Life history traits interact with landscape composition to influence population dynamics of a terrestrial arthropod: A simulation study
Bibliographic record
Abstract
:Population persistence of animals that have specific habitat requirements in certain life history stages is influenced by spatial and temporal availability of the required habitat. Accessibility of suitable habitats may be affected by their spatial distribution in the landscape and by dispersal ability of the animals. Animals with low dispersal power should be more vulnerable to spatial variation in the distribution of suitable habitats than animals with high dispersal ability. We investigated the effects of differential dispersal power and landscape composition on population dynamics of a terrestrial arthropod by use of a spatially explicit individual-based simulation model. The model was parameterized with data from a common spring breeding carabid beetle in European agroecosystems. Spring-breeding ground beetles depend largely on vegetated field boundaries for (winter) hibernation. We analyzed the effects of and interaction between dispersal rate, field size, and availability of hibernation sites on beetle population development and spatial dynamics. A landscape composed of small fields and a high boundary-to-field size ratio supported larger beetle populations than a landscape consisting of large fields. Animal populations were more robust to variation in spatial distribution of required habitat when dispersal power of individuals was high. In a landscape of large fields, beetles were found to associate with the field boundaries, whereas such an association was not observed in a landscape composed of small fields, suggesting that distance to hibernation sites may be a limiting factor for habitat use. Interactions between life history traits and essential habitat requirements may have profound effects on population dynamics and play an important role in predictions of effects of landscape changes on animal populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".