Deciding where to put them: Sensitivity tests and independent evaluation are critical when using species distribution models to inform conservation translocations
Bibliographic record
Abstract
Abstract Conservation translocations are an important tool for combating species declines and population losses. Species distribution models (SDMs) can facilitate the selection of suitable release sites for translocation programs. However, these models can be sensitive to several modelling decisions. In this study, we explore the impacts of three key modelling decisions on Maxent models developed to inform reintroductions of the long‐toed salamander ( Ambystoma macrodactylum ) in southwestern Alberta. We specifically test the sensitivity of model predictions to (1) the type of environmental variables used to generate models, (2) whether the background points used to calibrate the models reflects the potential bias in the input locality records and (3) the choice of geographic study extent. We use independent presence‐absence data from an extensive field survey to test the accuracy of models based on different decisions. Both model predictions and performance were sensitive to these modelling decisions. Models developed using local study extents were more accurate than those based on range‐wide extents. Both study extent and type of background points impacted model predictions and performance more than the set of environmental variables included in the models for this species. We further demonstrate the impacts of these decisions on the ranking of potential release sites and present a final set of recommendations that accounts for this uncertainty under both current and future climatic conditions. We specifically identify three sites that are expected to be suitable in both present and future time periods as potential release sites for salamander reintroductions in southwestern Alberta. Synthesis and applications : Our study adds to our understanding of how different modelling decisions impact SDMs and the downstream conclusions from these models while simultaneously demonstrating a rigorous approach for the use of SDMs in conservation translocation planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".