Spatial and species compositional networks for inferring connectivity patterns in ecological communities
Bibliographic record
Abstract
Abstract Aim Multiple spatial and non‐spatial processes are involved in determining the complex patterns underlying the spatial variation of individual species and their assemblages. This complexity, and the logistical challenges involved in following dispersal for multiple species across multiple sites, make it challenging to infer the processes underlying metacommunity spatial heterogeneity. The goal of our paper is to present a robust quantitative framework for inferring spatial patterns across multiple ecological communities. Innovation Unlike numerous metapopulation studies that have inferred migration rates based on landscape connectivity metrics which take into account the spatial positioning of occupied and empty patches, metacommunity studies have relied on spatial predictors built without considering such information. Here, we introduce a novel method called the multi‐species spatial network ( MSSN ) to detect and explain spatial variability in community assemblies using a graph‐theoretical approach. The MSSN approach can be best described as a reconciliation between the spatial positioning of sites and their patterns of patch occupation. Main conclusions Our simulation and real data analyses showed that our MSSN approach was better at detecting spatial patterns within metacommunities than the commonly used MEM method (Moran's eigenvector maps). In addition, our proposed framework is also useful in estimating the levels of spatial connectivity for each local community. Finally, our framework is flexible enough to incorporate different types of functions, metrics and algorithms to detect complex spatial patterns.
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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".