Incorporating behavior-based indices of connectivity into spatially explicit population models
Bibliographic record
Abstract
Measuring connectivity in fragmented landscapes remains a central problem in ecology. Connectivity metrics range from descriptors of landscape structure to direct observations of a species’ ability to move to and colonize a forest patch. We constructed individual-based spatially explicit population models for a guild of forest rodents in Indiana to test the ability of structural and actual, or behavioral, measures of connectivity to predict patch and landscape occupancy and abundance. Model accuracy was assessed using comparisons with data from trapping studies. Predicted abundances within patches correlated with empirical data for five out of six species, but predicted patterns of patch occupancy corresponded with observations for only one species. Discrepancies may be due to inaccurate parameter values or the absence from the models of ecological processes such as conspecific attraction and competition. Nonetheless, the models demonstrated the utility of patch immigration as a measure of connectivity in explaining population abundance in fragmented landscapes. We discuss potential methods of collecting these behavior-based data.
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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 | 0.001 |
| 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".