Sampling and asymptotic network properties of spatial multi‐trophic networks
Bibliographic record
Abstract
Collecting well‐resolved empirical trophic networks requires significant time, money and expertise, yet we are still lacking knowledge on how sampling effort and bias impact the estimation of network structure. Filling this gap is a critical first step towards creating accurate representations of ecological networks and for teasing apart the impact of sampling compared to ecological and evolutionary processes that are known to create spatio‐temporal variation in network structure. We use a well‐sampled spatial dataset of lake food webs to examine how sample effort influences network structure. Specifically, we predict asymptotic network properties (ANPs) for our dataset by comparing lake‐specific network metrics with increasing sampling effort. We then contrast three sampling strategies – random, smallest lake to largest lake or largest lake to smallest lake – to assess which strategy best captures the regional metaweb (i.e. network of all potential interactions) network properties. We demonstrate metric‐specific relationships between sample effort and network metrics, often diverging from the ANPs. For example, low sample effort can contribute to much lower and poorer estimates of closeness centralization, as compared to approximations of modularity with similar sample efforts. In fact, many network metrics (e.g. connectance) have a quadratic relationship with sample effort indicating a sampling ‘sweet spot', which represents optimal sample effort for a close approximation of the ANP. Further, we find that sampling larger lakes followed by smaller lakes is a more optimal sampling strategy for capturing metaweb properties in this lentic ecosystem. Overall, we provide clear ways to better understand the impacts of sampling bias in food‐web studies which may be particularly critical given the rapid increase in studies comparing food webs across space and time.
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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".