Diversity and suitability of existing methods and metrics for quantifying species range shifts
Bibliographic record
Abstract
Abstract Aim The quantification of species range shifts is critical for developing effective plans to conserve biodiversity. There are numerous methods and metrics for quantifying species range shifts, but we currently lack a comprehensive review of existing approaches used in species range shift studies. Location Global. Time period 2013 – 2014. Major taxa studied All taxa. Methods We conducted a quantitative literature review to first identify the methods currently used for defining a species' range over a particular time and then to identify metrics used for measuring changes in species ranges over time. We provide a roadmap for the selection of methods and metrics for measuring species ranges and species range shifts by discussing opportunities, assumptions and constraints of the different approaches. Results Our literature review revealed six main methods for defining species ranges: observational studies, grid‐based mapping, convex hull, kriging, species distribution modelling and hybrid methods. These methods are used with three main metric classes to measure species range shifts: changes in range limit, size and the probability of species occurrences or suitability. Most methods for defining species ranges and subsequent range shifts can be applied to different spatial extents and resolutions and taxa. However, only species distribution models (SDMs) and hybrid methods allow for the exploration of the relationship between species occurrence and environmental variables, and only these methods can be used for forecasting species ranges into future environments. Likewise, the inclusion of ecological processes in range shift calculations requires researchers to use hybrid methods or mechanistic models. Main conclusions Our review revealed a high diversity of methods and metrics used to quantify species range shifts. As these methods and metrics underlie many of the conservation strategies proposed for climate change mitigation (e.g., protection of refugia), we urge the conservation community to evaluate underlying approaches for defining species ranges and measuring species range shifts with an equal level of scrutiny as the conservation strategies that these methods and metrics enable.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".