GIS-Based Modeling Approaches to Identify Mitigation Placement Along Roads
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,005 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».