Concrete Jungle to Urban Oasis: Scale, Greenspace Size and Patchiness Influence Wildlife in Cities of Eastern Los Angeles County, California

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Elihu, Janel L. Ortiz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4909697/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban greenspaces are a haven for wildlife in densely populated cities. Wildlife use greenspaces for resource acquisition, shelter, and traveling across urbanized landscapes. Greenspace characteristics such as presence of woody or herbaceous landcover, size, edge density, and patchiness influence species richness. The goals of this study was to: 1) identify and quantify greenspace metrics to determine relationships with wildlife and 2) determine differences in greenspace patterns at various spatial scales. To monitor wildlife, twenty-six camera traps were set in eastern Los Angeles County, California; greenspace metrics were gathered using 3m land cover supervised classification. We used a generalized linear mixed model to determine the influence of greenspace metrics on richness at four scales (200m, 500m, 1km, and 2km). At larger scales, 1km and 2km, high herbaceous cover, whether as increasing aggregated patches or increased patchiness, and moderate levels of woody cover positively influence species richness. At smaller scales, 200m and 500m, low to moderate levels of herbaceous cover and high levels of woody cover strongly and positively influence species. These results suggest that wildlife are able to utilize urban areas with increasing fragmentation of greenspace habitat and require greenspace, either as a few, less fragmented patches or as many patches with high herbaceous cover in the urban matrix. From the perspective of urban planning, developing greenspaces from a broader ecological scale is important to ensure they function as stepping stones in the urban matrix. Understanding these patterns can improve greenspaces that support wildlife and therefore, ecological functions. urban wildlife patchiness greenspace fragmentation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Greenspaces in the urban matrix are critical to supplying ecosystem services needed for a healthy environment. These ecosystem services include carbon sequestration (Jo and McPherson 1995 ; McPherson 1998 ), regulation of the urban microclimate and the urban heat island effect (McPherson 1998 ; Oliveira et al. 2011 ), reducing the negative effects of noise pollution (Dzhambov and Dimitrova 2014 ), increase of water table infiltration which reduces impervious surface water run-off and increase evapotranspiration (Bolund and Hunhammar 1999 ), decrease air pollution (Nowak et al. 2006 ), and provide habitat for biodiversity (Goddard et al. 2010 ; Beninde et al. 2015 ). The degree in which urban greenspace provides effective habitat for biodiversity depends on several factors including greenspace type, connectivity in the urban matrix, size, and species present (Shanahan et al. 2011 ; Beninde et al. 2015 ). Ecosystems services contribute to healthier land, air, water, in urban areas for wildlife and humans alike, however there are specific benefits that are especially important for people as they connect with the natural environment. Species richness also differs along a gradient of urbanization. From the densely modified urban core of the inner city to natural ecosystems on the outskirts of cities, urban-to-rural gradients provide a method to quantify changes in urbanization as it relates to wildlife (McKinney 2002 ; Riem et al. 2012 ). Gradient analysis, first studied in plant communities, is a research approach to describe environmental variation within space, accounting for spatial patterns that preside over ecological system’s structure and function (Whittaker 1967 ; McDonnell and Pickett 1990 ). It has since been used to describe spatial patterns for many other taxa, including wildlife species. Urban wildlife studies are often conducted along a gradient of rural, suburbia, and urban areas to document how a species relates to changes of urban factors (Riem et al. 2012 ), such as presence anthropogenic food sources, light/sound pollution, and human activity. Urban describes areas of high impervious cover and highest human population density, suburbia usually has moderate levels of impervious cover and human population density and serve as a transition zone between urban and rural, while rural areas, like agriculture and natural lands, have the lowest impervious cover and human population density (Šálek et al. 2015 ). In the case of Los Angeles county, especially San Gabriel Valley, since there is limited agricultural land, we can say urban-to-natural gradient instead of rural. Natural lands consist of habitat that is undeveloped and has its original, unaltered vegetation in place. Several studies have shown that the urban core holds the lowest species diversity and diversity increases along the gradient as it moves closer to natural or remnant areas (McKinney 2002 ). For example, in small and medium size carnivore mammals, the home range size decreased along the gradient of natural lands to the urban core, while the population density increased for each species (Šálek et al. 2015 ). An urbanization gradient, also proves as a useful metric to study urban wildlife, because it can be compared to other gradients in urban regions, such as an income gradient (Magle et al. 2021 ). As Magle (2021) demonstrated, medium to large mammal species responded more strongly to the urbanization gradient than an income gradient and also found average species occupancy was highest at decreased levels of urban intensity (Magle et al. 2021 ). Results like this can help researchers identify patterns of adaptation, plasticity, fitness, evolutionary changes, in wildlife as well as necessary changes to the urban landscape such as increasing habitat connectivity, native vegetation, and improving access to wildlife for those in densely urban areas (Šálek et al. 2015 ). Until recently, the majority of urban wildlife studies were city specific, relating that city’s climate, geography, age, land-use and culture to the ecology of wildlife in that area (Magle et al. 2019 ). However limitations arise when trying to understand and compare large-scale and generalized patterns of wildlife ecology in urban spaces because there is no consistent data across varied study areas to draw consensus (Magle et al. 2019 ). Though cities vary in landscape, they hold similar structures, such as human-modified greenspace, suburban neighborhoods with yards, and concentrated grey space like industrial areas. General patterns between species presence and urban features amongst cities can be key identifiers in how wildlife relate to the urban landscape; for example Virginia opossum ( Didelphis virginiana) presence was found to increase with growing urban intensity (Markovchick-Nicholls et al. 2008 ). As urbanized as southern California is there is not a lot of research as to what wildlife inhabit urban ecosystems and how the structure of these urban systems effect wildlife. Urbanization has affected wildlife species differently based off their specific needs for resources and species have been categorized based on their relation to urban settings, such as urban exploiters, urban adapters, and urban avoiders (Hardin 2021). Urban exploiters are species that can exploit resources in urban or human-altered environments and reach their highest population densities in developed areas. Urban exploiters tend to be non-native species, including the house sparrow ( Passer domesticus) and the rock pigeon ( Columba livia) (Blair 1996 ). Some species, like the black rat ( Rattus rattus) , are also termed commensalist species, meaning they require human habitation to thrive (Aplin et al. 2011 ). Urban adapters are species that are able to survive and thrive in urban and natural settings (Blair 1996 ). Urban adapters, like the coyote ( Canis latrans) , Virginia opossum ( Didelphis virginiana) , and Northern raccoon ( Procyon lotor) benefit from the surplus of resources urban settings offer but require access to natural areas for feeding, denning or resting (McKinney 2002 , Gese et al. 2012 ). Coyotes also fall under the term ‘edge species’, as they live in urban areas but require access to natural habitat and must travel farther through urban regions to access nonurbanized areas (Riley 2003, Gese 2012). Differing degrees of urbanization affect species, like the coyote, which have been found to have larger home ranges within urban habitats, compared to rural coyotes, often because they must travel farther in urbanized regions to find patches of natural land (Gese 2012). Urban avoiders tend to avoid urban settings as much as possible and are especially more sensitive to altered and human-dominated environments (Blair 1996 ). Many large mammals, specifically predators like the mountain lion ( Puma concolor) , black bear ( Ursus americanus) , and bobcat ( Lynx rufus) , are urban avoiders, and will have higher population density in natural areas (McKinney 2002 ). Mountain lions prefer natural areas with minimal human presence; but increasingly have been found in urbanized areas based on the availability of their prey species, the California mule deer ( Odocoileus hemionus californicus ), which eat ornamental vegetation in urbanized areas (Riley et al. 2021 ). Desert cottontails ( Sylvilagus audubonii ) have consistent densities in urban greenspaces, like parks and yards, and do not vary between urban, urban edge, and natural spaces (Dunagan 2019). This has led bobcats to hunt for cottontails more frequently at night closer to the urban edge (Dunagan 2019). The influence of urbanization on wildlife behavior can lead to increased human-wildlife interactions, especially at urban to wildland transitional edges. For wildlife to survive in urban regions, as compared to natural areas, they must possess two important characteristics, be generalists and have a tolerance for human presence (Hardin 2021). Generalists thrive from a wide range of resources that provide food and shelter while also being plastic in their behavior (Hardin 2021). In contrast, specialists may be able to survive in urban areas with very specific resource requirements, limiting their range in urban settings. The consequences of urbanization have greatly altered the ecology of many wildlife species so much so that they are defined based on their tolerance to urban environments. Key impacts of urbanization are loss and fragmentation of natural habitats. Loss describes the decrease in area of the original habitat while fragmentation describes the process in which original habitat is divided or interrupted by other habitats, essentially breaking the original area into small pieces (Collinge 2009 ). Habitat conversion, a type of habitat loss, occurs when an area of an original habitat type is changed to another land use type, such as a region of rainforest being converted into farmland. Around the world about 21% of land area has been converted for human uses with greater than 50% loss seen in North America’s temperate and mixed forests and temperate grasslands (Hoekstra et al. 2005 ). A common example of fragmentation includes a freeway or road running through a previously undisturbed habitat, such as a desert or prairie, splitting the area. Fragmentation is frequent in Los Angeles County, and one impactful example is the 101 freeway which consists of ten lanes that separate the Santa Monica Mountain and Simi Hills habitat (Riley, 2021). This has proved challenging for many species, especially the mountain lion, who rely on travelling large distances as part of their territory to find mates (Riley, 2021). Fragmentation poses new risks and challenges to wildlife as they are forced to learn how to move across a modified habitat, while relearning how to get resources and avoid predation and death (Collinge 2009 ). Many species that have adapted to urban areas have modified their behavior to support their survival. Since urban greenspace habitat patches are often smaller and more interspersed, prey and predator species have been found to occupy the same patch, when normally prey would disperse farther to avoid the predator (Gallo et al. 2019 ). One camera trap study performed in Chicago, Illinois, found that eastern cottontails and white-tailed deer did not display differences in spatial occupancy from the coyote in urbanized regions (Gallo et al. 2019 ). Changes in morphology and song characteristics of the house finch (Haemorhous mexicanus) were tested and shown in urban areas that finches with narrower beaks produce lower-frequency songs that can be heard over the noise of urban environments, possibly suggesting that males will be chosen based off their altered song frequency to be heard by females in urban settings (Giraudeau et al. 2014 ). Research documenting radio-telemetry bobcats and coyotes in southern California around the Ventura Freeway (US 101) show that the freeway acts as a hard boundary for male and female bobcats, producing smaller home ranges and decreasing gene flow (Riley et al. 2006 ). Barriers in urban spaces have led to rapid changes in species morphology, behavior, and ecology as they adapt to human-built spaces. As natural habitats shrink and urban sprawl increases, it is important to consider the ecology of urban regions and how they function. Biologists around the world have noticed this change and have focused their interests on understanding the ecology of wildlife in urbanized areas. In this study, we will determine what greenspace characteristics influence urban wildlife richness by using remote camera traps to monitor wildlife diversity in greenspaces within San Gabriel Valley (eastern Los Angeles County, California). We expect a larger area of greenspace with less edge and frequency of greenspace patches to have higher species richness versus smaller, patchier, simpler greenspace. Recognizing the most influential characteristics will aid city planners and urban ecologists to better understand how to create or modify existing urban greenspace with the vision of maximizing benefits for wildlife and people to create sustainable urban ecosystems. MATERIALS AND METHODS Study Area San Gabriel Valley is a highly urbanized region of eastern Los Angeles County in Southern California, comprised of 31 cities covering an area of 1036 square kilometers with a population of over 1.7 million people (San Gabriel Valley- Los Angeles County Economic Development Corporation 2020). Historically, San Gabriel Valley was primarily farmland, consisting of citrus orchards, produce, and cattle ranches (Surls and Gerber 2016 ). Study Design and Site Selection One 30 km transect was created sampling twenty-six sites using camera traps to monitor wildlife along an urban to rural gradient from Diamond Bar to the San Gabriel Mountains covering 11 of the 31 cities in San Gabriel Valley (Fig. 1 ). The transect is based on degrees of impervious surface cover, like sidewalks and human-built structures (Chithra et al. 2015 ), ranging from densely urban (high impervious cover) to rural/natural land cover (low/zero impervious cover). A 2km buffer was created around the transect and all cameras were placed at least 1km from each other within the 2km buffer following the protocol of the Urban Wildlife Information Network (UWIN). Site selection and placement were based upon UWIN methodology and finalized based on site access and approval. Camera sites were defined within 5 greenspace categories (Fig. 2 ) which vary in size, location, amount of greenspace (GS) and human-built surfaces (HB), and average amount of human activity (HA- people/per trap night). These categories include 1) yard space: greenspace, primarily lawns, attached to houses surrounded by human-built structures with low human activity and foot traffic (GS: 54–76%, HB: 16–43%, HA: 15.59), 2) open-modified space: greenspace with low human foot traffic and access, including a landfill, private commercial nursery, and urban farmland (GS: 42–75%, HB: 6–55%, HA: 25.39), 3) natural areas: vegetated natural spaces, like trails, with low to moderate human use and access (GS: 89–99%, HB: 0%, HA: 0.014), 4) urban-wildland interface: natural greenspace adjacent to houses and human-development with low access and foot traffic (GS: 70–99%, HB: 0–11%. HA: 4.67), and 5) recreation: city parks with primarily ornamental vegetation (some with/without patches of natural land) with high access and foot traffic (GS: 54–82%, HB: 7–39%, HA: 226.82). Greenspace and human-built surface percentages were calculated based on a 155-meter buffer around each camera site which was standardized by using the smallest study site size. All final site selections were dependent upon Cal Poly Pomona’s Risk Management approval, site approval, and accessibility. Camera Trapping One Bushnell Core Low Glow Trail camera (Model- 119936C) was placed at each study site to monitor wildlife presence in the area. Cameras were deployed to captured photos for two, two-week time spans per month. Data was gathered for a total of one year during 2022 and 2023, sampling one month per season (Fall- October, Winter- January, Spring- April and Summer- July). No lures were used in this study. EcoAssist, an AI program, incorporates the model MegaDetector to detect wildlife presence in photos, and was used to identify and separate photos with an object, animal, or human in them from empty photos (van Lunteren 2023 ). Photos were analyzed and tagged twice for confirmation from two independent volunteers when looking for species presence, human presence, and objects. If there was disagreement between tags, photos are sent to validation from which AE would determine the correct tag or enter the correct tag if both are incorrect. Final taxonomic groups of interest were mammals and birds. This project is considered exempt from Cal Poly Pomona Institutional Animal Care and Use Committee (IACUC) since there was no manipulation or handling of animals. Land Cover Classification A supervised land cover classification raster was created using ERDAS Imagine 16.6 of 3m resolution imagery taken on December 1, 2021 from the Planet-Scope satellite (Planet Labs PBC). Land cover types were categorized into 5 classes including woody cover, herbaceous cover, human-built, bare ground, and water. A 69% accuracy land cover classification of eastern Los Angeles County was achieved with Google Maps using 250 random points. This accuracy is probably due to a larger survey area, eastern Los Angeles County (1,044 \(\:{km}^{2}\) ) being used in the assessment compared to using the smaller, study transect (60 \(\:{km}^{2}\) ). While 85% land cover accuracy attainment is often cited in literature, it is not widely applicable among all landcover analyses, not initially developed to support local, small scale landcover analyses, and might be unrealistically inflated (Foody 2008 ). Buffers of 200m (Desert cottontail), 500m (Virginia opossum), 1km (Northern raccoon), and 2km (Coyote) in radii were created and clipped around each camera site (Examples shown in Fig. 3 ) to account for patterns that influence wildlife diversity at different scales based on home ranges of common urban species (Haugen 1942 ; Prange 2003; Harmon et al. 2005 ; Gehrt 2009; Wright 2012). A buffer of 500m, 1km, and 2km accounts for intermediate dispersal and foraging distances of common urban adapted birds such as the Northern mockingbird ( Mimus polyglottos ), mourning dove ( Zenaida macroura ), house finch ( Carpodacus mexicanus ), and American crow ( Corvus brachyrhynchos ) (Crooks et al. 2004 ; Matthies et al. 2017 ; Aberle et al. 2020 ). Class level landscape characteristics, hereon known as metrics, (Online Resource 1), determined from literature review and the FRAGSTATS manual version 4 (McGarigal and Marks 1995 , McGarigal 2015 ), were extracted from the buffers and analyzed using FRAGSTATS 4.2 software. These metrics were chosen based on the most commonly used metrics relating biodiversity to landscape ecology (Neel et al. 2004; Schindler et al. 2008 ; Perotto-Baldivieso et al. 2009 ; Tolessa et al. 2016 ; Mata et al. 2018 ; Lombardi et al. 2020 ). Statistical Analysis We ran a non-metric multidimensional scaling (NMDS) analysis with Bray-Curtis dissimilarity for species richness data to determine similarities between mammal and bird species frequencies in relation to occupancy in greenspaces of different land use types. Pearson’s correlation coefficient (r) was calculated and a multidimensional scaling (MDS) analysis was conducted for landscape metrics to eliminate those that may be highly correlated to each other (> 0.7, < -0.7). Focusing on species richness for mammals and birds at each site, we used SpadeR (Species-richness Prediction And Diversity Estimation in R) from the online R application Shiny, to develop estimated species richness values factoring in the number of trap nights each camera was in use and species detection per trap night which included number of uniques (species detected in one sampling unit) and duplicates (species detected in two sampling units) (Chao 2019). We determined species detection by noting presence/absence of a species per trap day along with total trap days per site to create incidence-frequency data and chose the estimator iChao2 for species richness because it has an improved lower bound compared to Chao2, good accuracy, reduced bias compared to traditional estimators, and improves confidence interval coverage (Chiu et al. 2014 ; Chao 2019). Comparisons of richness values from camera trap data and iChao2 estimates (with 95% CI) per site and land use category are shown in Online Resource Fig. 2 . To understand the relationship between estimated species richness and landscape metrics, we used a generalized linear mixed model (GLMM) with a negative-binomial distribution. Estimated species richness was the response variable and percent of land cover of woody (PLAND Woody), herbaceous cover (PLAND Herbaceous), woody patch density (PD Woody), herbaceous patch density (PD Herbaceous), patch area mean woody/herbaceous (AREA_MN Woody/Herbaceous), edge density of woody/herbaceous cover (ED_Woody/Herbaceous), patch cohesion index of woody and herbaceous cover (COHESION Woody/Herbaceous), and largest patch index (LPI Woody/Herbaceous) were proposed fixed effects. Study sites nested within land use type were used as a random effect. To further narrow metric selection and prevent overfitting I used best-fit line plots and Akaike Information Criterion (AIC) values to compare and favor models with lower AIC values and normal residual diagnostic plots to determine the final model. The final data of the models for 200, 500m, 1km, and 2km examining estimated species richness and landscape metrics, contained percent landscape woody cover (PLAND_Woody), percent landscape herbaceous cover (PLAND_Herbaceous), patch density woody cover (PD_Woody), patch density herbaceous cover (PD_Herbaceous), edge density herbaceous (ED_Herbaceous) (Online Resource Table 2) and all interactions were rescaled for standardization (scale between − 1 to 1). Post model diagnostic plots of residuals and Q-Q plots were used to visualize the fit of each model and if inappropriate due to high dispersion or patterns in variance in diagnostic residuals, another metric was considered. All analyses were conducted in program R 4.2.0. RESULTS Data was collected from a total of 140 trap nights per camera from 26 cameras, one camera per site, in 11 cities from February 2022 to January 2023. Forty-five species were documented across all sites (14 mammals, 31 birds). Distribution and total detections of all species varied over all sites (Online Resource 3). Six species were seen in over 50% of all survey sites. The most ubiquitous species were the coyote ( Canis latrans ) and fox squirrel ( Sciurus niger ) appearing at 21 sites, with total detections of 489 and 360, respectively. California ground squirrel ( Otospermophilus beecheyi ) (16, 222), opossum ( Didelphis virginiana ) (16, 157), Desert cottontail ( Sylvilagus audubonii ) (15, 454), and Striped skunk ( Mephitis mephitis ) (15, 184) were species most detected across many of the sites. The NMDS analysis shows and supports which wildlife species of eastern Los Angeles County fall into the category of urban ‘exploiter’, ‘adapter’, and ‘avoider’ and how they orient to different human land use types (Fig. 4). The NMDS plot also provides additional support to understand which specific species are frequent in which human land use categories- answering the question of ‘who’ is at each site. For each camera site, metrics of woody and herbaceous cover were extracted at four scales: 200m, 500m, 1km, and 2km. Greenspace for all sites at 2km accounted for an average of 66% land cover. Remaining land cover includes human-built, bare ground, and water. Table 4 Significant main effects, 2-way, and 3-way interactions statistic summary produced from GLMM of landscape metrics and wildlife richness for each model at scales of 200m, 500m, 1km, and 2km Scale Variables Estimate Std. Error Z value Pr(>|z|) 200m Edge Density Herbaceous 0.335297 0.153095 2.190 0.0285 Percent Cover Herbaceous:Edge Density Herbaceous - 0.51249 0.223277 -2.295 0.0217 500m Percent Cover Herbaceous 0.65540 0.1658 3.951 7.79e-05 Percent Cover Woody 0.92577 0.2834 3.266 0.00109 Patch Density Woody 0.42229 0.1962 2.152 0.03142 Percent Cover Woody:Percent Cover Herbaceous: Patch Density Woody 0.81731 0.2911 2.807 0.00500 Percent Cover Density Woody: Percent Cover Density Herbaceous: Patch Density Herbaceous 1.30340 0.2518 5.176 2.27e-07 Percent Cover Herbaceous:Patch Density Woody: Patch Density Herbaceous 1.95356 0.3349 5.833 5.43e-09 1km Percent cover Woody -1.12220 0.5644 -1.988 0.0468 Percent Cover Woody:Percent Cover Herbaceous -1.60201 0.8005 -2.001 0.0454 2km Patch Density Woody -0.96493 0.3658 -2.638 0.008349 Percent Cover Woody:Percent Cover Herbaceous 1.77502 0.5343 3.322 0.000894 Percent Cover Woody:Patch Density Herbaceous 3.21374 0.8104 3.965 7.33e-05 Percent Cover Herbaceous:Patch Density Woody:Patch Density Herbaceous -1.95780 0.9371 -2.089 0.036707 200m Scale Edge density of herbaceous cover was a significant main effect on richness at 200m scale (Table 4). At 200m, species richness decreases as percent herbaceous increases when edge density of herbaceous is high (743 m/hectare) (Fig. 5A). As percent herbaceous cover increases with low (786 m/hectare) herbaceous edge, species richness slightly increases (Fig. 5A). A conceptual buffer (5B) displays how the landscape would appear based off the interaction graph. 500m Scale Percent of woody cover, percent herbaceous cover, and patch density of woody cover were all significant main effects on richness at the 500m scale (Table 4). There is a general pattern of increased species richness when percent of woody cover increases at most values of herbaceous cover and woody patch density (Fig. 5C-H). Medium (160/100 hectares) and high (270/ 100 hectares) woody patchiness, with medium (23%) and high (45%) of herbaceous cover, strongly and positively influences species richness as woody cover increases. Low (70/ 100 hectares) woody patchiness increases richness at high herbaceous cover but is minimally affected by levels of woody cover (Fig. 5C). Low (32%) herbaceous cover and low (200/ 100 hectares) herbaceous patch density with high (45%) woody cover influence high richness (Fig. 5E/5F). At medium (287/ 100 hectares) and high (386/ 100 hectares) levels of herbaceous patchiness, and medium (45%) and high (56%) herbaceous cover, richness increases with high (45%) levels of woody cover. Low richness can be seen with medium (287/ 100 hectares) and high (386/ 100 hectares) herbaceous patchiness, low (32%) herbaceous cover, and higher woody cover. Low richness is also seen at low (200/ 100 hectares) herbaceous patchiness, at high (56%) herbaceous cover, with increased woody cover (Fig. 5E/5F). Highest richness is expected at low (32%) and medium (45%) herbaceous cover, considering all levels (low- 200, medium- 287, high- 386/ 100 hectares) of herbaceous patch density and low (70/ 100 hectares) to high (270/ 100 hectares) woody patchiness (Fig. 5G/5H). Richness declines at high (56%) herbaceous cover, every level (low- 200, medium- 287, high- 386/ 100 hectares) of herbaceous patch density as woody patchiness increases, from low (70/ 100 hectares) to high (270/ 100 hectares) (Fig. 5G/5H). At a smaller scale of 500m, woody cover has a stronger positive effect on species richness compared to 1km and 2km. 1km Scale Percent of woody cover was a significant main effect on richness at the 1km scale (Table 4). Highest species richness is seen with increased herbaceous land cover (56%) and lower values of woody cover (0.77%), but as woody cover increases, richness drops dramatically (Fig. 5I). At low levels of herbaceous land cover (32%), as woody cover increases, species richness increases minimally (Fig. 5I).The buffer at 1km (5J) demonstrates an example landscape pattern between herbaceous landcover and percent woody cover contributing to high species richness. From 2km to 1km we see a similarity of high herbaceous cover influence positively richness, but it switches from high to low woody cover that predicts higher richness. 2km Scale Patch density of woody cover had a significant effect on richness at the 2km scale (Table 4). There was a significant 3-way interaction between Pland Herbaceous cover, PD Woody and PD Herbaceous cover as they influence species richness at 2km (Fig. 5O). Low amount of herbaceous land cover (32%) and low herbaceous patchiness, medium to high patch density of tree cover contributes to increased species richness. As herbaceous cover increases, high (>55%) and herbaceous patches increases, high (367/ 100 hectares), medium level (181/ 100 hectares) of woody patches we see a peak in species richness. There is a drastic drop in species richness in areas of greater herbaceous cover and increased presence of herbaceous patches, as woody cover reaches its highest patch density. At 2km, moderate levels of woody patch density positively influence species richness in areas with higher or lower herbaceous cover regardless of its patchiness. The buffer at 2km (5P) demonstrates an example landscape pattern between herbaceous landcover and patch density woody and herbaceous contributing to high species richness. Higher species richness is seen as herbaceous (>55%) and woody (~43%) cover increases (Fig. 5K- 5L). The lowest species richness is seen with high herbaceous cover (>55%) and low woody cover (1.3%), and low herbaceous cover (32%) and high woody cover (~43%). Increased richness towards low to high (1.3- 43%) percent woody cover regardless of whether patchiness of herbaceous is low or high (79- 270/ 100 hectares) (Fig. 5M- 5N). At extremes lows or highs of percent woody cover and patch density herbaceous, richness remains extremely low. DISCUSSION The landscape ecology of urban greenspaces impact and influence wildlife species richness in eastern Los Angeles County. This study focused on specific landscape metrics and vegetated landcover including woody and herbaceous patch density , percent land cover,, and edge density cover at different scales of 200m, 500m, 1km, and 2km, to identify patterns that may contribute to increased richness. The findings of this research fill a gap in knowledge about urban wildlife in eastern Los Angeles County to better understand how to create, sustain, and possibly modify urban greenspaces for wildlife. The NMDS plot (Fig. 4 ) demonstrates patterning of urban wildlife species in eastern Los Angeles county falling into the categories of urban avoider, adapter, and exploiter. Highly urban species like the opossum, house finch, and northern mockingbird are more commonly found in yard spaces while urban adapter species like the coyote and striped skunk are seen in larger greenspace patches like recreation and open modified space. Urban avoider species like the bobcat prefer areas of higher vegetation however they are increasingly becoming edge species due to increasing housing development and fragmentation, causing bobcats to cross more fragmented vegetated patches to reach larger vegetated patches (Zheng et al. 2024). Bobcats are noted for their behavioral plasticity and adaptability enabling them to be able to traverse urbanized landscapes to reach water, food, vegetated habitat, and mates reaching densities in densely human populated regions similar to small urban spaces (Crooks 2002 ; Lombardi 2017). As human population density increases in cities it is important to measure the scale at which specific species may be responding to changes within urbanized regions in order to better understand responses wildlife may have to greenspace. Scale of Greenspace Wildlife species richness was influenced by levels of herbaceous cover, herbaceous patchiness, woody cover, and woody patchiness to different degrees based on scale size. Varying scales of 200m, 500m, 1km, and 2km were critical to identify patterns and change in patterns between wildlife and greenspace. Only observing large scales for instance may make sweeping generalizations while losing important details that may be significant for species that live at smaller scales (Fidino et al. 2021 ). Depending on the species, a coyote may travel farther to acquire food or denning sites, but a fox squirrel may have an urban range limited to a few blocks. At 1km and 2km, we may suggest that some species are accustomed to low woody urban environments but benefit from low to high levels of herbaceous cover and patchiness. One study found that at buffers of 1km and 2km, native, terrestrial and forested birds were not strongly influenced by increased canopy cover (Humphrey et al. 2023 ). These species may be using human-built landcover like buildings to serve as shelter for nesting or power lines which substitute for trees as perching and nesting locations. A balance of herbaceous and woody cover appears to be needed at larger scales (Fig. 6 ). While perhaps tree cover improves urban habitat for wildlife, patterns from analysis may suggest that wildlife in eastern Los Angeles County predominantly rely on large herbaceous patches. Even though herbaceous cover becomes more fragmented, the increase of woody landcover positively influences species richness. This indicates that urban wildlife may prefer a balance of available herbaceous patches, either more or less aggregated, with moderate levels of woody cover present. At 500m, increased woody land cover and patchiness positively, strongly, influenced species richness, especially compared to scales of 1 km and 2km which saw higher richness at low to moderate woody cover/patchiness. The effect tree cover has on species richness is stronger within a smaller area of land (Fig. 6 ). Increased tree cover at smaller scales has been shown to support oak woodland and riparian dominant birds in San Diego county (Crooks et al. 2004 ). One study in Australia, found that bird richness was increasingly and strongly influenced by vegetation, nectar producing plants, in neighborhoods measured at smaller scales compared to larger scales at 1km (Luck 2013). This suggests that greenspaces designed within urban spaces should consider the available surrounding landscape at various scales. Patchiness of Landscape Urban landscapes are characterized by highly fragmented and heterogeneous patches of greenspace (Zhou et al. 2018 ). To a degree, urban wildlife are accustomed to anthropogenic fragmentation and understand how to find food and other resources throughout the matrix (Crooks 2002 ; Riley et al. 2003 ; Grubbs and Krausman 2009 ). For example, in the Santa Monica Mountains, coyotes and bobcats were collared and tracked to monitor their home range, and were found that over 80% of each species had home ranges inclusive of urban land (Riley et al. 2003 ). Also noted, urban adapted species are struck less by cars compared to species exposed to less traffic and roads in undisturbed habitats (Riley et al. 2003 ), which is one of the main impediments to navigating urban fragmentation. However, sensitivity to fragmentation is species specific and largely depends on the animal’s body size and food sources (Crooks 2002 ). These findings support the need to measure wildlife response to landscape effects at species specific scaled levels, because they consider body size and dispersal ability. Larger species from this study, like the black bear and mule deer, were found along predominantly urban wildland interface and natural sites, suggesting that at smaller scale of 500m low herbaceous patchiness and cover with high woody cover and moderate woody patchiness could support these species. As the scale increases at 2km, these species may encounter landcover patterns of medium to high herbaceous cover and patches as they look for food along the urban wildland edge but with decreased woody cover and patches, this would limit dispersal and ensure the need to return to habitat that can meet those needs. At a scale of 500m supports high richness if there is high herbaceous patchiness, high herbaceous cover, and high woody cover. Many species that tend to be generalists in food and habitat have a higher tolerance for human disturbance as they disperse through the landscape to find resources, relying on presence of herbaceous cover (Adams 2021 ). We also see species richness increase due to low herbaceous cover, low herbaceous patches, and high woody cover, and this may point to species that require trees as primary vegetation cover for resources and traveling. A study in southern California, focused on mesopredators such as coyotes, bobcats, mountain lions, opossums, and skunks, in patches less than \(\:{1km}^{2}\) to understand the effects of fragmentation. Species like the opossum, were determined as a key species signaling increased habitat disturbance, increased edge, and benefit from high levels of fragmentation (Crooks 2002 ; Markovchick-Nicholls et al. 2008 ). While coyotes, due to their high levels of adaptability, were not ideal to measure the effects of fragmentation, they did serve to identify the functional aspect of connectivity in dense urban regions (Crooks 2002 ). The bobcat was more sensitive to fragmentation than coyotes, but sustained presence in urban areas with the use of prevalent passageways to travel through and access large, natural areas (Crooks 2002 ). This study echoes patterns observed across many sites for this study, for example opossums were primarily seen in higher herbaceous fragmented patches of suburban yard and urban gardens that were frequently interrupted by human-built surface cover, like busy streets and housing. Bobcats were more frequent in urban wildland interface locations that had increased herbaceous and woody patches stemming from natural areas. These observations were supported by the NMDS plot that shows opossums closer to yard spaces and bobcats found in between natural sites and urban wildland interfaces. Many studies have suggested the concept of creating greenspaces as ‘stepping stones’ which would provide wildlife with resources in a fragmented urban landscape (Spellerberg and Gaywood 1993 ; Marzluff and Ewing 2001 ; Saura 2014; Lynch 2019 ). Stepping stones serve as a means for dispersal and resource acquisition for wildlife, and urban planners want to encourage the use of greenspace for wildlife habitat, not just to travel through (Lynch 2019 ). Step stones of greenspace habitats are beneficial to wildlife when they improve access to natural, undisturbed habitat patches (Lynch 2019 ). Planting native vegetation in patches to restore natural habitat as it is connects to nature reserves, improves biodiversity, as it helps to create gradients of resources for edge species to access and protects interior species (Marzluff and Ewing 2001 ). A sister to the concept of step stone habitats is ‘Ecological land-use complementation’ (ELC) which are urban greenspace patches arranged to support ecological functions and improve biodiversity within the urban matrix (Colding 2007 ). An example would include suburbia with yard space, a city park, and natural area, all aggregated to each other rather than being dispersed across dense impervious cover, like malls with parking lots or industrial areas (Colding 2007 ). Patterns of the stepping stone framework and ELC seem evident within this study and as shown by species richness strongly and positively influenced by various levels of herbaceous cover and patchiness along with low to high woody patches and high woody cover. This supports the idea that urban greenspaces function ecologically as a mosaic to support multiple species’ needs. ELC bolsters ecosystem functions such as seed dispersal and pollination by providing heterogeneity of resources for species that may use complementary habitats (Colding 2007 ). In addition, this concept reinforces the need for buffers along natural spaces as they border urbanized land. Urban greenspaces can serve as beneficial buffers that could reduce sensitivity of fragmentation for urban sensitive species and increase vegetated landcover as natural lands reduce due to development (Colding 2007 ). However, it is important to be selective about what type of buffer to use, one that would prevent anthropogenic effects, like invasive species or pesticide runoff, from permeating natural areas but also allow the migration of edge species and improve access for wildlife to increased food and habitat (Marzluff and Ewing 2001 ; Colding 2007 ). Limitations and Further Research Limitations exist with a small sample size of camera trap sites which possibly resulted in broad confidence intervals. Included with that was the difficulty and inability to acquire an equal number of sites per land use category due to restricted access, lack of permission from landowners, and uneven distribution of types of sites within the study area. Having a higher number of natural areas to sample from would highlight the pattern seen along the urban-natural gradient. However, considering this study focused on urban wildlife species primarily focused in urban greenspaces, the data obtained covers the scope of the research. Further research can include vegetation surveys of these greenspaces to determine influences of microhabitat and implementing additional survey methods for other taxonomic groups, like point count survey or acoustic monitoring for birds, to improve detections. As this project continues, including additional years of camera trap data would hopefully improve confidence of predictions suggested in this study. Including seasonal changes like temperature and rainfall during each camera trap month would provide additional variables to help understand the patterns of wildlife richness in eastern Los Angeles County across major environmental events. Additionally, utilizing this design to conduct a multi-species occupancy model to estimate species richness and compare these values to the iChao2 estimator and within the models to determine if predictions are consistent. Implications and Conclusion Greenspaces can serve as a rich reservoir of biodiversity and provide critical ecosystem functions in urban spaces. They vary incredibly by size, aggregation and edge characteristics in the urban matrix which affects resources for wildlife and thus species richness. This research serves to fill a gap of knowledge about urban wildlife in eastern Los Angeles County by identifying important landscape characteristics of urban greenspaces and surrounding areas that affect species richness. When designing urban planners use multiple scales to reflect the complex interactions that occur at the local, district, and regional levels that account for demographic, economic, social, ecological, and residential conditions (Wissen Hayek et al. 2015 ). This research aims to support design elements to create sustainable urbanscapes for wildlife and humans in which ecological functions and biodiversity are prioritized in urban regions, especially focusing on how greenspaces can become one of the primary conduits for this process. Local biodiversity can be supported and improved alongside the commitment to bolstering ecosystem services of urban greenspaces. Government, public, and scientific community participation at the city, county and regional level, is critical to enact long-standing change and implement ecologically sound greenspaces that support biodiversity, human well-being, and ecosystem functions. It is important to consider the role individual greenspaces serve as habitat within the entire matrix and to elicit help from biologists in urban planning. Incorporating ecology within urban planning and development is one piece of the puzzle to solving urban conservation. With group effort of community members, urban planners, ecologists, and local management agencies holistic decisions can be attained to cultivate coexistence between wildlife and people in urban regions (Marzluff and Ewing 2001 ). Declarations Funding This work was supported by the California State University and the CSUPERB research award. AE has received research support from scholarships provided by the Cal Poly Pomona Biological Sciences Department. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Adrianna Elihu and Janel Ortiz. The first draft of the manuscript was written by Adrianna Elihu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Aberle MA, Langwig KE, Adelman JS, Hawley DM (2020) Effects of bird feeder density on the foraging behaviors of a backyard songbird (the House Finch, Haemorhous mexicanus) subject to seasonal disease outbreaks. Can J Zool 98:611–621. https://doi.org/10.1139/cjz–2019–0282 Adams E (2021) Clark. Population Dynamics. Urban Wildlife Management. CRC, pp 181–215 Aplin KP, Suzuki H, Chinen AA et al (2011) Multiple Geographic Origins of Commensalism and Complex Dispersal History of Black Rats. PLoS ONE 6:e26357. https://doi.org/10.1371/journal.pone.0026357 Beninde J, Veith M, Hochkirch A (2015) Biodiversity in cities needs space: a meta-analysis of factors determining intra-urban biodiversity variation. Ecol Lett 18:581–592. https://doi.org/10.1111/ele.12427 Blair RB (1996) Land Use and Avian Species Diversity Along an Urban Gradient. Ecol Appl 6:506–519. https://doi.org/10.2307/2269387 Bolund P, Hunhammar S (1999) Ecosystem services in urban areas. Ecol Econ 29:293–301. https://doi.org/10.1016/S0921–8009(99 Chao A, Ma KH, Hsieh TC, Chiu C-H (2019) User’s Guide for Online Program SpadeR. Species-richness Prediction And Diversity Estimation in R Chiu C, Wang Y, Walther BA, Chao A (2014) An improved nonparametric lower bound of species richness via a modified good–turing frequency formula. Biometrics 70:671–682. https://doi.org/10.1111/biom.12200 Chithra SV, Harindranathan Nair MV, Amarnath A, Anjana NS (2015) Impacts of Impervious Surfaces on the Environment. Int J Eng Sci Invention 5:27–31 Colding J (2007) Ecological land-use complementation’ for building resilience in urban ecosystems. Landsc Urban Plann 81:46–55. https://doi.org/10.1016/j.landurbplan.2006.10.016 Collinge SK (2009) Ecology of Fragmented Landscapes. Johns Hopkins University Crooks KR (2002) Relative Sensitivities of Mammalian Carnivores to Habitat Fragmentation. Conserv Biol 16:488–502. https://doi.org/10.1046/j.1523–1739.2002.00386.x Crooks KR, Suarez AV, Bolger DT (2004) Avian assemblages along a gradient of urbanization in a highly fragmented landscape. Biol Conserv 115:451–462. https://doi.org/10.1016/S0006–3207(03)00162–9 Dunagan SP, Karels TJ, Moriarty JG et al (2019) Bobcat and rabbit habitat use in an urban landscape. J Mammal 100:401–409. https://doi.org/10.1093/jmammal/gyz062 Dzhambov A, Dimitrova D (2014) Urban green spaces′ effectiveness as a psychological buffer for the negative health impact of noise pollution: A systematic review. Noise Health 16:157. https://doi.org/10.4103/1463–1741.134916 Fidino M, Gallo T, Lehrer EW et al (2021) Landscape-scale differences among cities alter common species’ responses to urbanization. Ecol Appl 31:e02253. https://doi.org/10.1002/eap.2253 Foody GM (2008) Harshness in image classification accuracy assessment. Int J Remote Sens 29:3137–3158. https://doi.org/10.1080/01431160701442120 Gallo T, Fidino M, Lehrer EW, Magle S (2019) Urbanization alters predator-avoidance behaviours. J Anim Ecol 88:793–803. https://doi.org/10.1111/1365–2656.12967 Gehrt SD, Anchor C, White LA (2009) Home Range and Landscape Use of Coyotes in a Metropolitan Landscape: Conflict or Coexistence? J Mammal 90:1045–1057. https://doi.org/10.1644/08-MAMM-A–277.1 Gese EM, Morey PS, Gehrt SD (2012) Influence of the urban matrix on space use of coyotes in the Chicago metropolitan area. J Ethol 30:413–425. https://doi.org/10.1007/s10164-012-0339–8 Giraudeau M, Nolan PM, Black CE et al (2014) Song characteristics track bill morphology along a gradient of urbanization in house finches (Haemorhous mexicanus). Front Zool 11:83. https://doi.org/10.1186/s12983-014-0083–8 Goddard MA, Dougill AJ, Benton TG (2010) Scaling up from gardens: biodiversity conservation in urban environments. Trends Ecol Evol 25:90–98. https://doi.org/10.1016/j.tree.2009.07.016 Grubbs SE, Krausman PR (2009) Use of Urban Landscape by Coyotes. Southwest Nat 54:1–12. https://doi.org/10.1894/MLK–05.1 Hardin G, Population E, Adams (2021) CRC Press, pp. 165–178 Harmon LJ, Bauman K, McCloud M et al (2005) What free-ranging animals do at the zoo: a study of the behavior and habitat use of opossums (Didelphis virginiana) on the grounds of the St. Louis Zoo. Zoo Biol 24:197–213. https://doi.org/10.1002/zoo.20046 Haugen AO (1942) Home Range of the Cottontail Rabbit. Ecology 23:354–367. https://doi.org/10.2307/1930675 Hoekstra JM, Boucher TM, Ricketts TH, Roberts C (2005) Confronting a biome crisis: global disparities of habitat loss and protection. Ecol Lett 8:23–29. https://doi.org/10.1111/j.1461–0248.2004.00686.x Humphrey JE, Haslem A, Bennett AF (2023) Housing or habitat: what drives patterns of avian species richness in urbanized landscapes? Landsc Ecol 38:1919–1937. https://doi.org/10.1007/s10980-023-01666–2 Jo H-K, McPherson GE (1995) Carbon Storage and Flux in Urban Residential Greenspace. J Environ Manage 45:109–133. https://doi.org/10.1006/jema.1995.0062 Lombardi JV, Comer CE, Scognamillo DG, Conway WC (2017) Coyote, fox, and bobcat response to anthropogenic and natural landscape features in a small urban area. Urban Ecosyst 20:1239–1248 Lombardi JV, Tewes ME, Perotto-Baldivieso HL et al (2020) Spatial structure of woody cover affects habitat use patterns of ocelots in Texas. Mamm Res 65:555–563. https://doi.org/10.1007/s13364-020-00501–2 Luck GW, Smallbone LT, Sheffield KT (2013) Environmental and socio-economic factors related to urban bird communities. Austral Ecol 38(1):111–120 Lynch AJ (2019) Creating effective urban greenways and stepping-stones: four critical gaps in habitat connectivity planning research. J Plann Literature 34(2):131–155 Magle SB, Fidino M, Lehrer EW et al (2019) Advancing urban wildlife research through a multi-city collaboration. Front Ecol Environ 17:232–239. https://doi.org/10.1002/fee.2030 Magle SB, Fidino M, Sander HA et al (2021) Wealth and urbanization shape medium and large terrestrial mammal communities. Glob Change Biol 27:5446–5459. https://doi.org/10.1111/gcb.15800 Markovchick-Nicholls L, Regan HM, Deutschman DH et al (2008) Relationships between Human Disturbance and Wildlife Land Use in Urban Habitat Fragments: Human Disturbance in Habitat Fragments. Conserv Biol 22:99–109. https://doi.org/10.1111/j.1523–1739.2007.00846.x Marzluff JM, Ewing K (2001) Restoration of Fragmented Landscapes for the Conservation of Birds: A General Framework and Specific Recommendations for Urbanizing Landscapes. Restor Ecol 9:280–292. https://doi.org/10.1046/j.1526–100x.2001.009003280.x Mata JM, Perotto-Baldivieso HL, Hernández F et al (2018) Quantifying the spatial and temporal distribution of tanglehead (Heteropogon contortus) on South Texas rangelands. Ecol Process 7:2. https://doi.org/10.1186/s13717-018-0113–0 Matthies SA, Rüter S, Schaarschmidt F, Prasse R (2017) Determinants of species richness within and across taxonomic groups in urban green spaces. Urban Ecosyst 20:897–909. https://doi.org/10.1007/s11252-017-0642–9 McDonnell MJ, Pickett STA (1990) Ecosystem Structure and Function along Urban-Rural Gradients: An Unexploited Opportunity for Ecology. Ecology 71:1232–1237. https://doi.org/10.2307/1938259 McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR McGarigal K (2015) FRAGSTATS Help, Version 4.2 McKinney ML (2002) Urbanization, Biodiversity, and Conservation. Bioscience 52:883. https://doi.org/10.1641/0006 –3568(2002)052[0883:UBAC]2.0.CO;2 McPherson EG (1998) Atmospheric Carbon Dioxide Reduction by Sacramento’s Urban Forest. AUF 24:215–223. https://doi.org/10.48044/jauf.1998.026 Nowak DJ, Crane DE, Stevens JC (2006) Air pollution removal by urban trees and shrubs in the United States. Urban Forestry Urban Green 4:115–123. https://doi.org/10.1016/j.ufug.2006.01.007 Oliveira S, Andrade H, Vaz T (2011) The cooling effect of green spaces as a contribution to the mitigation of urban heat: A case study in Lisbon. Build Environ 46:2186–2194. https://doi.org/10.1016/j.buildenv.2011.04.034 Open Data Network https://www.opendatanetwork.com/entity/0500000US06037/Los_Angeles_County_CA/geographic.population.density?year=2018 Perotto-Baldivieso HL, Meléndez-Ackerman E, García MA et al (2009) Spatial distribution, connectivity, and the influence of scale: habitat availability for the endangered Mona Island rock iguana. Biodivers Conserv 18:905–917. https://doi.org/10.1007/s10531-008-9520–3 Planet Labs PBC (2018) Planet Application Program Interface: In Space for Like on Earth, https://api.planet.com . Image © 2021 Planet Labs PBC Prange S, Gehrt SD, Wiggers EP (2003) Demographic Factors Contributing to High Raccoon Densities in Urban Landscapes. J Wildl Manag 67:324. https://doi.org/10.2307/3802774 Riem JG, Blair RB, Pennington DN, Solomon NG (2012) Estimating Mammalian Species Diversity across an Urban Gradient. Am Midl Nat 168:315–332. https://doi.org/10.1674/0003-0031-168.2.315 Riley SPD, Pollinger JP, Sauvajot RM et al (2006) FAST-TRACK: A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol Ecol 15:1733–1741. https://doi.org/10.1111/j.1365–294X.2006.02907.x Riley SPD, Sauvajot RM, Fuller TK et al (2003) Effects of Urbanization and Habitat Fragmentation on Bobcats and Coyotes in Southern California. Conserv Biol 17:566–576. https://doi.org/10.1046/j.1523–1739.2003.01458.x Riley SPD, Sikich JA, Benson JF (2021) Big Cats in the Big City: Spatial Ecology of Mountain Lions in Greater Los Angeles. Jour Wild Mgmt 85:1527–1542. https://doi.org/10.1002/jwmg.22127 Šálek M, Drahníková L, Tkadlec E (2015) Changes in home range sizes and population densities of carnivore species along the natural to urban habitat gradient. Mammal Rev 45:1–14. https://doi.org/10.1111/mam.12027 San Gabriel (2020) Schindler S, Poirazidis K, Wrbka T (2008) Towards a core set of landscape metrics for biodiversity assessments: A case study from Dadia National Park, Greece. Ecol Ind 8:502–514. https://doi.org/10.1016/j.ecolind.2007.06.001 Shanahan DF, Miller C, Possingham HP, Fuller RA (2011) The influence of patch area and connectivity on avian communities in urban revegetation. Biol Conserv 144:722–729. https://doi.org/10.1016/j.biocon.2010.10.014 Saura S, Bodin Ö, Fortin MJ (2014) Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. J Appl Ecol 51:171–182. https://doi.org/10.1111/1365–2664.12179 Spellerberg IF, Gaywood MJ (1993) Linear features: linear habitats & wildlife corridors. Center for Environmental Sciences Surls R, Gerber J (2016) From COWS to CONCRETE: The Rise and Fall of Farming in Los Angeles, 43–46 Tolessa T, Senbeta F, Kidane M (2016) Landscape composition and configuration in the central highlands of Ethiopia. Ecol Evol 6:7409–7421. https://doi.org/10.1002/ece3.2477 Urban Wildlife Information Network: Conceptual Overview (2021) Conceptual Overview (2021_08_23 18_15_38 UTC).pdf Urban Wildlife Information Network Partners. https://www.urbanwildlifeinfo.org/partners Urban Wildlife Information Network Study Design. https://static1.squarespace.com/static/620557dccfabd22386bdbe51/t/624f100886fcfe5b9875fb54/1649348617933/UWIN+Database+Manual.pdf van Lunteren P (2023) EcoAssist: A no-code platform to train and deploy custom YOLOv5 object detection models. J Open Source Softw 8(88):5581 Whittaker RH (1967) GRADIENT ANALYSIS OF VEGETATION*. Biol Rev 42:207–264. https://doi.org/10.1111/j.1469–185X.1967.tb01419.x Wissen Hayek U, Efthymiou D, Farooq B et al (2015) Quality of urban patterns: Spatially explicit evidence for multiple scales. Landsc Urban Plann 142:47–62. https://doi.org/10.1016/j.landurbplan.2015.05.010 Wright JD, Burt MS, Jackson VL (2012) Influences of an Urban Environment on Home Range and Body Mass of Virginia Opossums (Didelphis virginiana). Northeastern Naturalist 19:77–86. https://doi.org/10.1656/045.019.0106 Zhou W, Wang J, Qian Y et al (2018) The rapid but invisible changes in urban greenspace: A comparative study of nine Chinese cities. Sci Total Environ 627:1572–1584. https://doi.org/10.1016/j.scitotenv.2018.01.335 Additional Declarations No competing interests reported. Supplementary Files SuppFig1.docx SuppTable1.docx SuppTable2.docx SuppTable3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4909697","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352253835,"identity":"02f35c29-0c30-4a66-8df7-6f1084ab5c90","order_by":0,"name":"Adrianna J. 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Ortiz","email":"","orcid":"","institution":"California State Polytechnic University-Pomona","correspondingAuthor":false,"prefix":"","firstName":"Janel","middleName":"L.","lastName":"Ortiz","suffix":""}],"badges":[],"createdAt":"2024-08-13 23:44:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4909697/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4909697/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64284198,"identity":"490140ed-7a44-4af3-98b8-5121dc946cf9","added_by":"auto","created_at":"2024-09-11 08:25:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCamera site locations (26) indicated by black dots along a 30 km transect, ranging from the San Gabriel Mountains to Diamond Bar, in San Gabriel Valley, Los Angeles County, California\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/46eb5155f91c1d3d1f3e82a2.jpg"},{"id":64285582,"identity":"4214307a-5b11-4dea-be32-b645b995b9e0","added_by":"auto","created_at":"2024-09-11 08:41:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLand use types along an urban to rural gradient. Land use types include: Open modified space, recreation, yard space, urban wildland interface, and natural area. Clipart image provided by iStock.com/Tetiana Lazunova, photos provided by Google Maps\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/300250f07287afdb5e85f2cc.jpg"},{"id":64284204,"identity":"e400063b-aea4-4cd9-9497-dbad3a0f25d4","added_by":"auto","created_at":"2024-09-11 08:25:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLand cover classification of (a) private homeowner categorized as urban-wildland interface and (b) Plant nursery as open-modified space\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/4f51c7aa9b12146b51e5db1b.jpg"},{"id":64284200,"identity":"d466ccc3-c1a0-4085-bb55-1014e286d7b1","added_by":"auto","created_at":"2024-09-11 08:25:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNMDS plot showingpatterns of mammal and bird species occurrence in relation to land use type ((1)- Natural area, (2) UWI, (3) Open Modified, (4) Recreation, (5) Yard space) for camera locations in eastern Los Angeles County, California from February 2022 to January 2023\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/d6391d9c5bad1b4574ce291a.jpg"},{"id":64284665,"identity":"8c80c0aa-c3ef-4d0c-9a26-3e4b8f4e042a","added_by":"auto","created_at":"2024-09-11 08:33:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":657647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e(a-p) Two and three-way interaction graphs with associated conceptual buffer to display the landscape patterns to support high species richness. (a) Two-way interaction at 200m scale demonstrating richness on percent herbaceous cover at low (red) and high (blue) levels of edge density herbaceous cover with 95% confidence intervals with (b) associated conceptual buffer. (c) Three-way interactions at 500m showing richness on percent woody cover at low (red), medium (blue), and high (green), levels of patch density woody across low (left panel), medium (middle), and high (right) percent herbaceous levels with 95% confidence intervals with (d) associated conceptual buffer. (e) Three-way interaction at 500m showing richness on percent woody cover at low (red), medium (blue), and high (green), levels of patch density herbaceous across low (left panel), medium (middle), and high (right) percent herbaceous levels with 95% confidence intervals with (f) associated conceptual buffer. (g) Three-way interaction at 500m showing richness on patch density woody cover at low (red), medium (blue), and high (green), levels of patch density herbaceous across low (left panel), medium (middle), and high (right) percent herbaceous levels with 95% confidence intervals with (h) associated conceptual buffer. (i) Two-way interaction at 1km scale illustrating richness on percent woody cover at low (red) and high (blue) levels of percent herbaceous cover with 95% confidence intervals with (j) associated buffer. (k) Two-way interaction at 2km demonstrating richness on Percent Woody at low (red) and high (blue) levels of Patch Density Herbaceous with (l) associated buffer. (m) Two-way interaction at 2km highlighting richness on Percent Woody at low (red) and high (blue) high levels of Patch Density Herbaceous levels with 95% confidence intervals with (n) associated conceptual buffer. (o) Three-way interaction at 2km illustrating wildlife richness on patch density woody at low (red), medium (blue), and high (green) levels of patch density herbaceous across low (left panel), medium (middle), and high (right) percent herbaceous levels with 95% confidence intervals with (p) associated conceptual buffer.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/fb2bad0a9a01a77ed7e3b9d8.png"},{"id":64284664,"identity":"62ece257-4d84-488f-9112-207a6d7b05fd","added_by":"auto","created_at":"2024-09-11 08:33:07","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88837,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual land cover demonstrating changes from 200 m to 2 km of increasing herbaceous cover, decreasing woody cover, and increasing vegetation patchiness.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/a8192a06372a18df4635b686.jpg"},{"id":65958666,"identity":"7957a07e-d67c-4bae-8dae-2c09d257cff8","added_by":"auto","created_at":"2024-10-05 02:53:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1466594,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/120e9391-6512-41ec-992f-fedf1556892e.pdf"},{"id":64284208,"identity":"7651e7d1-2878-4eef-88bb-83060938fbbb","added_by":"auto","created_at":"2024-09-11 08:25:08","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":140320,"visible":true,"origin":"","legend":"","description":"","filename":"SuppFig1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/c0d44972814f7e8f705ad5d0.docx"},{"id":64284206,"identity":"b8334a02-21a8-4b7f-9061-1f2ef18f5e62","added_by":"auto","created_at":"2024-09-11 08:25:08","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":14932,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/b71bb6878960b34c55d3288c.docx"},{"id":64284668,"identity":"4dfb6bb9-3f4d-4407-831f-2f7f3999e714","added_by":"auto","created_at":"2024-09-11 08:33:08","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":14696,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/3cca43ffd3e58c825ea15301.docx"},{"id":64284667,"identity":"0e9966d9-f0b4-48f1-ac31-e3533f166ab3","added_by":"auto","created_at":"2024-09-11 08:33:08","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":20092,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4909697/v1/6f92bb7ef2f21c93968c1c6e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Concrete Jungle to Urban Oasis: Scale, Greenspace Size and Patchiness Influence Wildlife in Cities of Eastern Los Angeles County, California","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGreenspaces in the urban matrix are critical to supplying ecosystem services needed for a healthy environment. These ecosystem services include carbon sequestration (Jo and McPherson \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; McPherson \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), regulation of the urban microclimate and the urban heat island effect (McPherson \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Oliveira et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), reducing the negative effects of noise pollution (Dzhambov and Dimitrova \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), increase of water table infiltration which reduces impervious surface water run-off and increase evapotranspiration (Bolund and Hunhammar \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), decrease air pollution (Nowak et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and provide habitat for biodiversity (Goddard et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Beninde et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The degree in which urban greenspace provides effective habitat for biodiversity depends on several factors including greenspace type, connectivity in the urban matrix, size, and species present (Shanahan et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Beninde et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Ecosystems services contribute to healthier land, air, water, in urban areas for wildlife and humans alike, however there are specific benefits that are especially important for people as they connect with the natural environment.\u003c/p\u003e \u003cp\u003eSpecies richness also differs along a gradient of urbanization. From the densely modified urban core of the inner city to natural ecosystems on the outskirts of cities, urban-to-rural gradients provide a method to quantify changes in urbanization as it relates to wildlife (McKinney \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Riem et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Gradient analysis, first studied in plant communities, is a research approach to describe environmental variation within space, accounting for spatial patterns that preside over ecological system\u0026rsquo;s structure and function (Whittaker \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; McDonnell and Pickett \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). It has since been used to describe spatial patterns for many other taxa, including wildlife species. Urban wildlife studies are often conducted along a gradient of rural, suburbia, and urban areas to document how a species relates to changes of urban factors (Riem et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), such as presence anthropogenic food sources, light/sound pollution, and human activity. Urban describes areas of high impervious cover and highest human population density, suburbia usually has moderate levels of impervious cover and human population density and serve as a transition zone between urban and rural, while rural areas, like agriculture and natural lands, have the lowest impervious cover and human population density (Š\u0026aacute;lek et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the case of Los Angeles county, especially San Gabriel Valley, since there is limited agricultural land, we can say urban-to-natural gradient instead of rural. Natural lands consist of habitat that is undeveloped and has its original, unaltered vegetation in place. Several studies have shown that the urban core holds the lowest species diversity and diversity increases along the gradient as it moves closer to natural or remnant areas (McKinney \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). For example, in small and medium size carnivore mammals, the home range size decreased along the gradient of natural lands to the urban core, while the population density increased for each species (Š\u0026aacute;lek et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An urbanization gradient, also proves as a useful metric to study urban wildlife, because it can be compared to other gradients in urban regions, such as an income gradient (Magle et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As Magle (2021) demonstrated, medium to large mammal species responded more strongly to the urbanization gradient than an income gradient and also found average species occupancy was highest at decreased levels of urban intensity (Magle et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Results like this can help researchers identify patterns of adaptation, plasticity, fitness, evolutionary changes, in wildlife as well as necessary changes to the urban landscape such as increasing habitat connectivity, native vegetation, and improving access to wildlife for those in densely urban areas (Š\u0026aacute;lek et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUntil recently, the majority of urban wildlife studies were city specific, relating that city\u0026rsquo;s climate, geography, age, land-use and culture to the ecology of wildlife in that area (Magle et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However limitations arise when trying to understand and compare large-scale and generalized patterns of wildlife ecology in urban spaces because there is no consistent data across varied study areas to draw consensus (Magle et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Though cities vary in landscape, they hold similar structures, such as human-modified greenspace, suburban neighborhoods with yards, and concentrated grey space like industrial areas. General patterns between species presence and urban features amongst cities can be key identifiers in how wildlife relate to the urban landscape; for example Virginia opossum (\u003cem\u003eDidelphis virginiana)\u003c/em\u003e presence was found to increase with growing urban intensity (Markovchick-Nicholls et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). As urbanized as southern California is there is not a lot of research as to what wildlife inhabit urban ecosystems and how the structure of these urban systems effect wildlife.\u003c/p\u003e \u003cp\u003eUrbanization has affected wildlife species differently based off their specific needs for resources and species have been categorized based on their relation to urban settings, such as urban exploiters, urban adapters, and urban avoiders (Hardin 2021). Urban exploiters are species that can exploit resources in urban or human-altered environments and reach their highest population densities in developed areas. Urban exploiters tend to be non-native species, including the house sparrow (\u003cem\u003ePasser domesticus)\u003c/em\u003e and the rock pigeon (\u003cem\u003eColumba livia)\u003c/em\u003e (Blair \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Some species, like the black rat (\u003cem\u003eRattus rattus)\u003c/em\u003e, are also termed commensalist species, meaning they require human habitation to thrive (Aplin et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Urban adapters are species that are able to survive and thrive in urban and natural settings (Blair \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Urban adapters, like the coyote (\u003cem\u003eCanis latrans)\u003c/em\u003e, Virginia opossum (\u003cem\u003eDidelphis virginiana)\u003c/em\u003e, and Northern raccoon (\u003cem\u003eProcyon lotor)\u003c/em\u003e benefit from the surplus of resources urban settings offer but require access to natural areas for feeding, denning or resting (McKinney \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Gese et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Coyotes also fall under the term \u0026lsquo;edge species\u0026rsquo;, as they live in urban areas but require access to natural habitat and must travel farther through urban regions to access nonurbanized areas (Riley 2003, Gese 2012). Differing degrees of urbanization affect species, like the coyote, which have been found to have larger home ranges within urban habitats, compared to rural coyotes, often because they must travel farther in urbanized regions to find patches of natural land (Gese 2012). Urban avoiders tend to avoid urban settings as much as possible and are especially more sensitive to altered and human-dominated environments (Blair \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Many large mammals, specifically predators like the mountain lion (\u003cem\u003ePuma concolor)\u003c/em\u003e, black bear (\u003cem\u003eUrsus americanus)\u003c/em\u003e, and bobcat (\u003cem\u003eLynx rufus)\u003c/em\u003e, are urban avoiders, and will have higher population density in natural areas (McKinney \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Mountain lions prefer natural areas with minimal human presence; but increasingly have been found in urbanized areas based on the availability of their prey species, the California mule deer (\u003cem\u003eOdocoileus hemionus californicus\u003c/em\u003e), which eat ornamental vegetation in urbanized areas (Riley et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Desert cottontails (\u003cem\u003eSylvilagus audubonii\u003c/em\u003e) have consistent densities in urban greenspaces, like parks and yards, and do not vary between urban, urban edge, and natural spaces (Dunagan 2019). This has led bobcats to hunt for cottontails more frequently at night closer to the urban edge (Dunagan 2019). The influence of urbanization on wildlife behavior can lead to increased human-wildlife interactions, especially at urban to wildland transitional edges. For wildlife to survive in urban regions, as compared to natural areas, they must possess two important characteristics, be generalists and have a tolerance for human presence (Hardin 2021). Generalists thrive from a wide range of resources that provide food and shelter while also being plastic in their behavior (Hardin 2021). In contrast, specialists may be able to survive in urban areas with very specific resource requirements, limiting their range in urban settings. The consequences of urbanization have greatly altered the ecology of many wildlife species so much so that they are defined based on their tolerance to urban environments.\u003c/p\u003e \u003cp\u003eKey impacts of urbanization are loss and fragmentation of natural habitats. Loss describes the decrease in area of the original habitat while fragmentation describes the process in which original habitat is divided or interrupted by other habitats, essentially breaking the original area into small pieces (Collinge \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Habitat conversion, a type of habitat loss, occurs when an area of an original habitat type is changed to another land use type, such as a region of rainforest being converted into farmland. Around the world about 21% of land area has been converted for human uses with greater than 50% loss seen in North America\u0026rsquo;s temperate and mixed forests and temperate grasslands (Hoekstra et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A common example of fragmentation includes a freeway or road running through a previously undisturbed habitat, such as a desert or prairie, splitting the area. Fragmentation is frequent in Los Angeles County, and one impactful example is the 101 freeway which consists of ten lanes that separate the Santa Monica Mountain and Simi Hills habitat (Riley, 2021). This has proved challenging for many species, especially the mountain lion, who rely on travelling large distances as part of their territory to find mates (Riley, 2021). Fragmentation poses new risks and challenges to wildlife as they are forced to learn how to move across a modified habitat, while relearning how to get resources and avoid predation and death (Collinge \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Many species that have adapted to urban areas have modified their behavior to support their survival. Since urban greenspace habitat patches are often smaller and more interspersed, prey and predator species have been found to occupy the same patch, when normally prey would disperse farther to avoid the predator (Gallo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). One camera trap study performed in Chicago, Illinois, found that eastern cottontails and white-tailed deer did not display differences in spatial occupancy from the coyote in urbanized regions (Gallo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Changes in morphology and song characteristics of the house finch \u003cem\u003e(Haemorhous mexicanus)\u003c/em\u003e were tested and shown in urban areas that finches with narrower beaks produce lower-frequency songs that can be heard over the noise of urban environments, possibly suggesting that males will be chosen based off their altered song frequency to be heard by females in urban settings (Giraudeau et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Research documenting radio-telemetry bobcats and coyotes in southern California around the Ventura Freeway (US 101) show that the freeway acts as a hard boundary for male and female bobcats, producing smaller home ranges and decreasing gene flow (Riley et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Barriers in urban spaces have led to rapid changes in species morphology, behavior, and ecology as they adapt to human-built spaces.\u003c/p\u003e \u003cp\u003eAs natural habitats shrink and urban sprawl increases, it is important to consider the ecology of urban regions and how they function. Biologists around the world have noticed this change and have focused their interests on understanding the ecology of wildlife in urbanized areas. In this study, we will determine what greenspace characteristics influence urban wildlife richness by using remote camera traps to monitor wildlife diversity in greenspaces within San Gabriel Valley (eastern Los Angeles County, California). We expect a larger area of greenspace with less edge and frequency of greenspace patches to have higher species richness versus smaller, patchier, simpler greenspace. Recognizing the most influential characteristics will aid city planners and urban ecologists to better understand how to create or modify existing urban greenspace with the vision of maximizing benefits for wildlife and people to create sustainable urban ecosystems.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eSan Gabriel Valley is a highly urbanized region of eastern Los Angeles County in Southern California, comprised of 31 cities covering an area of 1036 square kilometers with a population of over 1.7\u0026nbsp;million people (San Gabriel Valley- Los Angeles County Economic Development Corporation 2020). Historically, San Gabriel Valley was primarily farmland, consisting of citrus orchards, produce, and cattle ranches (Surls and Gerber \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Site Selection\u003c/h2\u003e \u003cp\u003eOne 30 km transect was created sampling twenty-six sites using camera traps to monitor wildlife along an urban to rural gradient from Diamond Bar to the San Gabriel Mountains covering 11 of the 31 cities in San Gabriel Valley (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The transect is based on degrees of impervious surface cover, like sidewalks and human-built structures (Chithra et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), ranging from densely urban (high impervious cover) to rural/natural land cover (low/zero impervious cover). A 2km buffer was created around the transect and all cameras were placed at least 1km from each other within the 2km buffer following the protocol of the Urban Wildlife Information Network (UWIN). Site selection and placement were based upon UWIN methodology and finalized based on site access and approval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCamera sites were defined within 5 greenspace categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) which vary in size, location, amount of greenspace (GS) and human-built surfaces (HB), and average amount of human activity (HA- people/per trap night). These categories include 1) yard space: greenspace, primarily lawns, attached to houses surrounded by human-built structures with low human activity and foot traffic (GS: 54\u0026ndash;76%, HB: 16\u0026ndash;43%, HA: 15.59), 2) open-modified space: greenspace with low human foot traffic and access, including a landfill, private commercial nursery, and urban farmland (GS: 42\u0026ndash;75%, HB: 6\u0026ndash;55%, HA: 25.39), 3) natural areas: vegetated natural spaces, like trails, with low to moderate human use and access (GS: 89\u0026ndash;99%, HB: 0%, HA: 0.014), 4) urban-wildland interface: natural greenspace adjacent to houses and human-development with low access and foot traffic (GS: 70\u0026ndash;99%, HB: 0\u0026ndash;11%. HA: 4.67), and 5) recreation: city parks with primarily ornamental vegetation (some with/without patches of natural land) with high access and foot traffic (GS: 54\u0026ndash;82%, HB: 7\u0026ndash;39%, HA: 226.82). Greenspace and human-built surface percentages were calculated based on a 155-meter buffer around each camera site which was standardized by using the smallest study site size. All final site selections were dependent upon Cal Poly Pomona\u0026rsquo;s Risk Management approval, site approval, and accessibility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCamera Trapping\u003c/h2\u003e \u003cp\u003eOne Bushnell Core Low Glow Trail camera (Model- 119936C) was placed at each study site to monitor wildlife presence in the area. Cameras were deployed to captured photos for two, two-week time spans per month. Data was gathered for a total of one year during 2022 and 2023, sampling one month per season (Fall- October, Winter- January, Spring- April and Summer- July). No lures were used in this study.\u003c/p\u003e \u003cp\u003eEcoAssist, an AI program, incorporates the model MegaDetector to detect wildlife presence in photos, and was used to identify and separate photos with an object, animal, or human in them from empty photos (van Lunteren \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Photos were analyzed and tagged twice for confirmation from two independent volunteers when looking for species presence, human presence, and objects. If there was disagreement between tags, photos are sent to validation from which AE would determine the correct tag or enter the correct tag if both are incorrect. Final taxonomic groups of interest were mammals and birds. This project is considered exempt from Cal Poly Pomona Institutional Animal Care and Use Committee (IACUC) since there was no manipulation or handling of animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLand Cover Classification\u003c/h2\u003e \u003cp\u003eA supervised land cover classification raster was created using ERDAS Imagine 16.6 of\u003c/p\u003e \u003cp\u003e3m resolution imagery taken on December 1, 2021 from the Planet-Scope satellite (Planet Labs PBC). Land cover types were categorized into 5 classes including woody cover, herbaceous cover, human-built, bare ground, and water. A 69% accuracy land cover classification of eastern Los Angeles County was achieved with Google Maps using 250 random points. This accuracy is probably due to a larger survey area, eastern Los Angeles County (1,044 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{km}^{2}\\)\u003c/span\u003e\u003c/span\u003e) being used in the assessment compared to using the smaller, study transect (60 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{km}^{2}\\)\u003c/span\u003e\u003c/span\u003e). While 85% land cover accuracy attainment is often cited in literature, it is not widely applicable among all landcover analyses, not initially developed to support local, small scale landcover analyses, and might be unrealistically inflated (Foody \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Buffers of 200m (Desert cottontail), 500m (Virginia opossum), 1km (Northern raccoon), and 2km (Coyote) in radii were created and clipped around each camera site (Examples shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to account for patterns that influence wildlife diversity at different scales based on home ranges of common urban species (Haugen \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1942\u003c/span\u003e; Prange 2003; Harmon et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gehrt 2009; Wright 2012). A buffer of 500m, 1km, and 2km accounts for intermediate dispersal and foraging distances of common urban adapted birds such as the Northern mockingbird (\u003cem\u003eMimus polyglottos\u003c/em\u003e), mourning dove (\u003cem\u003eZenaida macroura\u003c/em\u003e), house finch (\u003cem\u003eCarpodacus mexicanus\u003c/em\u003e), and American crow (\u003cem\u003eCorvus brachyrhynchos\u003c/em\u003e) (Crooks et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Matthies et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Aberle et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Class level landscape characteristics, hereon known as metrics, (Online Resource 1), determined from literature review and the FRAGSTATS manual version 4 (McGarigal and Marks \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, McGarigal \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), were extracted from the buffers and analyzed using FRAGSTATS 4.2 software. These metrics were chosen based on the most commonly used metrics relating biodiversity to landscape ecology (Neel et al. 2004; Schindler et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Perotto-Baldivieso et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Tolessa et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mata et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lombardi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe ran a non-metric multidimensional scaling (NMDS) analysis with Bray-Curtis dissimilarity for species richness data to determine similarities between mammal and bird species frequencies in relation to occupancy in greenspaces of different land use types. Pearson\u0026rsquo;s correlation coefficient (r) was calculated and a multidimensional scaling (MDS) analysis was conducted for landscape metrics to eliminate those that may be highly correlated to each other (\u0026gt;\u0026thinsp;0.7, \u0026lt; -0.7). Focusing on species richness for mammals and birds at each site, we used SpadeR (Species-richness Prediction And Diversity Estimation in R) from the online R application Shiny, to develop estimated species richness values factoring in the number of trap nights each camera was in use and species detection per trap night which included number of uniques (species detected in one sampling unit) and duplicates (species detected in two sampling units) (Chao 2019). We determined species detection by noting presence/absence of a species per trap day along with total trap days per site to create incidence-frequency data and chose the estimator iChao2 for species richness because it has an improved lower bound compared to Chao2, good accuracy, reduced bias compared to traditional estimators, and improves confidence interval coverage (Chiu et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Chao 2019). Comparisons of richness values from camera trap data and iChao2 estimates (with 95% CI) per site and land use category are shown in Online Resource Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo understand the relationship between estimated species richness and landscape metrics, we used a generalized linear mixed model (GLMM) with a negative-binomial distribution. Estimated species richness was the response variable and percent of land cover of woody (PLAND Woody), herbaceous cover (PLAND Herbaceous), woody patch density (PD Woody), herbaceous patch density (PD Herbaceous), patch area mean woody/herbaceous (AREA_MN Woody/Herbaceous), edge density of woody/herbaceous cover (ED_Woody/Herbaceous), patch cohesion index of woody and herbaceous cover (COHESION Woody/Herbaceous), and largest patch index (LPI Woody/Herbaceous) were proposed fixed effects. Study sites nested within land use type were used as a random effect. To further narrow metric selection and prevent overfitting I used best-fit line plots and Akaike Information Criterion (AIC) values to compare and favor models with lower AIC values and normal residual diagnostic plots to determine the final model. The final data of the models for 200, 500m, 1km, and 2km examining estimated species richness and landscape metrics, contained percent landscape woody cover (PLAND_Woody), percent landscape herbaceous cover (PLAND_Herbaceous), patch density woody cover (PD_Woody), patch density herbaceous cover (PD_Herbaceous), edge density herbaceous (ED_Herbaceous) (Online Resource Table\u0026nbsp;2) and all interactions were rescaled for standardization (scale between \u0026minus;\u0026thinsp;1 to 1). Post model diagnostic plots of residuals and Q-Q plots were used to visualize the fit of each model and if inappropriate due to high dispersion or patterns in variance in diagnostic residuals, another metric was considered. All analyses were conducted in program R 4.2.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eData was collected from a total of 140 trap nights per camera from 26 cameras, one camera per site, in 11 cities from February 2022 to January 2023. Forty-five species were documented across all sites (14 mammals, 31 birds). Distribution and total detections of all species varied over all sites (Online Resource 3). Six species were seen in over 50% of all survey sites. The most ubiquitous species were the coyote (\u003cem\u003eCanis latrans\u003c/em\u003e) and fox squirrel (\u003cem\u003eSciurus niger\u003c/em\u003e)\u0026nbsp;appearing at 21 sites, with total detections of 489 and 360, respectively. California ground squirrel (\u003cem\u003eOtospermophilus beecheyi\u003c/em\u003e) (16, 222), opossum (\u003cem\u003eDidelphis virginiana\u003c/em\u003e) (16, 157), Desert cottontail (\u003cem\u003eSylvilagus audubonii\u003c/em\u003e) (15, 454), and Striped skunk (\u003cem\u003eMephitis mephitis\u003c/em\u003e) (15, 184) were species most detected across many of the sites. The NMDS analysis shows and supports which wildlife species of eastern Los Angeles County fall into the category of urban \u0026lsquo;exploiter\u0026rsquo;, \u0026lsquo;adapter\u0026rsquo;, and \u0026lsquo;avoider\u0026rsquo; and how they orient to different human land use types (Fig. 4). The NMDS plot also provides additional support to understand which specific species are frequent in which human land use categories- answering the question of \u0026lsquo;who\u0026rsquo; is at each site.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each camera site, metrics of woody and herbaceous cover were extracted at four scales: 200m, 500m, 1km, and 2km. Greenspace for all sites at 2km accounted for an average of 66% land cover. Remaining land cover includes human-built, bare ground, and water.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Significant main effects, 2-way, and 3-way interactions statistic summary produced from GLMM of landscape metrics and wildlife richness for each model at scales of 200m, 500m, 1km, and 2km\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePr(\u0026gt;|z|)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e200m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003eEdge Density Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e0.335297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.153095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e2.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.0285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Herbaceous:Edge Density Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e- 0.51249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.223277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e-2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.0217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e500m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e0.65540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.1658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e3.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e7.79e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Woody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e0.92577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.2834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e3.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.00109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePatch Density Woody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e0.42229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.1962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e2.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.03142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Woody:Percent Cover Herbaceous: Patch Density Woody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e0.81731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.2911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e2.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.00500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover \u0026nbsp;Density Woody: Percent Cover Density Herbaceous: Patch Density Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e1.30340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.2518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e5.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e2.27e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Herbaceous:Patch Density Woody: Patch Density Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e1.95356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e5.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e5.43e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1km\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent cover Woody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e-1.12220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.5644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e-1.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.0468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Woody:Percent Cover Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e-1.60201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.8005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e-2.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.0454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2km\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePatch Density Woody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e-0.96493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.3658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e-2.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.008349\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Woody:Percent Cover Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e1.77502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.5343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e3.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.000894\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Woody:Patch Density Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e3.21374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.8104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e3.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e7.33e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.75716694772344%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"47.8920741989882%\" valign=\"top\"\u003e\n \u003cp\u003ePercent Cover Herbaceous:Patch Density Woody:Patch Density Herbaceous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.97301854974705%\" valign=\"top\"\u003e\n \u003cp\u003e-1.95780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\" valign=\"top\"\u003e\n \u003cp\u003e0.9371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.443507588532883%\" valign=\"top\"\u003e\n \u003cp\u003e-2.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.79258010118044%\" valign=\"top\"\u003e\n \u003cp\u003e0.036707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cu\u003e200m Scale\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eEdge density of herbaceous cover was a significant main effect on richness at 200m scale (Table 4). At 200m, species richness decreases as percent herbaceous increases when edge density of herbaceous is high (743 m/hectare) (Fig. 5A). As percent herbaceous cover increases with low (786 m/hectare) herbaceous edge, species richness slightly increases (Fig. 5A). A conceptual buffer (5B) displays how the landscape would appear based off the interaction graph.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e500m Scale\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePercent of woody cover, percent herbaceous cover, and patch density of woody cover were all significant main effects on richness at the 500m scale (Table 4). There is a general pattern of increased species richness when percent of woody cover increases at most values of herbaceous cover and woody patch density (Fig. 5C-H). Medium (160/100 hectares) and high (270/ 100 hectares) woody patchiness, with medium (23%) and high (45%) of herbaceous cover, strongly and positively influences species richness as woody cover increases. Low (70/ 100 hectares) woody patchiness increases richness at high herbaceous cover but is minimally affected by levels of woody cover (Fig. 5C).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLow (32%) herbaceous cover and low (200/ 100 hectares) herbaceous patch density with high (45%) woody cover influence high richness (Fig. 5E/5F). At medium (287/ 100 hectares) and high (386/ 100 hectares) levels of herbaceous patchiness, and medium (45%) and high (56%) herbaceous cover, richness increases with high (45%) levels of woody cover. Low richness can be seen with medium (287/ 100 hectares) and high (386/ 100 hectares) herbaceous patchiness, low (32%) herbaceous cover, and higher woody cover. Low richness is also seen at low (200/ 100 hectares) herbaceous patchiness, at high (56%) herbaceous cover, with increased woody cover (Fig. 5E/5F).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHighest richness is expected at low (32%) and medium (45%) herbaceous cover, considering all levels (low- 200, medium- 287, high- 386/ 100 hectares) of herbaceous patch density and low (70/ 100 hectares) to high (270/ 100 hectares) woody patchiness (Fig. 5G/5H). Richness declines at high (56%) herbaceous cover, every level (low- 200, medium- 287, high- 386/ 100 hectares) of herbaceous patch density as woody patchiness increases, from low (70/ 100 hectares) to high (270/ 100 hectares) (Fig. 5G/5H). At a smaller scale of 500m, woody cover has a stronger positive effect on species richness compared to 1km and 2km.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e1km Scale\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePercent of woody cover was a significant main effect on richness at the 1km scale (Table 4). Highest species richness is seen with increased herbaceous land cover (56%) and lower values of woody cover (0.77%), but as woody cover increases, richness drops dramatically (Fig. 5I). At low levels of herbaceous land cover (32%), as woody cover increases, species richness increases minimally (Fig. 5I).The buffer at 1km (5J) demonstrates an example landscape pattern between herbaceous landcover and percent woody cover contributing to high species richness. From 2km to 1km we see a similarity of high herbaceous cover influence positively richness, but it switches from high to low woody cover that predicts higher richness.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003e2km Scale\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePatch density of woody cover had a significant effect on richness at the 2km scale (Table 4). There was a significant 3-way interaction between Pland Herbaceous cover, PD Woody and PD Herbaceous cover as they influence species richness at 2km (Fig. 5O). Low amount of herbaceous land cover (32%) and low herbaceous patchiness, medium to high patch density of tree cover contributes to increased species richness. As herbaceous cover increases, high (\u0026gt;55%) and herbaceous patches increases, high (367/ 100 hectares), medium level (181/ 100 hectares) of woody patches we see a peak in species richness. There is a drastic drop in species richness in areas of greater herbaceous cover and increased presence of herbaceous patches, as woody cover reaches its highest patch density. At 2km, moderate levels of woody patch density positively influence species richness in areas with higher or lower herbaceous cover regardless of its patchiness. The buffer at 2km (5P) demonstrates an example landscape pattern between herbaceous landcover and patch density woody and herbaceous contributing to high species richness.\u003c/p\u003e\n\u003cp\u003eHigher species richness is seen as herbaceous (\u0026gt;55%) and woody (~43%) cover increases (Fig. 5K- 5L). The lowest species richness is seen with high herbaceous cover (\u0026gt;55%) and low woody cover (1.3%), and low herbaceous cover (32%) and high woody cover (~43%). Increased richness towards low to high (1.3- 43%) percent woody cover regardless of whether patchiness of herbaceous is low or high (79- 270/ 100 hectares) (Fig. 5M- 5N). At extremes lows or highs of percent woody cover and patch density herbaceous, richness remains extremely low.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe landscape ecology of urban greenspaces impact and influence wildlife species richness in\u003c/span\u003e eastern Los Angeles County. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis study focused on specific landscape metrics and vegetated landcover\u003c/span\u003e including \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ewoody and herbaceous patch density\u003c/span\u003e, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003epercent land\u003c/span\u003e cover,, \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand edge density cover at different scales of 200m, 500m, 1km, and 2km, to identify patterns that may contribute to increased richness. The findings of this research fill a gap in knowledge about urban wildlife in\u003c/span\u003e eastern Los Angeles County \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eto better understand how to create, sustain, and possibly modify urban greenspaces for wildlife.\u003c/span\u003e\u003c/p\u003e \u003cp\u003eThe NMDS plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrates patterning of urban wildlife species in eastern Los Angeles county falling into the categories of urban avoider, adapter, and exploiter. Highly urban species like the opossum, house finch, and northern mockingbird are more commonly found in yard spaces while urban adapter species like the coyote and striped skunk are seen in larger greenspace patches like recreation and open modified space. Urban avoider species like the bobcat prefer areas of higher vegetation however they are increasingly becoming edge species due to increasing housing development and fragmentation, causing bobcats to cross more fragmented vegetated patches to reach larger vegetated patches (Zheng et al. 2024). Bobcats are noted for their behavioral plasticity and adaptability enabling them to be able to traverse urbanized landscapes to reach water, food, vegetated habitat, and mates reaching densities in densely human populated regions similar to small urban spaces (Crooks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Lombardi 2017). As human population density increases in cities it is important to measure the scale at which specific species may be responding to changes within urbanized regions in order to better understand responses wildlife may have to greenspace.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eScale of Greenspace\u003c/h2\u003e \u003cp\u003eWildlife species richness was influenced by levels of herbaceous cover, herbaceous patchiness, woody cover, and woody patchiness to different degrees based on scale size. Varying scales of 200m, 500m, 1km, and 2km were critical to identify patterns and change in patterns between wildlife and greenspace. Only observing large scales for instance may make sweeping generalizations while losing important details that may be significant for species that live at smaller scales (Fidino et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Depending on the species, a coyote may travel farther to acquire food or denning sites, but a fox squirrel may have an urban range limited to a few blocks. At 1km and 2km, we may suggest that some species are accustomed to low woody urban environments but benefit from low to high levels of herbaceous cover and patchiness. One study found that at buffers of 1km and 2km, native, terrestrial and forested birds were not strongly influenced by increased canopy cover (Humphrey et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These species may be using human-built landcover like buildings to serve as shelter for nesting or power lines which substitute for trees as perching and nesting locations. A balance of herbaceous and woody cover appears to be needed at larger scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile perhaps tree cover improves urban habitat for wildlife, patterns from analysis may suggest that wildlife in eastern Los Angeles County predominantly rely on large herbaceous patches. Even though herbaceous cover becomes more fragmented, the increase of woody landcover positively influences species richness. This indicates that urban wildlife may prefer a balance of available herbaceous patches, either more or less aggregated, with moderate levels of woody cover present. At 500m, increased woody land cover and patchiness positively, strongly, influenced species richness, especially compared to scales of 1 km and 2km which saw higher richness at low to moderate woody cover/patchiness. The effect tree cover has on species richness is stronger within a smaller area of land (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Increased tree cover at smaller scales has been shown to support oak woodland and riparian dominant birds in San Diego county (Crooks et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). One study in Australia, found that bird richness was increasingly and strongly influenced by vegetation, nectar producing plants, in neighborhoods measured at smaller scales compared to larger scales at 1km (Luck 2013). This suggests that greenspaces designed within urban spaces should consider the available surrounding landscape at various scales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePatchiness of Landscape\u003c/h2\u003e \u003cp\u003eUrban landscapes are characterized by highly fragmented and heterogeneous patches of greenspace (Zhou et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To a degree, urban wildlife are accustomed to anthropogenic fragmentation and understand how to find food and other resources throughout the matrix (Crooks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Riley et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Grubbs and Krausman \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For example, in the Santa Monica Mountains, coyotes and bobcats were collared and tracked to monitor their home range, and were found that over 80% of each species had home ranges inclusive of urban land (Riley et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Also noted, urban adapted species are struck less by cars compared to species exposed to less traffic and roads in undisturbed habitats (Riley et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), which is one of the main impediments to navigating urban fragmentation. However, sensitivity to fragmentation is species specific and largely depends on the animal\u0026rsquo;s body size and food sources (Crooks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These findings support the need to measure wildlife response to landscape effects at species specific scaled levels, because they consider body size and dispersal ability. Larger species from this study, like the black bear and mule deer, were found along predominantly urban wildland interface and natural sites, suggesting that at smaller scale of 500m low herbaceous patchiness and cover with high woody cover and moderate woody patchiness could support these species. As the scale increases at 2km, these species may encounter landcover patterns of medium to high herbaceous cover and patches as they look for food along the urban wildland edge but with decreased woody cover and patches, this would limit dispersal and ensure the need to return to habitat that can meet those needs.\u003c/p\u003e \u003cp\u003eAt a scale of 500m supports high richness if there is high herbaceous patchiness, high herbaceous cover, and high woody cover. Many species that tend to be generalists in food and habitat have a higher tolerance for human disturbance as they disperse through the landscape to find resources, relying on presence of herbaceous cover (Adams \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We also see species richness increase due to low herbaceous cover, low herbaceous patches, and high woody cover, and this may point to species that require trees as primary vegetation cover for resources and traveling. A study in southern California, focused on mesopredators such as coyotes, bobcats, mountain lions, opossums, and skunks, in patches less than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{1km}^{2}\\)\u003c/span\u003e\u003c/span\u003e to understand the effects of fragmentation. Species like the opossum, were determined as a key species signaling increased habitat disturbance, increased edge, and benefit from high levels of fragmentation (Crooks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Markovchick-Nicholls et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). While coyotes, due to their high levels of adaptability, were not ideal to measure the effects of fragmentation, they did serve to identify the functional aspect of connectivity in dense urban regions (Crooks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The bobcat was more sensitive to fragmentation than coyotes, but sustained presence in urban areas with the use of prevalent passageways to travel through and access large, natural areas (Crooks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This study echoes patterns observed across many sites for this study, for example opossums were primarily seen in higher herbaceous fragmented patches of suburban yard and urban gardens that were frequently interrupted by human-built surface cover, like busy streets and housing. Bobcats were more frequent in urban wildland interface locations that had increased herbaceous and woody patches stemming from natural areas. These observations were supported by the NMDS plot that shows opossums closer to yard spaces and bobcats found in between natural sites and urban wildland interfaces.\u003c/p\u003e \u003cp\u003eMany studies have suggested the concept of creating greenspaces as \u0026lsquo;stepping stones\u0026rsquo; which would provide wildlife with resources in a fragmented urban landscape (Spellerberg and Gaywood \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Marzluff and Ewing \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Saura 2014; Lynch \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Stepping stones serve as a means for dispersal and resource acquisition for wildlife, and urban planners want to encourage the use of greenspace for wildlife habitat, not just to travel through (Lynch \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Step stones of greenspace habitats are beneficial to wildlife when they improve access to natural, undisturbed habitat patches (Lynch \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Planting native vegetation in patches to restore natural habitat as it is connects to nature reserves, improves biodiversity, as it helps to create gradients of resources for edge species to access and protects interior species (Marzluff and Ewing \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A sister to the concept of step stone habitats is \u0026lsquo;Ecological land-use complementation\u0026rsquo; (ELC) which are urban greenspace patches arranged to support ecological functions and improve biodiversity within the urban matrix (Colding \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). An example would include suburbia with yard space, a city park, and natural area, all aggregated to each other rather than being dispersed across dense impervious cover, like malls with parking lots or industrial areas (Colding \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatterns of the stepping stone framework and ELC seem evident within this study and as shown by species richness strongly and positively influenced by various levels of herbaceous cover and patchiness along with low to high woody patches and high woody cover. This supports the idea that urban greenspaces function ecologically as a mosaic to support multiple species\u0026rsquo; needs. ELC bolsters ecosystem functions such as seed dispersal and pollination by providing heterogeneity of resources for species that may use complementary habitats (Colding \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In addition, this concept reinforces the need for buffers along natural spaces as they border urbanized land. Urban greenspaces can serve as beneficial buffers that could reduce sensitivity of fragmentation for urban sensitive species and increase vegetated landcover as natural lands reduce due to development (Colding \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). However, it is important to be selective about what type of buffer to use, one that would prevent anthropogenic effects, like invasive species or pesticide runoff, from permeating natural areas but also allow the migration of edge species and improve access for wildlife to increased food and habitat (Marzluff and Ewing \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Colding \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Further Research\u003c/h2\u003e \u003cp\u003eLimitations exist with a small sample size of camera trap sites which possibly resulted in broad confidence intervals. Included with that was the difficulty and inability to acquire an equal number of sites per land use category due to restricted access, lack of permission from landowners, and uneven distribution of types of sites within the study area. Having a higher number of natural areas to sample from would highlight the pattern seen along the urban-natural gradient. However, considering this study focused on urban wildlife species primarily focused in urban greenspaces, the data obtained covers the scope of the research.\u003c/p\u003e \u003cp\u003eFurther research can include vegetation surveys of these greenspaces to determine influences of microhabitat and implementing additional survey methods for other taxonomic groups, like point count survey or acoustic monitoring for birds, to improve detections. As this project continues, including additional years of camera trap data would hopefully improve confidence of predictions suggested in this study. Including seasonal changes like temperature and rainfall during each camera trap month would provide additional variables to help understand the patterns of wildlife richness in eastern Los Angeles County across major environmental events. Additionally, utilizing this design to conduct a multi-species occupancy model to estimate species richness and compare these values to the iChao2 estimator and within the models to determine if predictions are consistent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications and Conclusion\u003c/h2\u003e \u003cp\u003eGreenspaces can serve as a rich reservoir of biodiversity and provide critical ecosystem functions in urban spaces. They vary incredibly by size, aggregation and edge characteristics in the urban matrix which affects resources for wildlife and thus species richness. This research serves to fill a gap of knowledge about urban wildlife in eastern Los Angeles County by identifying important landscape characteristics of urban greenspaces and surrounding areas that affect species richness. When designing urban planners use multiple scales to reflect the complex interactions that occur at the local, district, and regional levels that account for demographic, economic, social, ecological, and residential conditions (Wissen Hayek et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This research aims to support design elements to create sustainable urbanscapes for wildlife and humans in which ecological functions and biodiversity are prioritized in urban regions, especially focusing on how greenspaces can become one of the primary conduits for this process. Local biodiversity can be supported and improved alongside the commitment to bolstering ecosystem services of urban greenspaces.\u003c/p\u003e \u003cp\u003eGovernment, public, and scientific community participation at the city, county and regional level, is critical to enact long-standing change and implement ecologically sound greenspaces that support biodiversity, human well-being, and ecosystem functions. It is important to consider the role individual greenspaces serve as habitat within the entire matrix and to elicit help from biologists in urban planning. Incorporating ecology within urban planning and development is one piece of the puzzle to solving urban conservation. With group effort of community members, urban planners, ecologists, and local management agencies holistic decisions can be attained to cultivate coexistence between wildlife and people in urban regions (Marzluff and Ewing \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the California State University and the CSUPERB research award. AE has received research support from scholarships provided by the Cal Poly Pomona Biological Sciences Department.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Adrianna Elihu and Janel Ortiz. The first draft of the manuscript was written by Adrianna Elihu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAberle MA, Langwig KE, Adelman JS, Hawley DM (2020) Effects of bird feeder density on the foraging behaviors of a backyard songbird (the House Finch, Haemorhous mexicanus) subject to seasonal disease outbreaks. Can J Zool 98:611\u0026ndash;621. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/cjz\u0026ndash;2019\u0026ndash;0282\u003c/span\u003e\u003cspan address=\"10.1139/cjz\u0026ndash;2019\u0026ndash;0282\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams E (2021) Clark. Population Dynamics. Urban Wildlife Management. CRC, pp 181\u0026ndash;215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAplin KP, Suzuki H, Chinen AA et al (2011) Multiple Geographic Origins of Commensalism and Complex Dispersal History of Black Rats. PLoS ONE 6:e26357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0026357\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0026357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeninde J, Veith M, Hochkirch A (2015) Biodiversity in cities needs space: a meta-analysis of factors determining intra-urban biodiversity variation. Ecol Lett 18:581\u0026ndash;592. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ele.12427\u003c/span\u003e\u003cspan address=\"10.1111/ele.12427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlair RB (1996) Land Use and Avian Species Diversity Along an Urban Gradient. Ecol Appl 6:506\u0026ndash;519. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/2269387\u003c/span\u003e\u003cspan address=\"10.2307/2269387\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolund P, Hunhammar S (1999) Ecosystem services in urban areas. Ecol Econ 29:293\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0921\u0026ndash;8009(99\u003c/span\u003e\u003cspan address=\"10.1016/S0921\u0026ndash;8009(99\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChao A, Ma KH, Hsieh TC, Chiu C-H (2019) User\u0026rsquo;s Guide for Online Program SpadeR. Species-richness Prediction And Diversity Estimation in R\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu C, Wang Y, Walther BA, Chao A (2014) An improved nonparametric lower bound of species richness via a modified good\u0026ndash;turing frequency formula. Biometrics 70:671\u0026ndash;682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/biom.12200\u003c/span\u003e\u003cspan address=\"10.1111/biom.12200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChithra SV, Harindranathan Nair MV, Amarnath A, Anjana NS (2015) Impacts of Impervious Surfaces on the Environment. Int J Eng Sci Invention 5:27\u0026ndash;31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColding J (2007) Ecological land-use complementation\u0026rsquo; for building resilience in urban ecosystems. Landsc Urban Plann 81:46\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2006.10.016\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2006.10.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollinge SK (2009) Ecology of Fragmented Landscapes. Johns Hopkins University\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrooks KR (2002) Relative Sensitivities of Mammalian Carnivores to Habitat Fragmentation. Conserv Biol 16:488\u0026ndash;502. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1523\u0026ndash;1739.2002.00386.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1523\u0026ndash;1739.2002.00386.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrooks KR, Suarez AV, Bolger DT (2004) Avian assemblages along a gradient of urbanization in a highly fragmented landscape. Biol Conserv 115:451\u0026ndash;462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0006\u0026ndash;3207(03)00162\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1016/S0006\u0026ndash;3207(03)00162\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunagan SP, Karels TJ, Moriarty JG et al (2019) Bobcat and rabbit habitat use in an urban landscape. J Mammal 100:401\u0026ndash;409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jmammal/gyz062\u003c/span\u003e\u003cspan address=\"10.1093/jmammal/gyz062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDzhambov A, Dimitrova D (2014) Urban green spaces\u0026prime; effectiveness as a psychological buffer for the negative health impact of noise pollution: A systematic review. Noise Health 16:157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/1463\u0026ndash;1741.134916\u003c/span\u003e\u003cspan address=\"10.4103/1463\u0026ndash;1741.134916\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFidino M, Gallo T, Lehrer EW et al (2021) Landscape-scale differences among cities alter common species\u0026rsquo; responses to urbanization. Ecol Appl 31:e02253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/eap.2253\u003c/span\u003e\u003cspan address=\"10.1002/eap.2253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoody GM (2008) Harshness in image classification accuracy assessment. Int J Remote Sens 29:3137\u0026ndash;3158. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01431160701442120\u003c/span\u003e\u003cspan address=\"10.1080/01431160701442120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallo T, Fidino M, Lehrer EW, Magle S (2019) Urbanization alters predator-avoidance behaviours. J Anim Ecol 88:793\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365\u0026ndash;2656.12967\u003c/span\u003e\u003cspan address=\"10.1111/1365\u0026ndash;2656.12967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGehrt SD, Anchor C, White LA (2009) Home Range and Landscape Use of Coyotes in a Metropolitan Landscape: Conflict or Coexistence? J Mammal 90:1045\u0026ndash;1057. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1644/08-MAMM-A\u0026ndash;277.1\u003c/span\u003e\u003cspan address=\"10.1644/08-MAMM-A\u0026ndash;277.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGese EM, Morey PS, Gehrt SD (2012) Influence of the urban matrix on space use of coyotes in the Chicago metropolitan area. J Ethol 30:413\u0026ndash;425. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10164-012-0339\u0026ndash;8\u003c/span\u003e\u003cspan address=\"10.1007/s10164-012-0339\u0026ndash;8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiraudeau M, Nolan PM, Black CE et al (2014) Song characteristics track bill morphology along a gradient of urbanization in house finches (Haemorhous mexicanus). Front Zool 11:83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12983-014-0083\u0026ndash;8\u003c/span\u003e\u003cspan address=\"10.1186/s12983-014-0083\u0026ndash;8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoddard MA, Dougill AJ, Benton TG (2010) Scaling up from gardens: biodiversity conservation in urban environments. Trends Ecol Evol 25:90\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2009.07.016\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2009.07.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrubbs SE, Krausman PR (2009) Use of Urban Landscape by Coyotes. Southwest Nat 54:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1894/MLK\u0026ndash;05.1\u003c/span\u003e\u003cspan address=\"10.1894/MLK\u0026ndash;05.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardin G, Population E, Adams (2021) CRC Press, pp. 165\u0026ndash;178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarmon LJ, Bauman K, McCloud M et al (2005) What free-ranging animals do at the zoo: a study of the behavior and habitat use of opossums (Didelphis virginiana) on the grounds of the St. Louis Zoo. Zoo Biol 24:197\u0026ndash;213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/zoo.20046\u003c/span\u003e\u003cspan address=\"10.1002/zoo.20046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaugen AO (1942) Home Range of the Cottontail Rabbit. Ecology 23:354\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1930675\u003c/span\u003e\u003cspan address=\"10.2307/1930675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoekstra JM, Boucher TM, Ricketts TH, Roberts C (2005) Confronting a biome crisis: global disparities of habitat loss and protection. Ecol Lett 8:23\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1461\u0026ndash;0248.2004.00686.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1461\u0026ndash;0248.2004.00686.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumphrey JE, Haslem A, Bennett AF (2023) Housing or habitat: what drives patterns of avian species richness in urbanized landscapes? Landsc Ecol 38:1919\u0026ndash;1937. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10980-023-01666\u0026ndash;2\u003c/span\u003e\u003cspan address=\"10.1007/s10980-023-01666\u0026ndash;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJo H-K, McPherson GE (1995) Carbon Storage and Flux in Urban Residential Greenspace. J Environ Manage 45:109\u0026ndash;133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1006/jema.1995.0062\u003c/span\u003e\u003cspan address=\"10.1006/jema.1995.0062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLombardi JV, Comer CE, Scognamillo DG, Conway WC (2017) Coyote, fox, and bobcat response to anthropogenic and natural landscape features in a small urban area. Urban Ecosyst 20:1239\u0026ndash;1248\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLombardi JV, Tewes ME, Perotto-Baldivieso HL et al (2020) Spatial structure of woody cover affects habitat use patterns of ocelots in Texas. Mamm Res 65:555\u0026ndash;563. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13364-020-00501\u0026ndash;2\u003c/span\u003e\u003cspan address=\"10.1007/s13364-020-00501\u0026ndash;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuck GW, Smallbone LT, Sheffield KT (2013) Environmental and socio-economic factors related to urban bird communities. Austral Ecol 38(1):111\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLynch AJ (2019) Creating effective urban greenways and stepping-stones: four critical gaps in habitat connectivity planning research. J Plann Literature 34(2):131\u0026ndash;155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagle SB, Fidino M, Lehrer EW et al (2019) Advancing urban wildlife research through a multi-city collaboration. Front Ecol Environ 17:232\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/fee.2030\u003c/span\u003e\u003cspan address=\"10.1002/fee.2030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagle SB, Fidino M, Sander HA et al (2021) Wealth and urbanization shape medium and large terrestrial mammal communities. Glob Change Biol 27:5446\u0026ndash;5459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/gcb.15800\u003c/span\u003e\u003cspan address=\"10.1111/gcb.15800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarkovchick-Nicholls L, Regan HM, Deutschman DH et al (2008) Relationships between Human Disturbance and Wildlife Land Use in Urban Habitat Fragments: Human Disturbance in Habitat Fragments. Conserv Biol 22:99\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523\u0026ndash;1739.2007.00846.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523\u0026ndash;1739.2007.00846.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarzluff JM, Ewing K (2001) Restoration of Fragmented Landscapes for the Conservation of Birds: A General Framework and Specific Recommendations for Urbanizing Landscapes. Restor Ecol 9:280\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1526\u0026ndash;100x.2001.009003280.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1526\u0026ndash;100x.2001.009003280.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMata JM, Perotto-Baldivieso HL, Hern\u0026aacute;ndez F et al (2018) Quantifying the spatial and temporal distribution of tanglehead (Heteropogon contortus) on South Texas rangelands. Ecol Process 7:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13717-018-0113\u0026ndash;0\u003c/span\u003e\u003cspan address=\"10.1186/s13717-018-0113\u0026ndash;0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthies SA, R\u0026uuml;ter S, Schaarschmidt F, Prasse R (2017) Determinants of species richness within and across taxonomic groups in urban green spaces. Urban Ecosyst 20:897\u0026ndash;909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11252-017-0642\u0026ndash;9\u003c/span\u003e\u003cspan address=\"10.1007/s11252-017-0642\u0026ndash;9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonnell MJ, Pickett STA (1990) Ecosystem Structure and Function along Urban-Rural Gradients: An Unexploited Opportunity for Ecology. Ecology 71:1232\u0026ndash;1237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1938259\u003c/span\u003e\u003cspan address=\"10.2307/1938259\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGarigal K (2015) FRAGSTATS Help, Version 4.2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney ML (2002) Urbanization, Biodiversity, and Conservation. Bioscience 52:883. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1641/0006\u003c/span\u003e\u003cspan address=\"10.1641/0006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026ndash;3568(2002)052[0883:UBAC]2.0.CO;2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcPherson EG (1998) Atmospheric Carbon Dioxide Reduction by Sacramento\u0026rsquo;s Urban Forest. AUF 24:215\u0026ndash;223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48044/jauf.1998.026\u003c/span\u003e\u003cspan address=\"10.48044/jauf.1998.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowak DJ, Crane DE, Stevens JC (2006) Air pollution removal by urban trees and shrubs in the United States. Urban Forestry Urban Green 4:115\u0026ndash;123. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ufug.2006.01.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ufug.2006.01.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira S, Andrade H, Vaz T (2011) The cooling effect of green spaces as a contribution to the mitigation of urban heat: A case study in Lisbon. Build Environ 46:2186\u0026ndash;2194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.buildenv.2011.04.034\u003c/span\u003e\u003cspan address=\"10.1016/j.buildenv.2011.04.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpen Data Network \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.opendatanetwork.com/entity/0500000US06037/Los_Angeles_County_CA/geographic.population.density?year=2018\u003c/span\u003e\u003cspan address=\"https://www.opendatanetwork.com/entity/0500000US06037/Los_Angeles_County_CA/geographic.population.density?year=2018\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerotto-Baldivieso HL, Mel\u0026eacute;ndez-Ackerman E, Garc\u0026iacute;a MA et al (2009) Spatial distribution, connectivity, and the influence of scale: habitat availability for the endangered Mona Island rock iguana. Biodivers Conserv 18:905\u0026ndash;917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10531-008-9520\u0026ndash;3\u003c/span\u003e\u003cspan address=\"10.1007/s10531-008-9520\u0026ndash;3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlanet Labs PBC (2018) Planet Application Program Interface: In Space for Like on Earth, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://api.planet.com\u003c/span\u003e\u003cspan address=\"https://api.planet.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Image \u0026copy; 2021 Planet Labs PBC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrange S, Gehrt SD, Wiggers EP (2003) Demographic Factors Contributing to High Raccoon Densities in Urban Landscapes. J Wildl Manag 67:324. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3802774\u003c/span\u003e\u003cspan address=\"10.2307/3802774\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiem JG, Blair RB, Pennington DN, Solomon NG (2012) Estimating Mammalian Species Diversity across an Urban Gradient. Am Midl Nat 168:315\u0026ndash;332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1674/0003-0031-168.2.315\u003c/span\u003e\u003cspan address=\"10.1674/0003-0031-168.2.315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley SPD, Pollinger JP, Sauvajot RM et al (2006) FAST-TRACK: A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol Ecol 15:1733\u0026ndash;1741. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365\u0026ndash;294X.2006.02907.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365\u0026ndash;294X.2006.02907.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley SPD, Sauvajot RM, Fuller TK et al (2003) Effects of Urbanization and Habitat Fragmentation on Bobcats and Coyotes in Southern California. Conserv Biol 17:566\u0026ndash;576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1523\u0026ndash;1739.2003.01458.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1523\u0026ndash;1739.2003.01458.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley SPD, Sikich JA, Benson JF (2021) Big Cats in the Big City: Spatial Ecology of Mountain Lions in Greater Los Angeles. Jour Wild Mgmt 85:1527\u0026ndash;1542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jwmg.22127\u003c/span\u003e\u003cspan address=\"10.1002/jwmg.22127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eŠ\u0026aacute;lek M, Drahn\u0026iacute;kov\u0026aacute; L, Tkadlec E (2015) Changes in home range sizes and population densities of carnivore species along the natural to urban habitat gradient. Mammal Rev 45:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mam.12027\u003c/span\u003e\u003cspan address=\"10.1111/mam.12027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSan Gabriel (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchindler S, Poirazidis K, Wrbka T (2008) Towards a core set of landscape metrics for biodiversity assessments: A case study from Dadia National Park, Greece. Ecol Ind 8:502\u0026ndash;514. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecolind.2007.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ecolind.2007.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanahan DF, Miller C, Possingham HP, Fuller RA (2011) The influence of patch area and connectivity on avian communities in urban revegetation. Biol Conserv 144:722\u0026ndash;729. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.biocon.2010.10.014\u003c/span\u003e\u003cspan address=\"10.1016/j.biocon.2010.10.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaura S, Bodin \u0026Ouml;, Fortin MJ (2014) Stepping stones are crucial for species\u0026rsquo; long-distance dispersal and range expansion through habitat networks. J Appl Ecol 51:171\u0026ndash;182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1365\u0026ndash;2664.12179\u003c/span\u003e\u003cspan address=\"10.1111/1365\u0026ndash;2664.12179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpellerberg IF, Gaywood MJ (1993) Linear features: linear habitats \u0026amp; wildlife corridors. Center for Environmental Sciences\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSurls R, Gerber J (2016) From COWS to CONCRETE: The Rise and Fall of Farming in Los Angeles, 43\u0026ndash;46\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolessa T, Senbeta F, Kidane M (2016) Landscape composition and configuration in the central highlands of Ethiopia. Ecol Evol 6:7409\u0026ndash;7421. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.2477\u003c/span\u003e\u003cspan address=\"10.1002/ece3.2477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrban Wildlife Information Network: Conceptual Overview (2021) Conceptual Overview (2021_08_23 18_15_38 UTC).pdf\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrban Wildlife Information Network Partners. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.urbanwildlifeinfo.org/partners\u003c/span\u003e\u003cspan address=\"https://www.urbanwildlifeinfo.org/partners\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrban Wildlife Information Network Study Design. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://static1.squarespace.com/static/620557dccfabd22386bdbe51/t/624f100886fcfe5b9875fb54/1649348617933/UWIN+Database+Manual.pdf\u003c/span\u003e\u003cspan address=\"https://static1.squarespace.com/static/620557dccfabd22386bdbe51/t/624f100886fcfe5b9875fb54/1649348617933/UWIN+Database+Manual.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Lunteren P (2023) EcoAssist: A no-code platform to train and deploy custom YOLOv5 object detection models. J Open Source Softw 8(88):5581\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhittaker RH (1967) GRADIENT ANALYSIS OF VEGETATION*. Biol Rev 42:207\u0026ndash;264. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469\u0026ndash;185X.1967.tb01419.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469\u0026ndash;185X.1967.tb01419.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWissen Hayek U, Efthymiou D, Farooq B et al (2015) Quality of urban patterns: Spatially explicit evidence for multiple scales. Landsc Urban Plann 142:47\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landurbplan.2015.05.010\u003c/span\u003e\u003cspan address=\"10.1016/j.landurbplan.2015.05.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright JD, Burt MS, Jackson VL (2012) Influences of an Urban Environment on Home Range and Body Mass of Virginia Opossums (Didelphis virginiana). Northeastern Naturalist 19:77\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1656/045.019.0106\u003c/span\u003e\u003cspan address=\"10.1656/045.019.0106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou W, Wang J, Qian Y et al (2018) The rapid but invisible changes in urban greenspace: A comparative study of nine Chinese cities. Sci Total Environ 627:1572\u0026ndash;1584. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.01.335\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2018.01.335\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"urban wildlife, patchiness, greenspace, fragmentation","lastPublishedDoi":"10.21203/rs.3.rs-4909697/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4909697/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban greenspaces are a haven for wildlife in densely populated cities. Wildlife use greenspaces for resource acquisition, shelter, and traveling across urbanized landscapes. Greenspace characteristics such as presence of woody or herbaceous landcover, size, edge density, and patchiness influence species richness. The goals of this study was to: 1) identify and quantify greenspace metrics to determine relationships with wildlife and 2) determine differences in greenspace patterns at various spatial scales. To monitor wildlife, twenty-six camera traps were set in eastern Los Angeles County, California; greenspace metrics were gathered using 3m land cover supervised classification. We used a generalized linear mixed model to determine the influence of greenspace metrics on richness at four scales (200m, 500m, 1km, and 2km). At larger scales, 1km and 2km, high herbaceous cover, whether as increasing aggregated patches or increased patchiness, and moderate levels of woody cover positively influence species richness. At smaller scales, 200m and 500m, low to moderate levels of herbaceous cover and high levels of woody cover strongly and positively influence species. These results suggest that wildlife are able to utilize urban areas with increasing fragmentation of greenspace habitat and require greenspace, either as a few, less fragmented patches or as many patches with high herbaceous cover in the urban matrix. From the perspective of urban planning, developing greenspaces from a broader ecological scale is important to ensure they function as stepping stones in the urban matrix. Understanding these patterns can improve greenspaces that support wildlife and therefore, ecological functions.\u003c/p\u003e","manuscriptTitle":"Concrete Jungle to Urban Oasis: Scale, Greenspace Size and Patchiness Influence Wildlife in Cities of Eastern Los Angeles County, California","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-11 08:25:03","doi":"10.21203/rs.3.rs-4909697/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69d5943e-8512-4e06-9b47-783c6f2d2d54","owner":[],"postedDate":"September 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-05T02:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-11 08:25:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4909697","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4909697","identity":"rs-4909697","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}