Structural diversity is better associated with forest productivity than species or functional diversity

Understanding the relationship between biodiversity and productivity can be advanced by improving metrics used to quantify biodiversity. Structural diversity, that is, variation of size and form of plant organs, is an emerging biodiversity metric. However, compared with the other biodiversity metrics, its relative importance in specific components of forest productivity, for example, recruitment of new individuals, biomass net change after accounting for mortality, is largely unknown, particularly across a large spatial scale with multiple influential gradients. To address the knowledge gap, we used USDA Forest Service Forest Inventory and Analysis (FIA) data across the southcentral USA from 2008 to 2017. We calculated forest biomass increments due to recruitment and growth and net change in biomass. Then, we quantified the effects of a range of abiotic and biotic variables on the biomass increments and net change. Our results showed that (1) Structural diversity was negatively associated with the two biomass increments and net change in biomass. The negative effects were supported by increased occurrences of insects and diseases with greater structural diversity. (2) Compared with species and functional diversity, structural diversity showed a better association with biomass increments and net change, suggested by its larger absolute values of standardized coefficients, and the effects of structural diversity were negative in contrast to species diversity. (3) The effects of structural diversity, stand age, and elevation differed between natural and planted forests that may stem from the differences in stand development and species composition between the two forest types. Together, structural diversity may represent an important dimension of biodiversity impacts on plant productivity, which could be related to the exacerbated disturbances with greater structural diversity.

Can leaf drought tolerance predict species abundance and its changes in tropical-subtropical forests?

Climate change has resulted in an increase in drought severity in the species-rich tropical and subtropical forests of southern China. Exploring the spatiotemporal relationship between drought-tolerance trait and tree abundance provides a means to elucidate the impact of droughts on community assembly and dynamics. In this study, we measured the leaf turgor loss point (πtlp) for 399 tree species from three tropical forest plots and three subtropical forest plots. The plot area was 1 ha and tree abundance was calculated as total basal area per hectare according to the nearest community census data. The first aim of this study was to explore πtlp abundance relationships in the six plots across a range of precipitation seasonality. Additionally, three of the six plots (two tropical forests and one subtropical forest) had consecutive community censuses data (12-22 years) and the mortality ratios and abundance year slope of tree species were analyzed. The second aim was to examine whether πtlp is a predictor of tree mortality and abundance changes. Our results showed that tree species with lower (more negative) πtlp were more abundant in the tropical forests with relative high seasonality. However, πtlp was not related to tree abundance in the subtropical forests with low seasonality. Moreover, πtlp was not a good predictor of tree mortality and abundance changes in both humid and dry forests. This study reveals the restricted role of πtlp in predicting the response of forests to increasing droughts under climate change.

Dry Season Transpiration and Soil Water Dynamics in the Central Amazon

With current observations and future projections of more intense and frequent droughts in the tropics, understanding the impact that extensive dry periods may have on tree and ecosystem-level transpiration and concurrent carbon uptake has become increasingly important. Here, we investigate paired soil and tree water extraction dynamics in an old-growth upland forest in central Amazonia during the 2018 dry season. Tree water use was assessed via radial patterns of sap flow in eight dominant canopy trees, each a different species with a range in diameter, height, and wood density. Paired multi-sensor soil moisture probes used to quantify volumetric water content dynamics and soil water extraction within the upper 100 cm were installed adjacent to six of those trees. To link depth-specific water extraction patterns to root distribution, fine root biomass was assessed through the soil profile to 235 cm. To scale tree water use to the plot level (stand transpiration), basal area was measured for all trees within a 5 m radius around each soil moisture probe. The sensitivity of tree transpiration to reduced precipitation varied by tree, with some increasing and some decreasing in water use during the dry period. Tree-level water use scaled with sapwood area, from 11 to 190 L per day. Stand level water use, based on multiple plots encompassing sap flow and adjacent trees, varied from ∼1.7 to 3.3 mm per day, increasing linearly with plot basal area. Soil water extraction was dependent on root biomass, which was dense at the surface (i.e., 45% in the upper 5 cm) and declined dramatically with depth. As the dry season progressed and the upper soil dried, soil water extraction shifted to deeper levels and model projections suggest that much of the water used during the month-long dry-down could be extracted from the upper 2-3 m. Results indicate variation in rates of soil water extraction across the research area and, temporally, through the soil profile. These results provide key information on whole-tree contributions to transpiration by canopy trees as water availability changes. In addition, information on simultaneous stand level dynamics of soil water extraction that can inform mechanistic models that project tropical forest response to drought.

Evaluating multiple historical climate products in ecological models under current and projected temperatures

Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time have received less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, USA. We first compare the fit of, and support for, models employing observed temperatures, GHCP temperatures, and GHCP temperatures with an elevation adjustment, finding (1) greater support for, and better fit using, elevation-adjusted vs. raw temperature models and (2) overall similar fits of elevation-adjusted models employing temperatures from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation-adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2°C, finding good agreement among GHCPs though with between-GHCP differences and variation primarily at middle elevations (~1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models, particularly in mid-elevation areas where the position of treeline may be changing, suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.

Fire deficits have increased drought sensitivity in dry conifer forests: Fire frequency and tree-ring carbon isotope evidence from Central Oregon

A century of fire suppression across the Western United States has led to more crowded forests and increased competition for resources. Studies of forest thinning or stand conditions after mortality events have provided indirect evidence for how competition can promote drought stress and predispose forests to severe fire and/or bark beetle outbreaks. Here, we demonstrate linkages between fire deficits and increasing drought stress through analyses of annually resolved tree-ring growth, fire scars, and carbon isotope discrimination (Δ¹³ C) across a dry mixed-conifer forest landscape. Fire deficits across the study area have increased the sensitivity of leaf gas exchange to drought stress over the past >100 years. Since 1910, stand basal area in these forests has more than doubled and fire-return intervals have increased from 25 to 140 years. Meanwhile, the portion of interannual variation in tree-ring Δ¹³ C explained by the Palmer Drought Severity Index has more than doubled in ca. 300-500-year-old Pinus ponderosa as well as in fire-intolerant, ca. 90-190-year-old Abies grandis. Drought stress has increased in stands with a basal area of ≥25 m² /ha in 1910, as indicated by negative temporal Δ¹³ C trends, whereas stands with basal area ≤25 m² /ha in 1910, due to frequent or intense wildfire activity in decades beforehand, were initially buffered from increased drought stress and have benefited more from rising ambient carbon dioxide concentrations, [CO₂ ], as demonstrated by positive temporal Δ¹³ C trends. Furthermore, the average Δ¹³ C response across all P. ponderosa since 1830 indicates that photosynthetic assimilation rates and stomatal conductance have been reduced by ~10% and ~20%, respectively, compared to expected trends due to increasing [CO₂ ]. Although disturbance legacies contribute to local-scale intensity of drought stress, fire deficits have reduced drought resistance of mixed-conifer forests and made them more susceptible to challenges by pests and pathogens and other disturbances.

Historical harvests reduce neighboring old-growth basal area across a forest landscape

While advances in remote sensing have made stand, landscape, and regional assessments of the direct impacts of disturbance on forests quite common, the edge influence of timber harvesting on the structure of neighboring unharvested forests has not been examined extensively. In this study, we examine the impact of historical timber harvests on basal area patterns of neighboring old-growth forests to assess the magnitude and scale of harvest edge influence in a forest landscape of western Oregon, USA. We used lidar data and forest plot measurements to construct 30-m resolution live tree basal area maps in lower and middle elevation mature and old-growth forests. We assessed how edge influence on total, upper canopy, and lower canopy basal area varied across this forest landscape as a function of harvest characteristics (i.e., harvest size and age) and topographic conditions in the unharvested area. Upper canopy, lower canopy, and total basal area increased with distance from harvest edge and elevation. Forests within 75 m of harvest edges (20% of unharvested forests) had 4% to 6% less live tree basal area compared with forest interiors. An interaction between distance from harvest edge and elevation indicated that elevation altered edge influence in this landscape. We observed a positive edge influence at low elevations (<800 m) and a negative edge influence at moderate to high elevations (>800 m). Surprisingly, we found no or weak effects of harvest age (13-60 yr) and harvest area (0.2-110 ha) on surrounding unharvested forest basal area, implying that edge influence was relatively insensitive to the scale of disturbance and multi-decadal recovery processes. Our study indicates that the edge influence of past clearcutting on the structure of neighboring uncut old-growth forests is widespread and persistent. These indirect and diffuse legacies of historical timber harvests complicate forest management decision-making in old-growth forest landscapes by broadening the traditional view of stand boundaries. Furthermore, the consequences of forest harvesting may reach across ownership boundaries, highlighting complex governance issues surrounding landscape management of old-growth forests.

Integrating field and satellite data for spatially explicit inference on the density of threatened arboreal primates

Spatially explicit models of animal abundance are a critical tool to inform conservation planning and management. However, they require the availability of spatially diffuse environmental predictors of abundance, which may be challenging, especially in complex and heterogeneous habitats. This is particularly the case for tropical mammals, such as nonhuman primates, that depend on multi-layered and species-rich tree canopy coverage, which is usually measured through a limited sample of ground plots. We developed an approach that calibrates remote-sensing imagery to ground measurements of tree density to derive basal area, in turn used as a predictor of primate density based on published models. We applied generalized linear models (GLM) to relate 9.8-ha ground samples of tree basal area to various metrics extracted from Landsat 8 imagery. We tested the potential of this approach for spatial inference of animal density by comparing the density predictions for an endangered colobus monkey, to previous estimates from field transect counts, measured basal area, and other predictors of abundance. The best GLM had high accuracy and showed no significant difference between predicted and observed values of basal area. Our species distribution model yielded predicted primate densities that matched those based on field measurements. Results show the potential of using open-access and global remote-sensing data to derive an important predictor of animal abundance in tropical forests and in turn to make spatially explicit inference on animal density. This approach has important, inherent applications as it greatly magnifies the relevance of abundance modeling for informing conservation. This is especially true for threatened species living in heterogeneous habitats where spatial patterns of abundance, in relation to habitat and/or human disturbance factors, are often complex and, management decisions, such as improving forest protection, may need to be focused on priority areas.

Capturing spiral radial growth of conifers using the superellipse to model tree-ring geometric shape

Tree-rings are often assumed to approximate a circular shape when estimating forest productivity and carbon dynamics. However, tree rings are rarely, if ever, circular, thereby possibly resulting in under- or over-estimation in forest productivity and carbon sequestration. Given the crucial role played by tree ring data in assessing forest productivity and carbon storage within a context of global change, it is particularly important that mathematical models adequately render cross-sectional area increment derived from tree rings. We modeled the geometric shape of tree rings using the superellipse equation and checked its validation based on the theoretical simulation and six actual cross sections collected from three conifers. We found that the superellipse better describes the geometric shape of tree rings than the circle commonly used. We showed that a spiral growth trend exists on the radial section over time, which might be closely related to spiral grain along the longitudinal axis. The superellipse generally had higher accuracy than the circle in predicting the basal area increment, resulting in an improved estimate for the basal area. The superellipse may allow better assessing forest productivity and carbon storage in terrestrial forest ecosystems.

Across a macro-ecological gradient forest competition is strongest at the most productive sites

We tested the hypothesis that the effect of forest basal area on tree growth interacts with macro-ecological gradients of primary productivity, using a large dataset of eucalypt tree growth collected across temperate and sub- tropical mesic Australia. To do this, we derived an index of inter-tree competition based on stand basal area (stand BA) relative to the climatically determined potential basal area. Using linear mixed effects modeling, we found that the main effects of climatic productivity, tree size, and competition explained 26.5% of the deviance in individual tree growth, but adding interactions to the model could explain a further 8.9%. The effect of competition on growth interacts with the gradient of climatic productivity, with negligible effect of competition in low productivity environments, but marked negative effects at the most productive sites. We also found a positive interaction between tree size and stand BA, which was most pronounced in the most productive sites. We interpret these patterns as reflecting intense competition for light amongst maturing trees on more productive sites, and below ground moisture limitation at low productivity sites, which results in open stands with little competition for light. These trends are consistent with the life history and stand development of eucalypt forests: in cool moist environments, light is the most limiting resource, resulting in size-asymmetric competition, while in hot, low rainfall environments are open forests with little competition for light but where the amount of tree regeneration is limited by water availability.

Estimating Asymptotic Attributes of Forest Stands Based on Bio-Mathematical Rationales

An approach for estimating asymptotic forest stand yield, basal area, and tree density (number of stems per unit of area) is proposed. Available forest stand growth data are used to establish the reciprocal equation of Competition-Density (C-D) effect and develop equations relating the coefficients of C-D effect to stand top height. Asymptotic stand yield, basal area, and tree density are derived based on bio-mathematical rationales and expressed as functions of asymptotic top height. Asymptotic top height can be obtained for different site qualities and/or habitat types by evaluating a height growth model in the limit as age approaches infinity. Estimated asymptotes can be utilized to parameterize sigmoid-shaped growth functions (e.g., Richards growth model) for developing forest growth and yield models.