Limited spatiotemporal niche partitioning among mesocarnivores in Gorongosa National Park, Mozambique
Bibliographic record
Abstract
Abstract Competition drives community composition and structure in many ecosystems. Spatial and temporal niche partitioning, in which competing species divide the environment in space or time, are mechanisms that may allow for coexistence among ecologically similar species. Such division of resources may be especially important for carnivores in African savannas, which support diverse carnivore assemblages. We used camera traps to explore patterns of spatial and temporal niche partitioning among four mesocarnivore species in Mozambique's Gorongosa National Park: large‐spotted genet (Genetta maculata), African civet (Civettictis civetta), honey badger (Mellivora capensis) and marsh mongoose (Atilax paludinosus). We applied a multispecies occupancy model to evaluate spatial partitioning among mesocarnivores and to quantify the environmental factors that affect species‐specific habitat use, including relative lion (Panthera leo) activity. We also analyzed the temporal activity overlap of the four focal species. We identified species‐specific habitat covariates that influenced detection probabilities but found no evidence of spatial or temporal partitioning among mesocarnivores in the study system. Indeed, we found some evidence for spatial co‐occurrence between two of our focal species: African civet and marsh mongoose. There may be limited competition among mesocarnivores in this system, perhaps due to niche and diet differentiation among these species and an abundance of resources. While we found limited evidence that lion activity impacts mesocarnivores, ongoing monitoring of intraguild interactions is vital as apex predator populations recover in the system. This study adds to a growing understanding of African mesocarnivore ecology and highlights the importance of understanding these dynamics for effective multispecies conservation and restoration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".