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This data package includes raw survey variables from the 2021 implementation of the Phoenix Area Social Survey (PASS), which was established in 2001 as part of the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) project’s long-term monitoring program. Approximately every five years, the CAP LTER surveys households in select neighborhoods in metropolitan Phoenix to examine people’s perceptions, attitudes, and behaviors about landscape decisions, heat stress and climate change, and other risks and their management. Core goals of this research endeavor include minimizing environmental problems, fostering urban conservation, and enhancing human well-being within the fields of urban ecology and sustainability. The PASS also regularly collects data on predictor variables including environmental values or worldviews and socio-demographics. In 2021, new questions were added to better understand residents’ health and wellbeing, how people perceive and interact with wildlife, and how outdoor recreational activities changed during the COVID-19 pandemic. As background, the first PASS was piloted in 2001 in 8 neighborhoods (n= 302) in the City of Phoenix. The 2006 (n= 808) and 2011 (n= 806) samples then expanded to a broader range of neighborhoods (40-45), aiming for 20 respondents per neighborhood, which were strategically selected to represent the geography of the greater metropolitan area in terms of location, income, and other demographics. The revised sampling design allows for intensive neighborhood analyses that link residents’ perceptions, attitudes, and decisions to the local urban ecological infrastructure, as well as other biophysical features such as bird community composition and diversity measures. In 2017, the PASS sample was redesigned to target a larger number of people (~65) in fewer (12) neighborhoods across the region (final n = 496 with response rate of 39%). For PASS 2021, we achieved a total sample size of 509 with a 37% response rate. Across 2017–2021, 235 repeat respondents participated, amounting to a response rate of 46% of the longitudinal sampling frame (n=496). By linking the social survey data to various environmental datasets, the overarching question for the CAP LTER IV project, which spanned 2016 to 2022, was: *How do the services provided by dynamic urban ecosystems and their infrastructure affect human outcomes and behavior, and how do human actions affect patterns in urban ecosystem structure and function, and ultimately, urban sustainability?* For more information on PASS 2021 and the survey neighborhoods, review the PDF report (PASS2021report_final.pdf) in this data package, as well as the methods, protocols, and metadata. Materials include the codebook with footnotes and annotations for data sources and details on verbatim questions (e.g., when they were added or modified), in addition to the questionnaire mailed to residents (two versions for repeat households and newly drawn households). For time-series analyses, researchers can separately download the 2017 or earlier datasets and link them to respondents across each survey by unique CASE ID numbers. Although the data are published for public use and benefits, we request that researchers who use the survey data contact Kelli.Larson@asu.edu to avoid duplicate analyses and publications and to coordinate among a large network of scholars who use the PASS data. This is very important since investigators continue to analyze the datasets as a part of the CAP LTER. For a list of publications from PASS 2021 or earlier, please refer to the metadata file or visit our website. You can also email Kelli Larson for any publications released after July 2025. 

Abstract Sustaining biodiversity requires measuring the interacting spatial and temporal processes by which environmental factors shape wildlife community assembly. Declines in bird communities due to urban development and changing climate conditions are widely documented. However, the combined impacts of multiple environmental stressors on biodiversity remain unclear, especially in urbanized desert ecosystems. This is largely due to a lack of data at the scales necessary for predicting the consequences of environmental change for diverse species and functional groups, particularly those that provide ecosystem services such as seed dispersal, pest control, and pollination. Trends in the prevalence and diversity of different functional groups contribute to understanding how changes in bird communities impact well‐being through the lens of ecosystem services. Across the rapidly developing drylands of the metropolitan Phoenix, Arizona, USA, we ask the following question: How have inter‐ and intra‐annual landscape changes associated with urbanization and climate shaped the dynamic characteristics of bird communities, specifically the abundance and richness of species and their functional groups? We analyzed long‐term drivers of bird communities by combining a two‐decade, multi‐season spatial dataset of environmental conditions (urbanization, vegetation, temperature, etc.) with biotic data (species richness and abundance) collected seasonally during the same time periods (winter and spring; 2001–2016). Results show that increased impervious surface area and land surface temperature were negatively associated with overall bird abundance and species richness across the study period, especially during winter. However, these relationships varied among functional groups, with potentially mixed outcomes for ecosystem services and disservices provided by urban biodiversity. By improving knowledge of long‐term trends in multiple environmental drivers that shape wildlife community dynamics, these results facilitate effective evaluation of how landscape management practices in drylands influence the outcomes of evolving human‐wildlife relationships. 

This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 

This data package consists of multiple decades of bioclimatic raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). We sourced each bioclimatic variable from 1-km resolution gridded estimates of daily climatic data from NASA Daymet V4, including daily mean (ppt) and total precipitation (ppt_sum), daily maximum air temperature (temp_max), daily minimum air temperature (temp_min), incident shortwave radiation flux density (srad), and daily average partial pressure of water vapor (vp). For each of these six variables, we created temporally aggregated raster images by calculating mean pixel-values of each for each season and year, as well as producing a seventh variable of seasonally and annually summed precipitation (ppt_sum). Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 

This data package consists of multiple decades of land surface temperature (LST) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). We derived LST values based on the thermal band from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations: - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 

### overview This data package consists of multiple decades of normalized difference vegetation index (NDVI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona (USA), temporally aggregated by year and by four meteorological seasons (Winter, Spring, Summer, Fall). To serve as a proxy measurement of vegetation greenness and productivity across years and seasons, NDVI was derived from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code. ### citations - Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031 

Abstract Cities support abundant human and wildlife populations that are shaped indirectly and directly by human decisions, often resulting in unequal access to environmental services and accessible open spaces. Urban land cover drives biodiversity patterns across metropolitan areas, but at smaller scales that matter to local residents, neighborhood socio‐cultural factors can influence the presence and abundance of wildlife. Neighborhood income is associated with plant and animal diversity in some cities, but the influence of other social variables is less well understood, especially across desert ecosystems. We explored wildlife distribution across gradients of neighborhood ethnicity in addition to income and landscape characteristics within residential areas of metropolitan Phoenix, Arizona, USA. Utilizing data from 38 wildlife cameras deployed in public parks and undeveloped open spaces within or near suburban neighborhoods, we estimated occupancy and activity patterns of common mammal species, including species native to the Sonoran Desert (coyote [Canis latrans] and desert cottontail rabbit [Sylvilagus audubonii]), and non‐native domestic cat (Felis catus). Neighborhood ethnicity (percentage of Latino residents) appeared to exhibit a negative relationship with occupancy for coyotes and cottontail rabbits. Additionally, daily activity patterns of coyotes occurred later in the evenings and mornings in neighborhoods with higher proportions of Latino residents, but activity was unaffected by differences in neighborhood income. This study is one of the first to show that social‐ecological mechanisms associated with patterns of neighborhood ethnicity as well as income may help to shape wildlife distribution in cities. These findings have implications for equitable management and provisioning of ecosystem services for urban residents and highlight the importance of considering a range of social covariates to better understand biodiversity outcomes in urban and urbanizing areas. 

IntroductionHuman-wildlife coexistence in cities depends on how residents perceive and interact with wildlife in their neighborhoods. An individual’s attitudes toward and responses to wildlife are primarily shaped by their subjective cognitive judgments, including multi-faceted environmental values and perceptions of risks or safety. However, experiences with wildlife could also positively or negatively affect an individual’s environmental attitudes, including their comfort living near wildlife. Previous work on human-wildlife coexistence has commonly focused on rural environments and on conflicts with individual problem species, while positive interactions with diverse wildlife communities have been understudied. MethodsGiven this research gap, we surveyed wildlife attitudes of residents across twelve neighborhoods in the Phoenix Metropolitan Area, AZ to ask: how do the environments in which residents live, as well as their values, identities, and personal characteristics, explain the degree to which they are comfortable living near different wildlife groups (coyotes, foxes, and rabbits)? ResultsWe found that residents who were more comfortable living near wildlife commonly held pro-wildlife value orientations, reflecting the expectation that attitudes toward wildlife are primarily driven be an individual’s value-based judgements. However, attitudes were further influenced by sociodemographic factors (e.g., pet ownership, gender identity), as well as environmental factors that influence the presence of and familiarity with wildlife. Specifically, residents living closer to desert parks and preserves were more likely to have positive attitudes toward both coyotes and foxes, species generally regarded by residents as riskier to humans and domestic animals. DiscussionBy improving understanding of people’s attitudes toward urban wildlife, these results can help managers effectively evaluate the potential for human-wildlife coexistence through strategies to mitigate risk and facilitate stewardship. 

ABSTRACT Humans play key roles in shaping the structure and processes of ecosystems globally, especially in cities. This recognition has prompted a recent focus on understanding urban systemsviainteractions between human social systems and ecological and evolutionary processes. Most research has focused on interactions between two of these three domains. Here we present a framework for linking all three – social, ecological, and evolutionary – by focusing on phenotypic response and effect traits, illustrating the framework's utility in understanding wildlife dynamics in urban systems. We first present a generalized model for the social–ecological–evolutionary–phenotypic (SEEP) framework, then use urban climate as a specific example, provide guidance on how to implement this approach, and finally discuss emerging questions motivated by the framework and challenges in utilizing the approach.