Emerging horizons in predictive biogeography
Bibliographic record
Abstract
The notion that different branches of biological sciences -including ecology, macroecology, and biogeography -should adopt a predictive focus rather than merely aiming to describe and understand the natural world has gained traction over the past decades (Peters 1991, Shrader-Frechette andMcCoy 1993).This trend has been enabled both by technological advancement leading to new predictive frameworks, and by the pressing societal demands to anticipate and mitigate the effects of global change on biodiversity and the associated ecosystem services.An early example of this trend is the work by Sánchez-Cordero et al. ( 2004) who contributed a chapter on predictive biogeography for conservation applications in a seminal volume on biogeography (Lomolino and Heaney 2004).While the authors did not explicitly define the term predictive biogeography, their discussion emphasized how developments in statistical ecology and mapping had allowed the description of species distributions at large spatial scales.Similarly, Thuiller et al. (2006) employed the concept of predictive biogeography in the restricted context of describing the use of stacked species distribution models (SDMs) in predicting plant richness in South Africa.Dawson et al. (2011) subsequently highlighted SDMs as the most widely used predictive method in biogeography, but also called attention on the importance of establishing broader frameworks to anticipate changes in biodiversity, from species to ecosystems, in response to climate change.There are other biogeographic patterns that are widely used in a predictive context.Most notably, the species area relationships (SARs), which have also been important to understand and predict species extinctions (Drakare et al. 2006) driven by anthropogenic habitat fragmentation for example.However, the widespread use of SDMs, along with the fact that they remain the method of choice at large scales in ecology, has been repeatedly highlighted (Bellard et al. 2012, Araújo et al. 2019, Zurell et al. 2020, Soley-Guardia et al. 2024).Mapping biodiversity remains an essential component of large-scale spatial conservation planning
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".