Patterns of Structural Response to Simulated Partial Harvesting of Boreal Mixedwood Stands

Partial harvesting has been proposed as an approach for maintaining late-successional structure within managed boreal mixedwood stands. Although little long-term data is available to evaluate its effects in this stand type, recent advances in individual tree-based stand modeling provide an opportunity to simulate post-harvest stand development following different retention harvests. Using the stand dynamics model SORTIE-ND, we examined 40-year patterns of structural change in response to different intensities (30%, 50%, and 70% removal) and spatial patterns (uniform, small patch, large patch) of harvesting in aspen-dominated mixedwood stands. We assessed structural dynamics through a suite of variables representing the distribution of tree sizes, understory development, regeneration, standing and fallen dead wood characteristics, and within-stand heterogeneity. Partial harvesting induced a reciprocal increase in understory and downed woody debris development and decrease in overstory structure over the first 20 years after harvest, with this effect reversing after 25 years as harvest-induced regeneration reached the canopy. Densities of large trees and snags were reduced by harvesting, and did not recover to pre-harvest levels within 40 years. Harvesting promoted within-stand heterogeneity in the short and long term, and also produced transient increases in early-decay downed woody debris and ground exposure. These effects largely increased in proportion to harvest intensity. Although spatial pattern was of lesser importance than intensity, aggregated harvests induced somewhat less pronounced impacts on structure (with the exception of heterogeneity) than dispersed harvesting. These simulation results can form a basis for more detailed hypotheses regarding maintenance of late-successional stand structure and function through partial harvesting. Such hypotheses may in turn be translated into real-world silvicultural experiments to be evaluated, refined, and either accepted or rejected within an adaptive management framework

A critique of general allometry-inspired models for estimating forest carbon density from airborne LiDAR.

There is currently much interest in developing general approaches for mapping forest aboveground carbon density using structural information contained in airborne LiDAR data. The most widely utilized model in tropical forests assumes that aboveground carbon density is a compound power function of top of canopy height (a metric easily derived from LiDAR), basal area and wood density. Here we derive the model in terms of the geometry of individual tree crowns within forest stands, showing how scaling exponents in the aboveground carbon density model arise from the height-diameter (H-D) and projected crown area-diameter (C-D) allometries of individual trees. We show that a power function relationship emerges when the C-D scaling exponent is close to 2, or when tree diameters follow a Weibull distribution (or other specific distributions) and are invariant across the landscape. In addition, basal area must be closely correlated with canopy height for the approach to work. The efficacy of the model was explored for a managed uneven-aged temperate forest in Ontario, Canada within which stands dominated by sugar maple (Acer saccharum Marsh.) and mixed stands were identified. A much poorer goodness-of-fit was obtained than previously reported for tropical forests (R2 = 0.29 vs. about 0.83). Explanations for the poor predictive power on the model include: (1) basal area was only weakly correlated with top canopy height; (2) tree size distributions varied considerably across the landscape; (3) the allometry exponents are affected by variation in species composition arising from timber management and soil conditions; and (4) the C-D allometric power function was far from 2 (1.28). We conclude that landscape heterogeneity in forest structure and tree allometry reduces the accuracy of general power-function models for predicting aboveground carbon density in managed forests. More studies in different forest types are needed to understand the situations in which power functions of LiDAR height are appropriate for modelling forest carbon stocks

Structural changes and potential vertebrate responses following simulated partial harvesting of boreal mixedwood stands

Partial harvesting, where different numbers and arrangements of live trees are retained in forest stands, has been proposed for maintaining late-successional structure and associated vertebrate species within managed boreal forests. Using the stand dynamics model SORTIE-ND, we examined 80-year patterns of structural change in response to different intensities (30–70% basal area removal) and spatial patterns (22–273m2 mean patch size) of harvesting. We also applied habitat models for seven late-successional vertebrates to the structural conditions present after harvesting to assess potential species responses. Partial harvesting increased understory and downed woody debris (DWD) cover and decreased overstory structure for the first 25 years after harvest, in comparison to unharvested stands, with this effect subsequently reversing as harvest-induced regeneration reached the canopy. Although harvesting enhanced long-term structural development in this regard, large trees, large snags, and largeDWDall remained below unharvested levels throughout the simulation period. Harvesting also produced transient increases in early-decayDWDand ground exposure. Most changes in structural attributes increased in proportion to harvest intensity, but structural differencesamongharvest patterns were generally small. Dispersed harvesting induced somewhat less pronounced decreases in vertical structure, and produced more post-harvest slash, than aggregated harvesting. All seven vertebrate species decreased in abundance as harvest intensity increased from 30 to 70%. In comparison to their pre-harvest abundances in old stands, vertebrates associated with DWD (redback salamander, marten, red-backed vole) showed neutral or positive responses at one or more harvest intensities, whereas those associated with large trees and snags (brown creeper, flying squirrel) consistently exhibited substantial adverse impacts

Plant functional traits have globally consistent effects on competition.

Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits--wood density, specific leaf area and maximum height--consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.We are especially grateful to the researchers whose long-term commitment to establish and maintain forest plots and their associated databases made this study possible, and to those who granted us data access: forest inventories and permanent plots of New Zealand, Spain (MAGRAMA), France, Switzerland, Sweden, US and Canada (for the provinces of Quebec provided by the Ministère des Ressources Naturelles du Québec, Ontario provided by OnTAP’s Growth and Yield Program of the Ontario Ministry of Natural Resources, Saskatchewan, Manitoba, New Brunswick, Newfoundland and Labrador), CTFS (BCI and LTER-Luquillo), Taiwan (Fushan), Cirad (Paracou with funding by CEBA, ANR-10-LABX-25-01), Cirad, MEFCP and ICRA (M’Baïki) and Japan. We thank MPI-BGC Jena, who host TRY, and the international funding networks supporting TRY (IGBP, DIVERSITAS, GLP, NERC, QUEST, FRB and GIS Climate). G.K. was supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework Program (Demo-Traits project, no. 299340). The working group that initiated this synthesis was supported by Macquarie University and by Australian Research Council through a fellowship to M.W.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nature1647

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Global variability in leaf respiration in relation to climate, plant functional types and leaf traits

• Leaf dark respiration (Rdark) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of Rdark and associated leaf traits. • Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in Rdark. • Area-based Rdark at the prevailing average daily growth temperature (T) of each site increased only twofold from the Arctic to the tropics, despite a 20°C increase in growing T (8–28°C). By contrast, Rdark at a standard T (25°C, Rdark25) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher Rdark25 at a given photosynthetic capacity (Vcmax25) or leaf nitrogen concentration ([N]) than species at warmer sites. Rdark25 values at any given Vcmax25 or [N] were higher in herbs than in woody plants. • The results highlight variation in Rdark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of Rdark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs)

Modelling Effects of Partial Harvesting on Wildlife Species and their Habitat

In Canada’s eastern boreal forest region, partial-harvest silviculture has garnered increasing support for maintaining wildlife species and habitat structure associated with late-successional forests. If late-successional species can find suitable habitat in managed stands that retain a certain number, type, and pattern of live trees, then partial harvesting might represent a viable tool for maintaining species associated with old and complex forests. I used several indirect forms of inference to evaluate whether late-successional vertebrate species can be maintained within partially harvested stands in the eastern boreal forest. A meta-analysis of studies across North America showed that no bird species decreased in abundance by half where light harvesting retained at least 70% of live trees. However, adverse effects occurred at lower levels of retention, with some bird species unlikely to use harvested stands with less than 50% retention until appropriate habitat structure returned. A spatially explicit stand dynamics model showed that while partial harvesting can promote development of understory saplings, downed wood, and heterogeneity, it can also induce long-term decreases in the abundances of large trees and snags. Consequently, species dependent on the latter, such as brown creepers (Certhia americana) and northern flying squirrels (Glaucomys sabrinus), were projected to be more susceptible to partial harvesting than those associated with other types of structure. At a more detailed scale, a neighbourhood model developed from live-trapping data revealed that southern red-backed voles (Myodes gapperi) exhibited local associations with several late-successional features within boreal mixedwood stands. Their associations with some features depended on stand-level habitat conditions, which suggested that vole habitat in managed stands could be improved by retaining live trees and downed wood. A spatially explicit model of optimal home range establishment that incorporated these relationships fit vole abundance data marginally better than an aspatial habitat model. When the home range model was applied to simulated partially harvested stands, it predicted that spatial heterogeneity could have a positive effect on vole abundance, but only at harvest intensities of 70-90% with suppressed shrub cover. With careful attention to issues such as these, partial-harvest silviculture could be useful in maintaining vertebrate biodiversity within eastern boreal forests.Ph

Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery

Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral indices leads to more accurate predictions than indices derived from visible wavelengths alone and how the performance varies among three different tree species (Picea glauca, Pinus contorta, Populus tremuloides). Our results show that spectral information can be effectively used to predict tree mortality, with a random forest model producing a mean area under the receiver operating characteristic curve (AUC) of 89.8% and a balanced accuracy of 83.3%. The exclusion of multispectral indices worsened the model performance, but only slightly (AUC = 87.9%, balanced accuracy = 81.8%). We found variation in model performance among species, with higher accuracy for the broadleaf species (balanced accuracy = 85.2%) than the two conifer species (balanced accuracy = 73.3% and 77.8%). However, all models overpredicted tree mortality by a major degree, which limits the use for tree mortality predictions on an individual level. Further improvements such as long-term monitoring, the use of hyperspectral data and cost-sensitive learning algorithms, and training the model with a larger and more balanced data set are necessary. Nevertheless, our results demonstrate that imagery from UAVs has strong potential for predicting annual mortality for individual canopy trees