Spatiotemporal stability of two beetle populations in non‐farmed habitats in an agricultural landscape
Bibliographic record
Abstract
Summary Understanding the spatiotemporal ecology of invertebrates in agricultural ecosystems is important for developing management strategies to enhance biodiversity and reduce pesticide use. There is increasing recognition that studies need to be undertaken at scales larger than the field but the ability to do this is often resource limited. Pheromone traps, developed for pest management, can provide a relatively cheap means of collecting data across wide geographical scales. In this study we used such traps to examine the spatial stability of two species of agrioted beetles in non‐farmed habitats over a period of 3 years. The spatial distributions of adult male Agriotes lineatus and Agriotes obscurus were sampled from April until July at a number of sites across 950 ha on Westham Island, British Columbia, Canada. Data were assembled as spatially referenced cumulative trap counts by species and year, and analysed for evidence of aggregation and clustering using spatial analysis by distance indices (SADIE) methodology. Both intraspecific and interspecific association analyses were then carried out. A further set of analyses considered the spatial associations between different sampling dates in 2004. Agriotes lineatus was clustered into patches and gaps at the landscape scale in all 3 years, and A. obscurus showed similar spatial structure in 2002 and 2003. For both species, spatiotemporal stability at the landscape scale was evident. There was also a positive association between the two species in 2 out of the 3 years, suggesting that they were responding in a similar manner to the landscape environment. Synthesis and applications . This study showed that there was substantial temporal stability in the spatial structures of A. lineatus and A. obscurus in non‐farmed habitats, and that the two species respond in a broadly similar way to a heterogeneous agricultural landscape. This suggests that models to predict distributions within a landscape could be developed to guide decisions about where to grow vulnerable crops. Identification of the environmental factors that differentiate between clusters and gaps in their spatial distributions may also make it possible to manipulate populations and reduce their impact as pests without recourse to insecticides. Moreover, our results extend the spatial scales over which SADIE statistics have been used, and demonstrate the usefulness of pheromone trapping as a sampling method in addressing ecological questions at a landscape scale.
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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 | 0.000 | 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) | 0.000 | 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".