Modelling distribution and abundance with presence‐only data
Bibliographic record
Abstract
Summary Presence‐only data, for which there is no information on locations where the species is absent, are common in both animal and plant studies. In many situations, these may be the only data available on a species. We need effective ways to use these data to explore species distribution or species use of habitat. Many analytical approaches have been used to model presence‐only data, some inappropriately. We provide a synthesis and critique of statistical methods currently in use to both estimate and evaluate these models, and discuss the critical importance of study design in models where only presence can be identified Profile or envelope methods exist to characterize environmental covariates that describe the locations where organisms are found. Predictions from profile approaches are generally coarse, but may be useful when species records, environmental predictors and biological understanding are scarce. Alternatively, one can build models to contrast environmental attributes associated with known locations with a sample of random landscape locations, termed either ‘pseudo‐absences’ or ‘available’. Great care needs to be taken when selecting random landscape locations, because the way in which they are selected determines the modelling techniques that can be applied. Regression‐based models can provide predictions of the relative likelihood of occurrence, and in some situations predictions of the probability of occurrence. The logistic model is frequently applied, but can rarely be used directly to estimate these models; instead, case–control or logistic discrimination should be used depending on the sample design. Cross‐validation can be used to evaluate model performance and to assess how effectively the model reflects a quantity proportional to the probability of occurrence. However, more research is needed to develop a single measure or statistic that summarizes model performance for presence‐only data. Synthesis and applications. A number of statistical procedures are available to explore patterns in presence‐only data; the choice among them depends on the quality of the presence‐only data. Presence‐only records can provide insight into the vulnerability, historical distribution and conservation status of species. Models developed using these data can inform management. Our caveat is that researchers must be mindful of study design and the biases inherent in presence data, and be cautious in the interpretation of model predictions.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".