Testing a generalized leaf mass estimation method for diverse tree species and climates of the continental United States.

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available.

Alpine treeline ecotones are potential refugia for a montane pine species threatened by bark beetle outbreaks.

Warming-induced mountain pine beetle (Dendroctonus ponderosae; MPB) outbreaks have caused extensive mortality of whitebark pine (Pinus albicaulis; WBP) throughout the species' range. In the highest mountains where WBP occur, they cross alpine treeline ecotones (ATEs) where growth forms transition from trees to shrub-like krummholz, some of which survived recent MPB outbreaks. This observation motivated the hypothesis that ATEs are refugia for WBP because krummholz growth forms escape MPB attack and have the potential to produce viable seed. To test this hypothesis, we surveyed WBP mortality along transects from the ATE edge (locally highest krummholz WBP) downslope into the forest and, to distinguish if survival mechanisms are unique to ATEs, across other forest ecotones (OFEs) from the edge of WBP occurrence into the forest. We replicated this design at 10 randomly selected sites in the U.S. Northern Rocky Mountains. We also surveyed reproduction in a subset of ATE sites. Mortality was nearly absent in upper ATEs (mean ± SE percent dead across all sites of 0.03% ± 0.03% 0-100 m from the edge and 14.1% ± 1.7% 100-500 m from the edge) but was above 20% along OFEs (21.4 ± 5.2% 0-100 m and 32.4 ± 2.7% 100-500 m from the edge). We observed lower reproduction in upper ATEs (16 ± 9.9 cones/ha and 12.9 ± 5.3 viable seeds/cone 0-100 m from the edge) compared to forests below (317.1 ± 64.4 cones/ha and 32.5 ± 2.5 viable seeds/cone 100-500 m from the edge). Uniquely high WBP survival supports the hypothesis that ATEs serve as refugia because krummholz growth forms escape MPB attack. However, low reproduction suggests ATE refugia function over longer time periods. Beyond our WBP system, we propose that plant populations in marginal environments are candidate refugia if distinct phenotypes result in reduced disturbance impacts.

Critical analyses when modeling tree biomass to ensure additivity of its components.

It is presented the theme additivity of biomass of tree components. To evaluate and discuss this context, experimental information collected in forests of Acacia mearnsii De Wild. was used. Equations for components (stem and crown) and total biomass were fitted by means of two procedures: 1) generalized nonlinear least squares and 2) weighted-nonlinear seemingly unrelated regressions. Analyzing the performance of the estimators, it can be concluded that the two tested procedures are equivalent. On the other hand, this conclusion differs when evaluated the consistency and efficiency of the estimators. Fitting equations for the components and for the total biomass by an independent way is not realistic, because from a biological point of view the estimates of biomass are inconsistent, i.e., are not additive. The biomass estimates of the components and of the total, resulting from equations adjusted by means of systems of equations, provided narrower confidence intervals in relation to the equations adjusted independently, and is therefore more efficient. The second procedure presents better biological properties and statistics to estimate allometric equations for biomass of the components and for the total when compared with the independent estimation, thus it should be the method to be used.

Future global productivity will be affected by plant trait response to climate.

Plant traits are both responsive to local climate and strong predictors of primary productivity. We hypothesized that future climate change might promote a shift in global plant traits resulting in changes in Gross Primary Productivity (GPP). We characterized the relationship between key plant traits, namely Specific Leaf Area (SLA), height, and seed mass, and local climate and primary productivity. We found that by 2070, tropical and arid ecosystems will be more suitable for plants with relatively lower canopy height, SLA and seed mass, while far northern latitudes will favor woody and taller plants than at present. Using a network of tower eddy covariance CO2 flux measurements and the extrapolated plant trait maps, we estimated the global distribution of annual GPP under current and projected future plant community distribution. We predict that annual GPP in northern biomes (≥45 °N) will increase by 31% (+8.1 ± 0.5 Pg C), but this will be offset by a 17.9% GPP decline in the tropics (-11.8 ± 0.84 Pg C). These findings suggest that regional climate changes will affect plant trait distributions, which may in turn affect global productivity patterns.

Remote Sensing Derived Fire Frequency, Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia.

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m3 m(-3)) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species' ecological habitat niche across Australia.

Assessing the performance of sampling designs for measuring the abundance of understory plants.

Accurate estimation of responses of understory plants to disturbance is essential for understanding the efficacy of management activities. However, the ability to assess changes in the abundance of plants may be hampered by inappropriate sampling methodologies. Conventional methods for sampling understory plants may be precise for common species but may fail to adequately characterize abundance of less common species. We tested conventional (modified Whittaker plots and Daubenmire and point-line intercept transects) and novel (strip adaptive cluster sampling [SACS]) approaches to sampling understory plants to determine their efficacy for quantifying abundance on control and thinned-and-burned treatment units in Pinus ponderosa forests in western Montana, USA. For species grouped by growth-form and for common species, all three conventional designs were capable of estimating cover with a 50% relative margin of error with reasonable sample sizes (3-36 replicates for growth-form groups; 8-14 replicates for common species); however, increasing precision to 25% relative margin of error required sample sizes that may be infeasible (11-143 replicates for growth-form groups; 28-54 replicates for common species). All three conventional designs required enormous sample sizes to estimate cover of nonnative species as a group (29-60 replicates) and of individual less common species (62-118 replicates), even with a 50% relative margin of error. SACS was the only design that efficiently sampled less common species, requiring only 6-11% as many replicates relative to conventional designs. Conventional designs may not be effective for estimating abundance of the majority of forest understory plants, which are typically patchily distributed with low abundance, or of newly establishing nonnative plants. Novel methods such as SACS should be considered in investigations when cover of these species is of concern.