Tropical forest succession and associated changes in community composition are driven by species' demographic rates, but how demographic strategies shift during succession remains unclear. To identify generalities in demographic trade-offs and successional shifts in demographic strategies, we quantified demographic rates of 787 tree species from two wet and two dry Neotropical forests. Across all forests, we found two demographic trade-offs -- the growth--survival and the stature--recruitment trade-off -- enabling the data-driven assignment of species to five demographic strategies. Fast species dominated early in succession and were then replaced by long-lived pioneers in three forests. Intermediate and slow species increased in basal area over succession but in contrast to the current conceptual model, long-lived pioneers continued to dominate until the old-growth stage in all forests. The basal area of short-lived breeders was low across all successional stages. These results increase the mechanistic understanding and predictability of Neotropical forest succession.
All species must balance their allocation to growth, survival and recruitment. Among trees, evolution has resulted in different strategies of partitioning resources to these key demographic processes, i.e. demographic trade-offs. It is unclear whether the same demographic trade-offs structure tropical forests worldwide. Here, we used data from 13 large-scale and long-term tropical forest plots to estimate the principal trade-offs in growth, survival, recruitment, and tree stature at each site. For ten sites, two trade-offs appeared repeatedly. One trade-off showed a negative relationship between growth and survival, i.e. the well-known fast−slow continuum. The second trade-off distinguished between tall-statured species and species with high recruitment rates, i.e. a stature−recruitment trade-off. Thus, the fast-slow continuum and tree stature are two independent dimensions structuring most tropical tree communities. Our discovery of the consistency of demographic trade-offs and strategies across forest types in three continents substantially improves our ability to predict tropical forest dynamics worldwide.
Meta-analyses often encounter studies with incompletely reported variance measures (e.g. standard deviation values) or sample sizes, both needed to conduct weighted meta-analyses. Here, we first present a systematic literature survey on the frequency and treatment of missing data in published ecological meta-analyses showing that the majority of meta-analyses encountered incompletely reported studies. We then simulated meta-analysis data sets to investigate the performance of 14 options to treat or impute missing SDs and/or SSs. Performance was thereby assessed using results from fully informed weighted analyses on (hypothetically) complete data sets. We show that the omission of incompletely reported studies is not a viable solution. Unweighted and sample size-based variance approximation can yield unbiased grand means if effect sizes are independent of their corresponding SDs and SSs. The performance of different imputation methods depends on the structure of the meta-analysis data set, especially in the case of correlated effect sizes and standard deviations or sample sizes. In a best-case scenario, which assumes that SDs and/or SSs are both missing at random and are unrelated to effect sizes, our simulations show that the imputation of up to 90% of missing data still yields grand means and confidence intervals that are similar to those obtained with fully informed weighted analyses. We conclude that multiple imputation of missing variance measures and sample sizes could help overcome the problem of incompletely reported primary studies, not only in the field of ecological meta-analyses. Still, caution must be exercised in consideration of potential correlations and pattern of missingness.