Prey Distribution Mapping to Support Conservation Planning
Bibliographic record
Abstract
ABSTRACT Aim Biodiversity conservation broadly relies on protecting suitable habitat for species of concern, and species distribution models (SDM) are a common method for classifying potential habitat suitability. However, SDMs frequently omit resource availability and are therefore missing potentially useful information for planning successful conservation areas. Here, we aim to identify regions of high prey availability, and thus high resource availability, for several listed predator species across California dryland regions. This information could be used to support more refined estimates of valuable habitat for prey species that support listed species within Central California drylands. Location California, USA. Time Period 1945–2022. Major Taxa Studied Arthropoda. Methods We used a prey list for 11 listed species found in California drylands and compiled occurrence records for those species from the Global Biodiversity Information Facility. We fit individual SDMs for all species using Bayesian additive regression trees. These individual SDMs were then stacked to identify hotspots of prey density across California. Results We found the highest observed and predicted prey richness along the Southern California coast in grassland, savanna, and urban landcover classes. Only a small region (2.9%) contained suitable habitat for more than 50% of prey species. In contrast, 60% of the study region contained suitable habitat for at least 1 prey item for more than 50% of listed predator species. Main Conclusions Observed hotspots of high prey richness and regions where predicted prey richness could support multiple listed species identify potential regions for conservation efforts. Our results also highlight how mapping prey in addition to listed species can support planning, as our stacked‐SDMs identified a wide geographic extent that can support the listed species. Future research could use these stacked‐SDMs to identify sites for standardised field surveys for key variables including whether predicted prey items are present but also in high abundance.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".