GIS-Based Modeling Approaches to Identify Mitigation Placement Along Roads
Bibliographic record
Abstract
Decision-making in the design of effective wildlife passage structures is hampered by the sparse information currently available. There are several reasons for this deficiency. Monitoring wildlife passages is not often anticipated after construction. There are few methodological approaches to identify the placement of wildlife passages. Finally, there is an urgent need for mitigation procedures that contemplate the broad landscape context of road systems. When used in a geographic information system (GIS) environment, regional or landscape level connectivity models of sufficient resolution can help delineate placement of wildlife crossing structures. GIS tools and applications are becoming more popular among resource managers and transportation planners. An empirically based habitat linkage model is preferred to qualitative or conceptual models based on limited data. However, in many cases, the data necessary for empirically based models are not available. As a substitute, expert information can be used to develop simple, predictive, habitat linkage models in a relatively short period of time. Banff National Park is preparing for a new Trans-Canada highway (TCH) expansion and mitigation project. We need to be able to provide park managers with an empirical assessment of the impediments posed by transportation corridors to animal movements, and recommend the placement of mitigation measures. For some species there are empirical data, while for others there are little or no data. Given this situation, we developed several GIS approaches to model animal movements across transportation corridors in the Central Rocky Mountains. For a single species, we developed three different but spatially explicit habitat models to identify linkage areas across the TCH. One model was based on empirical data, and the other two models were based on expert opinion and expert literature. We used the empirical model as a yardstick to measure the accuracy of the expert-based models. Our tests showed the expert literaturebased model most closely approximated the empirical model, both in the results of statistical tests and the description of the linkages. For a similar exercise using empirical data, we developed a multi-scale GIS approach to model multiple species movements across the TCH and identify mitigation passage placement. Three steps were involved: 1) the creation of regional habitat suitability models for each of four large mammal species, 2) the development of a regional scale movement component to the models, and (3) nested within step 2, the construction of local-scale movement models of high spatial resolution within the transportation corridor. Recommendations regarding the location of potential mitigation based on the intersection of simulated pathways with transportation corridors and other human infrastructure were the result of the exercise. Our empirical and expert models represent useful tools for resource and transportation planners charged with determining the location of mitigation passages. Expert models were shown to be practical when baseline information is lacking and time constraints do not allow for pre-construction data collection. It is important to note the wide applicability of such models to other planning issues in the Central Rocky Mountains. The proposed models could be applied to other human infrastructure, such as railways, trails, or other road systems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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".