Multi-scale habitat selection of land cover proportion by farmland birds in Ontario
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".