Planting accelerates restoration of tropical forest but assembly mechanisms appear insensitive to initial composition
Bibliographic record
Abstract
Abstract Central to the success of restoration is how applied activities influence community assembly mechanisms. Phylogenetic and trait‐based approaches to community ecology are increasingly being used to test for non‐random community assembly and are now being applied to assessments of habitat restoration. A critical question for the restoration of tropical forests is how plantings influence the recruitment of new species, and specifically the phylogenetic and functional diversity of restored habitats. We examined 8 years (2006–2014) of tropical‐forest recruitment in two restoration planting compositions (12 animal‐dispersed and 12 wind‐dispersed tree species), with a control (no planting) in 24 plots in Los Tuxtlas, Mexico. Specifically, we assessed the influence of plantings on newly arriving individuals’ phylogenetic, functional, taxonomic diversity, abundance and the change of these measures during early succession. The recruiting individuals’ phylogenetic, functional, taxonomic diversity and abundance increased through succession. Both animal‐dispersed planting and wind‐dispersed planting appeared to accelerate forest succession more than controls (natural succession), and diversity in the animal‐dispersed plantings was marginally higher after 8 years. We did not find any difference in recruiting individuals’ phylogenetic and functional dispersion (measured as standardized effect sizes) in any given year, or when measured as turnover between successive pairs of years, measuring planting composition and control plots. Recruiting individuals were phylogenetically clustered during early forest restoration regardless of treatment. At the same time, the recruits transitioned from appearing randomly constructed to clustering according to functional traits, which suggests an increase in recruits’ functional similarity during early succession. Synthesis and applications . Both the animal and wind‐dispersed plantings accelerated the increase of recruiting individuals’ phylogenetic, functional, taxonomic diversity and abundance during early succession. However, planting treatment did not appear to alter community assembly mechanisms of recruiting individuals. Our findings support restoration planting by showing that planting trees with animal dispersal syndrome could accelerate forest restoration more than unassisted forest regeneration. Furthermore, communities appeared to be phylogenetically and functionally clustered during early succession regardless of initial planted composition. Thus, while overall diversity increased with planting, if a restoration goal is to maximize phylogenetic or functional dispersion, the planting composition tested did not provide means to achieve this goal, at least during early succession.
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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".