). While their success is generally attributed to their adaptability and generalist nature (Hody & Kays 2018), understanding whether their populations are composed of individual generalists or specialists with substantial individual behavioral variation has important management implications across urbanization gradients. Ecological theory would suggest large differences in niche partitioning and individual specialization as a result of changes in resource diversity and intra and interspecific competition, but we found surprisingly similar niche widths among coyotes occupying an urbanization gradient in Washington. Despite similar levels of diet diversity, we found evidence for specialization among individuals that varied substantially across these diverse landscape contexts. Specialization decreased as urbanization increased in the landscape, suggesting that these areas may have decreased competition for resources. Alternatively, prey availability may drive species consumption. Our wildland study areas had the highest degree of individual specialization and the lowest degrees of dietary overlap. These study areas were much larger (~3,000 km 2 each) than Bainbridge Island (~72 km 2 ) and the Seattle metropolitan area (~1200 km 2 ), and coyotes likely occurred at much lower densities in the wildland areas (Fischer et al. 2012, Šálek et al. 2014). Coyotes in more urbanized regions have significantly smaller home ranges, which may constrain the resources available to them and reduce the potential for specialization (Atwood et al. 2010, Šálek et al. 2014, Ward et al. 2018). While we do not know the exact home range size of coyotes in Seattle, coyotes in other urban regions such as Chicago have had recorded home ranges as small as 5 km 2 (Gehrt et al. 2009), whereas coyotes in our wildland areas had home ranges ~ 35 km 2 (n=34 coyotes; Prugh et al. 2023). Likewise, on a relatively small island such as Bainbridge, prey availability is likely to be more homogenous than across a larger wildland area with diverse topography and land cover (Tobler 1970). Thus, home ranges and territoriality may strongly influence coyote diet and access to individual resources throughout our study areas. Our findings suggest that coyotes in more urban areas may face much lower degrees of competition due to prolific anthropogenic resources and abundant non-native prey despite having increased intraspecific density and conspecific densities. While we do not have estimates of resource availability in our study regions, other works have demonstrated that resource availability is generally higher in urban regions than in non-urban regions (Hansen et al. 2020, Thompson et al. 2021). The increased specialization among wildland coyotes may reflect increased intraspecific competition due to lower diversity and abundance of resources, as well as increased interspecific competition with other mesocarnivores such as bobcats ( Lynx rufus ) and apex predators including wolves ( Canis lupus ) and mountain lions ( Puma concolor, Prugh et al. 2023). Dietary richness is often greater in urban regions as a result of access to both native and anthropogenic food sources (Murray et al. 2015, de Souza Laurindo & Vizentin-Bugoni 2020, Larson et al. 2020). However, little research has uncovered if this diversity is reflected at both population level and at the individual level. While we identified approximately 60% more vertebrate and plant taxa within our urban study system across all individuals, rarified individual diet diversity was only slightly lower in our wildland study areas compared to Bainbridge Island and Seattle metropolitan area coyotes. The lack of difference in individual dietary diversity across regions may further indicate that coyotes are constrained to the resources in their immediate vicinity. With significantly smaller home ranges in urban areas, there may be relatively few prey species available to any given individual as a result of this smaller foraging and hunting area despite the higher diversity of dietary items across the entire urban region. Additionally, seasonality and sample size may influence the overall population-level taxa richness among the study regions. Most samples for our urban and island study areas were collected during the summer when plant resources like berries are more abundant. 36% our samples for the wildland areas were collected during the winter and this may have reduced the overall species richness. However, only 40 unique taxa were identified across both winter and summer collection periods. 50% of these taxa were identified during the winter collection period (20 total taxa in winter, 30 total and 20 novel taxa in summer), reducing the likelihood that seasonality strongly influenced the dietary richness analyses. In urban and rural areas where conflict or perceived conflict with coyotes is high (Poessel et al. 2017b), this individualization has important implications. In areas with higher degrees of population-wide individual specialization like our wildland areas, removing ‘problem’ individuals who are depredating domestic animals can be more effective than population-wide culling at reducing conflict (Blejwas et al. 2002). For example, while consumption of anthropogenic foods was rare in our wildland areas, one individual accounted for half of all anthropogenic food consumed (n=4/8 occurrences of anthropogenic items in scat). In other systems, individual predators can have tremendous effects on prey populations. For instance, Festa-Bianchet et al. (2006) found that individual mountain lions were likely responsible for killing a large portion of a bighorn sheep ( Ovis canadensis ) population, leading to major declines. Similarly, extirpation of the Stephen’s Island wren ( Xenicus lyalli ) was the result of predation by a single domestic cat ( Felis catus ; Greenway 1967). In urban areas, coyote consumption of domestic cats and dogs is an important source of conflict, but removal of “problem” coyotes is unlikely to be effective due to low individual specialization among urban coyotes. Although some coyotes in the Seattle area consumed cats more frequently than others (0 - 46% of scats per coyote; mean=15%, sd=0.15), the vast majority of coyotes in the Seattle area had cat remains in their scats (26 of 35 individuals, 74%). Thus, other strategies such as keeping domestic cats indoors or dogs on leashes are essential to promoting co-existence in cities (Poessel et al. 2017a,b). While dietary richness can give us an idea of how varied an individual’s diet may be, niche breadth can indicate the dispersion of dietary diversity and point to either widespread consumption of many items or heavy consumption on relatively few taxa. Niche breadth was surprisingly similar among study areas and across individuals. The relatively narrow values for niche breadth compared to the number of resources available to them indicate that coyote diet is largely made up of a few key species, despite finding 64 taxa in the Seattle metropolitan area diet versus 27 taxa across our wildland study areas. For instance, Eastern cottontail rabbits ( Sylvilagus floridianus ) occurred in most scats in the Seattle area, and likewise cervids were widely consumed in the wildland study areas. Across all study areas, coyotes appear to be eating what is easily available to them within their home ranges and acting as dietary generalists at both the individual- and population-level. However, we could not assess actual prey distribution and availability across the landscape. To better understand the relationship between prey availability and niche breadth, further research should be conducted to estimate prey availability. Because scats represent a snapshot of an individual’s consumption, multiple samples from the same individual are needed to accurately characterize diets (Prugh et al. 2008). In our case, species accumulation models indicated we needed at least 8 scat samples per individual before dietary diversity began to level off. Longitudinal sampling of the same individual can be challenging, especially across large study areas or in areas with high turnover rates. In our wildland study areas for example, the number of coyotes with at least 8 successfully genotyped scats was limited because fieldwork covered about 3,000 km 2 in each of the two wildland areas, while the number of individuals on Bainbridge Island was likely constrained by its small land mass. Additionally, while metabarcoding offers a much finer resolution of diet compared to traditional scat analysis, capable of identifying most vertebrate species and many plant genera, a significant drawback is the need for repeated sampling of individuals. Other methods, such as stable isotope analysis, offer inferior taxonomic resolution but have the advantage of integrating dietary information over longer periods from single samples (Crawford et al. 2008). Future research should consider combining lower-resolution information such as stable isotope analysis in conjunction with metabarcoding to increase long-term understanding of dietary trends while being able to accurately understand diet diversity (Bonin et al. 2020). Understanding dietary specialization and diversity has important ecological and management implications. The patterns of prey consumption by individual predators can alter prey distribution, behavior, and population size (Packer et al. 2003, Salo et al. 2010), which can have cascading effects on the vegetative landscape (Schmitz et al. 2000). Many studies have pointed to dietary differences between urban and non-urban populations (Sugden et al. 2021), but few have investigated the individual behaviors and choices that scale up to these population-level differences or how these differences may arise (Romero-Vidal et al. 2023). Urban areas can support high densities of mesocarnivores due in large part to abundant resources available to wildlife, yet we have little understanding of how these mesocarnivore densities interplay with abundant resources and shape the landscape of competition. Romero-Vidal et al. (2023) found that dietary patterns were largely driven by intraspecific competition and individual behavior for birds, while urbanization generally did not have an effect. Our results indicate resources in urban areas may be abundant enough that coyote densities are limited by territoriality rather than resources, thereby relaxing the intensity of intraspecific competition and dietary partitioning. While lethal removal of coyotes in urban areas can be an effective management tool (Breck et al. 2017), lack of individual specialization may render this management option ineffective when conflict occurs due to consumption of dietary items that are preferred across the population. Supplement Table S1: Taxonomic families present within each study area with frequency of occurrence greater than 0.01. Scientific Name Taxa Type Common Name Seattle Metropolitan Area FOO Bainbridge Island FOO Okanagan FOO Northeast FOO Wildland Combined FOO Accipitridae Bird Hawks 0.01 – – – – Adoxaceae Plant Elderberry Family 0.05 0.02 0.02 – 0.02 Anacardiaceae Plant Cashew Family 0.01 – 0.02 – 0.01 Anatidae Bird Ducks 0.03 0.06 0.02 – 0.01 Apiaceae Plant Carrot Family 0.06 0.02 0.09 – 0.07 Aplodontidae Mammal Mountain Beavers 0.01 – – – – Berberidaceae Plant Barberry Family 0.01 – 0.02 0.09 0.04 Bovidae Mammal Cattle 0.06 0.04 0.06 – 0.04 Caprifoliaceae Plant Honeysuckle Family 0.03 0.01 0.08 0.23 0.11 Castoridae Mammal Beavers 0.03 – – – – Cervidae Mammal Deer & Elk 0.01 0.13 0.28 0.71 0.38 Charadriidae Bird Plovers & Lapwings – 0.01 – – – Clupeidae Fish Herrings & Sprats 0.01 – – – – Columbidae Bird Doves & Pigeons 0.01 0.01 – – – Convolvulaceae Plant Morning Glory Family 0.01 0.01 – – – Corvidae Bird Crows 0.03 – – – – Cricetidae Mammal Voles 0.22 0.65 0.30 0.09 0.24 Equidae Mammal Horses – 0.01 0.01 – 0.01 Ericaceae Plant Heath Family 0.03 0.10 0.15 0.14 0.15 Fabaceae Plant Legume Family 0.05 0.06 – 0.03 0.01 Fabaceae – Soy Plant Soy 0.05 0.01 – – – Felidae Mammal Cats 0.15 0.03 – – – Gasterosteidae Fish Stickleback 0.01 – – – – Grossulariaceae Plant Currant Family – – 0.05 0.23 0.09 Juglandaceae Plant Walnut Family 0.01 – – – – Leporidae Mammal Rabbits 0.49 0.16 0.32 0.37 0.33 Linaceae Plant Flax Family 0.01 – – – – Moraceae Plant Mulberry Family – 0.08 – – – Muridae Mammal Rats 0.12 0.04 – – – Mustelidae Mammal Weasels – 0.01 – – – Myricaceae Plant Sweet Gale Family 0.01 – – – – Paridae Bird Tits & Chickadees 0.01 – – – – Passerellidae Bird Passerines 0.05 0.10 0.06 0.03 0.05 Phasianidae Bird Pheasants, Chickens, & Turkeys 0.18 0.16 0.09 0.37 0.16 Picidae Bird Woodpeckers – – 0.01 – 0.01 Poaceae Plant Grass Family 0.02 – – – – Poaceae – Corn Plant Corn 0.03 0.01 – – – Polygonaceae Plant Buckwheat Family 0.10 0.14 0.03 0.03 0.03 Procyonidae Mammal Raccoons 0.01 0.01 – – – Rosaceae Plant Rose Family 0.89 0.85 0.26 0.83 0.40 Salmonidae Fish Salmon – 0.03 0.01 – 0.01 Sciuridae Mammal Squirrels 0.11 0.12 0.03 – 0.02 Sittidae Bird Nuthatches – 0.01 0.01 – 0.01 Soricidae Mammal Shrews 0.02 0.01 0.02 – 0.01 Suidae Mammal Pigs 0.07 0.04 0.01 – 0.01 Talpidae Mammal Moles 0.04 0.06 – – – Trochilidae Bird Hummingbirds – 0.01 – – – Turdidae Bird Robins & Thrushes 0.02 0.07 – – – Xiphiidae Fish Swordfishes – 0.01 – – – Table S2: Table of higher resolution taxonomy sorted alphabetically within taxa type. Each taxon is listed with its scientific and common name. An ‘X’ denotes presence within that study system while a ‘—’ denotes absence. For the wildland study area deer are listed under Odocoileus as both Odocoileus hemionus and Odocoileus virginianus are present in parts of the wildland study system and are not distinguishable using these methods. On Bainbridge Island and in the Seattle metropolitan area, only Odocoileus hemionus occurs. Taxa Type Scientific Name Common Name Seattle Metropolitan Area Bainbridge Island Wildland Combined Bird Aix sponsa Wood Duck X — — Anas Ducks X X X Bonasa umbellus Ruffed Grouse — — X Catharus ustaulatus Swainson’s Thrush X — — Colaptes auratus Northern Flicker X — X Columbidae Doves X — — Corvus Crows X — — Coturnix japonica Japanese Quail X — — Dendragapus fuliginosus Sooty Grouse — — X Gallus gallus Domestic Chicken X X — Ixoreus naevius Varied Thrush — X — Junco hyemalis Dark-eyed Junco X X X Meleagris gallopavo Turkey X — X Neurotrichus gibbsii American Shrew Mole X X — Paridae Tits X — — Phasianus colchicus Ring-necked Pheasant X — — Pipilo maculatus Spotted Towhee X X X Poecile Atricapilla Black-capped Chickadee X — — Regulus Kinglets X — — Sittidae Nuthatches X X Sturnus vulgaris Common Starling X — — Trochilidae Hummingbirds X — — Troglodytes Wrens X — — Turdus True Thrushes X X — Fish Ameiurus Bullheads X — — Brevoortia Brevoortia X — — Cyprinus Carps X — — Gasterosteus aculeatus Three-spined Stickleback X — — Micropterus salmoides Large-mouth Bass X — — Oncorhynchus Pacific Salmon & Trout — X — Oncorhynchus kisutch Coho Salmon — — X Perca Perch X — — Xiphias gladius Swordfish — X — Mammal Alces alces Moose — — X Aplodontia rufa Mountain Beaver X — — Bos taurus Domestic Cattle X — X Castor canadensis North American Beaver X — — Clethrionomys Slender Voles — — X Cricetidae Voles X X X Equus caballus Domestic Horse — — X Felis catus Domestic Cat X X — Lepus americanus Snowshoe Hare — — X Microtus longicaudus Long-tailed vole X — X Mus musculus House Mouse X — — Neogale vison American Mink — X — Odocoileus Deer (Both black and white tail occur in parts of wildland study areas) Odocoileus hemionus Odocoileus hemionus X Odocoileus hemionus Black-tailed Deer X X — Oryctolagus cuniculus European Rabbit X — — Ovis aries Domestic Sheep X — — Procyon lotor Raccoon X — — Rattus norvegicus Norway Rat X X — Rattus rattus Black Rat X X — Scanapus orarius Coast Mole X — — Scanapus townsendii Townsend’s Mole X — — Sciurus carolinensis Eastern Gray Squirrel X X — Sorex Long-tailed Shrews X X — Sus scrofa Domestic Pig X X — Sylvilagus floridianus Eastern Cottontail X X — Tamiasciurus douglasii Douglas Squirrel X — X Plant Actinidia Kiwi X — — Anacardium Cashews X — — Apiaceae Carrot Family X X — Arachis Peanuts X — — Avena Oats X — — Berberidaceae Barberry Family X — X Brassica Brassicas X — — Cornus Dogwoods X — — Crataegus Hawthorns X — X Ericaceae Heath Family X X — Fabaceae Legume Family X — — Fabaceae – Soy Soy, Glycine X — — Ipomoea Sweet Potatoes X X — Juglandaceae Walnut Family X — — Lactuca Lettuces X — — Lens Lentils X — — Linum Flaxes X — — Morus Mulberries X X — Musa Bananas X X — Oryza Rice X — — Piper Pepper X — — Poaceae – Corn Corn X — — Prunus Plums X X X Pyrus Pears X — — Rhus Sumacs — — X Ribes Currants X — X Rubus Brambles X X X Rumex Dock X X X Rutaceae Citrus Family X — Sambucus Elderberries X X X Sorghum Sorghums X — — Symphoricarpos Snowberries X — X Theobroma Chocolate X — — Trifolium Clovers X X X Vaccinium Huckleberries — — X Viburnum Honeysuckles X — — Reptile Trachemys scripta Red-eared Slider X — — References Alley, T. 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Keywords comparative molecular genetics population ecology sequencing terrestrial vertebrate Authors Affiliations Samantha Kreling 0000-0002-6298-2041 [email protected] University of Washington Seattle Campus View all articles by this author Ellen Reese University of Washington View all articles by this author Olivia Cavalluzzi University of Washington View all articles by this author Alexandra Renouard University of Washington View all articles by this author Yasmine Hentati 0000-0003-3531-6956 University of Washington Seattle Campus View all articles by this author Robert Long 0000-0003-4004-9573 Woodland Park Zoo View all articles by this author Laura Prugh 0000-0001-9045-3107 University of Washington View all articles by this author Metrics & Citations Metrics Article Usage 233 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Samantha Kreling, Ellen Reese, Olivia Cavalluzzi, et al. 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