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<p>Bird species occurrence datasets are a valuable resource for understanding the effects of urbanization on various biotic conditions (e.g., species occupancy and richness). Existing datasets offer promising opportunities to explore variations among cities and along the urban-rural gradient. However, there is a lack of observation data to systematically capture intra-urban variations at a fine spatial scale, especially in dense urban areas. Here, we describe the production and validation of a machine learning-generated bird occurrence dataset based on 10,691 hours of passive audio recordings systematically collected across different types of dense urban forms in Gothenburg, Sweden. The dataset is available in a standard Darwin Core Archive (DwC-A) format, to ensure data interoperability, and includes 239,597 occurrence records of 61 species from April 21 to June 16, 2024, across 30 sites in Gothenburg. We anticipate that this dataset will be a valuable resource for researchers in urban ecology, planning, and design to better understand the relationship between the characteristics of different types of dense urban forms and various biotic conditions in cities.</p>

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A bird species occurrence dataset from passive audio recordings across dense urban areas in Gothenburg, Sweden

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

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  • Ahmed Hazem Eldesoky,
  • Jorge Gil,
  • Oskar Kindvall,
  • Ioanna Stavroulaki,
  • Leif Jonasson,
  • David Bennett,
  • Wenqing Yang,
  • Alonso Francisco Martínez Diaz,
  • Rachel Lichter,
  • Frixos Petrou,
  • Meta Berghauser Pont

摘要

Bird species occurrence datasets are a valuable resource for understanding the effects of urbanization on various biotic conditions (e.g., species occupancy and richness). Existing datasets offer promising opportunities to explore variations among cities and along the urban-rural gradient. However, there is a lack of observation data to systematically capture intra-urban variations at a fine spatial scale, especially in dense urban areas. Here, we describe the production and validation of a machine learning-generated bird occurrence dataset based on 10,691 hours of passive audio recordings systematically collected across different types of dense urban forms in Gothenburg, Sweden. The dataset is available in a standard Darwin Core Archive (DwC-A) format, to ensure data interoperability, and includes 239,597 occurrence records of 61 species from April 21 to June 16, 2024, across 30 sites in Gothenburg. We anticipate that this dataset will be a valuable resource for researchers in urban ecology, planning, and design to better understand the relationship between the characteristics of different types of dense urban forms and various biotic conditions in cities.