The biogeography and filtering of woody plant functional diversity in North and South America

210

214

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.T o understand and predict the impacts of climate change on system Earth, it is essential to predict global vegetation distribution and its attributes. Vegetation determines the fluxes of energy, water, and CO 2 to and from terrestrial ecosystems. Socalled Dynamic Global Vegetation Models (DGVMs) (reviewed in ref. 1) are indispensable tools to make predictions on such biosphere-climate interactions. Despite their importance, DGVMs are among the most uncertain components of earth system models when predicting climate change (2).DGVMs have been built around the concept of Plant Functional Types (PFTs) (3). Traditionally, various functional attributes (or traits) were assumed to be constant for a given PFT. This assumption has various drawbacks (reviewed in ref. 4). For instance, it implies assuming that trait values used to parameterize PFTs are valid under past environmental conditions and will be valid under future conditions. As such, this assumption neglects acclimation and adaptation (5), nonrandom species extinction (6), and major differences in dispersal rates among species and within PFTs (7). Moreover, this assumption strongly hampers quantifying feedback mechanisms between vegetation and its environment.For these reasons, the application of traits in DGVMs is increasingly refined. Trait responses to, for example, different soil fertility conditions are desc...

Section: Resultsmentioning

confidence: 76%

A fully traits-based approach to modeling global vegetation distribution

Bodegom

Douma

Verheijen

2014

210

214

“…It has been shown that using trait distributions leads to different estimates of carbon dynamics (32) and that higher-order moments of trait distributions contribute to sustaining multiple ecosystem functions (33). While species-level mapping (21,23,24) does capture trait distributions, it has been limited geographically and restricted to subsets of functional groups.…”

Section: Significancementioning

confidence: 99%

“…Previous work has extrapolated trait measurements across continental or larger regions through three methodologies: (i) grouping measurements of individuals into larger categories that share a set of properties [a working definition of plant functional types (PFTs)] (4, 15), (ii) exploiting trait-environment relationships (e.g., leaf Nm and mean annual temperature) (1,(16)(17)(18)(19)(20), or (iii) restricting the analysis to species whose presence has been widely estimated on the ground (21)(22)(23)(24). Each of these methods has limitations-for example, trait-environment relationships do not well explain observed trait spatial patterns (1,25), while species-based approaches limit the scope of extrapolation to only areas with well-measured species abundance.…”

mentioning

confidence: 99%

Mapping local and global variability in plant trait distributions

Butler

Datta

Flores‐Moreno

et al. 2017

Self Cite

187

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50 × 50-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.odeling global climate and the carbon cycle with Earth system models (ESMs) requires maps of plant traits that play key roles in leaf-and ecosystem-level metabolic processes (1-4). Multiple traits are critical to both photosynthesis and respiration, foremost leaf nitrogen concentration (Nm ) and specific leaf area (SLA) (5-7). More recently, variation in leaf phosphorus concentration (Pm ) has also been linked to variation in photosynthesis and foliar respiration (7-12). Estimating detailed global geographic patterns of these traits and corresponding trait-environment relationships has been hampered by limited measurements (13), but recent improvements in data coverage (14) allow for greater detail in spatial estimates of these key traits.Previous work has extrapolated trait measurements across continental or larger regions through three methodologies: (i) grouping measurements of individuals into larger categories that share a set of properties [a working definition of plant functional types (PFTs)] (4, 15), (ii) exploiting trait-environment relationships (e.g., leaf Nm and mean annual temperature) (1,(16)(17)(18)(19)(20), or (iii) restricting the analysis to species whose presence has been widely estimated on the ground (21-24). Each of these methods has limitations-for example, trait-environment relationships do not well explain observed trait spatial patterns (1, 25), while species-based approaches limit the scope of extrapolation to only areas with well-measured species abundance. More critically, the first two global methodologies emp...

“…Studies have considered the adaptive mechanisms behind strategies within a single ecosystem, including predator avoidance, energetic constraints, diet quality, and interspecific competition (9,21). Meanwhile, although mapping functional traits has become a core technique in functional biogeography (22,23), surprisingly little is known about the biogeography of diel activity patterns, and the extent to which they are determined by geographic gradients in light and climate. Addressing such issues has become more pressing with growth in the evidence for a wide range of ecological impacts of both anthropogenic climatic change and nighttime light pollution (24)(25)(26)(27)(28).…”

mentioning

confidence: 99%

Biogeography of time partitioning in mammals

Bennie

Duffy

Inger

et al. 2014

253

209

Many animals regulate their activity over a 24-h sleep-wake cycle, concentrating their peak periods of activity to coincide with the hours of daylight, darkness, or twilight, or using different periods of light and darkness in more complex ways. These behavioral differences, which are in themselves functional traits, are associated with suites of physiological and morphological adaptations with implications for the ecological roles of species. The biogeography of diel time partitioning is, however, poorly understood. Here, we document basic biogeographic patterns of time partitioning by mammals and ecologically relevant large-scale patterns of natural variation in "illuminated activity time" constrained by temperature, and we determine how well the first of these are predicted by the second. Although the majority of mammals are nocturnal, the distributions of diurnal and crepuscular species richness are strongly associated with the availability of biologically useful daylight and twilight, respectively. Cathemerality is associated with relatively long hours of daylight and twilight in the northern Holarctic region, whereas the proportion of nocturnal species is highest in arid regions and lowest at extreme high altitudes. Although thermal constraints on activity have been identified as key to the distributions of organisms, constraints due to functional adaptation to the light environment are less well studied. Global patterns in diversity are constrained by the availability of the temporal niche; disruption of these constraints by the spread of artificial lighting and anthropogenic climate change, and the potential effects on time partitioning, are likely to be critical influences on species' future distributions.cathemeral | night N atural cycles of light and darkness structure the environment of the majority of eukaryotic organisms. The rotation of the Earth partitions time into regular cycles of day and night, and although all points on the Earth's surface receive roughly equal durations of light and darkness over the course of a year, at mid to high latitudes seasonal variation in day length imposes an uneven distribution throughout the annual cycle. During the hours when the sun is below the horizon, there is seasonal and latitudinal variation in the duration of "biologically useful semidarkness" in the form of twilight and moonlight (1), modified by both the lunar cycle and variable cloud cover, providing changing opportunities for animals able to use visual cues for key behaviors including foraging, predator avoidance, and reproduction (2-6). Activity during both daylight and semidarkness may be further constrained by covariance between the natural cycles of light and temperature; the metabolic costs of thermoregulation place constraints on the time available for activity (7). Thermal constraints may limit nocturnal activity when nighttime temperatures are low, and diurnal activity when temperatures are high. Furthermore, energetic constraints may force some species to be active throughout hours of both ...