Search for: All records

Understanding tropical forest dynamics and planning for their sustainable management require efficient, yet accurate, predictions of the joint dynamics of hundreds of tree species. With increasing information on tropical tree life histories, our predictive understanding is no longer limited by species data but by the ability of existing models to make use of it. Using a demographic forest model, we show that the basal area and compositional changes during forest succession in a neotropical forest can be accurately predicted by representing tropical tree diversity (hundreds of species) with only five functional groups spanning two essential trade-offs—the growth-survival and stature-recruitment trade-offs. This data-driven modeling framework substantially improves our ability to predict consequences of anthropogenic impacts on tropical forests. 

Most existing functional diversity indices focus on a single facet of functional diversity. Although these indices are useful for quantifying specific aspects of functional diversity, they often present some conceptual or practical limitations in estimating functional diversity. Here, we present a new functional extension and evenness (FEE) index that encompasses two important aspects of functional diversity. This new index is based on the straightforward notion that a community has high diversity when its species are distant from each other in trait space. The index quantifies functional diversity by evaluating the overall extension of species traits and the interspecific differences of a species assemblage in trait space. The concept of minimum spanning tree (MST) of points was adopted to obtain the essential distribution properties for a species assembly in trait space. We combined the total length of MST branches (extension) and the variation of branch lengths (evenness) into a raw FEE₀metric and then translated FEE₀to a species richness‐independent FEE index using a null model approach. We assessed the properties of FEE and used multiple approaches to evaluate its performance. The results show that the FEE index performs well in quantifying functional diversity and presents the following desired properties: (a) It allows a fair comparison of functional diversity across different species richness levels; (b) it preserves the essence of single‐facet indices while overcoming some of their limitations; (c) it standardizes comparisons among communities by taking into consideration the trait space of the shared species pool; and (d) it has the potential to distinguish among different community assembly processes. With these attributes, we suggest that the FEE index is a promising metric to inform biodiversity conservation policy and management, especially in applications at large spatial and/or temporal scales.

 

Forest dynamics arise from the interplay of environmental drivers and disturbances with the demographic processes of recruitment, growth, and mortality, subsequently driving biomass and species composition. However, forest disturbances and subsequent recovery are shifting with global changes in climate and land use, altering these dynamics. Changes in environmental drivers, land use, and disturbance regimes are forcing forests toward younger, shorter stands. Rising carbon dioxide, acclimation, adaptation, and migration can influence these impacts. Recent developments in Earth system models support increasingly realistic simulations of vegetation dynamics. In parallel, emerging remote sensing datasets promise qualitatively new and more abundant data on the underlying processes and consequences for vegetation structure. When combined, these advances hold promise for improving the scientific understanding of changes in vegetation demographics and disturbances. 

Symbiotic nitrogen (N)‐fixing trees can drive N and carbon cycling and thus are critical components of future climate projections. Despite detailed understanding of how climate influences N‐fixation enzyme activity and physiology, comparatively little is known about how climate influences N‐fixing tree abundance. Here, we used forest inventory data from theUSAand Mexico (>125,000 plots) along with climate data to address two questions: (1) How does the abundance distribution of N‐fixing trees (rhizobial, actinorhizal, and both types together) vary with mean annual temperature (MAT) and precipitation (MAP)? (2) How will changing climate shift the abundance distribution of N‐fixing trees? We found that rhizobial N‐fixing trees were nearly absent below 15°CMAT, but above 15°CMAT, they increased in abundance as temperature rose. We found no evidence for a hump‐shaped response to temperature throughout the range of our data. Rhizobial trees were more abundant in dry than in wet ecosystems. By contrast, actinorhizal trees peaked in abundance at 5–10°CMATand were least abundant in areas with intermediate precipitation. Next, we used a climate‐envelope approach to project how N‐fixing tree relative abundance might change in the future. The climate‐envelope projection showed that rhizobial N‐fixing trees will likely become more abundant in many areas by 2080, particularly in the southernUSAand western Mexico, due primarily to rising temperatures. Projections for actinorhizal N‐fixing trees were more nuanced due to their nonmonotonic dependence on temperature and precipitation. Overall, the dominant trend is that warming will increase N‐fixing tree abundance in much of theUSAand Mexico, with large increases up to 40° North latitude. The quantitative link we provide between climate and N‐fixing tree abundance can help improve the representation of symbiotic N fixation in Earth System Models.

 

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that permit the representation of plant demographics inESMs, and identify issues raised by these developments that highlight important gaps in ecological understanding. These issues inevitably translate into uncertainty in model projections but also allow models to be applied to new processes and questions concerning the dynamics of real‐world ecosystems. We argue that stronger and more innovative connections to data, across the range of scales considered, are required to address these gaps in understanding. The development of first‐generation land surface models as a unifying framework for ecophysiological understanding stimulated much research into plant physiological traits and gas exchange. Constraining predictions at ecologically relevant spatial and temporal scales will require a similar investment of effort and intensified inter‐disciplinary communication.