Robust metagenomic evidence that local assemblage richness increases with latitude in ground‐active invertebrates of North America
Bibliographic record
Abstract
Biodiversity monitoring is imperative for understanding how changing climate may impact the distributions of taxa from single species to the spatial distribution of biological diversity. Large‐scale and cross‐taxa biodiversity monitoring also allows an empirical understanding of biogeographic patterns across taxa. One such pattern, where in taxonomic richness peaks at tropical latitudes are typically treated as a biogeographical rule with few notable exceptions. Here we leveraged the invertebrate pitfall collections of the National Ecological Observatory Network (NEON) across North America to describe patterns of local taxonomic richness across taxa and across taxonomic scale. We focused on Arthropoda, Annelida and Mollusca. Additionally, we estimated regional species richness using expert‐identified samples of three NEON sentinel taxa: Carabidae, Culicidae and Ixodida. To sample pitfall animals, we filtered storage ethanol and employed environmental DNA‐barcoding methodologies to amplify and sequence extracted DNA from the filtrate for two regions of a mitochondrial gene. We assigned taxonomic names to these sequences at 97% similarity to reference sequences and calculated local taxonomic richness at the levels of species, genus, family and order. We calculated local species richness for 12 common invertebrate taxa. We used generalized linear models to describe the relationships between taxonomic richness and spatial, climatic and abundance predictor variables. At four taxonomic scales, ranging from species to order, taxonomic richnessincreased significantly as a function of latitude. Of the twelve invertebrate taxa weexamined, seven mirrored this positive latitudinal gradient in species richness. At theregional scale, two of three NEON Sentinel Taxa showed positive latitudinal gradientsin species richness. Temperature, precipitation, abundance and sequence read numberplayed minor roles in explaining patterns of taxonomic richness. When considering these mostly temperate sites that span 46 degrees of latitude, we found no support for the expected negative latitudinal gradients across taxa and taxonomic scales. Instead, for many of these taxa and taxonomic scales, we observed significant, positive richness gradients with increasing latitude among ground‐dwelling invertebrate communities. Thus, one of the most ‘general' patterns in biogeography was not found for most invertebrate taxa across temperate latitudes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".