An introduction to predictive distribution modelling for conservation to encourage novel perspectives
Bibliographic record
Abstract
ABSTRACT An introduction to predictive distribution modelling for conservation to encourage novel perspectives. The rapid pace and potentially irreversible consequences of global change create an urgent need to predict the spatial responses of biota for conservation to better inform the prioritization and management of terrestrial habitats and prevent future extinctions. Here, we provide an accessible entry point to the field to guide near-future work building predictive species distribution models (SDMs) by synthesizing a technical framework for the proactive conservation of avian biodiversity. Our framework offers a useful approach to navigate the challenges surrounding the large spatio-temporal resolution of datasets and datasets that favor hypothesis testing at broad spatio-temporal scales and coarse resolutions, which can affect our ability to assess the validity of current predicted distributions. We explain how to improve the accuracy of predictive models by determining the extent to which: 1) dispersal limitation impacts the rate of range shifts, 2) taxa are rare at their range limits, and 3) land use and climate change interact. Finally, we offer approaches to filling knowledge gaps by creatively leveraging existing methods and data sources. RESUMEN Una introducción a la modelización predictiva de la distribución para la conservación con el fin de fomentar nuevas perspectivas . El rápido ritmo y las consecuencias potencialmente irreversibles del cambio global crean una necesidad urgente de predecir las respuestas espaciales de la biota para la conservación, con el fin de informar mejor la priorización y gestión de los hábitats terrestres y prevenir futuras extinciones. Aquí proporcionamos un punto de entrada accesible al campo para guiar el trabajo del futuro próximo en la construcción de modelos predictivos de distribución de especies (SDM), sintetizando un marco técnico para la conservación proactiva de la biodiversidad aviar. Nuestro marco ofrece un enfoque útil para navegar por los retos que rodean a la gran resolución espacio-temporal de los conjuntos de datos y a los conjuntos de datos que favorecen la comprobación de hipótesis a escalas espacio-temporales amplias y resoluciones gruesas, lo que puede afectar a nuestra capacidad para evaluar la validez de las distribuciones predichas actuales. Explicamos cómo mejorar la precisión de los modelos predictivos determinando hasta qué punto 1) la limitación de la dispersión influye en el ritmo de los cambios de área de distribución, 2) los taxones son raros en los límites de su área de distribución, y 3) el uso del suelo y el cambio climático interactúan. Por último, proponemos enfoques para colmar las lagunas de conocimiento aprovechando de forma creativa los métodos y fuentes de datos existentes.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".