Multi-scale habitat selection of land cover proportion by farmland birds in Ontario
Notice bibliographique
Résumé
Modern agriculture is a central driver in species declines, and its harmonization with conservation is critical to achieving social and ecological sustainability. Due to agricultural land occupation and reduction of native vegetation covers, species like migratory birds use remaining habitats within agricultural landscapes to complete their life cycles. The consequent decrease in vital ecosystem functions linked to biodiversity (e.g., pollination, pest control) has prompted scientists and land managers to design strategies that benefit production and conservation. However, variation in species' perception of landscape fragmentation across agricultural areas makes difficult the delineation of favourable landscape attributes in agricultural systems. Species can interact with environmental factors at varying spatial and temporal scales, with highly context-dependent outcomes. Here, I aim to quantify farmland birds' selection of home range’s location (i.e., Second order selection) based on the proportion of six land covers (Tame grass, Native grass, Trees, Shrubs, Developed) measured at five spatial scales of varying extent: 200m, 400m, 800m, 1600m and 3200m radii. I address the questions 1) Do farmland birds in Southern Ontario select land cover proportion in the local landscape as a habitat cue? 2) Do scales selected by farmland birds in the local landscape differ according to their degree of specialization? And 3) How is the distribution of farmland birds that use local-landscape cover proportion as a habitat cue in Ontario? Using bird occurrence data from North America's Breeding Birds Surveys (BBS) between 2014 and 2019 and landcover data from Ontario's Annual crop inventory (ACI), Resource Selection Functions (RSF) were built for farmland birds in Ontario. I applied a semi-optimized modelling approach to determine the scales within farmland birds' perceived changes in land cover proportion and composition, making inferences on their plausible causes and management implications. Results indicate that multiple species select or avoid habitats using landscape-level cues at varying scales below 800m and above 1600m radii. Farmland birds' multi-scale selection also seems to differ between generalists and specialists, with specialists selecting landcover proportion at smaller scales (i.e., >800m radii) and generalists at both smaller and large scales (<200m and >1600m radii). These patterns contrasted with species preferences indicated that selection for landcover proportion at scales below the 800m radius could be associated with patch-level attributes and above the 1600m radius with landscape-level attributes. Moreover, prediction surfaces built from species top models showed that highly selected locations for farmland generalists and specialists concentrated between Western and Central Ontario were larger grasslands and varying landscape composition favour specialist and generalist occurrence. However, the extension of areas where multiple species concur on their occurrence was low compared to groups' mean occurrence, implying that there is not an exclusive landscape structure that favours all species. In order to improve the availability of attractive habitats for farmland birds, I suggest that rather than defining a top landscape structure, scientists and managers need to work together to determine multiple landscape arrangements adjusted to the availability of relevant factors like grassland proportion.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».