A time geographic approach to delineating areas of sustained wildlife use

Geographic information systems (GIS) are widely used for mapping wildlife movement patterns, and observed wildlife locations are surrogates for inferring on wildlife movement and habitat selection. We present a new approach to mapping areas where wildlife exhibit sustained use, which we term slow movement areas (SMAs). Nested within the habitat selection concepts of home range and core areas, SMAs are an additional approach to identifying areas important for wildlife. Our method for delineating SMAs is demonstrated on a grizzly bear (Ursus arctos) case study examining road density. Our results showed that subadult females had significantly higher road densities within SMAs than in their potential path area home ranges. The lowest road density was found in the SMAs of adult male grizzly bears. Given increased mortality risks associated with roads, female encampment near roads may have negative conservation implications. The methods presented in this manuscript compliment recent developments to identify movement suspension and intensively exploited areas defined from wildlife telemetry data. SMA delineation is sensitive to missing data and best applied to telemetry data collected with a consistent resolution.PostprintPeer reviewe

Municipal Cycling Plan Scan for AAA

This dataset is a single Excel file containing multiple separate tabs (sheets)

Cosine: A Tool for Constraining Spatial Neighbourhoods in Marine Environments

Spatial analysis methods used for detecting, interpolating, or predicting local patterns in geographic data require delineating a neighbourhood to define the extent of the spatial interaction. Certain spatial analysis methods, such as interpolation, have implemented the concept of directionality and barriers. However, not all approaches take into consideration geographic or environmental constraints such as impassable mountain ranges, road networks, or coastlines. Specifically, complex marine landscapes and coastlines pose problematic neighbourhood definitions for standard neighbourhood matrices used in the spatial analysis of marine environments. Here, we offer a new approach to constraining spatial neighbourhoods when conducting geographical analysis in marine environments. We developed methods and open source software (COnstraining SpatIal NEighbourhoods—COSINE) for modifying spatial neighbourhoods, and demonstrate their utility with a marine study of oil spills. The COSINE graphical user interface allows users to modify the most common standard spatial neighbourhood definitions such as fixed distance, inverse distance and k-nearest neighbour.</jats:p

Lactate transport across sarcolemmal vesicles isolated from rainbow trout white muscle

ABSTRACT Rainbow trout (Oncorhynchus mykiss) retain the majority of lactate produced during exhaustive exercise within white muscle. Previous studies have suggested that this retention is partially via a re-uptake of released lactate. The purpose of this work was to study lactate uptake using trout white muscle sarcolemmal vesicles. Lactate uptake by trout white muscle is partially through a low-affinity, high-capacity carrier (apparent Km=55.6 mmol l−1 and Vmax= 44.5 nmol mg−1 protein min−1). At high concentrations (20 and 50 mmol l−1), pyruvate partially (up to 39 %) inhibited lactate uptake, suggesting the involvement of a monocarboxylate carrier. The anion transport inhibitor 4-acetoamido-4′-isothiocyanstilbene-2,2′-disulphonic acid (SITS) and the monocarboxylate transport inhibitor α-cyano-4-hydroxycinnamate (CHC) stimulated apparent lactate uptake. The model developed suggests that lactate is taken up by the vesicles, at least in part by a pyruvate-sensitive monocarboxylate carrier, and that its subsequent efflux is inhibited by SITS and CHC, suggesting that lactate export from trout white muscle is also carrier-mediated.</jats:p

What does crowdsourced data tell us about bicycling injury? A case study in a mid-sized Canadian city

With only ∼20 % of bicycling crashes captured in official databases, studies on bicycling safety can be limited. New datasets on bicycling incidents are available via crowdsourcing applications, with opportunity for analyses that characterize reporting patterns. Our goal was to characterize patterns of injury in crowdsourced bicycle incident reports from BikeMaps.org. We extracted 281 incidents reported on the BikeMaps.org global mapping platform and analyzed 21 explanatory variables representing personal, trip, route, and crash characteristics. We used a balanced random forest classifier to classify three outcomes: (i) collisions resulting in injury requiring medical treatment; (ii) collisions resulting in injury but the bicyclist did not seek medical treatment; and (iii) collisions that did not result in injury. Results indicate the ranked importance and direction of relationship for explanatory variables. By knowing conditions that are most associated with injury we can target interventions to reduce future risk. The most important reporting pattern overall was the type of object the bicyclist collided with. Increased probability of injury requiring medical treatment was associated with collisions with animals, train tracks, transient hazards, and left-turning motor vehicles. Falls, right hooks, and doorings were associated with incidents where the bicyclist was injured but did not seek medical treatment, and conflicts with pedestrians and passing motor vehicles were associated with minor collisions with no injuries. In Victoria, British Columbia, Canada, bicycling safety would be improved by additional infrastructure to support safe left turns and around train tracks. Our findings support previous research using hospital admissions data that demonstrate how non-motor vehicle crashes can lead to bicyclist injury and that route characteristics and conditions are factors in bicycling collisions. Crowdsourced data have potential to fill gaps in official data such as insurance, police, and hospital reports.This BikeMaps.org research and outreach has been funded by a grant from the Public Health Agency of Canada (PHAC). We acknowledge Taylor Denouden, Darren Boss, Colin Ferster, and Ayan Mitra in creating and maintaining the technology used to collect BikeMaps.org incident data, and the Capital Regional District for their support of outreach. We thank all members of the BikeMaps.org team whose outreach has helped BikeMaps.org reach a broad number of bicyclists in numerous locations. We also thank everyone who took the time to report an incident on BikeMaps.org.FacultyReviewe