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26 results for “Roadkill”
Data from: Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India
<p>This dataset contains animal roadkill data (2011-13) from the Valparai Plateau and Anamalai Tiger Reserve, Western Ghats, India. Occurrence records were gathered in the field by researchers of the <a href="https://www.ncf-india.org">Nature Conservation Foundation, India</a>. The dataset corresponds to the following publication:</p> <p>Jeganathan, P., Mudappa, D., Kumar, M. A., and Raman, T. R. S. 2018. <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Seasonal variation in wildlife roadkills in plantations and tropical rainforest in the Anamalai Hills, Western Ghats, India</a>. <em>Current Science</em> 114(3): 619-626. DOI: 10.18520/cs/v114/i03/619-626</p> <p>CONTACT #1<br> 1. Name: P. Jeganathan<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: jegan@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-0238-0655</p> <p>CONTACT #2<br> 1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p>CONTACT #3<br> 1. Name: M. Ananda Kumar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: anand@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-7094-1314</p> <p>CONTACT #4<br> 1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>Keywords: </strong>tropical rainforest, plantations, Anamalai Hills, animal roadkill, linear infrastructure intrusions, highways, road ecology, animal-vehicle collisions </p> <p><strong>Geographic Coverage:</strong><br> 1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India<br> 2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><strong>Temporal Coverage:</strong><br> 1. Begins: 2011-06-01 (Year, Month, Day)<br> 2. Ends: 2013-05-31 (Year, Month, Day)</p> <p><strong>Methods:</strong></p> <p>Methods involved repeated surveys along the road routes searching for roadkills and habitat sampling as described in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018),<em> Current Science</em> 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626</p> <p><strong>Files included:</strong></p> <p>Besides this 00_README.txt file, the dataset includes the following six files as explained below:<br> 1) 01_habitat_length.csv -- details of road routes surveyed as line transects<br> 2) 02_sampling_events.csv -- details of individual line transect sample surveys along road routes<br> 3) 03_roadkill_data_final.csv -- roadkill occurrence data from sample surveys along road routes<br> 4) 04_canopy_and_habitat.csv -- canopy and habitat readings along road routes (transects) surveyed<br> 5) 05_roadkill_transects_all.kml -- KML file containing geographic tracks of 11 road routes surveyed as roadkill transects<br> 6) 06_road_transects_map.jpg -- Map of surveyed routes corresponding to Figure 1 in Jeganathan et al. (2018)</p> <p><strong>01_habitat_length.csv</strong><br> transect: name of road route surveyed as a line transect<br> route_description: description of road route<br> tlength_km: transect length along road in kilometres (km)<br> tlength_m: transect length along road in metres (m)<br> forest: extent of the road in metres (m) with forest on both sides<br> forest_tea: extent of the road in metres (m) with forest on one side, tea on the other<br> coffee_forest: extent of the road in metres (m) with forest on one side, coffee plantation on the other<br> tea: extent of the road in metres (m) with tea plantation on both sides<br> coffee: extent of the road in metres (m) with coffee plantation on both sides<br> eucalyptus: extent of the road in metres (m) with eucalyptus plantation on both sides<br> eucalyptus_tea: extent of the road in metres (m) with eucalyptus on one side, tea plantation on the other</p> <p><strong>02_sampling_events.csv</strong><br> season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon)<br> transect: name of road route surveyed as a line transect<br> transect: name of road route surveyed as a line transect<br> tcode: unique code for each individual survey of a road route (transect) coevered on a specific date<br> eventDate: date of road survey<br> tlength: transect length along road in kilometres (km)</p> <p><strong>03_roadkilldata_final.csv</strong><br> sno: serial number of observation<br> season: monsoon (June to December 2011) or summer (March to June 2012 prior to the onset of 2012 monsoon)<br> transect: name of road route surveyed as a line transect<br> tcode: unique code for each individual survey of a road route (transect) coevered on a specific date<br> eventDate: date of road survey<br> fielddate: date of road survey as initially noted (for two surveys completed over two successive days, the initial date was recorded as eventDate for 2011-06-17 = 2011-06-16 and eventDate for 2011-07-06 = 2011-07-05<br> tlength: transect length along road in kilometres (km)<br> verbatimIdentification: original identification of roadkilled taxon<br> vernacularName: common name of taxon<br> scientificName: scientific name of taxon for corresponding taxonomic level of identification<br> taxonRank: rank of taxon indicating for corresponding taxonomic level of identification<br> taxonRemarks: category of taxon as noted for analysis<br> verbatimCoordinateSystem: coordinate system used for initial data collection<br> verbatimSRS: SRS of the location data collected (EPSG:32643/WGS84)<br> georeferenceRemarks: note indicating locations were converted from UTM (zone 43 N) to latitude longitude using QGIS software<br> verbatimLongitude: UTM longitude (Easting) as originally recorded<br> verbatimLatitude: UTM latitude (Northing) as originally recorded<br> decimalLongitude: longitude in decimal degree East<br> decimalLatitude: latitude in decimal degrees North<br> habitat: habitat on either side of the road (forest - forest on both sides; forest_tea - forest on one side, tea on the other; human - human settlements; coffee - coffee plantation on both sides; coffee_forest - coffee on one side, forest on the other; eucalyptus - eucalyptus plantation on both sides; eucalyptus_tea - eucalyptus on one side, tea on the other; tea - tea plantation)<br> individualCount: number of individuals recorded as roadkill (0 if no roadkills in that survey)<br> occurrenceStatus: indicated as 'present' for roadkills, or 'absent' if no roadkills recorded<br> occurrenceRemarks: notes and remarks if any</p> <p><strong>04_canopy_and_habitat.csv</strong><br> transect: name of road route surveyed as a line transect<br> verbatimroute: route name as originally noted<br> sno: serial number<br> canopycover: 0 if tree canopy absent, 1 if tree canopy present above point of observation<br> canopyoverlap: horizontal overlap of tree canopy above point of observation ranked as 0 - no canopy above; 1 canopy present but barely touching or overrlapping; 2 - canopy overlapping with sky still visible through leaves; 3 - canopy overlaps overhead densely with sky scarcely visible<br> verticaloverlap: vertical gap between canopy or branches of trees above point of observation ranked as 0 - very wide; 1 - barely touching, 2 - significant vertical overlap, 3 - substantial and dense vertical overlap<br> habcode: two letter alphabetical code with each letter indicating habitat on one side of the road at the point of observation with f - forest, t - tea, c - coffee, e - eucalyptus, v - village or human habitation, m - dam or reservoir<br> habno: numeric category for habitat on either side coded as 1 for monocultures (ee, tt); 2 for mixed forest and plantation (ef, ft, etc.); 3 for forest (ff), and 4 for coffee plantation (cc)<br> longitude: longitude in decimal degrees east<br> latitude: latitude in decimal degrees north</p> <p><strong>05_roadkill_transects_all.kml</strong><br> This KML file contains all 11 road routes surveyed as roadkill transects.</p> <p><strong>06_road_transects_map.jpg</strong><br> This map illustrating the surveyed road routes corresponds to Figure 1 in <a href="https://doi.org/10.18520/cs/v114/i03/619-626">Jeganathan et al. (2018), <em>Current Science</em> 114(3): 619-626</a>, DOI: 10.18520/cs/v114/i03/619-626</p>
Open-population models for estimating roadkill rates - Data and R Code
<p>Roadkill carcass capture-recapture data, capture histories for four and eight-occasion designs, and R code (with JAGS code) for roadkill rates estimation.</p>
Fig. 3 in Danger under wheels: mammal roadkills in the threaten lowland Atlantic Forest in southeast Brazil
Fig. 3. Roadkill hotspots of mammal species recorded along the RJ-122 highway (from Km 1 to Km 34) between October 2017 and January 2020, in the state of Rio de Janeiro, Brazil. The largest number of roadkills is concentrated in two regions (black location icon), Km 23/24 (N = 22 and N = 24) and Km 28/29 (N = 18 and N = 22).
Fig. 1 in Danger under wheels: mammal roadkills in the threaten lowland Atlantic Forest in southeast Brazil
Fig. 1. Biplot of the five most roadkilled species of mammals (Didelphis aurita, Coendou insidiosus, Cerdocyon thous, Dasypus novemcinctus and Callithrix jacchus) along the RJ-122 highway between October 2017 and January 2020, in the state of Rio de Janeiro, Brazil. Circle: mammal species; Triangle: months of the year.
Fig. 2 in Danger under wheels: mammal roadkills in the threaten lowland Atlantic Forest in southeast Brazil
Fig. 2. Frequency of roadkill mammals over every kilometer along the RJ- 122 highway between October 2017 and January 2020, in state of Rio de Janeiro, Brazil. Highlights are km 23, km 24, km 28 and km 29.
Roadkill Dataset 2014-2023 Quality level 2
<p>Dies sind die Daten zu überfahrenen Wirbeltieren aus dem Citizen Science Projekt Roadkill (https://roadkill.at) aus den Jahren 2014-2023, welche der Qualitätsstufe 2 entsprechen. Daten der Qualitätsstufe 1 wurden über GBIF veröffentlicht und sind hier zu finden: https://www.gbif.org/dataset/d0d5ef85-71b2-4da6-b6f6-c1c3d60987d3 </p> <p>Jeden zweiten Tag wurden die in das Projekt Roadkill eingegebenen Daten von Mitglieder*innen des Projektteams validiert, um falsche oder inkonsistente Einträge über das Backend der Website zu korrigieren oder, falls der Datensatz nicht korrigiert werden kann, den Datensatz zu löschen. Die Korrektur von Daten erfolgte (i) durch das Projektteam selbst, wenn Fehler offensichtlich war (z. B. Tier auf dem eingereichten Bild stimmt nicht mit der angeführten Artbestimmung überein) oder (ii) durch die Teilnehmer*innen selbst, nachdem sie vom Projektteam darauf hingewiesen wurden, dass eine Korrektur notwendig ist (z. B. wenn der Roadkill nicht an einer Straße liegt). Da die Teilnehmer*innen die Daten während ihrer täglichen Routine sammelten, handelt es sich bei den vorgelegten Daten um so genannte Präsenzdaten. </p> <p>Um die Qualität der Daten zu sichern, haben wir ein schrittweises Auswahlverfahren angewandt, mit dem wir die eingereichten Daten am Ende dieses Prozesses in drei Qualitätsstufen einordnen konnten:</p> <ul> <li>Qualitätsstufe 1: Datensätze mit korrekter Tierbestimmung (entweder durch Experten oder durch Bilder) und konsistenten Daten</li> <li>Qualitätsstufe 2: Datensätze mit konsistenten Daten, aber keiner möglichen Validierung des Tieres</li> <li>Gelöscht: Datensätze mit inkonsistenten Daten und keiner möglichen Validierung des Tieres</li> </ul> <p><br>Wir danken allen Citizen Scientists, welche die Daten gemeldet und bei der Identifizierung der Tiere geholfen haben. Ohne die viele freiwillige Arbeit der Citizen Scientists wäre dieses Projekt nicht möglich.</p> <p>________________________________________________________________</p> <p>These are the data on roadkill vertebrates from the citizen science project Roadkill collected in the years 2014-2023, which correspond to quality level 2. Quality level 1 data are published via GBIF and can be found here: https://www.gbif.org/dataset/d0d5ef85-71b2-4da6-b6f6-c1c3d60987d3. </p> <p>Every other day, data entered into the Roadkill project were validated by members of the project team to correct incorrect or inconsistent entries via the backend of the website or, if the record cannot be corrected, to delete the record. Correction of data was done (i) by the project team itself when errors were obvious (e.g., animal in the submitted image does not match the species identification listed) or (ii) by the participants themselves after being informed by the project team that a correction is needed (e.g., if the roadkill is not on a road). Since the participants collected the data during their daily routine, the submitted data are so-called presence-only data. </p> <p>To ensure the quality of the data, we used a stepwise selection process that allowed us to classify the submitted data into three quality levels at the end of this process:</p> <ul> <li>Quality Level 1: Records with correct species identification (either by experts or by images) and consistent data.</li> <li>Quality level 2: Records with consistent data, but no possible validation of the species</li> <li>Deleted: records with inconsistent data and no possible validation of the species</li> </ul> <p><br>We thank all the citizen scientists who reported the data and helped identify the species. Without the voluntary work of the citizen scientists this project would not be possible.</p>
Fig. 4 in Persistence of snake carcasses on roads and its potential effect on estimating roadkills in a megadiverse country
Fig. 4. Survival curves (i.e., probability of persistence on the road) for snake carcasses on primary and secondary roads in the municipality of Filandia, department of Quindío, Central Andes of Colombia. Kaplan-Meier estimates based on a sample size N = 81 snake carcasses.
Fig. 2 in Persistence of snake carcasses on roads and its potential effect on estimating roadkills in a megadiverse country
Fig. 2. Images of some species of snakes present in the study area and used in the field experiments. (A) Mastigodryas boddaerti, (B) Tantilla melanocephala, (C) Dipsas sanctijoannis, (D) Erythrolamprus epinephelus, (E) Leptodeira annulata, (F) Oxyrophus petolarius. Photos by Lina M. Robayo-Palacio (A), Fernando Vargas-Salinas (B–D), Ana María Ospina-L (E), and Wolfgang Buitrago-González (F).
Fig. 1 in Persistence of snake carcasses on roads and its potential effect on estimating roadkills in a megadiverse country
Fig. 1. Geographic location of the study area. Primary road (Autopista del Café) and the secondary road connecting Autopista del Café with the town of Filandia in the department of Quindío, Central Andes of Colombia. Adapted from SIG Quindío 2016. http://190.85.164.56/sigquindioiii/
Fig. 3 in Persistence of snake carcasses on roads and its potential effect on estimating roadkills in a megadiverse country
Fig. 3. (A) Relationship between weight and body length (Ln = natural logarithm) of snake carcasses used in this study. (B) Relationship between body length of snake carcasses and their persistence time on two roads with different levels of vehicular traffic.
Fig. 5 in Overview of roadkills in the Serra da Macaca Park Road (SP-139), state of São Paulo, Brazil
Fig. 5. Seasonality of the roadkills. Homeotherms = birds and mammals. Poikilotherms = amphibians and reptiles.
Fig. 1 in Overview of roadkills in the Serra da Macaca Park Road (SP-139), state of São Paulo, Brazil
Fig. 1. Map of the study area. Focus on the Parque Serra da Macaca Road, which crosses the Parque Estadual Carlos Botelho (PECB), State of SÃo Paulo, Brazil.
Fig. 4 in Overview of roadkills in the Serra da Macaca Park Road (SP-139), state of São Paulo, Brazil
Fig. 4. Mean (points) and standard deviation (bars) for the vehicle flow recorded on the Serra da Macaca Park Road (SP-139), State of SÃo Paulo, Brazil, between November 2015 and October 2016. The differences are statisticallY significant according to the t-test (t = - 2.765, g.l. = 10, p = 0.02).
Fig. 3 in Overview of roadkills in the Serra da Macaca Park Road (SP-139), state of São Paulo, Brazil
Fig. 3. Correlation between the number of roadkills and vehicle flow on the Serra da Macaca Park Road (SP-139), State of SÃo Paulo, Brazil: r2 = 0.72 and Y = 9.864/(1+exp(-(-3.912+1.536x))).
Fig. 2 in Roadkilled mammals in the northern Amazon region and comparisons with roadways in other regions of Brazil
Fig. 2. Number of articles evaluated with records of the 13 species identified in this studY and the distribution in each of the six Brazilian eco-regions (Amazonia, Caatinga, Cerrado, Atlantic Forest, Pampa and Pantanal).
Fig. 1 in Roadkilled mammals in the northern Amazon region and comparisons with roadways in other regions of Brazil
Fig. 1. StudY area located on roadwaY BR 364 in central region of state of Rondônia in southwestern portion of Amazon biome. The stretch sampled is in bold in the figure.
Data and code from: Three decades of wildlife-vehicle collisions in a protected area: main roads and long-distance commuting trips to migratory prey increase spotted hyena roadkills in the Serengeti
<p>This is the first release. Potential updates will be available on GitHub: <a href="https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area">https://github.com/MarwanNaciri/Three_decades_of_spotted_hyena_roadkill_in_a_protected_area</a></p>
Fleas of wild mammals carrying pathogenic bacteria in Argentinian Patagonia: a study based on wildlife roadkill
Open the record for dataset details and reuse information.
Roadkill risk in European birds: The role of detection and plumage colouration
Open the record for dataset details and reuse information.
Supporting data for the article 'Three-years monitoring of roadkills trends in a road adjacent to a national park in Panama'
<p><span><span><span><span><span><span><span><span><span><span><span>Roadkill monitoring can provide important information about spatial and temporal trends, including influential factors on the probability of wildlife – collisions. Such data are important for applying mitigation measures that reduce the mortality of species of conservation concern. Unfortunately, road ecology is not a mainstream discipline in some regions of the world and Central America represents one of those cases. I aimed to monitor roadkills in a road next to a national Park in Panama for three years. I examined whether there was variation in the number of roadkills across taxa (birds, mammals and reptiles), year (2017 – 2020), season (dry vs rainy) and whether monthly average precipitation and temperature influenced the probability of roadkill occurrence. Additionally, I performed a spatial analysis to identify roadkill hotspots. Mammals and reptiles were the most common roadkills. Roadkills tended to decrease with increases in temperature and precipitation. A separate analysis of the two most commons roadkills (iguanas and tamanduas) provided similar trends. The spatial analysis helped to identify a hotspot located in a curved section of the road and surrounded by water. This is the first study to examine for any road in Panama - through statistical modeling – factors </span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>that may influence the occurrence of roadkill events. The results suggest that vehicle collisions </span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>exert a similar pressure on all taxa and the geometry of the road or distance to the water are also influential. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>These data sets include monthly roadkill counts with average monthly precipitation and temperature during the dry and rainy season, over a period of three years (ALL.DATA). Two separate data sets (IGUANAS and TAMANDUAS) contain data as described for the tab ALL.DATA but only with data for green iguanas (Iguana iguana) and Northern Tamandua (Tamandua mexicana), respectively. For the spatial analysis, the data set COORDINATES show the location of each roadkill event, identified by latitude and longitude, date, taxa, IUCN status, scientific name and common name.</span></span></span></span></span></span></span></span></span></span></span></p>
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