An 11-year spatiotemporal roadkill dataset derived from citizen reports in Tsukuba, Japan

An 11-year spatiotemporal roadkill dataset derived from citizen reports in Tsukuba, Japan | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF data-descriptor An 11-year spatiotemporal roadkill dataset derived from citizen reports in Tsukuba, Japan Shoma JINGU, Minami UNNO, Yui OGAWA, Ryota AIZAWA, Keisuke YAMAMOTO, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9517196/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 Roadkill data are vital for assessing biodiversity and human–wildlife conflicts. However, datasets are often constrained by linear sampling along major highways, a lack of missing-data mechanism quantification, and geographical bias toward Western regions. This Data Descriptor presents a high-resolution dataset of 14,249 roadkill records collected over 11 years (2005–2007 and 2016–2025) in Tsukuba, Japan. Unlike traditional surveys, this dataset was generated through an “unintended citizen science,” in which citizens report carcasses to the municipal administration, triggering collection by contracted professionals. This mechanism ensured comprehensive “planar” coverage of the entire 284 km² administrative area. This standardized dataset features three methodological strengths: (1) a dual-resolution georeferencing system that assigns “point” and “polygon” precisions to detailed and ambiguous records, respectively; (2) a missing-data proxy via the retention of invalid reports; and (3) a three-tier taxonomic verification process involving citizens, contractors, and experts. The dataset offers a baseline for analyzing the impacts of urbanization, monitoring the invasion of non-native species, and evaluating the reliability of other administrative biodiversity monitoring systems. Figures Figure 1 Figure 2 Figure 3 Background & Summary Biodiversity conservation is a paramount global challenge, as underscored by the Kunming–Montreal Global Biodiversity Framework (KM-GBF), adopted in 2022 at the Fifteenth Meeting of the Conference of the Parties 1,2 . The framework aims to halt and reverse biodiversity loss to achieve a “Nature Positive” world by 2030, emphasizing the need for whole-of-society engagement 3 . Citizen science, which also known as community science and is defined as “scientific work undertaken by members of the general public 4 ,” is one strategy for attaining Nature Positive 2030 5 . Although citizen science is a recognized strategy for monitoring biodiversity at scale 6–9 , traditional initiatives often rely on a narrow demographic of nature enthusiasts, leaving the broader public uninvolved 10–13 . To bridge this gap, “unintended” citizen science data, which are often produced through routine interactions between the general public and nature, are frequently sourced from social media and similar platforms 14–16 . Roadkill, the death of wild animals on roads due to traffic accidents, is a prime example of unintended citizen science 17 . It serves as extensive evidence of human–wildlife encounters and provides an important source of ecological data 17,18 . Traditionally, professional monitoring is often logistically prohibited given the vast extent of road networks. Instead, citizen science projects on roadkill are increasingly outperforming expert surveys, demonstrating that public participation enables large-scale spatiotemporal surveys 19,20 . Building upon these initiatives, the present researchers seek to extend the scope of participation to the majority of the general public beyond individuals with a pre-existing interest in nature. Citizens’ reports of animal-vehicle collisions made to road management authorities constitute a massive yet underutilized resource 21–23 . However, the applicability of existing roadkill datasets has been constrained by geographical and methodological limitations. Such studies have been geographically skewed toward Western countries, leaving Asian urban landscapes underrepresented 24 . Methodologically, previous research has predominantly focused on major highways 25 . These linear datasets often lack a clearly defined spatial extent. When utilizing opportunistic citizen science data, the challenge shifts to interpreting the absence of reports, especially in distinguishing between a true absence of roadkill and a simple lack of observation or reporting, making it difficult to quantify missing data and “survey effort,” which is a critical parameter for robust ecological inference 26 , such as density estimation. Without a clear understanding of a survey’s monitoring intensity and coverage area, the application of its data to broader biodiversity assessments remains limited 27 . To overcome these fundamental limitations, this Data Descriptor presents a spatially dense, temporally extensive dataset of 14,249 roadkill records collected over an 11-year period in Tsukuba, Japan. While the geographic scope was localized, the meticulousness and reproducibility of this dataset will allow for novel applications that were previously unattainable. Covering the municipality’s entire 284 km² administrative area, this dataset represents a significant departure from traditional linear surveys. 24 Very few datasets globally achieve this level of spatiotemporal density by including over a decade of records on a complete municipal road network ranging from arterial highways to narrow residential streets. Data collection was enabled by a pre-existing systematic administrative process: citizens reported carcasses to the city government, which then deployed road managers to retrieve them. This year-round and comprehensive citywide operation according to a fixed format established a consistent level of survey effort. Consequently, this dataset provides a rare opportunity to analyze wildlife distribution, long-term annual fluctuations, and population dynamics in a continuous “planar” rather than a fragmented “linear” context, serving as an ideal testbed for urban ecology and spatial modeling. Another notable feature of this dataset is its rigorous quality approach. Species misidentification is a common concern in citizen science based monitoring 28–30 . Previous research has also highlighted the necessity of validating roadkill records collected by road maintenance personnel, who are typically nonspecialists in wildlife biology 31 . This indicates a dual challenge in data quality assurance: the initial risk of misidentification by nonexpert citizen reporters is compounded by the potential for error when nonspecialist road maintenance staff validate and process these reports. To address these multilayered challenges, the researchers implemented a three-tier identification process for a subset of records, documenting the species as identified by the reporting citizen, responding road maintenance manager, and expert authors. The preliminary study using an initial two-year subset of these data successfully characterized the geographic distribution of records and elucidated specific trends in citizens’ species misidentification 32,33 . Building on this foundation, the present dataset expanded its scope to 11 years, providing a robust, high-resolution resource for urban wildlife management. Therefore, this dataset also offers a universally applicable methodological platform for quantifying error structures and missing-data mechanisms in unintended citizen science programs worldwide. Methods Study Area This study was conducted in Tsukuba (36°05'01.1"N, 140°04'36.9"E), Ibaraki Prefecture, which is located approximately 50 km northeast of Tokyo (Figure 1) and has a temperate climate. By 2024, the city had a population of 259,618 34 . The municipal territory covers an area of 284 km². The northern part of the city includes Mt. Tsukuba (877 m), which serves as the centerpiece of the Suigo-Tsukuba Quasi-National Park. The majority of the city lies on the Tsukuba Plateau and its surrounding lowlands, with elevations ranging from 20 to 30 m above sea level. This southern region is characterized by a mosaic landscape of forests, agricultural fields, and urban areas. Tsukuba was planned and developed as a “science city” in the late 1960s, in which national and private research institutes are concentrated, transforming a rural landscape on a flat plateau. Planned nature conservation measures were implemented in the city’s design phase 36 . Policies have mandated the preservation of certain areas of forests and grasslands derived from the original rural landscape, integrating them into urban parks and green spaces that are accessible to residents 36 . This unique local history made Tsukuba an appropriate area for studying human-nature interactions and the effectiveness of citizen science in an urban environment where green space was intentionally conserved. The study area, Tsukuba, is administratively divided into more than 350 local subdistricts (known as Oaza or Chome in Japan, Figure 1a). Tsukuba’s road network has been extensively developed, primarily across the flat terrain of the plateau and lowlands (Figure 1b). The total managed road length in the study area was approximately 3,956 km, comprising 282 km of national and prefectural roads and 3,674 km of municipal roads. Note that Figure 1b excludes toll expressways, elevated road sections, tunnels, and pedestrian pathways, as these areas fall outside the jurisdiction of municipal roadkill collection services. The final outcome of this study was the roadkill data shown in Figure 3c, which was obtained through the following steps. Data Source and Collection Workflow Roadkill data were derived from Tsukuba’s administrative records. When a citizen reports an animal carcass on a public road (excluding toll expressways and private land), the city government dispatches a contracted company to collect it. This service operates year-round (excluding New Year’s Day) between 08:30 and 17:00. The reporting process follows a specific and straightforward protocol: City officials receive the call, record the species name and location as reported by the citizen, and forward these details to the contractor. Upon arrival, the contractor collected the carcass, recorded the actual location, time of collection, and species name, and captured a photograph for the final report. Importantly, the primary purpose of this system is road safety management and retrieval of lost pets managed by the Clean Environment Promotion Division, not ecological monitoring. In fact, the contractor was a private pet funeral company and not a wildlife research agency. Although they possess extensive experience in carcass handling, their taxonomic expertise is not at the same specialist level as that of a biologist. Furthermore, because most records rely on reports from passersby rather than motorists who have injured the animal, the recorded time reflects the collection time and not the exact time of the accident. Data Digitization and Integration The researchers compiled records from two primary sources covering two periods: April 1, 2005–August 31, 2007 and April 1, 2016–December 31, 2025. The source materials included analog reception memos from the city hall and monthly reports from the contractor. To verify details and resolve ambiguities, the researchers also conducted monthly interviews with city officials. These records were manually digitized into a unified database, linking the “service demand” (i.e., initial citizen report: reported species and location) with the “response” (i.e., contractor report: collection date, identified species, and photographs). Reporting citizens’ personal data (e.g., names, phone numbers, and addresses) was strictly removed during this stage to ensure privacy protection. Detailed metadata regarding the data acquisition process are summarized in Table 1 and full descriptions provided in the data repository (TFTsukubaRoadkill_dataDigitization.csv). These data delineated the chronological periods of their collection, the designated personnel responsible for digitization, the formats of the original source documents, and the availability and formats of photographic evidence (e.g., color, grayscale, and none) used to substantiate taxonomic identifications. Table 1. Metadata regarding the data digitization workflow. Field Description Range/Levels Example period Specific month and year corresponding to roadkill records 2005-04–2007-08 and 2016-04–2025-12 2016-04 digitizedBy Initials of author(s) responsible for manually digitizing and verifying records for a period Author names Shoma Jingu sourceType Type of administrative documents used as primary source for digitization Contractor reports (monthly reports only), both (integrated from contractor reports and city memos) Both documentType Indicates the availability and color format of the original administrative documents (including photograph) used for digitization and taxonomic identification color, grayscale, and none color Data Filtering and Quality Control The raw data were screened to ensure that they represented genuine roadkill incidents on public roads. To ensure data integrity while preserving information on administrative survey efforts, the researchers scrutinized each record and applied a logical flagging system rather than discarding problematic records. For example, records were retained when a site visit was conducted by a contractor following a report from a citizen but no carcass was found (e.g., the animal had been removed). These were flagged as “isUnconfirmedReport” to serve as a quantifiable proxy for the survey effort and missing data invested by road managers. Similarly, carcasses determined to result from nonvehicular causes (e.g., predation) were flagged as “isNotRoadkill.” To maintain data quality, records were flagged as “hasUnreliableLocation” when the specific location of the collision could not be verified. This included cases with ambiguous address descriptions, and instances where citizens transported the carcass from the road to a secondary location (e.g., bringing it to their residence in a box) before reporting it. In such scenarios, the recorded location reflected the collection point rather than the actual accident site, rendering the exact roadkill location indeterminable. Records that failed other quality checks were flagged as “isOtherInvalid.” These determinations were based on remarks from administrative logs, photographic evidence, and supplementary interviews. Consequently, the final dataset enables users to filter data according to specific analytical requirements, such as species distribution modeling. In addition, we conducted data screening and correction by OpenRefine ( http://openrefine.org ). Georeferencing Geospatial coordinates were derived using a two-tier approach depending on the availability of source documents. This resulted in two levels of spatial precision: “point” and “polygon.” Point-level Precision (Citizen Reports) When an initial citizen report was available, it typically provided a higher level of spatial precision. During the reporting process, city officials created temporary maps based on citizens’ descriptions to guide contractors. The most probable point was determined from internal reference maps. However, these coordinates represented locations reported by citizens and not GPS-confirmed locations. This means that spatial errors ranging from negligible to hundred meters may have occurred. Importantly, while the reported point may shift, the identity of the reported road is almost accurate. This uncertainty therefore primarily represents a linear error margin along a specific road rather than a simple radius. To reflect this quantitatively under Darwin Core (DwC) standards 37 , the field coordinateUncertaintyInMeters was assigned an estimated value of 100. Additionally, the qualitative field georeferenceRemarks was populated to explicitly indicate that these coordinates represent a specific point with an estimated 100-meter linear uncertainty. Polygon-level Precision (Contractor Reports) For records where detailed citizen reports were unavailable, the researchers relied on the locality information (e.g., Oaza or Chome; town/village subdistrict) shown in Figure 1a and recorded in the contractor’s field report. These spatial units correspond to the standardized “Cho-Aza” boundaries defined in the 2020 Population Census of Japan 38 . Each record was assigned a unique spatial identifier (“localityID”). This key allows dataset users to cross-reference the main data with the Supplementary Information (summarized in Table 2 and full descriptions provided in the data repository; TFTsukubaRoadkill_locality.csv), which provides detailed geometric attributes for each census tract, including administrative codes and area classifications. These records were assigned the geospatial centroid of the corresponding census polygon to “decimalLatitude” and “decimalLongitude.” To appropriately reflect this polygon-level resolution, the field “coordinateUncertaintyInMeters” was assigned a generalized value of 1000. This value was determined using the average area of the subdistricts in the study area, with a 1000-meter buffer sufficiently encompassing the vast majority of these polygons. “GeoreferenceRemarks” was concurrently utilized to indicate that the coordinates represented the centroid of a local subdistrict, preventing users from misinterpreting them as exact occurrences. Table 2. Metadata regarding the administrative units corresponding to records with polygon-level spatial precision. This table provides specific administrative details for records where the exact collision point was indeterminate (linked via the localityID field in the main dataset). The KEY_CODE is a unique 11-digit identifier defined in the 2020 Population Census of Japan 38 . This code directly links the roadkill dataset with official GIS boundary polygons (shapefiles) and aggregate demographic statistics available from the Portal Site of Official Statistics of Japan. Field Description Range/Levels Example localityID Unique spatial identifier linked to main roadkill dataset 343 records 100 KEY_CODE 11-digit linkage code connecting map figures and aggregate data defined in the 2020 Population Census of Japan 311 records 82200020 localityJP Area description (e.g., Oaza or Chome subdistricts) in kanji 343 records 研究学園１丁目 localityKana Phonetic reading of locality name in katakana 343 records kenkyugakuen 1 precision Classification of administrative unit level (e.g., Oaza, Chome) including exclave (tobichi), a spatially detached portion of an administrative unit Oaza, Chome, Exclave Oaza Spatial Resolution Adjustment for Sensitive Records Regardless of the availability of detailed citizen reports, records flagged as “isUnconfirmedReport,” “isNotRoadkill,” “hasUnreliableLocation,” or “isOtherInvalid” were automatically assigned polygon-level precision (the centroid of the administrative unit). This downscaling protocol served two critical purposes. First, it reflected scientific uncertainty; without confirmed carcass data, precise point data lack biological validation. Second, and most importantly, it protected the privacy of reporters. For example, records flagged as “hasUnreliableLocation” often involved cases where citizens reported carcasses found at boundaries between public roads and private properties. Publishing precise coordinates derived from these reports would pose a risk of revealing reporters’ residential locations. To mitigate this privacy risk, researchers strictly masked the precise location of all flagged records, replacing them with a broader administrative unit centroid. Given that DwC format data are finally distributed through open-access and species-searchable platforms like the Global Biodiversity Information Facility (GBIF), rigorous spatial masking protocol was implemented to protect threatened and endangered species. Records involving taxa designated on the national or prefectural Red Lists (e.g., Vulnerable, Endangered, and Critically Endangered) were systematically generalized to prevent potential conservation risks like illegal collection, poaching, or targeted harassment. For these sensitive occurrences, exact coordinates (“decimalLatitude,” “decimalLongitude”), detailed locality names (“locality”), and spatial uncertainty metrics (“coordinateUncertaintyInMeters”) were completely withheld in DwC dataset. The spatial resolution was generalized to the broad municipal level (“Tsukuba”) using a designated generic “localityID.” To ensure transparency and strict adherence to DwC 37 , the rationale for this spatial obfuscation was explicitly documented in the “georeferenceRemarks” field, ensuring that future users will comprehend the reason behind the deliberate absence of high-resolution spatial data for these records. However, applying blanket spatial masking to roadkill occurrences can establish a counterproductive precedent in road ecology, where data concealment may be prioritized over actionable conservation. Therefore, we adopted a dual release strategy. In the complete primary dataset hosted on Figshare accessed through this Data Descriptor, we provide the exact, unmasked coordinates for all records, including those of threatened species. By providing the unmasked full dataset within the scientific context of this study, we aim to maximize its utility for robust ecological modeling and effective, localized biodiversity conservation. Expert Species Identification Taxonomic identification was performed by researchers with expertise in wildlife monitoring and relevant licenses. The identifications relied on high-resolution photographs provided in the contractor’s final reports. The researchers focused on identifying 39 species (predominantly mammals) with distinct morphological characteristics. In cases where species were morphologically similar or photographic evidence was inconclusive, multiple experts cross-verified the identification, with the lead author making the final determination. Carcass Condition Ranking To assess how carcass condition influenced identification accuracy, the researchers classified the damage of the mammal carcasses into three levels based on photographic evidence: Level A (Intact): No major damage; original morphology largely maintained. Level B (Damaged): Morphology is preserved but with visible defects or partial damage. Level C (Dismembered): Morphology is significantly compromised; species characteristics are difficult to discern. Cases where the extent of damage could not be ascertained from photographs alone were labeled as “unidentifiable” and excluded from the condition-based analysis. Nonexpert Identification Records To evaluate identification errors, the researchers compared expert identifications with two nonexpert sources: Citizen Reports: Initial identification provided by citizens via telephone. These often included vague categories (e.g., “raccoon dog” or “cat”) or were stated as “unknown.” Contractor Reports: Identification recorded by waste management personnel at collection. These observations were not entirely independent; contractors were generally informed of the species reported by citizens prior to arrival, which may have influenced their judgment. However, contractors also had the advantage of visual inspection and sufficient time to photograph the carcass. Taxonomy and Conservation Status The researchers cross-referenced all species’ names with the Global Biodiversity Information Facility’s (GBIF) Backbone Taxonomy using Java and the GBIF’s API to rectify classification errors and standardize the taxonomy. This process included additional fields such as kingdom, phylum, and scientific authorship. It also permitted researchers to gather comprehensive taxonomic information to address any gaps within the datasets. For species not automatically matched, the researchers manually searched for correct synonyms when available. Conservation status was determined by cross-referencing three databases: the International Union for Conservation of Nature’s (IUCN) Red List 39 , the Japanese Ministry of the Environment Red List 40 , and the Ibaraki Prefecture Red List 41 . This multiscale approach enabled the evaluation of vulnerability at the global, national, and local levels. Regarding non-native species, the researchers prioritized domestic ecological definitions. Based on Japanese standards, species classified as non-native to Japan were strictly categorized as “unlisted,” regardless of their Red List status. Ethics Statement Assertion of Legal and Authorized Acquisition The dataset used in this study was derived from administrative records of Tsukuba City. The reports, comprising photographs and text, were obtained through a formal Information Disclosure Request (Joho Kaiji Seikyu) in accordance with Japanese administrative laws. Furthermore, the maps and attached memos used to extract spatial coordinates, species names, and observation dates were provided by the Clean Environment Promotion Division of Tsukuba City with prior consent. The analysis and publication of the results were conducted based on mutual agreement between the Clean Environment Promotion Division and the author. Together, the formal disclosure request and the authorized collaborative process ensure a fully transparent, authorized, and legitimate provenance for all source materials without any ethical or legal issues. Assertion of Secondary Use Rights In alignment with the Japanese Government’s “Electronic Government Open Data Strategy” and the “Government Standard Terms of Use (Version 2.0),” these public records may be used secondarily for research and public benefit, provided that appropriate attribution is given. The authors have transformed these administrative records into a standardized scientific format, constituting an original intellectual synthesis of the underlying administrative information. Assertion of Research Integrity The data processing and decoding were conducted in accordance with the “Guidelines for Responding to Misconduct in Research Activities” by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). The authors have implemented a rigorous verification protocol for information extraction from the permitted maps and the disclosed reports, and have thoroughly documented all georeferencing and digitization procedures to ensure the highest standards of research integrity and reproducibility. Assertion of Privacy and Ethical Considerations To respect privacy and strictly comply with the protection of personal information, all personal identifiers present in the original administrative documents have been thoroughly de-identified and removed. Crucially, to prevent the identification of residential locations of the citizens who provided the reports, the spatial coordinates of the occurrence locations have been generalized (rounded) strictly in cases where such identification was deemed possible. These privacy-masked records are explicitly distinguished and separated within the dataset. Furthermore, following international guidelines for the publication of sensitive biodiversity data, spatial coordinates for threatened species have also been generalized to prevent potential harm to local populations. This dual geographic masking approach protects both the privacy of the human reporters and the vulnerable species, balancing scientific transparency with rigorous ethical protections. Data Records The data presented in this study are available at Figshare 42 under the identifier 10.6084/m9.figshare.31580410. The dataset consisted of a single comma-separated value file, TFTsukubaRoadkill_Dataset.csv. The field names and descriptions were primarily defined in accordance with previous research 24,43 to ensure global interoperability. To accurately reflect the unique requirements of this study, certain fields and terms were specifically developed as necessary. All field names and descriptions summarized in Table 3. The dataset contained 14,249 records but 11,464 records and 34 fields filtered from the main data standardized according to the DwC 37 terminology to ensure interoperability with global biodiversity databases 44 (occurrence data are currently being registered to the GBIF via the Japan Node, and the DOI will be provided prior to publication). To facilitate both ecological analysis and methodological assessment, the fields were categorized into the following four primary groups. Dataset Attributes (DwC format) Standard DwC fields were used to document the dataset of each record: Identifier: “occurrenceID” and “catalogNumber” provide unique identifiers for an occurrence. Nature: “basisOfRecord” indicates the specific nature of the record. Spatiotemporal and Taxonomic Information (DwC format) Standard DwC fields document the “when,” “where,” and “what” of each record: Location: decimalLatitude, decimalLongitude, and locality (e.g., district names) provide spatial coordinates and administrative context. The set including “continent,” “countryCode,” “country,” “stateProvince,” “municipality,” and “geodeticDatum” shows the one record in this dataset. Spatial accuracy: “coordinateUncertaintyInMeters” and “georeferenceRemarks” provide quantitative and qualitative descriptions of the location, indicating whether those coordinates represent a specific point or the centroid of a subdistrict. Time: “year,” “month,” and “day” record the date of the report. Taxonomy: “scientificName” provides the final taxonomic identification verified by the authors with the standard DwC field related to taxonomy (“kingdom,” “phylum,” “class,” “order, “family, “genus,” “specificEpithet,” “infraspecificEpithet,” “acceptedNameUsage,” “scientificNameAuthorship,” “vernacularName,” and “taxonRank”). Statement about an organism: “dynamicProperties” provides the Red List category, “establishmentMeans” shows whether an organism was introduced to the location, “identificationVerificationStatus” includes categorical indicators of the observer’s confidence in the taxonomic identification, and “carcassCondition” is a qualitative assessment of the carcass’s state. Identification and Validation History (Original) To preserve the provenance of species identification described in the Methods section, the dataset included fields that tracked the multistage validation process: “verbatimCitizenTaxon” and “verbatimManagerTaxon”: The original taxon name provided by the citizen and manager if misidentified mentioned by “misidentifiedByCitizen” and “misidentifiedByManager”. “identifiedBy”: The name of the primary expert who performed the initial identification. “verifiedBy”: Name of the secondary verifier. “taxonVerifiedBy”: The name of the advanced specialist who resolved difficult or doubtful identifications. Data Quality and Logical Flags (Original) Custom logical flags and condition fields designed to support data filtering and the calculation of survey efforts and missing data were unique to this dataset. These include the following: “localityID”: A unique identifier for a locality used to connect the data with that of the 2020 Population Census of Japan 38 . “isUnconfirmedReport”: A Boolean flag (TRUE/FALSE) indicating records where a site visit was conducted but no carcass was found. “isNotRoadkill”: A flag identifying records where the cause of death was unrelated to vehicle collisions (e.g., predation). “hasUnreliableLocation” & “isOtherInvalid”: Flags mark records with insufficient spatial precision or other quality issues, allowing users to filter high-precision analyses. Full definitions, data types, and examples for all fields are provided in Table 3. Table 3. Definitions and descriptions of data fields. This table outlines the variable names, definitions, value ranges or levels, and examples of each field in the published dataset. The field names and definitions followed DwC standards to ensure interoperability. Field Description Range/Levels Example occurrenceID Unique identifier for an occurrence 14,249 records TF:TsukubaRoadkill:10000 catalogNumber Unique identifier for an occurrence within the data set 14,249 records 10000 basisOfRecord Specific nature of the data record 1 basis of record HumanObservation continent Continent where the data were recorded 1 continents Asia countryCode Standard code for the country in which the DwC term “Location” occurs 1 country codes JP country Country where the data were recorded 1 country Japan stateProvince Province where the data were recorded 1 province Ibaraki Prefecture municipality Municipality where the data were recorded 1 municipality Tsukuba locality Specific description of area (e.g., Oaza or Chome subdistricts) where roadkill was recorded 343 localities Amakubo 1 decimalLatitude Geographic latitude (in decimal degrees, using spatial reference system in the geodetic datum) of roadkill, standardized and georeferenced by the authors 35.94834 to 36.23086 36.0127627 decimalLongitude Geographic longitude (WGS84 in decimal degrees, using spatial reference system in the geodetic datum) of roadkill, standardized and georeferenced by the authors 140.0009 to 140.1685 140.1325247 geodeticDatum Coordinate system and set of reference points upon which geographic coordinates were based 1 coordinate system WGS84 coordinateUncertaintyInMeters Quantitative description of the spatial accuracy of location, indicating whether the coordinates represent a specific point or centroid of a local subdistrict 100 or 1000 100 georeferenceRemarks Qualitative description of the spatial accuracy of location, indicating whether the coordinates represent a specific point or centroid of a local subdistrict Point or Polygon Point kingdom Full scientific name of kingdom in which the taxon is classified 1 kingdom Animalia phylum Full scientific name of phylum in which the taxon is classified 1 phylum Chordata class Full scientific name of class in which the taxon is classified 4 classes Mammalia order Full scientific name of order in which the taxon is classified 18 orders Carnivora family Full scientific name of family in which the taxon is classified 32 families Canidae scientificName Full scientific name of species and subspecies in which the taxon is classified 52 names Nyctereutes procyonoides (Gray, 1834) genus Genus name of the species and subspecies in which the taxon is classified 39 genera Nyctereutes specificEpithet Name of the species within the genus 38 records procyonoides infraspecificEpithet Name of the lowest or terminal infraspecific epithet of the scientificName, excluding any rank designation 7 records japonica acceptedNameUsage Full name, with authorship and date information (if known), of the currently valid (zoological) or accepted (botanical) taxon 50 records Nyctereutes procyonoides (Gray, 1834) scientificNameAuthorship Authorship information for the scientificName formatted according to the conventions of the applicable dwc:nomenclaturalCode 26 records (Gray, 1834) dynamicProperties Red List Category in IUCN, Japanese Ministry of the Environments, and Ibaraki Prefecture Least Concern, Near Threatened, Vulnerable, Data Deficient, Critically Endangered, or unlisted {"iucnStatus":"Least Concern", "envStatus":"Vulnerable", "prefStatus":"Critically Endangered"} establishmentMeans Statement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans native, non-native, vagrant, or uncertain native vernacularName Common or vernacular name (English) 53 names raccoon dog taxonRank Species or subspecies 7 SPECIES year Year of event 2005–2007, 2016–2025 2025 month Month of event 12 months 7 day Day of event 31 days 4 identificationVerificationStatus Categorical indicator of the extent to which the taxonomic identification has been verified to be correct uncertain, verified by observer, or not applicable uncertain organismRemarks Concatenated notes regarding the physical state of the individual organism, integrating assessments of carcass condition, the presence of a collar, and visual signs of sarcoptic mange Carcass condition: intact, damaged, dismembered, unidentifiable, or not applicable; Collar presence and Sarcoptic mange: 1, 0, unidentifiable, or not applicable Carcass condition: damaged; Collar presence: 0; Sarcoptic mange: 1 localityID Unique identifier for a locality used in this study 344 records 1 misidentifiedByCitizen Binary flag indicating if the initial report from a citizen contained taxonomic misidentification 1, 0, or not applicable 1 verbatimCitizenTaxon Original taxon name provided by citizen common or vernacular name, matched, or not applicable cat misidentifiedByManager Binary flag indicating if identification by contractor was incorrect 1, 0, or not applicable 0 verbatimManagerTaxon Original taxon name provided by contractor before correction common or vernacular name, matched, or not applicable cat_or_raccoon_dog isUnconfirmedReport Logical flag indicating a citizen report was received and a site visit was conducted, but no carcass was found TRUE, FALSE TRUE isNotRoadkill Logical flag for records where the cause of death was determined to be nonvehicular collision (e.g., natural death and predation) TRUE, FALSE TRUE hasUnreliableLocation Logical flag indicating that geographic coordinates were too uncertain or invalid for spatial analysis TRUE, FALSE TRUE isOtherInvalid Logical flag for records that did not meet other data quality standards (e.g., incomplete data) TRUE, FALSE TRUE identifiedBy Name of specialist who performed primary identification and data verification name Keisuke Yamamoto verifiedBy Name of person who performed the secondary double-check of the record name or not applicable Shoma Jingu taxonVerifiedBy Name of specialist with advanced expertise who performed third check for difficult or doubtful taxonomic identifications name or not applicable Tomoro Mukai Data Overview The final dataset comprised 14,249 records spanning an 11-year period (April 2005–August 2007 and April 2016–December 2025). To analyze the confirmed and accurately georeferenced roadkill occurrences, the total dataset was filtered using established logical flags. Records were flagged if they represented unconfirmed reports (isUnconfirmedReport; n = 1,281), nonvehicular mortality (isNotRoadkill; n = 637), ambiguous spatial precision (hasUnreliableLocation; n = 895), or other data inconsistencies (isOtherInvalid; n = 22). Because multiple flags could be assigned to a single record, excluding these flagged entries yielded a robust subset of 11,464 valid records. Table 4 shows the taxonomic overview and spatial resolution for each group: amphibia (n = 9), reptilia (n = 86), aves (n = 1,144), and mammalia (n = 10,215). It lists each identifiable species and 10 additional entries that could not be identified from the records. Among mammals, the domestic cat ( Felis catus ) was the most frequently recorded species (n = 4,348), accounting for the largest proportion of the dataset. The raccoon dog ( Nyctereutes procyonoides ), was the most abundant native species (n = 2,856). The non-native masked palm civet ( Paguma larvata ) was also highly prevalent (n = 1,444). These three species collectively constitute the core of roadkill in Tsukuba. The data also captured a significant number of raccoons ( Procyon lotor ; n = 592), a designated invasive species under Japanese law. Figure 1c shows the spatial distribution of all 11,464 records across Tsukuba. A key characteristic of this dataset is its dual-resolution spatial structure. Point-level records (red dots in Figure 2 and the number of “Point” in Table 4) represent high-precision locations derived from citizen reports. Polygon-level records (blue shaded areas in Figure 1c and the number of “Polygon” in Table 4) represent records aggregated at the administrative units derived from contractor reports. These different styles ensured that roadkill occurrences were documented even in areas or situations where precise location details were unavailable. Table 4. Taxonomic overview and spatial resolution of the valid Tsukuba roadkill dataset, , including the total number of occurrences per species and corresponding spatial precision levels. Masked records represent species subject to conservation concerns in DwC dataset. Class Order Family Genus ScientificName VernacularName establishmentMeans taxonRank Number of valid roadkill Point Polygon Masked Total Amphibia Anura Anura frog uncertain ORDER 4 5 0 9 Reptilia Squamata snake uncertain ORDER 18 19 0 37 Testudines turtle uncertain ORDER 21 28 0 49 Aves Aves bird uncertain CLASS 82 114 0 196 Accipitriformes Accipitridae Accipiter Accipiter nisus nisosimilis sparrow hawk native SUBSPECIES 0 1 0 1 Milvus Milvus migrans (Boddaert, 1783) kite native SPECIES 1 1 0 2 Anseriformes Anatidae Anatidae duck uncertain FAMILY 41 37 0 78 Anas Anas zonorhyncha Swinhoe, 1866 grey duck native SPECIES 2 3 0 5 Cygnus Cygnus swan uncertain GENUS 0 1 0 1 Columbiformes Columbidae Columbidae pigeon uncertain FAMILY 59 70 0 129 Coraciiformes Alcedinidae Alcedo Alcedo atthis (Linnaeus, 1758) kingfisher native SPECIES 0 1 0 1 Falconiformes Falconidae Falco Falco tinnunculus Linnaeus, 1758 common kestrel native SPECIES 1 0 0 1 Galliformes Phasianidae Gallus Gallus gallus domesticus (Linnaeus, 1758) chicken non-native SUBSPECIES 1 1 0 2 Phasianus Phasianus versicolor Vieillot, 1825 pheasant native SPECIES 37 42 0 79 Gruiformes Rallidae Fulica Fulica atra Linnaeus, 1758 eurasian coot native SPECIES 1 1 0 2 Gallinula Gallinula chloropus (Linnaeus, 1758) common moorhen native SPECIES 0 1 0 1 Zapornia Zapornia fusca (Linnaeus, 1766) ruddy crake native SPECIES 0 0 1 1 Passeriformes Corvidae Corvus Corvus Linnaeus, 1758 crow native GENUS 239 295 0 534 Hirundinidae Hirundo Hirundo rustica Linnaeus, 1758 swallow native SPECIES 1 2 0 3 Leiothrichidae Leiothrix Leiothrix lutea (Scopoli, 1786) red-billed leiothrix non-native SPECIES 0 1 0 1 Paridae Parus Parus minor Temminck & Schlegel, 1848 Japanese tit native SPECIES 2 0 0 2 Passeridae Passer Passer montanus (Linnaeus, 1758) sparrow native SPECIES 25 19 0 44 Pycnonotidae Microscelis Microscelis amaurotis (Temminck, 1830) bulbul native SPECIES 4 2 0 6 Turdidae Turdus Turdus chrysolaus Temminck, 1832 brown-headed thrush native SPECIES 0 1 0 1 Turdus eunomus Temminck, 1831 dusky thrush native SPECIES 1 2 0 3 Zoothera Zoothera aurea (Holandre, 1825) white's thrush native SPECIES 1 1 0 2 Zosteropidae Zosterops Zosterops japonicus Temminck & Schlegel, 1845 Japanese white-eye native SPECIES 3 0 0 3 Pelecaniformes Ardeidae Ardeidae heron native FAMILY 24 10 0 34 Ardea Ardea cinerea jouyi A.H.Clark, 1907 grey heron native SUBSPECIES 2 5 0 7 Procellariiformes Procellariidae Procellariidae shearwater vagrant FAMILY 0 1 0 1 Strigiformes Strigidae Strix Strix uralensis Pallas, 1771 owl native SPECIES 3 1 0 4 Mammalia Artiodactyla Suidae Sus Sus scrofa leucomystax Temminck, 1842 boar native SUBSPECIES 9 16 0 25 Carnivora Canidae Canis Canis lupus subsp. familiaris dog non-native SUBSPECIES 30 206 0 236 Nyctereutes Nyctereutes procyonoides (Gray, 1834) raccoon dog native SPECIES 1267 1589 0 2856 Vulpes Vulpes vulpes japonica J.E.Gray, 1868 Japanese red fox native SUBSPECIES 15 23 0 38 Felidae Felis Felis catus Linnaeus, 1758 cat non-native SPECIES 1584 2764 0 4348 Mustelidae Martes Martes melampus (Wagner, 1840) marten native SPECIES 2 1 0 3 Meles Meles anakuma Temminck, 1844 badger native SPECIES 16 25 0 41 Mustela Mustela itatsi Temminck, 1844 weasel native SPECIES 35 50 0 85 Procyonidae Procyon Procyon lotor (Linnaeus, 1758) raccoon non-native SPECIES 322 270 0 592 Viverridae Paguma Paguma larvata (C.E.H.Smith, 1827) masked palm civet non-native SPECIES 595 849 0 1444 Chiroptera Chiroptera bat uncertain ORDER 3 1 0 4 Lagomorpha Leporidae Lepus Lepus brachyurus Temminck, 1844 hare native SPECIES 253 266 0 519 Oryctolagus Oryctolagus cuniculus (Linnaeus, 1758) domestic rabbit non-native SPECIES 2 0 0 2 Rodentia Muridae Muridae mouse uncertain FAMILY 3 10 0 13 Rodentia Sciuridae Tamias Tamias sibiricus (Laxmann, 1769) chipmunk non-native SPECIES 0 1 0 1 Soricomorpha Talpidae Talpidae mole uncertain FAMILY 5 3 0 8 Others uncertain PHYLUM 8 2 0 10 4722 6741 1 11464 Technical Validation Assessment of Identification Accuracy A critical concern in citizen science is the reliability of species identification. To validate the quality of this dataset, the researchers used a three-tier identification structure, as described in the Methods section. By comparing the reported species (citizen/contractor input) with expert verifications, users can quantitatively assess identification error rates. A previous analysis of a subset of this data revealed that while citizens often misidentified morphologically similar species (e.g., raccoon dogs vs. badgers), the inclusion of photographic evidence in the final reports allowed experts to correct these errors with high confidence 32 . The species fields indicated records that underwent the most rigorous level of scrutiny, ensuring that even difficult-to-identify species were assigned with high reliability. Survey Consistency and Missing-Data Mechanisms Unlike ad hoc volunteer surveys, which often suffer from spatiotemporal gaps and inconsistent effort based on individual availability 26 , the data collection for this dataset was driven by a systematic and pre-existing administrative mandate: public road safety management. This ensured a consistent level of survey effort across the entire 11-year period and the complete 284 km² administrative area. Furthermore, understanding the specific mechanisms underlying missing or excluded data is critical for building robust inferential models. To facilitate this interpretation, the present dataset explicitly retained unverified or invalid reports rather than discarding them. By providing specific logical flags (e.g., “isUnconfirmedReport,” “isNotRoadkill,” “hasUnreliableLocation,” and “isOtherInvalid”), users can better understand the reasonable for the exclusion of a record from the main occurrence subset. For instance, the inclusion of “isUnconfirmedReport” = TRUE (instances where contractors responded but found no carcasses) acted as a “null” event demonstrating the system’s continuous responsiveness, while also allowing researchers to model false-positive reporting rates. Collectively, these transparency flags enable data users to evaluate missing-data mechanisms, treat the dataset as a continuous time series rather than opportunistic sightings, and apply appropriate statistical treatments for missingness in advanced ecological or spatial modeling. Data Quality and Usage of Logical Flags To ensure the applicability of the dataset for ecological modeling, researchers implemented logical flags to isolate potential biases. The “isNotRoadkill” flag allows users to exclude nonvehicular mortality (e.g., disease and predation), ensuring that analyses of road impacts are not confounded by other mortality factors. The “hasUnreliableLocation” flag identifies records where the address description was too vague for precise georeferencing. Finally, the “carcassCondition” field (Levels A–C) allows users to filter specimens that were too damaged for reliable identification, thereby reducing uncertainties in species distribution models. By filtering based on these flags, users can generate a “clean” subset of high-precision, confirmed roadkill occurrences suitable for detailed spatial analyses. Usage Notes Interpretation of Observation Biases Users should be aware that this dataset represents citizen-reported presence-only data. Moreover, a roadkill record was generated only after surviving a multistep “filtration” process of detection and reporting (Figure 3) 22,27,45–47 . Consequently, the absence of records for a specific area or period does not necessarily indicate the absence of a species or roadkill events. Data users should consider inherent biases when using data for distribution modeling or population estimation. Multistep Detection Process A roadkill event was recorded in this dataset only if it passed through the filters depicted in Figure 3. Failure at any stage resulted in “nondetection.” This could occur because of a variety of factors. Occurrence (Event Probability) However, not all road crossings lead to mortality. Most individuals cross safely, whereas others may be injured but retreat into underbrush before death. Only animals that died on the road or were knocked onto the shoulder were potentially detectable. Driver Reporting Bias Drivers are more likely to report collisions with large mammals (e.g., deer and boar) owing to potential vehicle damage and insurance requirements. Conversely, collisions with smaller species are frequently ignored or unnoticed. Moreover, the probability of roadkill is often higher at night, and reduced visibility and driver awareness during dark hours significantly lower the likelihood of immediate reporting. Carcass Persistence (Availability): Carcasses are frequently removed by scavengers (e.g., crows) before they can be noticed by other citizens or collected by road managers 22 . Furthermore, vehicle impact may knock the carcass off the road surface into vegetation, rendering it invisible to passersby. Citizen Detection and Action If a carcass remained on the road, it had to be noticed by a passerby or a subsequent driver before being reported (if the initial driver did not report it). Local residents or volunteers could bury or dispose of carcasses without notifying the city administration. Such roadkill events were not recorded. Declarations Funding This study was supported by the JSPS KAKENHI Grant Number 24K20980. Code Availability The Java code used to process and validate the dataset for covering taxonomic information is sorted from https://github.com/PORBIOTA/PORBIOTA-ICNF/tree/main/DwC_Creation_Helpers . Author Contribution S.J. conceptualized the study, conducted the formal analysis, developed the software, and prepared the visualizations. S.J. and Y.O. developed the methodology. S.J., M.U., Y.O., R.A., K.Y., T.M., and S.S. curated the data and performed the validation. S.J., M.U., Y.O., K.Y., T.M., and Y.W. conducted the investigation. S.J. and M.U. provided the resources. S.J. and Y.W. acquired the funding, administered the project, and supervised the research. S.J. wrote the main manuscript text, and S.J. and Y.O. reviewed and edited the manuscript. All authors reviewed the manuscript. Acknowledgement We would like to thank the Clean Environment Promotion Division of Tsukuba City Government for data contribution. Data Availability The data generated and analyzed in this study are available at Figshare under the identifier 10.6084/m9.figshare.31580410. This repository includes the main roadkill dataset (TFTsukubaRoadkill_Dataset.csv), along with the detailed metadata (TFTsukubaRoadkill_dataDigitization.csv and TFTsukubaRoadkill_locality.csv), all provided as comma-separated values files. Furthermore, the species occurrence data, extracted and standardized in strict accordance with DwC, have been registered and are publicly accessible through the GBIF (DOI to be added during the revision process). The original administrative reports and photographs of individual carcasses serving as the basis for this dataset are not publicly shared, as the ownership and disclosure rights are held by the municipal government of Tsukuba. However, these materials may be provided by the corresponding author upon reasonable request, subject to necessary permissions from municipal authorities. 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An assessment of wildlife road casualties – the potential discrepancy between numbers counted and numbers killed. Web Ecol. 3 , 33–42 (2002). https://doi.org/10.5194/we-3-33-2002 Santos, R. A. L., Mota-Ferreira, M., Aguiar, L. M. S. & Ascensão, F. Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models. Sci. Total Environ. 642 , 629–637 (2018). https://doi.org/10.1016/J.SCITOTENV.2018.06.107 Additional Declarations No competing interests reported. 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. 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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-9517196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"data-descriptor","associatedPublications":[],"authors":[{"id":633343746,"identity":"b599273c-e11d-44a1-ae1b-e405c95d90a4","order_by":0,"name":"Shoma JINGU","email":"data:image/png;base64,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","orcid":"","institution":"Forestry and Forest Products Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Shoma","middleName":"","lastName":"JINGU","suffix":""},{"id":633343752,"identity":"ef87bf2c-6825-4bd4-a7df-dd1096e7a8ea","order_by":1,"name":"Minami UNNO","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Minami","middleName":"","lastName":"UNNO","suffix":""},{"id":633343772,"identity":"46eb7c1e-2115-4a3a-b6f8-2ef24efcdaa9","order_by":2,"name":"Yui OGAWA","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Yui","middleName":"","lastName":"OGAWA","suffix":""},{"id":633343775,"identity":"fe7545f1-8c80-4a97-8206-57165f61ee00","order_by":3,"name":"Ryota AIZAWA","email":"","orcid":"","institution":"Japan Wildlife Research Center","correspondingAuthor":false,"prefix":"","firstName":"Ryota","middleName":"","lastName":"AIZAWA","suffix":""},{"id":633343778,"identity":"1347fdc6-8544-4402-9d08-6e5cf8bf4766","order_by":4,"name":"Keisuke YAMAMOTO","email":"","orcid":"","institution":"Kai Kemono Shachu LLC","correspondingAuthor":false,"prefix":"","firstName":"Keisuke","middleName":"","lastName":"YAMAMOTO","suffix":""},{"id":633343782,"identity":"b2d58dc5-f135-484c-a255-6ca79bc05e5f","order_by":5,"name":"Tomoro MUKAI","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Tomoro","middleName":"","lastName":"MUKAI","suffix":""},{"id":633343785,"identity":"713808bf-678f-413e-8c51-0d297f1fb72d","order_by":6,"name":"Saya SUNADA","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Saya","middleName":"","lastName":"SUNADA","suffix":""},{"id":633343790,"identity":"bae0c709-4b8b-4874-b91a-0f80db0f8e1c","order_by":7,"name":"Yuya WATARI","email":"","orcid":"","institution":"Forestry and Forest Products Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Yuya","middleName":"","lastName":"WATARI","suffix":""}],"badges":[],"createdAt":"2026-04-24 12:24:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9517196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9517196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108476690,"identity":"c5418e08-0b61-4806-8b12-bf04cfc64699","added_by":"auto","created_at":"2026-05-05 07:02:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":540804,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area in Tsukuba, Kanto region, Japan. Japan and Kanto region maps are CS 3D-Map based on the Digital Map (Basic Geospatial Information) published by the Geospatial Information Authority of Japan. Dark brown means high elevation, high slope, and high curvature. \u003cstrong\u003e(a)\u003c/strong\u003e Administrative boundaries of local subdistricts. \u003cstrong\u003e(b)\u003c/strong\u003e Road network distribution (road centerlines). This map is based on “Road Centerline Vector Tiles\u003csup\u003e35\u003c/sup\u003e.” \u003cstrong\u003e(c)\u003c/strong\u003e Spatial distribution of roadkill occurrences aggregated by subdistrict. The size of the blue circles is proportional to the total number of recorded incidents within each area.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9517196/v1/d366732c4b0acddd12fe65d4.png"},{"id":108494031,"identity":"5fd391d6-76a8-4539-a740-bae4317aef17","added_by":"auto","created_at":"2026-05-05 10:02:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":222652,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of roadkill records in Tsukuba. Point-level data represented by dots, indicating records where precise collision locations were identified through citizen reports.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9517196/v1/0b58e1c2fe37a08b5102f7a6.png"},{"id":108476692,"identity":"a1ae9c74-e600-42cf-9334-13420bb39002","added_by":"auto","created_at":"2026-05-05 07:02:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67927,"visible":true,"origin":"","legend":"\u003cp\u003eMultistep detection and reporting process for roadkill. This diagram illustrates the filtration mechanism through which roadkill becomes a data record. A record is generated only if it survives multiple stages: (1) Occurrence: mortality must occur on the road surface; (2) Reporting: a driver or passerby must decide to report the incident to the city government; (3) Persistence: the carcass must remain on the road without being removed by scavengers or unreported human activity; and (4) Collection: the contractor must successfully locate the carcass. Each step introduces potential observational biases, such as taxonomic bias in reporting rates (e.g., larger animals are reported more frequently) or loss of evidence.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9517196/v1/d2d574d9a4ec240ea8da164d.png"},{"id":108495196,"identity":"7173d588-1b5e-4cf7-9cb3-c45cd726890e","added_by":"auto","created_at":"2026-05-05 10:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9517196/v1/a1f58337-8cc1-4b22-a355-3ee2937bb826.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An 11-year spatiotemporal roadkill dataset derived from citizen reports in Tsukuba, Japan","fulltext":[{"header":"Background \u0026 Summary","content":"\u003cp\u003eBiodiversity conservation is a paramount global challenge, as underscored by the Kunming\u0026ndash;Montreal Global Biodiversity Framework (KM-GBF), adopted in 2022 at the Fifteenth Meeting of the Conference of the Parties\u003csup\u003e1,2\u003c/sup\u003e. The framework aims to halt and reverse biodiversity loss to achieve a \u0026ldquo;Nature Positive\u0026rdquo; world by 2030, emphasizing the need for whole-of-society engagement\u003csup\u003e3\u003c/sup\u003e. Citizen science, which also known as community science and is defined as \u0026ldquo;scientific work undertaken by members of the general public\u003csup\u003e4\u003c/sup\u003e,\u0026rdquo; is one strategy for attaining Nature Positive 2030\u003csup\u003e5\u003c/sup\u003e. Although citizen science is a recognized strategy for monitoring biodiversity at scale\u003csup\u003e6\u0026ndash;9\u003c/sup\u003e, traditional initiatives often rely on a narrow demographic of nature enthusiasts, leaving the broader public uninvolved\u003csup\u003e10\u0026ndash;13\u003c/sup\u003e. To bridge this gap, \u0026ldquo;unintended\u0026rdquo; citizen science data, which are often produced through routine interactions between the general public and nature, are frequently sourced from social media and similar platforms\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRoadkill, the death of wild animals on roads due to traffic accidents, is a prime example of unintended citizen science\u003csup\u003e17\u003c/sup\u003e. It serves as extensive evidence of human\u0026ndash;wildlife encounters and provides an important source of ecological data\u003csup\u003e17,18\u003c/sup\u003e. Traditionally, professional monitoring is often logistically prohibited given the vast extent of road networks. Instead, citizen science projects on roadkill are increasingly outperforming expert surveys, demonstrating that public participation enables large-scale spatiotemporal surveys\u003csup\u003e19,20\u003c/sup\u003e. Building upon these initiatives, the present researchers seek to extend the scope of participation to the majority of the general public beyond individuals with a pre-existing interest in nature.\u003c/p\u003e\n\u003cp\u003eCitizens\u0026rsquo; reports of animal-vehicle collisions made to road management authorities constitute a massive yet underutilized resource\u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. However, the applicability of existing roadkill datasets has been constrained by geographical and methodological limitations. Such studies have been geographically skewed toward Western countries, leaving Asian urban landscapes underrepresented\u003csup\u003e24\u003c/sup\u003e. Methodologically, previous research has predominantly focused on major highways\u003csup\u003e25\u003c/sup\u003e. These linear datasets often lack a clearly defined spatial extent. When utilizing opportunistic citizen science data, the challenge shifts to interpreting the absence of reports, especially in distinguishing between a true absence of roadkill and a simple lack of observation or reporting, making it difficult to quantify missing data and \u0026ldquo;survey effort,\u0026rdquo; which is a critical parameter for robust ecological inference\u003csup\u003e26\u003c/sup\u003e, such as density estimation. Without a clear understanding of a survey\u0026rsquo;s monitoring intensity and coverage area, the application of its data to broader biodiversity assessments remains limited\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo overcome these fundamental limitations, this Data Descriptor presents a spatially dense, temporally extensive dataset of 14,249 roadkill records collected over an 11-year period in Tsukuba, Japan. While the geographic scope was localized, the meticulousness and reproducibility of this dataset will allow for novel applications that were previously unattainable. Covering the municipality\u0026rsquo;s entire 284 km\u0026sup2; administrative area, this dataset represents a significant departure from traditional linear surveys.\u003csup\u003e24\u003c/sup\u003e Very few datasets globally achieve this level of spatiotemporal density by including over a decade of records on a complete municipal road network ranging from arterial highways to narrow residential streets. Data collection was enabled by a pre-existing systematic administrative process: citizens reported carcasses to the city government, which then deployed road managers to retrieve them. This year-round and comprehensive citywide operation according to a fixed format established a consistent level of survey effort. Consequently, this dataset provides a rare opportunity to analyze wildlife distribution, long-term annual fluctuations, and population dynamics in a continuous \u0026ldquo;planar\u0026rdquo; rather than a fragmented \u0026ldquo;linear\u0026rdquo; context, serving as an ideal testbed for urban ecology and spatial modeling.\u003c/p\u003e\n\u003cp\u003eAnother notable feature of this dataset is its rigorous quality approach. Species misidentification is a common concern in citizen science based monitoring\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e. Previous research has also highlighted the necessity of validating roadkill records collected by road maintenance personnel, who are typically nonspecialists in wildlife biology\u003csup\u003e31\u003c/sup\u003e. This indicates a dual challenge in data quality assurance: the initial risk of misidentification by nonexpert citizen reporters is compounded by the potential for error when nonspecialist road maintenance staff validate and process these reports. To address these multilayered challenges, the researchers implemented a three-tier identification process for a subset of records, documenting the species as identified by the reporting citizen, responding road maintenance manager, and expert authors. The preliminary study using an initial two-year subset of these data successfully characterized the geographic distribution of records and elucidated specific trends in citizens\u0026rsquo; species misidentification\u003csup\u003e32,33\u003c/sup\u003e. Building on this foundation, the present dataset expanded its scope to 11 years, providing a robust, high-resolution resource for urban wildlife management. Therefore, this dataset also offers a universally applicable methodological platform for quantifying error structures and missing-data mechanisms in unintended citizen science programs worldwide.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Area\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in Tsukuba (36\u0026deg;05\u0026apos;01.1\u0026quot;N, 140\u0026deg;04\u0026apos;36.9\u0026quot;E), Ibaraki Prefecture, which is located approximately 50 km northeast of Tokyo (Figure 1) and has a temperate climate. By 2024, the city had a population of 259,618\u003csup\u003e34\u003c/sup\u003e. The municipal territory covers an area of 284 km\u0026sup2;. The northern part of the city includes Mt. Tsukuba (877 m), which serves as the centerpiece of the Suigo-Tsukuba Quasi-National Park. The majority of the city lies on the Tsukuba Plateau and its surrounding lowlands, with elevations ranging from 20 to 30 m above sea level. This southern region is characterized by a mosaic landscape of forests, agricultural fields, and urban areas.\u003c/p\u003e\n\u003cp\u003eTsukuba was planned and developed as a \u0026ldquo;science city\u0026rdquo; in the late 1960s, in which national and private research institutes are concentrated, transforming a rural landscape on a flat plateau. Planned nature conservation measures were implemented in the city\u0026rsquo;s design phase\u003csup\u003e36\u003c/sup\u003e. Policies have mandated the preservation of certain areas of forests and grasslands derived from the original rural landscape, integrating them into urban parks and green spaces that are accessible to residents\u003csup\u003e36\u003c/sup\u003e. This unique local history made Tsukuba an appropriate area for studying human-nature interactions and the effectiveness of citizen science in an urban environment where green space was intentionally conserved.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study area, Tsukuba, is administratively divided into more than 350 local subdistricts (known as Oaza or Chome in Japan, Figure 1a). Tsukuba\u0026rsquo;s road network has been extensively developed, primarily across the flat terrain of the plateau and lowlands (Figure 1b). The total managed road length in the study area was approximately 3,956 km, comprising 282 km of national and prefectural roads and 3,674 km of municipal roads. Note that Figure 1b excludes toll expressways, elevated road sections, tunnels, and pedestrian pathways, as these areas fall outside the jurisdiction of municipal roadkill collection services. The final outcome of this study was the roadkill data shown in Figure 3c, which was obtained through the following steps.\u003c/p\u003e\n\u003ch2\u003eData Source and Collection Workflow\u003c/h2\u003e\n\u003cp\u003eRoadkill data were derived from Tsukuba\u0026rsquo;s administrative records. When a citizen reports an animal carcass on a public road (excluding toll expressways and private land), the city government dispatches a contracted company to collect it. This service operates year-round (excluding New Year\u0026rsquo;s Day) between 08:30 and 17:00.\u003c/p\u003e\n\u003cp\u003eThe reporting process follows a specific and straightforward protocol: City officials receive the call, record the species name and location as reported by the citizen, and forward these details to the contractor. Upon arrival, the contractor collected the carcass, recorded the actual location, time of collection, and species name, and captured a photograph for the final report.\u003c/p\u003e\n\u003cp\u003eImportantly, the primary purpose of this system is road safety management and retrieval of lost pets managed by the Clean Environment Promotion Division, not ecological monitoring. In fact, the contractor was a private pet funeral company and not a wildlife research agency. Although they possess extensive experience in carcass handling, their taxonomic expertise is not at the same specialist level as that of a biologist. Furthermore, because most records rely on reports from passersby rather than motorists who have injured the animal, the recorded time reflects the collection time and not the exact time of the accident.\u003c/p\u003e\n\u003ch2\u003eData Digitization and Integration\u003c/h2\u003e\n\u003cp\u003eThe researchers compiled records from two primary sources covering two periods: April 1, 2005\u0026ndash;August 31, 2007 and April 1, 2016\u0026ndash;December 31, 2025. The source materials included analog reception memos from the city hall and monthly reports from the contractor. To verify details and resolve ambiguities, the researchers also conducted monthly interviews with city officials.\u003c/p\u003e\n\u003cp\u003eThese records were manually digitized into a unified database, linking the \u0026ldquo;service demand\u0026rdquo; (i.e., initial citizen report: reported species and location) with the \u0026ldquo;response\u0026rdquo; (i.e., contractor report: collection date, identified species, and photographs). Reporting citizens\u0026rsquo; personal data (e.g., names, phone numbers, and addresses) was strictly removed during this stage to ensure privacy protection. Detailed metadata regarding the data acquisition process are summarized in Table 1 and full descriptions provided in the data repository (TFTsukubaRoadkill_dataDigitization.csv). These data delineated the chronological periods of their collection, the designated personnel responsible for digitization, the formats of the original source documents, and the availability and formats of photographic evidence (e.g., color, grayscale, and none) used to substantiate taxonomic identifications.\u0026nbsp;\u003c/p\u003e\n\u003ch5\u003eTable 1. Metadata regarding the data digitization workflow.\u003c/h5\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eField\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 213px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003eRange/Levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eExample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eperiod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 213px;\"\u003e\n \u003cp\u003eSpecific month and year corresponding to roadkill records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003e2005-04\u0026ndash;2007-08 and 2016-04\u0026ndash;2025-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2016-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003edigitizedBy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 213px;\"\u003e\n \u003cp\u003eInitials of author(s) responsible for manually digitizing and verifying records for a period\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003eAuthor names\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eShoma Jingu\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003esourceType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 213px;\"\u003e\n \u003cp\u003eType of administrative documents used as primary source for digitization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003eContractor reports (monthly reports only), both (integrated from contractor reports and city memos)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003edocumentType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 213px;\"\u003e\n \u003cp\u003eIndicates the availability and color format of the original administrative documents (including photograph) used for digitization and taxonomic identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 199px;\"\u003e\n \u003cp\u003ecolor, grayscale, and none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003ecolor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch2\u003eData Filtering and Quality Control\u003c/h2\u003e\n\u003cp\u003eThe raw data were screened to ensure that they represented genuine roadkill incidents on public roads. To ensure data integrity while preserving information on administrative survey efforts, the researchers scrutinized each record and applied a logical flagging system rather than discarding problematic records. For example, records were retained when a site visit was conducted by a contractor following a report from a citizen but no carcass was found (e.g., the animal had been removed). These were flagged as \u0026ldquo;isUnconfirmedReport\u0026rdquo; to serve as a quantifiable proxy for the survey effort and missing data invested by road managers. Similarly, carcasses determined to result from nonvehicular causes (e.g., predation) were flagged as \u0026ldquo;isNotRoadkill.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eTo maintain data quality, records were flagged as \u0026ldquo;hasUnreliableLocation\u0026rdquo; when the specific location of the collision could not be verified. This included cases with ambiguous address descriptions, and instances where citizens transported the carcass from the road to a secondary location (e.g., bringing it to their residence in a box) before reporting it. In such scenarios, the recorded location reflected the collection point rather than the actual accident site, rendering the exact roadkill location indeterminable. Records that failed other quality checks were flagged as \u0026ldquo;isOtherInvalid.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThese determinations were based on remarks from administrative logs, photographic evidence, and supplementary interviews. Consequently, the final dataset enables users to filter data according to specific analytical requirements, such as species distribution modeling. In addition, we conducted data screening and correction by OpenRefine (\u003cu\u003ehttp://openrefine.org\u003c/u\u003e).\u003c/p\u003e\n\u003ch2\u003eGeoreferencing\u003c/h2\u003e\n\u003cp\u003eGeospatial coordinates were derived using a two-tier approach depending on the availability of source documents. This resulted in two levels of spatial precision: \u0026ldquo;point\u0026rdquo; and \u0026ldquo;polygon.\u0026rdquo;\u003c/p\u003e\n\u003ch3\u003ePoint-level Precision (Citizen Reports)\u003c/h3\u003e\n\u003cp\u003eWhen an initial citizen report was available, it typically provided a higher level of spatial precision. During the reporting process, city officials created temporary maps based on citizens\u0026rsquo; descriptions to guide contractors. The most probable point was determined from internal reference maps. However, these coordinates represented locations reported by citizens and not GPS-confirmed locations. This means that spatial errors ranging from negligible to hundred meters may have occurred. Importantly, while the reported point may shift, the identity of the reported road is almost accurate. This uncertainty therefore primarily represents a linear error margin along a specific road rather than a simple radius. To reflect this quantitatively under Darwin Core (DwC) standards\u003csup\u003e37\u003c/sup\u003e, the field coordinateUncertaintyInMeters was assigned an estimated value of 100. Additionally, the qualitative field georeferenceRemarks was populated to explicitly indicate that these coordinates represent a specific point with an estimated 100-meter linear uncertainty.\u003c/p\u003e\n\u003ch3\u003ePolygon-level Precision (Contractor Reports)\u003c/h3\u003e\n\u003cp\u003eFor records where detailed citizen reports were unavailable, the researchers relied on the locality information (e.g., Oaza or Chome; town/village subdistrict) shown in Figure 1a and recorded in the contractor\u0026rsquo;s field report. These spatial units correspond to the standardized \u0026ldquo;Cho-Aza\u0026rdquo; boundaries defined in the 2020 Population Census of Japan\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEach record was assigned a unique spatial identifier (\u0026ldquo;localityID\u0026rdquo;). This key allows dataset users to cross-reference the main data with the Supplementary Information (summarized in Table 2 and full descriptions provided in the data repository; TFTsukubaRoadkill_locality.csv), which provides detailed geometric attributes for each census tract, including administrative codes and area classifications. These records were assigned the geospatial centroid of the corresponding census polygon to \u0026ldquo;decimalLatitude\u0026rdquo; and \u0026ldquo;decimalLongitude.\u0026rdquo; To appropriately reflect this polygon-level resolution, the field \u0026ldquo;coordinateUncertaintyInMeters\u0026rdquo; was assigned a generalized value of 1000. This value was determined using the average area of the subdistricts in the study area, with a 1000-meter buffer sufficiently encompassing the vast majority of these polygons. \u0026ldquo;GeoreferenceRemarks\u0026rdquo; was concurrently utilized to indicate that the coordinates represented the centroid of a local subdistrict, preventing users from misinterpreting them as exact occurrences.\u003c/p\u003e\n\u003ch5\u003eTable 2. Metadata regarding the administrative units corresponding to records with polygon-level spatial precision. This table provides specific administrative details for records where the exact collision point was indeterminate (linked via the localityID field in the main dataset). The KEY_CODE is a unique 11-digit identifier defined in the 2020 Population Census of Japan\u003csup\u003e38\u003c/sup\u003e. This code directly links the roadkill dataset with official GIS boundary polygons (shapefiles) and aggregate demographic statistics available from the Portal Site of Official Statistics of Japan.\u003c/h5\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eField\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRange/Levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eExample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003elocalityID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUnique spatial identifier linked to main roadkill dataset\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e343 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eKEY_CODE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11-digit linkage code connecting map figures and aggregate data defined in the 2020 Population Census of Japan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e311 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e82200020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003elocalityJP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eArea description (e.g., Oaza or Chome subdistricts) in kanji\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e343 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e研究学園１丁目\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003elocalityKana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePhonetic reading of locality name in katakana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e343 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ekenkyugakuen 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eprecision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eClassification of administrative unit level (e.g., Oaza, Chome) including exclave (tobichi), a spatially detached portion of an administrative unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOaza, Chome, Exclave\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOaza\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eSpatial Resolution Adjustment for Sensitive Records\u003c/h3\u003e\n\u003cp\u003eRegardless of the availability of detailed citizen reports, records flagged as \u0026ldquo;isUnconfirmedReport,\u0026rdquo; \u0026ldquo;isNotRoadkill,\u0026rdquo; \u0026ldquo;hasUnreliableLocation,\u0026rdquo; or \u0026ldquo;isOtherInvalid\u0026rdquo; were automatically assigned polygon-level precision (the centroid of the administrative unit). This downscaling protocol served two critical purposes. First, it reflected scientific uncertainty; without confirmed carcass data, precise point data lack biological validation. Second, and most importantly, it protected the privacy of reporters. For example, records flagged as \u0026ldquo;hasUnreliableLocation\u0026rdquo; often involved cases where citizens reported carcasses found at boundaries between public roads and private properties. Publishing precise coordinates derived from these reports would pose a risk of revealing reporters\u0026rsquo; residential locations. To mitigate this privacy risk, researchers strictly masked the precise location of all flagged records, replacing them with a broader administrative unit centroid.\u003c/p\u003e\n\u003cp\u003eGiven that DwC format data are finally distributed through open-access and species-searchable platforms like the Global Biodiversity Information Facility (GBIF), rigorous spatial masking protocol was implemented to protect threatened and endangered species. Records involving taxa designated on the national or prefectural Red Lists (e.g., Vulnerable, Endangered, and Critically Endangered) were systematically generalized to prevent potential conservation risks like illegal collection, poaching, or targeted harassment. For these sensitive occurrences, exact coordinates (\u0026ldquo;decimalLatitude,\u0026rdquo; \u0026ldquo;decimalLongitude\u0026rdquo;), detailed locality names (\u0026ldquo;locality\u0026rdquo;), and spatial uncertainty metrics (\u0026ldquo;coordinateUncertaintyInMeters\u0026rdquo;) were completely withheld in DwC dataset. The spatial resolution was generalized to the broad municipal level (\u0026ldquo;Tsukuba\u0026rdquo;) using a designated generic \u0026ldquo;localityID.\u0026rdquo; To ensure transparency and strict adherence to DwC\u003csup\u003e37\u003c/sup\u003e, the rationale for this spatial obfuscation was explicitly documented in the \u0026ldquo;georeferenceRemarks\u0026rdquo; field, ensuring that future users will comprehend the reason behind the deliberate absence of high-resolution spatial data for these records.\u003c/p\u003e\n\u003cp\u003eHowever, applying blanket spatial masking to roadkill occurrences can establish a counterproductive precedent in road ecology, where data concealment may be prioritized over actionable conservation. Therefore, we adopted a dual release strategy. In the complete primary dataset hosted on Figshare accessed through this Data Descriptor, we provide the exact, unmasked coordinates for all records, including those of threatened species. By providing the unmasked full dataset within the scientific context of this study, we aim to maximize its utility for robust ecological modeling and effective, localized biodiversity conservation.\u003c/p\u003e\n\u003ch2\u003eExpert Species Identification\u003c/h2\u003e\n\u003cp\u003eTaxonomic identification was performed by researchers with expertise in wildlife monitoring and relevant licenses. The identifications relied on high-resolution photographs provided in the contractor\u0026rsquo;s final reports. The researchers focused on identifying 39 species (predominantly mammals) with distinct morphological characteristics. In cases where species were morphologically similar or photographic evidence was inconclusive, multiple experts cross-verified the identification, with the lead author making the final determination.\u003c/p\u003e\n\u003ch2\u003eCarcass Condition Ranking\u003c/h2\u003e\n\u003cp\u003eTo assess how carcass condition influenced identification accuracy, the researchers classified the damage of the mammal carcasses into three levels based on photographic evidence:\u003c/p\u003e\n\u003cp\u003eLevel A (Intact): No major damage; original morphology largely maintained.\u003c/p\u003e\n\u003cp\u003eLevel B (Damaged): Morphology is preserved but with visible defects or partial damage.\u003c/p\u003e\n\u003cp\u003eLevel C (Dismembered): Morphology is significantly compromised; species characteristics are difficult to discern.\u003c/p\u003e\n\u003cp\u003eCases where the extent of damage could not be ascertained from photographs alone were labeled as \u0026ldquo;unidentifiable\u0026rdquo; and excluded from the condition-based analysis.\u003c/p\u003e\n\u003ch2\u003eNonexpert Identification Records\u003c/h2\u003e\n\u003cp\u003eTo evaluate identification errors, the researchers compared expert identifications with two nonexpert sources:\u003c/p\u003e\n\u003cp\u003eCitizen Reports: Initial identification provided by citizens via telephone. These often included vague categories (e.g., \u0026ldquo;raccoon dog\u0026rdquo; or \u0026ldquo;cat\u0026rdquo;) or were stated as \u0026ldquo;unknown.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eContractor Reports: Identification recorded by waste management personnel at collection.\u003c/p\u003e\n\u003cp\u003eThese observations were not entirely independent; contractors were generally informed of the species reported by citizens prior to arrival, which may have influenced their judgment. However, contractors also had the advantage of visual inspection and sufficient time to photograph the carcass.\u003c/p\u003e\n\u003ch2\u003eTaxonomy and Conservation Status\u003c/h2\u003e\n\u003cp\u003eThe researchers cross-referenced all species\u0026rsquo; names with the Global Biodiversity Information Facility\u0026rsquo;s (GBIF) Backbone Taxonomy using Java and the GBIF\u0026rsquo;s API to rectify classification errors and standardize the taxonomy. This process included additional fields such as kingdom, phylum, and scientific authorship. It also permitted researchers to gather comprehensive taxonomic information to address any gaps within the datasets. For species not automatically matched, the researchers manually searched for correct synonyms when available.\u003c/p\u003e\n\u003cp\u003eConservation status was determined by cross-referencing three databases: the International Union for Conservation of Nature\u0026rsquo;s (IUCN) Red List\u003csup\u003e39\u003c/sup\u003e, the Japanese Ministry of the Environment Red List\u003csup\u003e40\u003c/sup\u003e, and the Ibaraki Prefecture Red List\u003csup\u003e41\u003c/sup\u003e. This multiscale approach enabled the evaluation of vulnerability at the global, national, and local levels.\u003c/p\u003e\n\u003cp\u003eRegarding non-native species, the researchers prioritized domestic ecological definitions. Based on Japanese standards, species classified as non-native to Japan were strictly categorized as \u0026ldquo;unlisted,\u0026rdquo; regardless of their Red List status.\u003c/p\u003e\n\u003ch3\u003eEthics Statement\u003c/h3\u003e\n\u003ch4\u003eAssertion of Legal and Authorized Acquisition\u003c/h4\u003e\n\u003cp\u003eThe dataset used in this study was derived from administrative records of Tsukuba City. The reports, comprising photographs and text, were obtained through a formal Information Disclosure Request (Joho Kaiji Seikyu) in accordance with Japanese administrative laws. Furthermore, the maps and attached memos used to extract spatial coordinates, species names, and observation dates were provided by the Clean Environment Promotion Division of Tsukuba City with prior consent. The analysis and publication of the results were conducted based on mutual agreement between the Clean Environment Promotion Division and the author. Together, the formal disclosure request and the authorized collaborative process ensure a fully transparent, authorized, and legitimate provenance for all source materials without any ethical or legal issues.\u003c/p\u003e\n\u003ch4\u003eAssertion of Secondary Use Rights\u003c/h4\u003e\n\u003cp\u003eIn alignment with the Japanese Government\u0026rsquo;s \u0026ldquo;Electronic Government Open Data Strategy\u0026rdquo; and the \u0026ldquo;Government Standard Terms of Use (Version 2.0),\u0026rdquo; these public records may be used secondarily for research and public benefit, provided that appropriate attribution is given. The authors have transformed these administrative records into a standardized scientific format, constituting an original intellectual synthesis of the underlying administrative information.\u003c/p\u003e\n\u003ch4\u003eAssertion of Research Integrity\u003c/h4\u003e\n\u003cp\u003eThe data processing and decoding were conducted in accordance with the \u0026ldquo;Guidelines for Responding to Misconduct in Research Activities\u0026rdquo; by the Ministry of Education, Culture, Sports, Science and Technology (MEXT). The authors have implemented a rigorous verification protocol for information extraction from the permitted maps and the disclosed reports, and have thoroughly documented all georeferencing and digitization procedures to ensure the highest standards of research integrity and reproducibility.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eAssertion of Privacy and Ethical Considerations\u003c/h4\u003e\n\u003cp\u003eTo respect privacy and strictly comply with the protection of personal information, all personal identifiers present in the original administrative documents have been thoroughly de-identified and removed. Crucially, to prevent the identification of residential locations of the citizens who provided the reports, the spatial coordinates of the occurrence locations have been generalized (rounded) strictly in cases where such identification was deemed possible. These privacy-masked records are explicitly distinguished and separated within the dataset. Furthermore, following international guidelines for the publication of sensitive biodiversity data, spatial coordinates for threatened species have also been generalized to prevent potential harm to local populations. This dual geographic masking approach protects both the privacy of the human reporters and the vulnerable species, balancing scientific transparency with rigorous ethical protections.\u003c/p\u003e"},{"header":"Data Records","content":"\u003cp\u003eThe data presented in this study are available at Figshare\u003csup\u003e42\u003c/sup\u003e under the identifier 10.6084/m9.figshare.31580410. The dataset consisted of a single comma-separated value file, TFTsukubaRoadkill_Dataset.csv. The field names and descriptions were primarily defined in accordance with previous research\u003csup\u003e24,43\u003c/sup\u003e to ensure global interoperability. To accurately reflect the unique requirements of this study, certain fields and terms were specifically developed as necessary. All field names and descriptions summarized in Table 3. The dataset contained 14,249 records but 11,464 records and 34 fields filtered from the main data standardized according to the DwC\u003csup\u003e37\u003c/sup\u003e terminology to ensure interoperability with global biodiversity databases\u003csup\u003e44\u003c/sup\u003e (occurrence data are currently being registered to the GBIF via the Japan Node, and the DOI will be provided prior to publication). To facilitate both ecological analysis and methodological assessment, the fields were categorized into the following four primary groups.\u003c/p\u003e\n\u003ch3\u003eDataset Attributes (DwC format)\u003c/h3\u003e\n\u003cp\u003eStandard DwC fields were used to document the dataset of each record:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Identifier: \u0026ldquo;occurrenceID\u0026rdquo; and \u0026ldquo;catalogNumber\u0026rdquo; provide unique identifiers for an occurrence.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Nature: \u0026ldquo;basisOfRecord\u0026rdquo; indicates the specific nature of the record.\u003c/p\u003e\n\u003ch3\u003eSpatiotemporal and Taxonomic Information (DwC format)\u003c/h3\u003e\n\u003cp\u003eStandard DwC fields document the \u0026ldquo;when,\u0026rdquo; \u0026ldquo;where,\u0026rdquo; and \u0026ldquo;what\u0026rdquo; of each record:\u003c/p\u003e\n\u003cp\u003eLocation: decimalLatitude, decimalLongitude, and locality (e.g., district names) provide spatial coordinates and administrative context. The set including \u0026ldquo;continent,\u0026rdquo; \u0026ldquo;countryCode,\u0026rdquo; \u0026ldquo;country,\u0026rdquo; \u0026ldquo;stateProvince,\u0026rdquo; \u0026ldquo;municipality,\u0026rdquo; and \u0026ldquo;geodeticDatum\u0026rdquo; shows the one record in this dataset.\u003c/p\u003e\n\u003cp\u003eSpatial accuracy: \u0026ldquo;coordinateUncertaintyInMeters\u0026rdquo; and \u0026ldquo;georeferenceRemarks\u0026rdquo; provide quantitative and qualitative descriptions of the location, indicating whether those coordinates represent a specific point or the centroid of a subdistrict.\u003c/p\u003e\n\u003cp\u003eTime: \u0026ldquo;year,\u0026rdquo; \u0026ldquo;month,\u0026rdquo; and \u0026ldquo;day\u0026rdquo; record the date of the report.\u003c/p\u003e\n\u003cp\u003eTaxonomy: \u0026ldquo;scientificName\u0026rdquo; provides the final taxonomic identification verified by the authors with the standard DwC field related to taxonomy (\u0026ldquo;kingdom,\u0026rdquo; \u0026ldquo;phylum,\u0026rdquo; \u0026ldquo;class,\u0026rdquo; \u0026ldquo;order, \u0026ldquo;family, \u0026ldquo;genus,\u0026rdquo; \u0026ldquo;specificEpithet,\u0026rdquo; \u0026ldquo;infraspecificEpithet,\u0026rdquo; \u0026ldquo;acceptedNameUsage,\u0026rdquo; \u0026ldquo;scientificNameAuthorship,\u0026rdquo; \u0026ldquo;vernacularName,\u0026rdquo; and \u0026ldquo;taxonRank\u0026rdquo;).\u003c/p\u003e\n\u003cp\u003eStatement about an organism: \u0026ldquo;dynamicProperties\u0026rdquo; provides the Red List category, \u0026ldquo;establishmentMeans\u0026rdquo; shows whether an organism was introduced to the location, \u0026ldquo;identificationVerificationStatus\u0026rdquo; includes categorical indicators of the observer\u0026rsquo;s confidence in the taxonomic identification, and \u0026ldquo;carcassCondition\u0026rdquo; is a qualitative assessment of the carcass\u0026rsquo;s state.\u003c/p\u003e\n\u003ch3\u003eIdentification and Validation History (Original)\u003c/h3\u003e\n\u003cp\u003eTo preserve the provenance of species identification described in the Methods section, the dataset included fields that tracked the multistage validation process:\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;verbatimCitizenTaxon\u0026rdquo; and \u0026ldquo;verbatimManagerTaxon\u0026rdquo;: The original taxon name provided by the citizen and manager if misidentified mentioned by \u0026ldquo;misidentifiedByCitizen\u0026rdquo; and \u0026ldquo;misidentifiedByManager\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;identifiedBy\u0026rdquo;: The name of the primary expert who performed the initial identification.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;verifiedBy\u0026rdquo;: Name of the secondary verifier.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;taxonVerifiedBy\u0026rdquo;: The name of the advanced specialist who resolved difficult or doubtful identifications.\u003c/p\u003e\n\u003ch3\u003eData Quality and Logical Flags (Original)\u003c/h3\u003e\n\u003cp\u003eCustom logical flags and condition fields designed to support data filtering and the calculation of survey efforts and missing data were unique to this dataset. These include the following:\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;localityID\u0026rdquo;: A unique identifier for a locality used to connect the data with that of the 2020 Population Census of Japan\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;isUnconfirmedReport\u0026rdquo;: A Boolean flag (TRUE/FALSE) indicating records where a site visit was conducted but no carcass was found.\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;isNotRoadkill\u0026rdquo;: A flag identifying records where the cause of death was unrelated to vehicle collisions (e.g., predation).\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;hasUnreliableLocation\u0026rdquo; \u0026amp; \u0026ldquo;isOtherInvalid\u0026rdquo;: Flags mark records with insufficient spatial precision or other quality issues, allowing users to filter high-precision analyses.\u003c/p\u003e\n\u003cp\u003eFull definitions, data types, and examples for all fields are provided in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003ch5\u003eTable 3. Definitions and descriptions of data fields. This table outlines the variable names, definitions, value ranges or levels, and examples of each field in the published dataset. The field names and definitions followed DwC standards to ensure interoperability. \u0026nbsp;\u003c/h5\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eField\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRange/Levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExample\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eoccurrenceID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnique identifier for an occurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14,249 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTF:TsukubaRoadkill:10000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecatalogNumber\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnique identifier for an occurrence within the data set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14,249 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ebasisOfRecord\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpecific nature of the data record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 basis of record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHumanObservation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003econtinent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eContinent where the data were recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 continents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecountryCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard code for the country in which the DwC term \u0026ldquo;Location\u0026rdquo; occurs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 country codes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCountry where the data were recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003estateProvince\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProvince where the data were recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 province\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIbaraki Prefecture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emunicipality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMunicipality where the data were recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 municipality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTsukuba\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elocality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpecific description of area (e.g., Oaza or Chome subdistricts) where roadkill was recorded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e343 localities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAmakubo 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edecimalLatitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeographic latitude (in decimal degrees, using spatial reference system in the geodetic datum) of roadkill, standardized and georeferenced by the authors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.94834 to 36.23086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.0127627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edecimalLongitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGeographic longitude (WGS84 in decimal degrees, using spatial reference system in the geodetic datum) of roadkill, standardized and georeferenced by the authors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140.0009 to 140.1685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140.1325247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003egeodeticDatum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCoordinate system and set of reference points upon which geographic coordinates were based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 coordinate system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWGS84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecoordinateUncertaintyInMeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQuantitative description of the spatial accuracy of location, indicating whether the coordinates represent a specific point or centroid of a local subdistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100 or 1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003egeoreferenceRemarks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQualitative description of the spatial accuracy of location, indicating whether the coordinates represent a specific point or centroid of a local subdistrict\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoint or Polygon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePoint\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ekingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull scientific name of kingdom in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnimalia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ephylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull scientific name of phylum in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 phylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChordata\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eclass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull scientific name of class in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 classes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMammalia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull scientific name of order in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18 orders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCarnivora\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull scientific name of family in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 families\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCanidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003escientificName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull scientific name of species and subspecies in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52 names\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNyctereutes procyonoides (Gray, 1834)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003egenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGenus name of the species and subspecies in which the taxon is classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39 genera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNyctereutes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003especificEpithet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eName of the species within the genus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eprocyonoides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003einfraspecificEpithet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eName of the lowest or terminal infraspecific epithet of the scientificName, excluding any rank designation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ejaponica\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eacceptedNameUsage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFull name, with authorship and date information (if known), of the currently valid (zoological) or accepted (botanical) taxon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNyctereutes procyonoides (Gray, 1834)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003escientificNameAuthorship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAuthorship information for the scientificName formatted according to the conventions of the applicable dwc:nomenclaturalCode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(Gray, 1834)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edynamicProperties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRed List Category in IUCN, Japanese Ministry of the Environments, and Ibaraki Prefecture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeast Concern, Near Threatened, Vulnerable, Data Deficient, Critically Endangered, or unlisted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e{\u0026quot;iucnStatus\u0026quot;:\u0026quot;Least Concern\u0026quot;, \u0026quot;envStatus\u0026quot;:\u0026quot;Vulnerable\u0026quot;, \u0026quot;prefStatus\u0026quot;:\u0026quot;Critically Endangered\u0026quot;}\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eestablishmentMeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStatement about whether an organism has been introduced to a given place and time through the direct or indirect activity of modern humans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003enative, non-native, vagrant, or uncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003evernacularName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCommon or vernacular name (English)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53 names\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eraccoon dog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etaxonRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpecies or subspecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eyear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYear of event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2005\u0026ndash;2007, 2016\u0026ndash;2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emonth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMonth of event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eday\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDay of event\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eidentificationVerificationStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCategorical indicator of the extent to which the taxonomic identification has been verified to be correct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003euncertain, verified by observer, or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eorganismRemarks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConcatenated notes regarding the physical state of the individual organism, integrating assessments of carcass condition, the presence of a collar, and visual signs of sarcoptic mange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCarcass condition: intact, damaged, dismembered, unidentifiable, or not applicable; Collar presence and Sarcoptic mange: 1, 0, unidentifiable, or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCarcass condition: damaged; Collar presence: 0; Sarcoptic mange: 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003elocalityID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUnique identifier for a locality used in this study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e344 records\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emisidentifiedByCitizen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBinary flag indicating if the initial report from a citizen contained taxonomic misidentification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1, 0, or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003everbatimCitizenTaxon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOriginal taxon name provided by citizen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecommon or vernacular name, matched, or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emisidentifiedByManager\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBinary flag indicating if identification by contractor was incorrect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1, 0, or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003everbatimManagerTaxon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOriginal taxon name provided by contractor before correction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecommon or vernacular name, matched, or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ecat_or_raccoon_dog\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eisUnconfirmedReport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLogical flag indicating a citizen report was received and a site visit was conducted, but no carcass was found\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE, FALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eisNotRoadkill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLogical flag for records where the cause of death was determined to be nonvehicular collision (e.g., natural death and predation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE, FALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ehasUnreliableLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLogical flag indicating that geographic coordinates were too uncertain or invalid for spatial analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE, FALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eisOtherInvalid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLogical flag for records that did not meet other data quality standards (e.g., incomplete data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE, FALSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eidentifiedBy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eName of specialist who performed primary identification and data verification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ename\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKeisuke Yamamoto\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003everifiedBy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eName of person who performed the secondary double-check of the record\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ename or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eShoma Jingu\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003etaxonVerifiedBy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eName of specialist with advanced expertise who performed third check for difficult or doubtful taxonomic identifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ename or not applicable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTomoro Mukai\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Data Overview","content":"\u003cp\u003eThe final dataset comprised 14,249 records spanning an 11-year period (April 2005\u0026ndash;August 2007 and April 2016\u0026ndash;December 2025). To analyze the confirmed and accurately georeferenced roadkill occurrences, the total dataset was filtered using established logical flags. Records were flagged if they represented unconfirmed reports (isUnconfirmedReport; n = 1,281), nonvehicular mortality (isNotRoadkill; n = 637), ambiguous spatial precision (hasUnreliableLocation; n = 895), or other data inconsistencies (isOtherInvalid; n = 22). Because multiple flags could be assigned to a single record, excluding these flagged entries yielded a robust subset of 11,464 valid records. Table 4 shows the taxonomic overview and spatial resolution for each group: amphibia (n = 9), reptilia (n = 86), aves (n = 1,144), and mammalia (n = 10,215). It lists each identifiable species and 10 additional entries that could not be identified from the records. Among mammals, the domestic cat (\u003cem\u003eFelis catus\u003c/em\u003e) was the most frequently recorded species (n = 4,348), accounting for the largest proportion of the dataset. The raccoon dog (\u003cem\u003eNyctereutes procyonoides\u003c/em\u003e), was the most abundant native species (n = 2,856). The non-native masked palm civet (\u003cem\u003ePaguma larvata\u003c/em\u003e) was also highly prevalent (n = 1,444). These three species collectively constitute the core of roadkill in Tsukuba. The data also captured a significant number of raccoons (\u003cem\u003eProcyon lotor\u003c/em\u003e; n = 592), a designated invasive species under Japanese law. Figure 1c shows the spatial distribution of all 11,464 records across Tsukuba. A key characteristic of this dataset is its dual-resolution spatial structure. Point-level records (red dots in Figure 2 and the number of \u0026ldquo;Point\u0026rdquo; in Table 4) represent high-precision locations derived from citizen reports. Polygon-level records (blue shaded areas in Figure 1c and the number of \u0026ldquo;Polygon\u0026rdquo; in Table 4) represent records aggregated at the administrative units derived from contractor reports. These different styles ensured that roadkill occurrences were documented even in areas or situations where precise location details were unavailable.\u0026nbsp;\u003c/p\u003e\n\u003ch5\u003eTable 4. Taxonomic overview and spatial resolution of the valid Tsukuba roadkill dataset, , including the total number of occurrences per species and corresponding spatial precision levels. Masked records represent species subject to conservation concerns in DwC dataset.\u003c/h5\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFamily\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGenus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eScientificName\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eVernacularName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eestablishmentMeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003etaxonRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eNumber of valid roadkill\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePoint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePolygon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMasked\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAmphibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAnura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAnura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003efrog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eORDER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eReptilia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSquamata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003esnake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eORDER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTestudines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eturtle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eORDER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAves\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ebird\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCLASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAccipitriformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAccipitridae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eAccipiter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eAccipiter nisus nisosimilis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003esparrow hawk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSUBSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMilvus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMilvus migrans (Boddaert, 1783)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ekite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAnseriformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAnatidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAnatidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003educk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFAMILY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eAnas\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eAnas zonorhyncha Swinhoe, 1866\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003egrey duck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCygnus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCygnus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eswan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGENUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eColumbiformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eColumbidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eColumbidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003epigeon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFAMILY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCoraciiformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eAlcedinidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eAlcedo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eAlcedo atthis (Linnaeus, 1758)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ekingfisher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFalconiformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFalconidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFalco\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFalco tinnunculus Linnaeus, 1758\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ecommon kestrel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGalliformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePhasianidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGallus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGallus gallus domesticus (Linnaeus, 1758)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003echicken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSUBSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePhasianus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePhasianus versicolor Vieillot, 1825\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003epheasant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGruiformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRallidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFulica\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFulica atra Linnaeus, 1758\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eeurasian coot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGallinula\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eGallinula chloropus (Linnaeus, 1758)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ecommon moorhen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eZapornia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eZapornia fusca (Linnaeus, 1766)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eruddy crake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePasseriformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCorvidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCorvus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCorvus Linnaeus, 1758\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ecrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eGENUS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eHirundinidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eHirundo\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eHirundo rustica Linnaeus, 1758\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eswallow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLeiothrichidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eLeiothrix\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eLeiothrix lutea (Scopoli, 1786)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ered-billed leiothrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eParidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eParus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eParus minor Temminck \u0026amp; Schlegel, 1848\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eJapanese tit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePasseridae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePasser\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePasser montanus (Linnaeus, 1758)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003esparrow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePycnonotidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMicroscelis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMicroscelis amaurotis (Temminck, 1830)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ebulbul\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTurdidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTurdus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTurdus chrysolaus Temminck, 1832\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ebrown-headed thrush\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTurdus eunomus Temminck, 1831\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003edusky thrush\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eZoothera\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eZoothera aurea (Holandre, 1825)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ewhite\u0026apos;s thrush\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eZosteropidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eZosterops\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eZosterops japonicus Temminck \u0026amp; Schlegel, 1845\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eJapanese white-eye\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePelecaniformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eArdeidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eArdeidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eheron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFAMILY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eArdea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eArdea cinerea jouyi A.H.Clark, 1907\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003egrey heron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSUBSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProcellariiformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProcellariidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProcellariidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eshearwater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003evagrant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFAMILY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eStrigiformes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eStrigidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eStrix\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eStrix uralensis Pallas, 1771\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eowl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMammalia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eArtiodactyla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSuidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eSus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eSus scrofa leucomystax Temminck, 1842\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eboar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSUBSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCarnivora\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eCanidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCanis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eCanis lupus subsp. familiaris\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003edog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSUBSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eNyctereutes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eNyctereutes procyonoides (Gray, 1834)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eraccoon dog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eVulpes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eVulpes vulpes japonica J.E.Gray, 1868\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eJapanese red fox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSUBSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFelidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFelis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eFelis catus Linnaeus, 1758\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ecat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMustelidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMartes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMartes melampus (Wagner, 1840)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emarten\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMeles\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMeles anakuma Temminck, 1844\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ebadger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMustela\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eMustela itatsi Temminck, 1844\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eweasel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eProcyonidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eProcyon\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eProcyon lotor (Linnaeus, 1758)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eraccoon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eViverridae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePaguma\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003ePaguma larvata (C.E.H.Smith, 1827)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emasked palm civet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eChiroptera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eChiroptera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ebat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eORDER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLagomorpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eLeporidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eLepus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eLepus brachyurus Temminck, 1844\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ehare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eOryctolagus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eOryctolagus cuniculus (Linnaeus, 1758)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003edomestic rabbit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRodentia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMuridae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMuridae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFAMILY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRodentia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSciuridae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTamias\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eTamias sibiricus (Laxmann, 1769)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003echipmunk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003enon-native\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSPECIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eSoricomorpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTalpidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eTalpidae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003emole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFAMILY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003euncertain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePHYLUM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e11464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Technical Validation","content":"\u003ch2\u003eAssessment of Identification Accuracy\u003c/h2\u003e\n\u003cp\u003eA critical concern in citizen science is the reliability of species identification. To validate the quality of this dataset, the researchers used a three-tier identification structure, as described in the Methods section. By comparing the reported species (citizen/contractor input) with expert verifications, users can quantitatively assess identification error rates.\u003c/p\u003e\n\u003cp\u003eA previous analysis of a subset of this data revealed that while citizens often misidentified morphologically similar species (e.g., raccoon dogs vs. badgers), the inclusion of photographic evidence in the final reports allowed experts to correct these errors with high confidence\u003csup\u003e32\u003c/sup\u003e. The species fields indicated records that underwent the most rigorous level of scrutiny, ensuring that even difficult-to-identify species were assigned with high reliability.\u003c/p\u003e\n\u003ch2\u003eSurvey Consistency and Missing-Data Mechanisms\u003c/h2\u003e\n\u003cp\u003eUnlike ad hoc volunteer surveys, which often suffer from spatiotemporal gaps and inconsistent effort based on individual availability\u003csup\u003e26\u003c/sup\u003e, the data collection for this dataset was driven by a systematic and pre-existing administrative mandate: public road safety management. This ensured a consistent level of survey effort across the entire 11-year period and the complete 284 km\u0026sup2; administrative area. Furthermore, understanding the specific mechanisms underlying missing or excluded data is critical for building robust inferential models. To facilitate this interpretation, the present dataset explicitly retained unverified or invalid reports rather than discarding them. By providing specific logical flags (e.g., \u0026ldquo;isUnconfirmedReport,\u0026rdquo; \u0026ldquo;isNotRoadkill,\u0026rdquo; \u0026ldquo;hasUnreliableLocation,\u0026rdquo; and \u0026ldquo;isOtherInvalid\u0026rdquo;), users can better understand the reasonable for the exclusion of a record from the main occurrence subset. For instance, the inclusion of \u0026ldquo;isUnconfirmedReport\u0026rdquo; = TRUE (instances where contractors responded but found no carcasses) acted as a \u0026ldquo;null\u0026rdquo; event demonstrating the system\u0026rsquo;s continuous responsiveness, while also allowing researchers to model false-positive reporting rates. Collectively, these transparency flags enable data users to evaluate missing-data mechanisms, treat the dataset as a continuous time series rather than opportunistic sightings, and apply appropriate statistical treatments for missingness in advanced ecological or spatial modeling.\u003c/p\u003e\n\u003ch2\u003eData Quality and Usage of Logical Flags\u003c/h2\u003e\n\u003cp\u003eTo ensure the applicability of the dataset for ecological modeling, researchers implemented logical flags to isolate potential biases. The \u0026ldquo;isNotRoadkill\u0026rdquo; flag allows users to exclude nonvehicular mortality (e.g., disease and predation), ensuring that analyses of road impacts are not confounded by other mortality factors. The \u0026ldquo;hasUnreliableLocation\u0026rdquo; flag identifies records where the address description was too vague for precise georeferencing. Finally, the \u0026ldquo;carcassCondition\u0026rdquo; field (Levels A\u0026ndash;C) allows users to filter specimens that were too damaged for reliable identification, thereby reducing uncertainties in species distribution models. By filtering based on these flags, users can generate a \u0026ldquo;clean\u0026rdquo; subset of high-precision, confirmed roadkill occurrences suitable for detailed spatial analyses.\u003c/p\u003e"},{"header":"Usage Notes","content":"\u003ch2\u003eInterpretation of Observation Biases\u003c/h2\u003e\n\u003cp\u003eUsers should be aware that this dataset represents citizen-reported presence-only data. Moreover, a roadkill record was generated only after surviving a multistep \u0026ldquo;filtration\u0026rdquo; process of detection and reporting (Figure 3)\u003csup\u003e22,27,45\u0026ndash;47\u003c/sup\u003e. Consequently, the absence of records for a specific area or period does not necessarily indicate the absence of a species or roadkill events. Data users should consider inherent biases when using data for distribution modeling or population estimation.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eMultistep Detection Process\u003c/h3\u003e\n\u003cp\u003eA roadkill event was recorded in this dataset only if it passed through the filters depicted in Figure 3. Failure at any stage resulted in \u0026ldquo;nondetection.\u0026rdquo; This could occur because of a variety of factors.\u003c/p\u003e\n\u003ch4\u003eOccurrence (Event Probability)\u003c/h4\u003e\n\u003cp\u003eHowever, not all road crossings lead to mortality. Most individuals cross safely, whereas others may be injured but retreat into underbrush before death. Only animals that died on the road or were knocked onto the shoulder were potentially detectable.\u003c/p\u003e\n\u003ch4\u003eDriver Reporting Bias\u003c/h4\u003e\n\u003cp\u003eDrivers are more likely to report collisions with large mammals (e.g., deer and boar) owing to potential vehicle damage and insurance requirements. Conversely, collisions with smaller species are frequently ignored or unnoticed. Moreover, the probability of roadkill is often higher at night, and reduced visibility and driver awareness during dark hours significantly lower the likelihood of immediate reporting.\u003c/p\u003e\n\u003ch4\u003eCarcass Persistence (Availability):\u003c/h4\u003e\n\u003cp\u003eCarcasses are frequently removed by scavengers (e.g., crows) before they can be noticed by other citizens or collected by road managers\u003csup\u003e22\u003c/sup\u003e. Furthermore, vehicle impact may knock the carcass off the road surface into vegetation, rendering it invisible to passersby.\u003c/p\u003e\n\u003ch4\u003eCitizen Detection and Action\u003c/h4\u003e\n\u003cp\u003eIf a carcass remained on the road, it had to be noticed by a passerby or a subsequent driver before being reported (if the initial driver did not report it). Local residents or volunteers could bury or dispose of carcasses without notifying the city administration. Such roadkill events were not recorded.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the JSPS KAKENHI Grant Number 24K20980.\u003c/p\u003e\n\u003ch2\u003eCode Availability\u003c/h2\u003e\n\u003cp\u003eThe Java code used to process and validate the dataset for covering taxonomic information is sorted from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/PORBIOTA/PORBIOTA-ICNF/tree/main/DwC_Creation_Helpers\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.J. conceptualized the study, conducted the formal analysis, developed the software, and prepared the visualizations. S.J. and Y.O. developed the methodology. S.J., M.U., Y.O., R.A., K.Y., T.M., and S.S. curated the data and performed the validation. S.J., M.U., Y.O., K.Y., T.M., and Y.W. conducted the investigation. S.J. and M.U. provided the resources. S.J. and Y.W. acquired the funding, administered the project, and supervised the research. S.J. wrote the main manuscript text, and S.J. and Y.O. reviewed and edited the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the Clean Environment Promotion Division of Tsukuba City Government for data contribution.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data generated and analyzed in this study are available at Figshare under the identifier 10.6084/m9.figshare.31580410. This repository includes the main roadkill dataset (TFTsukubaRoadkill_Dataset.csv), along with the detailed metadata (TFTsukubaRoadkill_dataDigitization.csv and TFTsukubaRoadkill_locality.csv), all provided as comma-separated values files. Furthermore, the species occurrence data, extracted and standardized in strict accordance with DwC, have been registered and are publicly accessible through the GBIF (DOI to be added during the revision process). The original administrative reports and photographs of individual carcasses serving as the basis for this dataset are not publicly shared, as the ownership and disclosure rights are held by the municipal government of Tsukuba. However, these materials may be provided by the corresponding author upon reasonable request, subject to necessary permissions from municipal authorities. Details regarding the availability of original reports and photographic evidence for specific data collection periods are summarized in the detailed metadata (TFTsukubaRoadkill_dataDigitization.csv).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHughes, A. C. The post‐2020 global biodiversity framework: How did we get here, and where do we go next? \u003cem\u003eIntegr. Conserv.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 1\u0026ndash;9 (2023). https://doi.org/10.1002/inc3.16\u003c/li\u003e\n\u003cli\u003eStephens, T. The Kunming\u0026ndash;Montreal Global Biodiversity Framework. \u003cem\u003eInt. Leg. Mater.\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 868\u0026ndash;887 (2023). https://doi.org/10.1017/ilm.2023.16\u003c/li\u003e\n\u003cli\u003eIPBES. \u003cem\u003eGlobal Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services\u003c/em\u003e. \u003cu\u003ehttps://zenodo.org/record/6417333\u003c/u\u003e (2019) https://doi.org/10.5281/zenodo.6417333\u003c/li\u003e\n\u003cli\u003eOxford University Press. 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Serv.\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 1\u0026ndash;16 (2017). https://doi.org/10.1016/j.ecoser.2017.09.008\u003c/li\u003e\n\u003cli\u003eUnited Nations Convention on Biological Diversity. Monitoring framework for the Kunming-Montreal global biodiversity framework. in \u003cem\u003eProceedings of the fifteenth meeting of the conference of the parties to the convention on biological diversity (part two)\u003c/em\u003e 20\u0026ndash;45 (United Nations Convention on Biological Diversity, 2022).\u003c/li\u003e\n\u003cli\u003eOgawa, Y. \u003cem\u003eet al.\u003c/em\u003e Optimizing the frequency of question items for bird species in quiz-style online training. \u003cem\u003eEcol. Inform.\u003c/em\u003e \u003cstrong\u003e85\u003c/strong\u003e, 102908 (2025). https://doi.org/10.1016/j.ecoinf.2024.102908\u003c/li\u003e\n\u003cli\u003ePocock, M. J. O., Tweddle, J. C., Savage, J., Robinson, L. D. \u0026amp; Roy, H. E. 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Total Environ.\u003c/em\u003e \u003cstrong\u003e642\u003c/strong\u003e, 629\u0026ndash;637 (2018). https://doi.org/10.1016/J.SCITOTENV.2018.06.107\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-9517196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9517196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Roadkill data are vital for assessing biodiversity and human–wildlife conflicts. However, datasets are often constrained by linear sampling along major highways, a lack of missing-data mechanism quantification, and geographical bias toward Western regions. This Data Descriptor presents a high-resolution dataset of 14,249 roadkill records collected over 11 years (2005–2007 and 2016–2025) in Tsukuba, Japan. Unlike traditional surveys, this dataset was generated through an “unintended citizen science,” in which citizens report carcasses to the municipal administration, triggering collection by contracted professionals. This mechanism ensured comprehensive “planar” coverage of the entire 284 km² administrative area. This standardized dataset features three methodological strengths: (1) a dual-resolution georeferencing system that assigns “point” and “polygon” precisions to detailed and ambiguous records, respectively; (2) a missing-data proxy via the retention of invalid reports; and (3) a three-tier taxonomic verification process involving citizens, contractors, and experts. The dataset offers a baseline for analyzing the impacts of urbanization, monitoring the invasion of non-native species, and evaluating the reliability of other administrative biodiversity monitoring systems.","manuscriptTitle":"An 11-year spatiotemporal roadkill dataset derived from citizen reports in Tsukuba, Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 07:02:06","doi":"10.21203/rs.3.rs-9517196/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":"49e80bf8-cee5-4435-a06b-238047b659bf","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[{"type":"editorAssigned","content":"","date":"2026-05-13T15:46:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-02T12:40:24+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T07:02:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 07:02:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9517196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9517196","identity":"rs-9517196","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}