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Large projected increases in forest disturbance pose a major threat to future wood fiber supply and carbon sequestration in the cold-limited, Canadian boreal forest ecosystem. Given the large sensitivity of tree growth to temperature, warming-induced increases in forest productivity have the potential to reduce these threats, but research efforts to date have yielded contradictory results attributed to limited data availability, methodological biases, and regional variability in forest dynamics. Here, we apply a machine learning algorithm to an unprecedented network of over 1 million tree growth records (1958 to 2018) from 20,089 permanent sample plots distributed across both Canada and the United States, spanning a 16.5 °C climatic gradient. Fitted models were then used to project the near-term (2050 s time period) growth of the six most abundant tree species in the Canadian boreal forest. Our results reveal a large, positive effect of increasing thermal energy on tree growth for most of the target species, leading to 20.5 to 22.7% projected gains in growth with climate change under RCP 4.5 and 8.5. The magnitude of these gains, which peak in the colder and wetter regions of the boreal forest, suggests that warming-induced growth increases should no longer be considered marginal but may in fact significantly offset some of the negative impacts of projected increases in drought and wildfire on wood supply and carbon sequestration and have major implications on ecological forecasts and the global economy.

It is well known that temperatures across the globe are rising, but climatic conditions are becoming more variable as well. Forecasts of species range shifts, however, often focus on average climatic changes while ignoring increasing climatic variability. In particular, many species distribution models use space-for-time substitution, which focuses exclusively on the effect of average climatic conditions on the target species across a geographic range, and is blind to the possibility of range-wide population collapse with increasing drought frequency, drought severity, or climate effects on other co-occurring species. Relegated to assessments of broad demographic patterns that ignore underlying biological responses to increasing climatic variability, this prevalent method of distribution forecasting may systematically underpredict climate change impacts. We compare six models of survival and abundance of a subcanopy tree species, Taxus brevifolia, over 40 years of past climate change to disentangle multiple sources of uncertainty: model formulation, scale of climate effect, and level of biological organization. We show that drought extremes increased Taxus individual- and population-scale mortality across a wide geographic climate gradient, precluding detection of a monotonic relationship with average climate. Individual-scale climatic extremes models derived from longitudinal data had the highest predictive accuracy (82%), whereas mean climate models had the lowest accuracy (< 65%). Our results highlight that conclusions drawn from forecasts of average warming alone likely underpredict climate change impacts by ignoring indicators of range-wide population declines for species sensitive to increasing climatic variability.

Large areas of African moist forests are being logged in the context of supposedly sustainable management plans. It remains however controversial whether harvesting a few trees per hectare can be maintained in the long term while preserving other forest services as well. We used a unique 24 year silvicultural experiment, encompassing 10 4 ha plots established in the Central African Republic, to assess the effect of disturbance linked to logging (two to nine trees ha−1 greater than or equal to 80 cm DBH) and thinning (11–41 trees ha−1 greater than or equal to 50 cm DBH) on the structure and dynamics of the forest. Before silvicultural treatments, above-ground biomass (AGB) and timber stock (i.e. the volume of commercial trees greater than or equal to 80 cm DBH) in the plots amounted 374.5 ± 58.2 Mg ha−1 and 79.7 ± 45.9 m3 ha−1, respectively. We found that (i) natural control forest was increasing in AGB (2.58 ± 1.73 Mg dry mass ha−1 yr−1) and decreasing in timber stock (−0.33 ± 1.57 m3 ha−1 yr−1); (ii) the AGB recovered very quickly after logging and thinning, at a rate proportional to the disturbance intensity (mean recovery after 24 years: 144%). Compared with controls, the gain almost doubled in the logged plots (4.82 ± 1.22 Mg ha−1 yr−1) and tripled in the logged + thinned plots (8.03 ± 1.41 Mg ha−1 yr−1); (iii) the timber stock recovered slowly (mean recovery after 24 years: 41%), at a rate of 0.75 ± 0.51 m3 ha−1 yr−1 in the logged plots, and 0.81 ± 0.74 m3 ha−1 yr−1 in the logged + thinned plots. Although thinning significantly increased the gain in biomass, it had no effect on the gain in timber stock. However, thinning did foster the growth and survival of small- and medium-sized timber trees and should have a positive effect over the next felling cycle.

The carbon stock in living forest biomass was quantified based on first-cycle State Forest Inventory (SFI) measurements in permanent sample plots. The total carbon stock in above- and below-ground living biomass was assessed to be 46.9 ±0.4 × 10 tons C and average carbon stock at 52.1 ±0.5 t C ha–1 as of 2020. The State Forest Register (SFR), the primary source of consolidated information on Russia’s forests, estimates the forest growing stock to be 83.1 × 10 . The total growing stock volume in the forests, according to the SFI amounted to 113.1 × 10 m3. Owing to the updated and significantly higher growing stock volume, the estimate of carbon stock in living bio-mass is approximately 35% higher than previously reported. The uncertainty of the total and average carbon stocks based on SFI data was substantially lower (approximately ±1%) than that reported in previous studies (±15–30%). Methods of accounting for the carbon stock in living biomass, the results of calculations for forest lands throughout the country, units of the administrative division, and forest zoning were considered. Assessment of living biomass based on representative sampling can substantially improve the relevance and reliability of national forest reporting.

Can morphological plant functional traits predict demographic rates (e.g., growth) within plant communities as diverse as tropical forests? This is one of the most important next-step questions in trait-based ecology and particularly for global reforestation efforts. Due to the diversity of tropical tree species and their longevity, it is difficult to predict their performance prior to reforestation efforts. In this study, we investigate if simple leaf traits are predictors of the more complex ecological process of plant growth in regenerating selectively logged natural forest within the Wet Tropics (WTs) bioregion of Australia. This study used a rich historical data set to quantify tree growth within plots located at Danbulla National Park and State Forest on the Atherton Tableland. Leaf traits were collected from trees that have exhibited fast or slow growth over the last ~50 yr of measurement. Leaf traits were found to be poor predictors of tree growth for trees that have entered the canopy; however, for sub-canopy trees, leaf traits had a stronger association with growth rates. Leaf phosphorus concentrations were the strongest predictor of Periodic Annual Increment (PAI) for trees growing within the sub-canopy, with trees with higher leaf phosphorus levels showing a higher PAI. Sub-canopy tree leaves also exhibited stronger trade-offs between leaf traits and adhere to theoretical predictions more so than for canopy trees. We suggest that, in order for leaf traits to be more applicable to reforestation, size dependence of traits and growth relationships need to be more carefully considered, particularly when reforestation practitioners assign mean trait values to tropical tree species from multiple canopy strata.

At global and regional scales, tree mortality rates are positively correlated with forest net primary productivity (NPP). Yet causes of the correlation are unknown, in spite of potentially profound implications for our understanding of environmental controls of forest structure and dynamics and, more generally, our understanding of broad-scale environmental controls of population dynamics and ecosystem processes. Here we seek to shed light on the causes of geographic patterns in tree mortality rates, and we consider some implications of the positive correlation between mortality rates and NPP. To reach these ends, we present seven hypotheses potentially explaining the correlation, develop an approach to help distinguish among the hypotheses, and apply the approach in a case study comparing a tropical and temperate forest. Based on our case study and literature synthesis, we conclude that no single mechanism controls geographic patterns of tree mortality rates. At least four different mechanisms may be at play, with the dominant mechanisms depending on whether the underlying productivity gradients are caused by climate or soil fertility. Two of the mechanisms are consequences of environmental selection for certain combinations of life-history traits, reflecting trade-offs between growth and defense (along edaphic productivity gradients) and between reproduction and persistence (as manifested in the adult tree stature continuum along climatic and edaphic gradients). The remaining two mechanisms are consequences of environmental influences on the nature and strength of ecological interactions: competition (along edaphic gradients) and pressure from plant enemies (along climatic gradients). For only one of these four mechanisms, competition, can high mortality rates be considered to be a relatively direct consequence of high NPP. The remaining mechanisms force us to adopt a different view of causality, in which tree growth rates and probability of mortality can vary with at least a degree of independence along productivity gradients. In many cases, rather than being a direct cause of high mortality rates, NPP may remain high in spite of high mortality rates. The independent influence of plant enemies and other factors helps explain why forest biomass can show little correlation, or even negative correlation, with forest NPP.

Stem exclusion and understory reinitiation are commonly described, but poorly understood, stages of forest development. It is assumed that overstory trees exert strong controls on understory herbs and shrubs during the transition from open- to closed-canopy forests, but long-term observations of this process are rare. We use long-term data from 188 plots to explore patterns and correlates of variation in understory richness and abundance 15-45 years after clear-cut logging and burning of two experimental watersheds in western Oregon, USA. We test whether variation in the temporal dynamics of plots can be explained by topoedaphic factors that influence resource availability (insolation and soil moisture), variation in the pace and intensity of overstory development, or characteristics of the vegetation prior to canopy closure. Changes in forest structure were substantial over the study period; canopy cover increased fourfold, stem density by 75%, and bole biomass by two orders of magnitude, although trends were highly variable among individual plots. In contrast, understory richness, foliar cover, and biomass declined only 30-40%, driven by loss of early-seral colonists, not residual forest species. Canopy closure occurred earlier on north aspects but declines in understory biomass, reflecting loss of colonizing shrubs (without concomitant increases in forest shrubs), were limited to south aspects. In contrast, variation in effective soil moisture had little influence on the pace of decline. Temporal trends were highly asynchronous among plots: nearly 50% of plots experienced some form of decline, but >35% showed no discernible trend. Declines were more likely in plots with greater tree influence before or at peak overstory development, but also in plots with greater understory development prior to canopy closure. Quantile regression models indicated weak relationships between understory biomass and overstory structure at most points in time. Our long-term data support a model of understory dynamics in which characteristics of the pre-closure vegetation are as important as overstory structure in determining the timing and nature of decline. Long-term studies are critical for elucidating patterns and processes that cannot be inferred from short-term experiments or space-for-time substitutions.

The Hyrcanian forests of Iran contain many species-rich communities that can only be maintained through an understanding of the renewal and development of these forests. Located in the Jojadeh section of the Farim forest in northern Iran, individual tree growth of five distinct species [(Oriental beech (Fagus orientalis Lipsky), chestnut-leaved oak (Quercus castaneifolia Coss. ex J.Gay), Persian maple (Acer velutinum Boiss.), common hornbeam (Carpinus betulus L.) and Caucasian alder (Alnus subcordata C.A.Mey.)] were measured on 313 permanent sample plots (0.1 ha) over a 10-year period (2003–2013). In this analysis, various tree-level predictions were investigated using the available data with application of parametric models and two artificial neural networks [i.e., the multilayer perceptron (MLP) and radial basis function (RBF) networks]. Individual tree diameter growth models showed a robust negative relationship with basal area in larger trees, which was relatively consistent across species. A total height model indicated that the examined species did not differ for a given set of covariates. In the survival model, the survival probability of Oriental beech was lower than the other species, while the ingrowth model revealed sapling density of all species increased with greater basal area. The artificial neural network based on the MLP was superior for all models and predicted more accurately than the RBF. Furthermore, the models based on the MLP were also superior to the parametric individual tree models developed using mixed-effect regression. The use of these developed models in forest planning and management is imperative, particularly for uneven-aged stands, but assessment of long-term projection behavior across the contrasting statistical approaches used is warranted despite the general superiority of the nonparametric models.

The sustainable management of the southern African woodlands is closely linked to the livelihoods of over 150 M people. Findings from the Socio‐Ecological Observatory for the Southern African Woodlands (SEOSAW) will underpin the sustainability of two of the largest industries on the continent: woodfuels and timber. SEOSAW will also improve our understanding of how human use shapes the biogeography and functioning of these ecosystems. Societal Impact Statement The sustainable management of the southern African woodlands is closely linked to the livelihoods of over 150 M people. Findings from the Socio‐Ecological Observatory for the Southern African Woodlands (SEOSAW) will underpin the sustainability of two of the largest industries on the continent: woodfuels and timber. SEOSAW will also improve our understanding of how human use shapes the biogeography and functioning of these ecosystems. Summary Here we describe a new network of researchers and long‐term, in situ, measurements that will characterize the changing socio‐ecology of the woodlands of southern Africa. These woodlands encompass the largest savanna in the world, but are chronically understudied, with few long‐term measurements. A network of permanent sample plots (PSPs) is required to: (a) address management issues, particularly related to sustainable harvesting for energy and timber; (b) understand how the woodlands are responding to a range of global and local drivers, such as climate change, CO2 fertilization, and harvesting; and (c) answer basic questions about biogeography, ecosystem function, and the role humans play in shaping the ecology of the region. We draw on other successful networks of PSPs and adapt their methods to the specific challenges of working in southern African woodlands. In particular we suggest divergences from established forest monitoring protocols that are needed to (a) adapt to a high level of ecosystem structural diversity (from open savanna to dry forest); (b) quantify the chronic disturbances by people, fire, and herbivores; (c) quantify the diversity and function of the understory of grasses, forbs, and shrubs; (d) understand the life histories of resprouting trees; and (e) conduct work in highly utilized, human‐dominated landscapes. We conclude by discussing how the SEOSAW network will integrate with remote sensing and modeling approaches. Throughout, we highlight the challenges inherent to integrating work by forest and savanna ecologists, and the wide range of skills needed to fully understand the socio‐ecology of the southern African woodlands.

Forest degradation leads to the gradual reduction of forest carbon stocks, function, and biodiversity following anthropogenic disturbance. Whilst tropical degradation is a widespread problem, it is currently very under-studied and its magnitude and extent are largely unknown. This is due, at least in part, to the lack of developed and tested methods for monitoring degradation. Due to the relatively subtle and ongoing changes associated with degradation, which can include the removal of small trees for fuelwood or understory clearance for agricultural production, it is very hard to detect using Earth Observation. Furthermore, degrading activities are normally spatially heterogeneous and stochastic, and therefore conventional forest inventory plots distributed across a landscape do not act as suitable indicators: at best only a small proportion of plots (often zero) will actually be degraded in a landscape undergoing active degradation. This problem is compounded because the metal tree tags used in permanent forest inventory plots likely deter tree clearance, biasing inventories toward under-reporting change. We have therefore developed a new forest plot protocol designed to monitor forest degradation. This involves a plot that can be set up quickly, so a large number can be established across a landscape, and easily remeasured, even though it does not use tree tags or other obvious markers. We present data from a demonstration plot network set up in Jalisco, Mexico, which were measured twice between 2017 and 2018. The protocol was successful, with one plot detecting degradation under our definition (losing greater than 10% AGB but remaining forest), and a further plot being deforested for Avocado ( Persea americana ) production. Live AGB ranged from 8.4 Mg ha –1 to 140.8 Mg ha –1 in Census 1, and from 0 Mg ha –1 to 144.2 Mg ha –1 Census 2, with four of ten plots losing AGB, and the remainder staying stable or showing slight increases. We suggest this protocol has great potential for underpinning appropriate forest plot networks for degradation monitoring, potentially in combination with Earth Observation analysis, but also in isolation.