Incorporating species population dynamics into static prioritization: Targeting species undergoing rapid change
Bibliographic record
Abstract
Abstract Conservation planning has generally used models with a static spatial distribution of species to predict the likely occurrence of species. However, static data do not usually capture rapid changes in the abundance of endangered species or cryptic life stages such as the dormancy stage of eggs and seeds. Little is known about how neglecting dynamic population processes, such as recovery processes, can affect the outcomes of spatial prioritization in conservation planning. In this study, we investigated the distribution of 62 aquatic plant species, including 23 threatened species, in 415 agricultural ponds for 37 years to examine the importance of including recovery processes in conservation planning. Using long‐term historical presence–absence data and seedbank longevity data for aquatic macrophytes, we estimated rates of population disappearance and recovery for each species in each pond over the next 100 years. Average rates of recovery exceeded 0, 0.2, and 0.4 in 85.4%, 40.3%, and 4.8% of aquatic plant species, respectively. Simulation results suggested that the extinction risk for a species greatly decreased when recovery processes were considered. We found that including recovery processes in target species populations increased the performance of spatial prioritization by protecting more species in a smaller number of protected ponds. Spatial ranking of ponds for species conservation differed substantially among scenarios that included and excluded population recovery processes, suggesting that conservation priorities based on complementarity analysis are sensitive to underlying assumptions. Synthesis and applications . Our dynamic approach, which considers recovery processes of species, contributes to more effective conservation planning by reducing bias in the prediction of species’ state, given that cryptic life stages are ubiquitous among many plants and some animals. Time‐series presence/absence data on target species are quite useful for the approach and are often archived by participatory monitoring. Thus, the opportunities for applying our method are broad, especially for conservation prioritization and decision‐making at the local scale.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".