Metacommunity size influences aquatic community composition in a natural mesocosm landscape
Bibliographic record
Abstract
Ecosystems are often arranged in naturally patchy landscapes with habitat patches linked by dispersal of species in a metacommunity. The size of a metacommunity, or number of patches, is predicted to influence community dynamics and therefore the structure and function of local communities. However, such predictions have yet to be experimentally tested using full food webs in natural metacommunities. We used the natural mesocosm system of aquatic macroinvertebrates in bromeliad phytotelmata to test the effect of the number of patches in a metacommunity on species richness, abundance, and community composition. We created metacommunities of varying size using fine mesh cages to enclose a gradient from a single bromeliad up to the full forest. We found that species richness, abundance, and biomass increased from enclosed metacommunities to the full forest size and that diversity and evenness also increased in larger enclosures. Community composition was affected by metacommunity size across the full gradient, with a more even detritivore community in larger metacommunities, and taxonomic groups such as mosquitoes going locally extinct in smaller metacommunities. We were able to divide the effects of metacommunity size into aquatic and terrestrial habitat components and found that the importance of each varied by species; those with simple life cycles were only affected by local aquatic habitat whereas insects with complex life cycles were also affected by the amount of terrestrial matrix. This differential survival of obligate and non‐obligate dispersers allowed us to partition the beta‐diversity between metacommunities among functional groups. Our study is one of the first tests of metacommunity size in a natural metacommunity landscape and shows that both diversity and community composition are significantly affected by metacommunity size. Synthesis Natural food webs are sensitive to meta‐community size, i.e. the number of patches connected through dispersal. We provide an empirical test using the aquatic foodweb associated within bromeliads as a model system. When we reduced the number of bromeliad patches connect through dispersal, we found a clear change of the foodweb in terms of population sizes, beta diversity, community composition and predator‐prey ratios. The response of individual taxa was predictable based on species traits including dispersal modes, life cycle, and adult resource requirements. Our study demonstrates that community structure is strongly influenced by the interplay of species traits and landscape properties.
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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".