Alternative Quantifications of Landscape Complementation to Model Gene Flow in Banded Longhorn Beetles [Typocerus v. velutinus (Olivier)]
Bibliographic record
Abstract
Rapid progression of human socio-economic activities has altered structure and function of natural landscapes. Species that rely on multiple, complementary habitat types to complete their life cycle may be especially at risk. However, such landscape complementation has received little attention in the context of landscape connectivity modelling. A previous study on flower longhorn beetles (Cerambycidae: Lepturinae) integrated landscape complementation into a continuous habitat suitability ‘surface’, which was used to quantify landscape connectivity between pairs of sampling sites using gradient-surface metrics. This connectivity model was validated with molecular genetic data collected for the banded longhorn beetle (Typocerus v. velutinus) in Indiana, USA. However, this approach has not been compared to alternative models in a landscape genetics context. Here, we used a discrete land cover map to calculate landscape metrics related to landscape complementation based on a patch mosaic model as an alternative to the previously published, continuous habitat suitability model. We evaluated the habitat suitability model surface with gradient surface metrics and with two resistance-based models based on least cost path and commute distance, in addition to an isolation-by-distance model based on Euclidean distance. We compared the ability of these competing models of connectivity to explain pairwise genetic distances (RST) previously calculated from ten microsatellite genotypes of 454 beetles collected from 17 sites across Indiana. Model selection with maximum likelihood population effects models found that gradient surface metrics were most effective at explaining pairwise genetic distances as a proxy for gene flow across the landscape, followed by landscape metrics calculated from the patch mosaic model, whereas the least cost path model performed worse than the commute distance and the isolation by distance model. We argue that the analysis of a continuous habitat suitability model with gradient surface metrics might perform better because of their combined ability to effectively represent and quantify the continuous degree of landscape complementation (i.e. availability of complementary habitats in vicinity) found at and in-between sites, on which these beetles depend. Our findings may inform future studies that seek to model habitat connectivity in complex heterogeneous landscapes as natural habitats continue to become more fragmented in the Anthropocene.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".