DOI: 10.5061/dryad.p8cz8wb59
title: Concrete jungle to urban oasis: evaluating scale, vegetation cover, and aggregation of urban greenspaces on wildlife

description: Urban greenspaces are a haven for wildlife in densely populated cities.
 Wildlife use greenspaces for resource acquisition, shelter, and travel
 across urbanized landscapes. Greenspace metrics such as herbaceous or
 woody landcover, size, patchiness, and human land use influence species
 richness. Considering these metrics in urban greenspace design could
 influence valuable resources for wildlife, therefore the goals of this
 study were to: 1) determine wildlife communities in different greenspace
 types 2) identify and quantify greenspace metrics to determine
 relationships with wildlife 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 landcover supervised
 classification and FRAGSTATS. Non-metric multidimensional scaling was used
 to determine wildlife community composition and a generalized linear mixed
 model was selected to determine influence of greenspace metrics on
 richness at four scales (200m, 500m, 1km, and 2km). Urban dweller adapted
 species like the Northern mockingbird, Virginia opossum, and desert
 cottontail, were commonly found in suburban yards and urban open modified
 greenspaces like a commercial plant nursery. Whereas species like the
 band-tailed pigeon, gray fox, and black bear had increased presence in
 natural greenspaces. Large scale greenspaces, 1km and 2km in size, with
 high herbaceous cover, either as increasing aggregated patches or
 increased patchiness, and moderate levels of woody cover, positively
 influence species richness. At smaller scales, 200m and 500m, low and high
 levels of herbaceous cover and high levels of woody cover, strongly and
 positively influence richness. These results suggest fragmented
 greenspaces with varying levels of herbaceous and woody cover contribute
 to increasing wildlife richness in urban regions. From the perspective of
 urban planning, consideration of  ecological scale is important
 to ensure developing greenspaces can support wildlife and ecological
 functions.
description: One 30 km transect was created sampling twenty-six sites using
 camera traps to monitor wildlife from Diamond Bar to the San Gabriel
 Mountains covering 11 of the 31 cities in San Gabriel Valley (Fig. 1).
 Camera site selection was based on standardized methodology developed by
 the Urban Wildlife Information Network (Magle et al. 2019). A 2km buffer
 was created around the transect and all cameras were placed randomly at
 least 1km from each other within the buffer.  Finalized site selection and
 placement were based on randomized locations generated initially with
 available vegetation for camera placement, available site access and
 approval from public, private, and city landowners.
 Camera sites (n=26) were defined within 5 greenspace categories
 after site selection  (Fig. 2) and vary in size, location, amount of
 greenspace (GS) and human-built surfaces (HB), and average amount of human
 activity (HA- people/per camera trap night captured during all five
 seasons). Additional land cover types, such as bareground and water were
 measured per site but were not metrics of interest in this study. In order
 to categorize camera sites into the 5 greenspace categories, they were
 each assessed within a 155 meter buffer (smallest study site size) to
 standardize greenspace and human-built surface percentages, around each
 camera site using the land cover classification on ArcMap 10.7 (ESRI,
 Redlands, CA, USA). These categories include 1) yard space: greenspace,
 primarily lawns, attached to houses surrounded by human-built structures
 with low human activity and foot traffic (n=4; GS: 54- 76%, HB: 16-43%,
 HA: 15.59), 2) open-modified space: greenspace with low to moderate human
 foot traffic and access, including a landfill, private commercial nursery,
 and urban farmland (n=5; GS: 42- 75%, HB: 6-55%, HA: 25.39), 3) natural
 areas: vegetated natural spaces, like trails, with very low human use and
 access (n=2; 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 (n=7; 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
 (n=8; GS: 54- 82%, HB: 7-39%, HA: 226.82). All final sites were dependent
 upon California State Polytechnic University-Pomona’s Risk Management
 approval and signed agreements with  landowner or management personnel,
 and accessibility during daylight hours. One Bushnell
 (Overland Park, KS) Core Low Glow Trail camera (Model- 119936C) was placed
 at each study site to monitor wildlife presence in the area. Settings for
 cameras were: image size (4K), capture number (1 photo), interval (30
 sec), and sensor level (normal). Cameras were secured to a tree or T-post
 0.5 -1 meter above the ground, faced towards any animal sign at initial
 set-up (e.g., animal trail, scat),  and set at an angle to capture
 approximately 50% of the  ground and 50% of the air in the photos to
 assess for birds and mammals. Cameras were deployed to capture photos for
 once per month, with a camera check mid-month to swap SD cards and
 batteries. Data was gathered for a total of one year from February 2022 to
 January 2023, sampling one month per season (Fall- October, Winter-
 January and February, 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 animal, human, or object from
 empty photos (van Lunteren 2023). Photos were then 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 were sent to validation from which the primary author
 (AE) determined the correct tag or entered the correct tag if both were
 incorrect. Final taxonomic groups of interest were mammals and birds as
 camera traps were placed and angled to capture both groups using both
 ground and air. This project was exempt from Cal Poly Pomona Institutional
 Animal Care and Use Committee (IACUC) since there was no manipulation or
 handling of animals. IRB approval to capture human activity was not
 necessary since all landowners approved of camera locations and signs were
 placed in public locations indicating individual photos may be taken from
 the wildlife camera. <strong>Land
 Cover Classification</strong> A supervised land
 cover classification raster was created using ERDAS Imagine 16.6 (Hexagon,
 Stockholm, Sweden) 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. These classes were initially chosen
 from urban wildlife studies then narrowed down to common land cover types
 across the camera sites in San Gabriel Valley and based on species
 presence from the camera traps (Kie 2000; Livingston 2003; Cadenasso 2007;
 Liu 2014; Dennis 2018; Moll 2018). It was important to distinguish
 greenspace as both herbaceous and woody cover to account for mammals that
 use greenspace to traverse across the urban matrix and for bird species
 that utilize tree canopy (Blair 1996; Kie 2000; Livingston 2003; Cadenasso
 2007; Lombardi 2018). Herbaceous cover was primarily composed of natural
 grasses in natural areas and manicured lawns, such as in recreation sites
 and yard spaces, while woody cover consisted of trees and shrubs. A 69%
 accuracy land cover classification of eastern Los Angeles County was
 achieved by assessing aerial images from Google Maps using 250 random
 points. An accuracy assessment was conducted as a pre-processing step in
 order to determine if the spatial model was truly representative of what
 is on the ground. Land cover percentages were extracted after the fact for
 analysis purposes. This accuracy is probably due to a larger survey area,
 eastern Los Angeles County (1,044 ) being used in the assessment compared to using the smaller, study transect (60  ). While 85% land cover accuracy attainment is often cited in literature, it is not widely applicable among all land cover analyses, not initially developed to support local, small scale land cover analyses, and might be unrealistically inflated (Foody 2008). Buffers of 200m (Desert cottontail, adult female during breeding season 22.5 acres within woodlands of Michigan, USA) (Haugen 1942), 500m (Virginia opossum, adult female ~15-35 ha, adult male ~20- 70 ha, urban setting in Missouri) (Harmon et al. 2004, Wright 2012), 1km (Northern raccoon, adults 25- 50 ha in urban regions of Illinois) (Prange 2004), and 2km (Coyote, breeding adult male and female 3.5 - 4.5 km2 in metropolitan Illinois) (Gehrt 2009) 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 circular home ranges of common urban species. A buffer of 500m, 1km, and 2km accounts for intermediate dispersal and foraging distances of common urban adapted birds such as the Northern mockingbird (<em>Mimus polyglottos</em>), mourning dove (<em>Zenaida macroura</em>), house finch (<em>Carpodacus mexicanus</em>), and American crow (<em>Corvus brachyrhynchos</em>) (Crooks et al. 2004; Matthies et al. 2017; Aberle et al. 2020). Buffers were created using the ‘Create Buffers’ tool around each camera site in ArcMap 10.7 (ESRI, Redlands, CA, USA) and the land cover classification raster was clipped at the buffer sizes using the Clip Raster tool for use in FRAGSTATS. Class level landscape characteristics, hereon known as metrics, (Table 1), determined from literature review and the FRAGSTATS manual version 4 (McGarigal and Marks 1995, McGarigal 2015), were extracted from each of the clipped buffers from the land cover raster 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). Patch area and distance from another patch were found to be the strongest predictors of bobcat, coyote, gray fox, raccoon, striped skunk, and Virginia opossum distribution (Crooks 2002). The influence of percent land cover of herbaceous, tree, urban, and water, oftentimes is species specific with some species like the bobcat and gray fox to prefer increased herbaceous cover (Rodriguez et al. 2021). Amongst the importance of studying landscape metrics at a local scale, is analyzing metrics  like heterogeneity of habitat, fragmentation, and landcover at the class-level to determine create biodiversity assessments (Schindler et. al 2007).  We ran a non-metric multidimensional scaling (NMDS) analysis with Bray-Curtis dissimilarity using R (Vienna, Austria) 4.2.0 (vegan package) to detect distinctions of mammal and bird species communities within greenspaces of different land use types. The stress value for the NMDS data was 0 &lt; x &lt;0.2. Pearson’s correlation coefficient (r) was calculated and a multidimensional scaling (MDS) analysis was conducted in R 4.2.0 (magrittr, dplyr, ggpubr packages) for landscape metrics to eliminate those that may be highly correlated to each other (&gt; 0.7, &lt; -0.7).  Focusing on species richness for mammals and birds at each site, we used SpadeR (Species-richness Prediction And Diversity Estimation in R) (Chao 2015) from the online R application Shiny (Chao 2015), 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 trap night) and duplicates (species detected in two trap nights) (Chao 2019). Species detection was determined by noting presence/absence of a species per trap day along with total trap days per site to create incidence-frequency data. Chao2 estimator, developed as a lower bound, uses uniques and duplicates to estimate the number of undetected species and has been used in another study to estimate species richness of mammals and birds for camera trap data (Chengcheng et al. 2018). iChao2 estimator was selected to estimate 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 Supporting Information. To understand the relationship between estimated species richness and landscape metrics, we used a generalized linear mixed model (GLMM) with a negative-binomial distribution in R 4.2.0 (lme4 package) for each scale. 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 we plotted linear relationships for response-fixed factor relationships. We established a threshold of three or more positive or negative correlations among the site types (Tredennick et al. 2021). Akaike Information Criterion (AIC) values were used to compare and favor models with lower AIC values and normal residual diagnostic plots to determine fixed effects for the final model. The final models for 200m, 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) (Table 1) and all interactions were rescaled for standardization (scale between -1 to 1). For visualization purposes we developed a constant at low (-1), medium (0), and high levels (1) of fixed effects to demonstrate the influence on richness for outcomes of the GLMMs and present an landscape buffer example from existing sites to visualize landscapes that represent the graphs as a whole. Two and three way interaction graphs factor in the dependence of each of the fixed effects to each other in order to predict multiple fixed effects on species richness. Two and three way interactions take priority over a single fixed effect. Post model diagnostic plots of residuals and Q-Q plots were used to visualize the fit of each model and if high dispersion or high variance were found those models were eliminated.
description: # Concrete jungle to urban oasis: evaluating scale, vegetation cover, and
 aggregation of urban greenspaces on wildlife Dataset DOI:
 [10.5061/dryad.p8cz8wb59](https://doi.org/10.5061/dryad.p8cz8wb59) ##
 Description of the data and file structure This dataset contains:
 landscape metrics derived from FRAGSTATS after conducting a supervised
 land cover classification at 3 meter resolution imagery taken on December
 2021. The metrics are taken at four different buffer sizes around each
 camera site, 200 meters, 500 meters, 1km, and 2km.   NMDS data of wildlife
 species is given based on relation to site type and higher prevalence of
 species per site type. iChao2 estimated richness data is given of species
 richness per site. On sheet2 is the raw richness from each camera site and
 the associated iChao2 richness value for that site, rounded up. Sheet 3,
 include the lower and upper confidence intervals. FinalRData includes the
 R code for final models at associated buffer sizes, producing results for
 likely associated relationships with richness, landscape metrics, and
 landscape cover. ### Files and variables #### File:
 ChosenMetrics_HerbWoody_Class1km.xlsx **Description:** Chosen landscape
 metrics in **bold** (evaluated in the R model) calculated from FRAGSTATS
 per site at 1km buffer. ##### Variables * 4-letter acronym of camera site,
 ie. ALGL, BRAD, SFTT. Camera sites are abbreviated for privacy of
 landowner and location. * TYPE describes landscape cover within the class
 level. 1- Herbaceous (grass and forb cover), 2- Woody (vegetation with
 woody stems, shrubs and trees), 3- Human built (human built surfaces,
 concrete, asphalt, buildings), 4- bare ground (dirt or non-vegetated
 ground, not built on), 5- Water (any body of water, man-made or natural).
 Here only 1- Herbaceous and 2-Woody are focused on for this specific
 study. * Tabs are grouped by human land use type, with associated camera
 sites within each tab. UWI- urban wildland interface (dwellings that
 boarder natural landscape/greenspace), NAT- natural land (unmodified
 landscape without buildings), YARD- yard spaces (vegetated areas attached
 to houses), OPENMOD- open modified space (modified open spaces usually
 vegetated that have various human uses), REC- recreation (urban parks,
 sports fields, and areas designated for human recreation) * Bolded
 landscape metrics, PLAND- percent landscape cover (measures percent this
 landcover type covers in (% percent)), AREA_MN- mean area coverage
 (average area coverage of landscape cover in (hectare)), ED- edge density
 (measures amount of edge for the specific landscape cover
 (meters/hectare)), PD- patch density (measures how many patches of a
 specific landscape type within a certain buffer size (number per
 hectare)), FRAC_AM- FRAC is a perimeter-area method that quantifies the
 degree of complexity of planar shapes across a variety of scales. FRAC_AM
 is the area-weighted mean patch fractal dimension used for class and
 landscape level by weighing patches according to their size (no unit).
 SHAPE_AM- SHAPE index measures the complexity of the patch shape compared
 to a standard shape like a square of the same size. Area-weighted mean
 patch shape index, provide a landscape focused perspective of landscape
 structure as they reflect the average conditions of a pixel chosen at
 random measure of shape complexity based on relative amount of perimeter
 per unit area, as a perimeter-to-area ratio called a fractal dimension (no
 unit). COHESION- measures connectedness of the patch (measures physical
 connectedness of the patch type, no unit), LPI- largest patch index
 (measures the largest patch within the determined buffer (% percent)).
 Final metrics are PLAND, ED, and PD based on GLMM R model. See paper for
 more details. #### File: ChosenMetricsClass_1km.xlsx
 **Description:** Chosen landscape metrics in **bold** (evaluated in the R
 model) calculated from FRAGSTATS per site at 1km buffer. ##### Variables *
 4-letter acronym of camera site, ie. ALGL, BRAD, SFTT. Camera sites are
 abbreviated for privacy of landowner and location. * TYPE describes
 landscape cover within the class level. 1- Herbaceous (grass and forb
 cover), 2- Woody (vegetation with woody stems, shrubs and trees), 3- Human
 built (human built surfaces, concrete, asphalt, buildings), 4- bare ground
 (dirt or non-vegetated ground, not built on), 5- Water (any body of water,
 man-made or natural). Here only 1- Herbaceous and 2-Woody are focused on
 for this specific study. * Tabs are grouped by human land use type, with
 associated camera sites within each tab. UWI- urban wildland interface
 (dwellings that boarder natural landscape/greenspace), NAT- natural land
 (unmodified landscape without buildings), YARD- yard spaces (vegetated
 areas attached to houses), OPENMOD- open modified space (modified open
 spaces usually vegetated that have various human uses), REC- recreation
 (urban parks, sports fields, and areas designated for human recreation) *
 Bolded landscape metrics, PLAND- percent landscape cover (measures percent
 this landcover type covers in (% percent)), AREA_MN- mean area coverage
 (average area coverage of landscape cover in (hectare)), ED- edge density
 (measures amount of edge for the specific landscape cover
 (meters/hectare)), PD- patch density (measures how many patches of a
 specific landscape type within a certain buffer size (number per
 hectare)), FRAC_AM- FRAC is a perimeter-area method that quantifies the
 degree of complexity of planar shapes across a variety of scales. FRAC_AM
 is the area-weighted mean patch fractal dimension used for class and
 landscape level by weighing patches according to their size (no unit).
 SHAPE_AM- SHAPE index measures the complexity of the patch shape compared
 to a standard shape like a square of the same size. Area-weighted mean
 patch shape index, provide a landscape focused perspective of landscape
 structure as they reflect the average conditions of a pixel chosen at
 random measure of shape complexity based on relative amount of perimeter
 per unit area, as a perimeter-to-area ratio called a fractal dimension (no
 unit). COHESION- measures connectedness of the patch (measures physical
 connectedness of the patch type, no unit), LPI- largest patch index
 (measures the largest patch within the determined buffer (% percent)).
 Final metrics are PLAND, ED, and PD based on GLMM R model. See paper for
 more details. #### File: ChosenMetrics_HerbWoody_Class2km.xlsx
 **Description:** Chosen landscape metrics in **bold** (evaluated in the R
 model) calculated from FRAGSTATS per site at 2km buffer. ##### Variables *
 4-letter acronym of camera site, ie. ALGL, BRAD, SFTT. Camera sites are
 abbreviated for privacy of landowner and location. * TYPE describes
 landscape cover within the class level. 1- Herbaceous (grass and forb
 cover), 2- Woody (vegetation with woody stems, shrubs and trees), 3- Human
 built (human built surfaces, concrete, asphalt, buildings), 4- bare ground
 (dirt or non-vegetated ground, not built on), 5- Water (any body of water,
 man-made or natural). Here only 1- Herbaceous and 2-Woody are focused on
 for this specific study. * Tabs are grouped by human land use type, with
 associated camera sites within each tab. UWI- urban wildland interface
 (dwellings that boarder natural landscape/greenspace), NAT- natural land
 (unmodified landscape without buildings), YARD- yard spaces (vegetated
 areas attached to houses), OPENMOD- open modified space (modified open
 spaces usually vegetated that have various human uses), REC- recreation
 (urban parks, sports fields, and areas designated for human recreation) *
 Bolded landscape metrics, PLAND- percent landscape cover (measures percent
 this landcover type covers in (% percent)), AREA_MN- mean area coverage
 (average area coverage of landscape cover in (hectare)), ED- edge density
 (measures amount of edge for the specific landscape cover
 (meters/hectare)), PD- patch density (measures how many patches of a
 specific landscape type within a certain buffer size (number per
 hectare)), FRAC_AM- FRAC is a perimeter-area method that quantifies the
 degree of complexity of planar shapes across a variety of scales. FRAC_AM
 is the area-weighted mean patch fractal dimension used for class and
 landscape level by weighing patches according to their size (no unit).
 SHAPE_AM- SHAPE index measures the complexity of the patch shape compared
 to a standard shape like a square of the same size. Area-weighted mean
 patch shape index, provide a landscape focused perspective of landscape
 structure as they reflect the average conditions of a pixel chosen at
 random measure of shape complexity based on relative amount of perimeter
 per unit area, as a perimeter-to-area ratio called a fractal dimension (no
 unit). COHESION- measures connectedness of the patch (measures physical
 connectedness of the patch type, no unit), LPI- largest patch index
 (measures the largest patch within the determined buffer (% percent)).
 Final metrics are PLAND, ED, and PD based on GLMM R model. See paper for
 more details. #### File: ChosenMetrics_Class2km.xlsx
 **Description:** Chosen landscape metrics in **bold** (evaluated in the R
 model) calculated from FRAGSTATS per site at 2km buffer. ##### Variables *
 4-letter acronym of camera site, ie. ALGL, BRAD, SFTT. Camera sites are
 abbreviated for privacy of landowner and location. * TYPE describes
 landscape cover within the class level. 1- Herbaceous (grass and forb
 cover), 2- Woody (vegetation with woody stems, shrubs and trees), 3- Human
 built (human built surfaces, concrete, asphalt, buildings), 4- bare ground
 (dirt or non-vegetated ground, not built on), 5- Water (any body of water,
 man-made or natural). Here only 1- Herbaceous and 2-Woody are focused on
 for this specific study. * Tabs are grouped by human land use type, with
 associated camera sites within each tab. UWI- urban wildland interface
 (dwellings that boarder natural landscape/greenspace), NAT- natural land
 (unmodified landscape without buildings), YARD- yard spaces (vegetated
 areas attached to houses), OPENMOD- open modified space (modified open
 spaces usually vegetated that have various human uses), REC- recreation
 (urban parks, sports fields, and areas designated for human recreation) *
 Bolded landscape metrics, PLAND- percent landscape cover (measures percent
 this landcover type covers in (% percent)), AREA_MN- mean area coverage
 (average area coverage of landscape cover in (hectare)), ED- edge density
 (measures amount of edge for the specific landscape cover
 (meters/hectare)), PD- patch density (measures how many patches of a
 specific landscape type within a certain buffer size (number per
 hectare)), FRAC_AM- FRAC is a perimeter-area method that quantifies the
 degree of complexity of planar shapes across a variety of scales. FRAC_AM
 is the area-weighted mean patch fractal dimension used for class and
 landscape level by weighing patches according to their size (no unit).
 SHAPE_AM- SHAPE index measures the complexity of the patch shape compared
 to a standard shape like a square of the same size. Area-weighted mean
 patch shape index, provide a landscape focused perspective of landscape
 structure as they reflect the average conditions of a pixel chosen at
 random measure of shape complexity based on relative amount of perimeter
 per unit area, as a perimeter-to-area ratio called a fractal dimension (no
 unit). COHESION- measures connectedness of the patch (measures physical
 connectedness of the patch type, no unit), LPI- largest patch index
 (measures the largest patch within the determined buffer (% percent)).
 Final metrics are PLAND, ED, and PD based on GLMM R model. See paper for
 more details. #### File: ChosenMetricsClass_200m.xlsx
 **Description:** Chosen landscape metrics in **bold** (evaluated in the R
 model) calculated from FRAGSTATS per site at 200m buffer. ##### Variables
 * 4-letter acronym of camera site, ie. ALGL, BRAD, SFTT. Camera sites are
 abbreviated for privacy of landowner and location. * TYPE describes
 landscape cover within the class level. 1- Herbaceous (grass and forb
 cover), 2- Woody (vegetation with woody stems, shrubs and trees), 3- Human
 built (human built surfaces, concrete, asphalt, buildings), 4- bare ground
 (dirt or non-vegetated ground, not built on), 5- Water (any body of water,
 man-made or natural). Here only 1- Herbaceous and 2-Woody are focused on
 for this specific study. * Tabs are grouped by human land use type, with
 associated camera sites within each tab. UWI- urban wildland interface
 (dwellings that boarder natural landscape/greenspace), NAT- natural land
 (unmodified landscape without buildings), YARD- yard spaces (vegetated
 areas attached to houses), OPENMOD- open modified space (modified open
 spaces usually vegetated that have various human uses), REC- recreation
 (urban parks, sports fields, and areas designated for human recreation) *
 Bolded landscape metrics, PLAND- percent landscape cover (measures percent
 this landcover type covers in (% percent)), AREA_MN- mean area coverage
 (average area coverage of landscape cover in (hectare)), ED- edge density
 (measures amount of edge for the specific landscape cover
 (meters/hectare)), PD- patch density (measures how many patches of a
 specific landscape type within a certain buffer size (number per
 hectare)), FRAC_AM- FRAC is a perimeter-area method that quantifies the
 degree of complexity of planar shapes across a variety of scales. FRAC_AM
 is the area-weighted mean patch fractal dimension used for class and
 landscape level by weighing patches according to their size (no unit).
 SHAPE_AM- SHAPE index measures the complexity of the patch shape compared
 to a standard shape like a square of the same size. Area-weighted mean
 patch shape index, provide a landscape focused perspective of landscape
 structure as they reflect the average conditions of a pixel chosen at
 random measure of shape complexity based on relative amount of perimeter
 per unit area, as a perimeter-to-area ratio called a fractal dimension (no
 unit). COHESION- measures connectedness of the patch (measures physical
 connectedness of the patch type, no unit), LPI- largest patch index
 (measures the largest patch within the determined buffer (% percent)).
 Final metrics are PLAND, ED, and PD based on GLMM R model. See paper for
 more details. #### File: ChosenMetricsClass_500m.xlsx
 **Description:** Chosen landscape metrics in **bold** (evaluated in the R
 model) calculated from FRAGSTATS per site at 500m buffer. ##### Variables
 * 4-letter acronym of camera site, ie. ALGL, BRAD, SFTT. Camera sites are
 abbreviated for privacy of landowner and location. * TYPE describes
 landscape cover within the class level. 1- Herbaceous (grass and forb
 cover), 2- Woody (vegetation with woody stems, shrubs and trees), 3- Human
 built (human built surfaces, concrete, asphalt, buildings), 4- bare ground
 (dirt or non-vegetated ground, not built on), 5- Water (any body of water,
 man-made or natural). Here only 1- Herbaceous and 2-Woody are focused on
 for this specific study. * Tabs are grouped by human land use type, with
 associated camera sites within each tab. UWI- urban wildland interface
 (dwellings that boarder natural landscape/greenspace), NAT- natural land
 (unmodified landscape without buildings), YARD- yard spaces (vegetated
 areas attached to houses), OPENMOD- open modified space (modified open
 spaces usually vegetated that have various human uses), REC- recreation
 (urban parks, sports fields, and areas designated for human recreation) *
 Bolded landscape metrics, PLAND- percent landscape cover (measures percent
 this landcover type covers in (% percent)), AREA_MN- mean area coverage
 (average area coverage of landscape cover in (hectare)), ED- edge density
 (measures amount of edge for the specific landscape cover
 (meters/hectare)), PD- patch density (measures how many patches of a
 specific landscape type within a certain buffer size (number per
 hectare)), FRAC_AM- FRAC is a perimeter-area method that quantifies the
 degree of complexity of planar shapes across a variety of scales. FRAC_AM
 is the area-weighted mean patch fractal dimension used for class and
 landscape level by weighing patches according to their size (no unit).
 SHAPE_AM- SHAPE index measures the complexity of the patch shape compared
 to a standard shape like a square of the same size. Area-weighted mean
 patch shape index, provide a landscape focused perspective of landscape
 structure as they reflect the average conditions of a pixel chosen at
 random measure of shape complexity based on relative amount of perimeter
 per unit area, as a perimeter-to-area ratio called a fractal dimension (no
 unit). COHESION- measures connectedness of the patch (measures physical
 connectedness of the patch type, no unit), LPI- largest patch index
 (measures the largest patch within the determined buffer (% percent)).
 Final metrics are PLAND, ED, and PD based on GLMM R model. See paper for
 more details. #### File: NMDS_SpeciesSpecific.xlsx
 **Description:** Non-metric multidimensional scaling (NMDS) of urban
 wildlife species per land use type. ##### Variables * Species count summed
 up per species per site type #### File: iChao2est_richness.xlsx
 **Description:** iChao2 estimated richness data is given of species
 richness per site. On sheet2 is the raw richness from each camera site and
 the associated iChao2 richness value for that site, rounded up. Sheet 3,
 include the lower and upper confidence intervals. ##### Variables * each
 individual camera site is listed as a 4 letter acronym, ie ALGL, CATR,
 HOPA * Richness is shown as a whole number. iChao2 estimated richness
 value is given as well as a rounded number. Raw richness value is listed
 on the 2nd sheet per site. #### File: FinalRData_ModelsRichLand_txt.txt
 #### File: FinalRData_ModelsRichLand.docx **Description:** Final R model
 of GLMM, with negative binomial distribution, richness as response
 variable, and fragstat metrics as proposed fixed effects. Study sites were
 nested within land use types as a random effect. ## Code/software
 RStudio, R 4.2.0 (lme4 package). Used to conduct and run final richness
 and landscape model, NMDS model. iCaho2 is calculated in R package SpadeR
 FRAGSTATS 4.2-64 ArcGIS Pro for mapping and finalizing land cover
 classification. Used to extract FRAGSTATS metric from  ERDAS Imagine to
 create supervised land cover classification