A Landscape-Level Assessment of Restoration Resource Allocation for the Eastern Monarch Butterfly
Bibliographic record
Abstract
The Monarch butterfly eastern population ( Danaus plexippus ) is in decline primarily due to habitat loss. Current habitat restoration programs focus on re-establishing milkweed, the primary food resource for Monarch caterpillars, in the central United States of America. However, individual components of the Monarch life cycle function as part of an integrated whole. Here we develop the MOBU-SDyM, a migration-wide systems dynamics model of the Monarch butterfly migratory cycle to explore alternative management strategies’ impacts. Our model offers several advances over previous efforts, considering complex variables such as dynamic temperature-dependent developmental times, dynamic habitat availability, and weather-related mortality across the entire range. We first explored whether the predominant focus of milkweed restoration in the mid-range of the Monarch’s migration could be overestimating the Monarch’s actual habitat requirements. Second, we examined the robustness of using the recommended 1.2–1.6 billion milkweed stems as a policy objective when accounting for factors such as droughts, changes in temperature, and the stems’ effective usability by the Monarchs. Third, we used the model to estimate the number and distribution of stems across the northern, central, and southern regions of the breeding range needed to reach a self-sustainable long-term Monarch population of six overwintering hectares. Our analysis revealed that concentrating milkweed growth in the central region increases the size of the overwintering colonies more so than equivalent growth in the south region, with growth in the northern region having a negligible effect. However, even though simulating an increase in milkweed stems in the south did not play a key role in increasing the size of the overwintering colonies, it plays a paramount role in keeping the population above a critically small size. Abiotic factors considerably influenced the actual number of stems needed, but, in general, our estimates of required stems were 43–91% larger than the number of stems currently set as a restoration target: our optimal allocation efforts were 7.35, 92, and 0.15% to the south, central, and northern regions, respectively. Systems dynamics’ analytical and computational strengths provided us with new avenues to investigate the Monarch’s migration as a complex biological system and to contribute to more robust restoration policies for this unique species.
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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 | 0.001 | 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 | 0.001 |
| 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".