Snakes on a lane: Road type and edge habitat predict hotspots of snake road mortality

2021 ◽  
Vol 61 ◽  
pp. 125978
Author(s):  
Ryan B. Wagner ◽  
Carl R. Brune ◽  
Viorel D. Popescu
Keyword(s):  
Road Mortality ◽  
Edge Habitat ◽  
Road Type

scholarly journals Proximity to Hibernacula and Road Type Influence Potential Road Mortality of Snakes in Southwestern Saskatchewan

2013 ◽  
Vol 126 (3) ◽  
pp. 194 ◽  
Author(s):  
Ashley N. Fortney ◽  
Ray G. Poulin ◽  
Jessica A. Martino ◽  
Dennilyn L. Parker ◽  
Christopher M. Somers
Keyword(s):  
National Park ◽  
Home Ranges ◽  
Road Mortality ◽  
Traffic Patterns ◽  
Factors Affecting ◽  
Conservation Implications ◽  
Multivariate Modelling ◽  
Radio Transmitters ◽  
Road Type ◽  
Park Area

The behaviour of snakes makes them vulnerable to road mortality, which may have conservation implications for some species. We examined factors affecting road mortality of snakes around Grasslands National Park of Canada (GNP), Saskatchewan, an area inhabited by a unique snake community within Canada, including the threatened Eastern Yellow-bellied Racer (Coluber constrictor flaviventris). Systematic surveys (n = 45) of roads in the Grasslands National Park area in 2009 yielded 36 dead and 18 live snakes. Multivariate modelling revealed that proximity to hibernacula was positively correlated with presence of snakes on roads. Paved roads had disproportionately higher numbers of snake mortalities, suggesting that traffic patterns are a bigger risk factor than road use per se. Some radio-tracked Eastern Yellow-bellied Racers (2 of 17; 12%) and Bullsnakes (Pituophis catenifer sayi) (4 of 5; 80%) captured at emergence from hibernacula had road areas in their home ranges. These individuals equipped with radio-transmitters used roads and immediately adjacent areas significantly more than expected, based on their availability, suggesting possible selection for roads. Strategies to reduce road mortality of snakes should focus on key stretches of roads, such as those near winter hibernacula or riparian zones. The placement of paved roads in sensitive areas like those in and around Grasslands National Park should be carefully considered to minimize snake mortality.

Road mortality in Swedish mammals: results of a drivers' questionnaire

2004 ◽  
Vol 10 (1) ◽  
pp. 225-233 ◽  
Author(s):  
Andreas Seiler ◽  
J-O. Helldin ◽  
Christiane Seiler
Keyword(s):  
Road Mortality

scholarly journals Barriers for big snakes: Incorporating animal behaviour and morphology into road mortality mitigation design

2021 ◽  
Vol 26 ◽  
pp. e01471
Author(s):  
Matthew R. Macpherson ◽  
Jacqueline D. Litzgus ◽  
Patrick J. Weatherhead ◽  
Stephen C. Lougheed
Keyword(s):  
Animal Behaviour ◽  
Road Mortality ◽  
Mitigation Design

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

Road Parameter-Based Driver Assistance System for Safe Driving

2019 ◽  
Vol 2 (4) ◽  
pp. 253-262
Author(s):  
Sai Charan Addanki ◽  
Keyword(s):  
Driver Assistance System ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Safe Driving ◽  
The Road ◽  
Lane Tracking ◽  
Road Type ◽  
Key Aspects ◽  
Type Classification

One of the key aspects of Advanced Driver Assistance Systems (ADAS) is ensuring the safety of the driver by maintaining a safe drivable speed. Overspeeding is one of the critical factors for accidents and vehicle rollovers, especially at road turns. This article aims to propose a driver assistance system for safe driving on Indian roads. In this regard, a camera-based classification of the road type combined with the road curvature estimation helps the driver to maintain a safe drivable speed primarily at road curves. Three Deep Convolutional Neural Network (CNN) models viz. Inception-v3, ResNet-50, and VGG-16 are being used for the task of road type classification. In this regard, the models are validated using a self-created dataset of Indian roads and an optimal performance of 83.2% correct classification is observed. For the calculation of road curvature, a lane tracking algorithm is used to estimate the curve radius of a structured road. The road type classification and the estimated road curvature values are given as inputs to a simulation-based model, CARSIM (vehicle road simulator to estimate the drivable speed). The recommended speed is then compared and analyzed with the actual speeds obtained from subjective tests.

11. Mitigation of Wildlife Collision Mortality at Little Cataraqui Creek Wetland

2018 ◽  
Author(s):  
Erin Keenan ◽  
Jeffy Henderson ◽  
Lauren Malo ◽  
Diana Pedersen
Keyword(s):  
Coastal Wetland ◽  
Public Awareness ◽  
Population Decline ◽  
Urban Traffic ◽  
Coastal Marsh ◽  
Road Mortality ◽  
Wetland Ecosystems ◽  
Turtle Species ◽  
Wetland Species ◽  
Specific Species

               Our research team is concerned with potential reptile and amphibian road mortality through Little Cataraqui Creek wetland along Front Rd. and Bath Rd. in Kingston, Ontario. The coastal marsh is a provincially significant wetland that supports species of migrating and breeding waterfowl, wetland dependent reptiles and amphibians, and may support some of Ontario’s threatened turtle species. The coastal wetland also provides a protected nesting area for a migrating species within Lake Ontario. Specific species of turtles, salamanders and frogs will be investigated.  Our team will identify general breeding, nesting and migratory behavior patterns of associated wetland species, and establish potential indirect effects on the wetland ecosystem due to population decline. Traffic volume and speed limits of Front Rd. and Bath Rd. at Little Cataraqui Creek Wetland will be identified, and any current wildlife crossing systems in place will be evaluated. Based on this research, our team will establish the significance of wildlife road mortality within Little Cataraqui Creek Wetland at Front Rd. and Bath Rd., and propose suitable mitigation techniques. These techniques may include implementing structural design solutions such as road barriers, culverts, etc., as well as increasing public awareness of wildlife crossing on Bath Rd. and Front Rd. by proposing appropriate traffic laws in the area. Themes include wetland species breeding and nesting behavior, and urban/traffic development issues surrounding wetland ecosystems.

Relation of Road Type to Tire Wear

1928 ◽  
Vol 92 (1) ◽  
pp. 854-871
Author(s):  
O. L. Waller ◽  
H. E. Phelps
Keyword(s):  
Tire Wear ◽  
Road Type

scholarly journals Spatiotemporal Patterns and Mechanisms of Chinese Tallowtree (Triadica sebifera) Spread along Edge Habitat in a Coastal Landscape, Mississippi, USA

2018 ◽  
Vol 11 (3) ◽  
pp. 117-126 ◽  
Author(s):  
Zhaofei Fan ◽  
Shaoyang Yang ◽  
Xia Liu
Keyword(s):  
Seed Dispersal ◽  
Negative Binomial ◽  
Seedling Recruitment ◽  
Light Availability ◽  
Spatiotemporal Patterns ◽  
Tree Age ◽  
Triadica Sebifera ◽  
Factors Affecting ◽  
Edge Habitat ◽  
Dispersal Mechanism

AbstractChinese tallowtree [Triadica sebifera(L.) Small] has reached unprecedented prevalence in coastal landscapes in the Gulf of Mexico, especially along edge habitat with low competition and abundant resource (e.g., light) availability. This study investigated the spatiotemporal patterns and mechanisms ofT. sebiferaspread along roadways and fire lines.Triadica sebiferaindividuals and landscape and community features were surveyed in equally spaced, spatially mapped plots. AllT. sebiferaindividuals were felled to determine tree age and status (seed trees or non-seed bearing trees), andT. sebiferaseed and seedling (≤2 yr old) densities and community and landscape features (over- and understory conditions, distance to seed trees) were measured. A zero-inflated negative binomial model was used to evaluate factors affectingT. sebiferaseed dispersal and seedling recruitment contributing to the observed spatiotemporal patterns. Introduced into the Grand Bay National Wildlife Refuge around 30 yr ago,T. sebiferatrees distribute in clustered patterns along roadways and fire lines and exhibit an exponential growth in density. HighT. sebiferaseed and seedling densities mainly occurred in sites that are ≤250 m from seed trees or have sparse overstory and high understory grass/herb coverage. With respect to the avian seed dispersal mechanism, the spatiotemporal patterns ofT. sebiferaspread along roadways and fire lines could be simply characterized by using landscape and community features that influence avian behaviors, including distance to seed trees, overstory tree density, and ground grass/herb coverage.

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