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Record W4318833443 · doi:10.15788/ndot2022.06

Long-term responses of an ecological community to highway mitigation measures

2022· report· en· W4318833443 on OpenAlexfundno aff
Adam T. Ford, Marcel P. Huijser, Anthony P. Clevenger

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicWildlife-Road Interactions and Conservation
Canadian institutionsnot available
FundersOregon Department of TransportationNevada Department of TransportationArizona Department of TransportationNew Mexico Department of TransportationParks CanadaMinnesota Department of TransportationFederal Highway AdministrationMichigan Department of TransportationU.S. Department of Transportation
KeywordsFencingWildlifeHabitatGeographyCarnivoreEcologyTerm (time)Environmental resource managementEnvironmental sciencePredationComputer scienceBiology

Abstract

fetched live from OpenAlex

In road mitigation systems characterized by multiple wildlife crossing structures (CS) and multiple-focal species, these species-specific design criteria are important to meeting management goals. CS types and locations are fixed in place and cannot be manipulated experimentally; long term studies may offer the best chance to inform evidence-based designs for new CS projects in the future. Long-term data from Banff National Park are uniquely posed to answer these critical questions. More recently, highway mitigation along US93 in Montana provides an additional case study with which to understand the responses of large animals to different CS designs. The purpose of this study is to identify factors affecting movement of large mammals through CS using data sets from both mitigation projects. Year-round monitoring of CS use was used in an analytical framework to address questions regarding species-specific and community level use of CS; design and habitat factors that best explain species-specific variation; and whether importance of design parameters changes over time. Over the 17 years of the Banff study, and the six years of the Montana study, CS facilitated over 200,000 crossing events at 55 locations. There were significant changes in annual crossing events over time. Variables associated with CS passage rates were species specific, but aligned with a few clusters of preference. With the exception of coyotes, all large carnivore species preferred open span bridges or overpasses to other CS types. In Montana, fencing was positively associated with passage rates for black bears and cougars. We found that wider CS tend to be preferred by most species, irrespective of their location. We also found that wider CS tend to have shorter ‘adaptation’ curves than narrower ones for grizzly bears, coyotes, cougars, and moose. Depending on the heterogeneity of the landscape near the highway, more CS may not create more crossing opportunities if local habitat conditions do not favor animals’ access to the road. At the scale of ecological communities, the flows of mass and energy are likely enough to alter the distribution of ecological processes in the Banff and Montana ecosystems. Our results highlight the value of long-term monitoring for assessing the effectiveness of mitigation measures. Our work confirms the species-specific nature of measure CS performance, leading to our primary recommendation that a diversity of CS designs be considered an essential part of a well-designed mitigation system for the large mammals of western North America. Short-term monitoring efforts may fail to accurately portray the ecological benefits of mitigation for populations and ecological communities. Our results will help to inform design and aid in the establishment of robust, long-term performance measures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.065
GPT teacher head0.325
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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