Railways offer grain on a silver platter to wildlife, but at what cost?
Bibliographic record
Abstract
It is well known that transportation corridors affect wildlife in a variety of ways, both directly and indirectly. Road ecology is a well-developed field of transportation ecology, and over the past few decades, there has been substantial research focused on investigating the influence of roads on wildlife and ecological processes (Rytwinski & Fahrig, 2015). Railway ecology on the other hand is a highly neglected aspect of transportation ecology, with little known about the effects of railways on wildlife (Popp & Boyle, 2017). Like roads, railways are known to fragment habitat as well as to lead to wildlife mortality through vehicle collisions (van der Grift, 1999; Mateo-Sánchez, Cushman & Saura, 2014), however, wildlife-railway related investigations have been very sparse in the literature (Popp & Boyle, 2017). This issue's Feature paper, ‘Grain spilled from moving trains create a substantial wildlife attractant in protected areas’ (Gangadharan et al., 2017), presents an excellent example of why it is important to study the effects of railways on wildlife. Gangadharan et al. (2017) investigated grain spillage from trains travelling along 134 km of railway in Canada and highlight the implications to wildlife, specifically grizzly bears (Ursus arctos). The authors explain that, based on their calculations, enough grain is spilled from moving trains on the railway every year to feed 42–52 grizzly bears all of their caloric needs! But that is, of course, if these bears can get to all of the grain first. Many other animals presumably take advantage of this abundance of food that is essentially presented on a silver platter. In my own current research in progress, I have documented an abundance of wildlife species using an Ontario railway, including medium to large mammals and many species of birds. I myself have seen first hand grain trails left along the rail, and often observe birds taking full advantage of the easy food source. But at what cost does this optimal foraging opportunity come? Aside from the more well-known issues associated with feeding wildlife (e.g. unnatural food reliance, reduced intake of natural forage, increased risk of disease or parasites associated with the close proximity of conspecifics) (Putman & Staines, 2004), a plethora of cascading effects are likely to exist. One of the obvious repercussions of railway use by wildlife is train-induced mortality (Bertch & Gibeau, 2010), which is the leading cause of mortality for grizzly bears in the Gangadharan et al. (2017) study area. My current research in progress has shown that train collisions are also the leading cause of mortality for reintroduced elk (Cervus elaphus) in north-central Ontario. In addition to the mortality risk trains present, prey animals attracted to grain may become more spatially predictable to predators. Wolves have been found to select for areas within 25 m of railways (Whittington, St. Clair & Mercer, 2005). Birds have also been found to alter their use of space in response to railways, and their species richness and abundance have been shown to be greater closer to railways (Li et al., 2010). Although not yet documented, carrion-feeding species may also be attracted to railways, as scavenging opportunities may increase through the availability of carcasses from train collisions. But at what rate do trains kill wildlife? How often are animals using railways in comparison to how often mortality occurs? The risk of wildlife mortality on railways is relatively unknown, however, the implications are important, especially for species at risk like grizzly bears. Transportation corridors may be used by wildlife for reasons other than optimal foraging opportunities. For example, in accordance with the Law of Least Effort (Zipf, 1949), transportation corridors likely create easy travel path opportunities for wildlife, especially in winter when snow depths in surrounding environments may impede animal movement (Andreassan, Gunderson & Storaas, 2005). Roads and railways are both linear corridors but they may provide very different environments to animals, however, such comparisons have rarely been made. These differences may result in one type of transportation corridor being perceived by wildlife as less risky, which could result in different rates of use, and potentially, mortality. These are some of the many questions in transportation ecology we should address but currently do not have the answers to. Gangadharan et al. (2017) present one of the very few studies that assess the potential impacts of railways on wildlife. This study is a novel, important contribution to a very poorly understood topic in ecology and by example, provides invitation for curious ecologists to delve more deeply into this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".