Geographical variation in the trait‐based assembly patterns of multitrophic invertebrate communities
Bibliographic record
Abstract
Abstract It has been argued that the mechanisms structuring ecological communities may be more generalizable when based on traits than on species identities. If so, patterns in the assembly of community‐level traits along environmental gradients should be similar in different places in the world. Alternatively, geographical change in the species pool and regional variation in climate might result in site‐specific relationships between community traits and local environments. These competing hypotheses are particularly untested for animal communities. Here we test the geographical constancy of trait‐based assembly patterns using a widespread multi‐trophic community: aquatic macroinvertebrates within bromeliads. We used data on 615 invertebrate taxa from 1,656 bromeliads in 26 field sites from Mexico to Argentina. We summarized invertebrate traits with four orthogonal axes, and used these trait axes to examine trait convergence and divergence assembly patterns along three environmental gradients: detrital biomass and water volume in bromeliads, and canopy cover over bromeliads. We found no overall signal of trait‐based assembly patterns along any of the environmental gradients. However, individual sites did show trait convergence along detrital and water gradients, and we built predictive models to explore these site differences. Sites that showed trait convergence along detrital gradients were all north of the Northern Andes. This geographical pattern may be related to phylogeographical differences in bromeliad morphology. Bromeliads with low detritus were dominated by detritivorous collectors and filter feeders, where those with high detritus had more sclerotized and predatory invertebrates. Sites that showed the strongest trait convergence along gradients in bromeliad water were in regions with seasonal precipitation. In such sites, bromeliads with low water were dominated by soft‐bodied, benthic invertebrates with simple life cycles. In less seasonal sites, traits associated with short‐term desiccation resistance, such as hard exoskeletons, were more important. In summary, we show that there are strong geographical effects on the trait‐based assembly patterns of this invertebrate community, driven by the biogeography of their foundational plant species as well as by regional climate. We suggest that inclusion of biogeography and climate in trait‐based community ecology could help make it a truly general theory. Read the free Plain Language Summary for this article on the Journal blog.
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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) | 0.004 | 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".