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Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction. 

Vegetation processes are fundamentally limited by nutrient and water availability, the uptake of which is mediated by plant roots in terrestrial ecosystems. While tropical forests play a central role in global water, carbon, and nutrient cycling, we know very little about tradeoffs and synergies in root traits that respond to resource scarcity. Tropical trees face a unique set of resource limitations, with rock-derived nutrients and moisture seasonality governing many ecosystem functions, and nutrient versus water availability often separated spatially and temporally. Root traits that characterize biomass, depth distributions, production and phenology, morphology, physiology, chemistry, and symbiotic relationships can be predictive of plants’ capacities to access and acquire nutrients and water, with links to aboveground processes like transpiration, wood productivity, and leaf phenology. In this review, we identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils. We also identify interesting paradoxes in tropical forest root responses to changing resources that merit further exploration. For example, specific root length, which typically increases under resource scarcity to expand the volume of soil explored, instead can increase with greater base cation availability, both across natural tropical forest gradients and in fertilization experiments. Also, nutrient additions, rather than reducing mycorrhizal colonization of fine roots as might be expected, increased colonization rates under scenarios of water scarcity in some forests. Efforts to include fine root traits and functions in vegetation models have grown more sophisticated over time, yet there is a disconnect between the emphasis in models characterizing nutrient and water uptake rates and carbon costs versus the emphasis in field experiments on measuring root biomass, production, and morphology in response to changes in resource availability. Closer integration of field and modeling efforts could connect mechanistic investigation of fine-root dynamics to ecosystem-scale understanding of nutrient and water cycling, allowing us to better predict tropical forest-climate feedbacks. 

Recent work has shown that evaluating functional trait distinctiveness, the average trait distance of a species to other species in a community offers promising insights into biodiversity dynamics and ecosystem functioning. However, the ecological mechanisms underlying the emergence and persistence of functionally distinct species are poorly understood. Here, we address the issue by considering a heterogeneous fitness landscape whereby functional dimensions encompass peaks representing trait combinations yielding positive population growth rates in a community. We identify four ecological cases contributing to the emergence and persistence of functionally distinct species. First, environmental heterogeneity or alternative phenotypic designs can drive positive population growth of functionally distinct species. Second, sink populations with negative population growth can deviate from local fitness peaks and be functionally distinct. Third, species found at the margin of the fitness landscape can persist but be functionally distinct. Fourth, biotic interactions (positive or negative) can dynamically alter the fitness landscape. We offer examples of these four cases and guidelines to distinguish between them. In addition to these deterministic processes, we explore how stochastic dispersal limitation can yield functional distinctiveness. Our framework offers a novel perspective on the relationship between fitness landscape heterogeneity and the functional composition of ecological assemblages.

 

Many plant species exhibit strong association with topographic habitats at local scales. However, the historical biogeographic and physiological drivers of habitat specialization are still poorly understood, and there is a need for relatively easy‐to‐measure predictors of species habitat niche breadth. Here, we explore whether species geographic range, climatic envelope, or intraspecific variability in leaf traits is related to the degree of habitat specialization in a hyperdiverse tropical tree community in Amazonian Ecuador. Contrary to our expectations, we find no effect of the size of species geographic ranges, the diversity of climate a species experiences across its range, or intraspecific variability in leaf traits in predicting topographic habitat association in the ~300 most common tropical tree species in a 25‐ha tropical forest plot. In addition, there was no phylogenetic signal to habitat specialization. We conclude that species geographic range size, climatic niche breadth, and intraspecific variability in leaf traits fail to capture the habitat specialization patterns observed in this highly diverse tropical forest.

 

A major challenge remains to understand the relative contributions of history, dispersal, and environmental filtering to the assembly of hyperdiverse communities across spatial scales. Here, we examine the extent to which biogeographical history and habitat specialization have generated turnover among and within lineages of Amazonian trees across broad geographic and environmental gradients. We replicated standardized tree inventories in 102 0.1‐ha plots located in two distant regions—the western Amazon and the eastern Guiana shield. Within each region, we used a nested design to replicate plots on contrasted habitats: white‐sand, terra firme, and seasonally flooded forests. Our plot network encompassed 26,386 trees that together represented 2,745 distinct taxa, which we standardized across all plots and regions. We combined taxonomic and phylogenetic data with detailed soil measurements and climatic data to: (1) test whether patterns of taxonomic and phylogenetic composition are consistent with recent or historical processes, (2) disentangle the relative effects of habitat, environment, and geographic distance on taxonomic and phylogenetic turnover among plots, and (3) contrast the proportion of habitat specialists among species from each region. We found substantial species turnover between Peru and French Guiana, with only 8.8% of species shared across regions; genus composition remained differentiated across habitats and regions, whereas turnover at higher taxonomic levels (family, order) was much lower. Species turnover across plots was explained primarily by regions, but also substantially by habitat differences and to a lesser extent by spatial distance within regions. Conversely, the composition of higher taxonomic levels was better explained by habitats (especially comparing white‐sand forests to other habitats) than spatial distance. White‐sand forests harbored most of the habitat specialists in both regions, with stronger habitat specialization in Peru than in French Guiana. Our results suggest that recent diversification events have resulted in extremely high turnover in species and genus composition with relatively little change in the composition of higher lineages. Our results also emphasize the contributions of rare habitats, such as white‐sand forests, to the extraordinary diversity of the Amazon and underline their importance as conservation priorities.