DOI: 10.5061/dryad.0p2ngf2b6
title: The impact of urbanization on avian communities during the breeding season in the Huanghuai Plain of China

description: This dataset was collected during the 2022–2023 breeding seasons in the
 Huanghuaihai Plain, China, to assess the impact of urbanization on bird
 communities. It includes species occurrence and abundance records across
 multiple line transects, as well as calculated diversity indices including
 species richness, Shannon-Wiener index, Simpson index, and Pielou’s
 evenness. Environmental variables were recorded for each transect,
 including building index (BI), environmental noise (EN), human disturbance
 index (DI), and distance to county center (DCC), which were combined to
 form an urbanization synthetic index (USI). The dataset also incorporates
 five species-level ecological and life-history traits: body mass (g), diet
 category, clutch size, nest site, and provincial distribution. Analyses of
 these data indicate significant differences in species diversity and
 functional traits among urban, suburban, and rural habitats. USI was
 negatively correlated with species richness and Shannon-Wiener diversity,
 but showed no significant relationship with functional traits.
 Environmental noise, distance to county center, and the proportion of
 buildings within a 250-m radius were identified as key factors influencing
 species diversity, while environmental noise and distance to county center
 were the strongest predictors of functional traits. These data can be
 reused to examine the effects of urbanization on avian community
 composition and diversity, to compare biodiversity patterns across
 regions, or to inform conservation and urban planning strategies.
description: # The impact of urbanization on avian communities during the breeding
 season in the Huanghuai Plain of China Date of data collection: from June
 to August in 2022 and 2023 Location: 150 line transects across the
 Huanghuai Plain, with 50 line transects allocated to each habitat (urban,
 suburban, rural). All the data files were created on December 30, 2024.
 original_data.csv: all data employed in this experiment, including
 Shannon-Wiener diversity index, Simpson diversity, species richness,
 Pielou evenness index, building index (four scales), environmental noise,
 disturbance index, the distance to county center, urbanization synthetic
 index, and site. R_Code.Rmd: R codes for "The impact of urbanization
 on avian communities during the breeding season in the Huanghuai Plain of
 China", there are codes for the data analysis in this dataset. The
 same analysis is presented only once to serve as an example for reference.
 See below for definitions of variables. Description of methods for data
 collection and data processing: Bird surveys were conducted by two
 experienced researchers during 4 h from dawn and 3 h before sunset in good
 weather (e.g., no wind and rain) in summer during June to August 2022. The
 observers walked along each line transect at a constant speed of
 approximately 1.0km/h – 2.0km/h and used binoculars for direct observation
 to identify birds, and additionally, observers also utilized camera to
 document bird that were unidentifiable within a 50-m radius, while not
 including those flying over the head. During June to August 2023, we
 carried out repeat bird surveys by using the same methods. The composition
 of land use types of each line transect did not change during our surveys.
 The identification and classification of birds based on A Checklist on the
 Classification and Distribution of Birds of China and A Field Guide to the
 Birds of China (Mackinnon et al., 2000). The levels of endangerment and
 conservation status are based on The List of National Key Protected
 Wildlife, The IUCN Red List of Threatened Species (IUCN;
 [https://www.iucnredlist.org/](https://www.iucnredlist.org/)), and The Red
 List of Biodiversity in China: Vertebrates. We selected 5 ecological and
 life history characteristics (i.e., body mass, diet, clutch size, nest
 site, and distributed provinces) for our study. See below for definitions
 of variables. 1.Body mass **Definition**: The average body weight of the
 bird species. **Unit**: grams (g). 2.Diet **Definition**: The primary
 feeding guild or dietary category of the species. Indicates the main food
 resources consumed by the species. **Unit**: categorical (i.e.,
 insectivorous, carnivorous, omnivorous, insectivorous&carnivorous).
 3.Clutch size **Definition**: The typical number of eggs laid per breeding
 attempt. Reflects the reproductive capacity of the species. **Unit**:
 number of eggs (count). 4.Nest site **Definition**: The typical nesting
 location used by the species.Describes where birds usually build their
 nests. **Unit**: categorical (e.g., ground, shrubbery, crown, water,
 rock-wall). 5.Distributed provinces **Definition**: The provinces or
 administrative regions where the species is recorded within China.Provides
 the known geographic distribution at the provincial level. **Unit**: text
 list of provinces. We imported all line transects into ArcGIS 10.8 and
 established four buffer zones around each line transect, with a radius of
 250m, 500m, 1000m, and 2000m, respectively, to estimate the proportion of
 buildings surrounding the lines. And we used the method of summing the
 weighted building proportions to calculate the building index: The
 Building Index (BI) = 250 m of building area%*1 + 500 m of building
 area%0.5 + 1000 m of building area% * 0.25 + 2000 m of building area%*
 0.125. While conducting field surveys along each line transect, we
 measured the environmental noise using a decibel meter. Measurements were
 conducted once during each of the three time periods: morning, midday, and
 evening, with each measurement lasting for 10 minutes. The average of the
 noise values obtained from each line transect was then taken as the
 environmental noise. Meanwhile, we also carried out the collection of the
 disturbance index. The observation at each line transect, human traffic
 was recorded, with each observation lasting for 10 minutes. The average
 value was then adopted. The study divided human disturbance into five
 levels, with level 1 indicating the absence of human; Level 2 indicating
 human traffic of 1-2 people per minute; Level 3 indicating human traffic
 of 3-7 people per minute; Level 4 indicating human traffic of 8-17 people
 per minute; and Level 5 indicating human traffic of 18 people per minute
 or more. Our study divided the study area into county-level units, with
 the government hall at each county serving as the central point. The
 distance to the county center (DCC) was measured as the straight-line from
 line transects to the government hall of each county (km) with Google map.
 Following other studies, an urbanization synthetic index (USI) for our
 study is as follows: USI= BI100 / 2 + EN + DI*20 + 100 / DCC. We adjusted
 parameter values to the range of 0-100, with higher numbers representing a
 higher level of urbanization. In detail, the BI value ranged from 0 to 2,
 we standardized it by multiplying by 100 and then dividing by 2; The EN
 value ranged from 0 to 100, so it remained unchanged; The DI value ranged
 from 1-5, so it was multiplied by 20; the DCC value from 0 to 60, so it
 was taken the reciprocal and multiplied 100. For each line transect, we
 calculated the species richness, abundance of every species,
 Shannon-Wiener diversity index, Pielou evenness index, and Simpson
 diversity index. We then conducted linear mixed models (LMMs) to explore
 the relationship between the urbanization synthetic index and bird species
 diversity, with the USI as the fixed effect and the research sites as a
 random factor. This experiment also employed linear mixed models to
 determine which factors in the USI (i.e., BI, EN, DI, DCC) influenced
 species diversity and their relative importance, with the study sites
 served as the random factor. We employed the Akaike Information Criterion
 (AIC) to determine which of the building index proportions within four
 different radii had the greatest impact, and we integrated the lowest AIC
 value into linear mixed models for further analyses. The analysis yielded
 a series of models. We then selected and ranked models basing on the
 cumulative difference values of the Akaike Information Criterion (ΔAICc ≤
 2), the candidate models selected were equivalent. Subsequently, model
 averaging was conducted to obtain the relative importance of each
 parameter within a 95% confidence interval, along with the model estimates
 and standard errors, in order to mitigate uncertainty in model selection.