• Energy Research

Abstract Lianas (woody vines) are an important component of tropical forests, with a strong impact on forest dynamics, but their responses during forest succession have received relatively little attention. Here, we present an analysis of the changes in stem density, biomass, and species richness of lianas and self-supporting plants during tropical forest succession. We surveyed lianas ≥0.5 cm diameter at breast height (dbh) and self-supporting plants ≥2.5 cm dbh in 0.1 ha inventory plots in a chronosequence of 30 sites in northeastern Costa Rica, 23 sites on abandoned pastures 10–44 years of age, and seven sites in old-growth forest. Stem density of self-supporting plants showed no predictable chronosequence trend, but liana stem density declined significantly with forest age. Aboveground biomass of self-supporting vegetation increased rapidly during succession, with forests 31–44 years exhibiting higher levels of biomass than old-growth forests. Liana biomass accumulated more slowly, with the highest levels in old-growth sites. Species richness of self-supporting vegetation increased significantly during succession, but species richness of lianas showed no change or a slight decline with forest age, depending on the method of assessment. The differences between tree and liana responses during succession stem from the unique physiology and life history traits of lianas.

We demonstrate use of the ratio of red to far-red (R:FR) light in assessing understory light levels in tropical forests. We determined the sensitivity of the ratio to bias when used under a variety of light conditions. We also calibrated the relationship between percent transmittance, as measured with traditional paired-instrument methods, and the R:FR ratio under a wide range of canopy cover conditions. Finally, we used the relationship to measure light levels under a closed canopy in a secondary tropical wet forest and assessed the usefulness of the measurements in determining spatial structure of light in a 1 ha plot, compared with percent transmittance values. Across a wide range of canopy conditions, red:far-red ratios explained 97% of the variation in percent diffuse transmittance under cloudy skies. Predictions of percent transmittance made under sunny skies were consistently lower than those made during cloudy conditions, even under a closed canopy, indicating that the method can be used reliably only with diffuse light. We used the R:FR ratio to characterize the spatial structure in understory light levels in a wet tropical forest in Costa Rica, explaining more variation in spatial structure than did a model fit to percent transmittance at a height of 1.5 m. The ratio provides a quick way to assess with high sensitivity the quantity and quality of light without the inconvenience and potential errors that can be introduced with the use of paired instruments or hemispherical photographs.

Understanding how human-impacted landscapes are changing is crucial for effective adaptive management and payment for ecosystem services programs. Landscape-level shifts in land use pose challenges not seen in typical ecological studies of well-protected forests. In human-modified landscapes, forests are often monitored using unique sets of randomized plots at each visit rather than re-censusing in the same permanent plots. We contrast field-based forest change monitoring using these two techniques and investigate whether sampling more plots or bigger plots better detects forest changes. Our empirical analysis employs long-term data sets from old-growth, second-growth, and managed tropical forests. We find that resampling in permanent plots reduces variation among subsequent censuses, but more importantly, it enables more powerful statistical tests. Increasing the number of plots improves detection of forest biomass changes more effectively than enlarging existing plot sizes, cost considerations being equal. This effect arises from more extensive capture of spatial heterogeneity by sampling in a greater number of locations. We further show that typical sampling techniques poorly assess the biodiversity of tropical forests and struggle to identify big changes in populations of common species. We conclude with practical suggestions for forest sampling in human-impacted tropical landscapes, including defining monitoring goals and delineating forests vs. entire landscapes as study areas.

AbstractUnderstanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

AbstractThe density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.