Partial river flow recovery with forest age is rare in the decades following establishment.

Forest regeneration and expansion are occurring in many countries, with 80 million ha established from 2000 to 2012 under the Bonn accord and 17.5 million ha established from 1990 to 2005 according to the Food and Agriculture Organisation. Multiple reviews have linked increasing forest cover with reduced river flow and potentially detrimental effects downstream. Previous reviews have investigated trends in river flow response over time, but the influence of forest age remains uncertain. Partial river flow recovery (towards non-forested conditions) has been reported in decades following forest establishment, but the role of climate in driving these trends has not been explored. Here, we evaluate river flow trends in 43 studies following forest establishment, which provide sufficient information to distinguish the effects of ageing forests from variable climate. Our meta-analysis supports previous findings showing that forestation reduces annual river flow (by 23% after 5 years and 38% after 25 years) with greater reductions in catchments with higher mean annual precipitation, larger increases in forest cover, and which were idle, rather than agricultural land, prior to forestation. The impact of forests on river flow is sensitive to annual precipitation and potential evapotranspiration, but responses are highly variable. Forests affect river flow less when annual precipitation is low, and sensitivity to precipitation decreases as catchment aridity increases. The majority of catchments demonstrated persistent river flow declines after forest establishment. However, nine catchments showed partial flow recovery after an initial decrease, with peak flow reductions at an average age of 15 and across a range of tree species. The mean rate of recovery was 34 mm/year over 5 years. Partial flow recovery with forest age cannot be commonly expected, however, and forestation programmes should take into account that changes to annual river flow are likely to persist for up to five decades.This work was funded by a grant from IUCN (International Union for the Conservation of Nature) through the KNOWFOR project (grant number: GB-1-203034

Mapping tree carbon with airborne remote sensing

Forests are a major component of the global carbon cycle, and accurate estimation of forest carbon stocks and fluxes is important in the context of anthropogenic global change. Airborne laser scanning (ALS) data sets are increasingly recognized as outstanding data sources for high-fidelity mapping of carbon stocks at regional scales.We develop a tree-centric approach to carbon mapping, based on identifying individual tree crowns (ITCs) and species from airborne remote sensing data, from which individual tree carbon stocks are calculated. We identify ITCs from the laser scanning point cloud using a region-growing algorithm and identifying species from airborne hyperspectral data by machine learning. For each detected tree, we predict stem diameter from its height and crown-width estimate. From that point on, we use well-established approaches developed for field-based inventories: above-ground biomasses of trees are estimated using published allometries and summed within plots to estimate carbon density.We show this approach is highly reliable: tests in the Italian Alps demonstrated a close relationship between field- and ALS-based estimates of carbon stocks (r2 = 0·98). Small trees are invisible from the air, and a correction factor is required to accommodate this effect.An advantage of the tree-centric approach over existing area-based methods is that it can produce maps at any scale and is fundamentally based on field-based inventory methods, making it intuitive and transparent. Airborne laser scanning, hyperspectral sensing and computational power are all advancing rapidly, making it increasingly feasible to use ITC approaches for effective mapping of forest carbon density also inside wider carbon mapping programs like REDD++.We thank Dr L. Frizzera for help with field-data collection. ALS data acquisition was supported by the European Commission (Alpine Space 2-3-2-FR NEWFOR). MD was supported by Trees4Future (European Union FP7 284181) and a NERC grant NE/K016377/1. DAC was also supported by a grant from BBSRC and DEFRA to study ash dieback.This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1111/2041-210X.1257

Tropical nature reserves are losing their buffer zones, but leakage is not to blame.

Tropical forests provide important ecosystem services to humanity, yet are threatened by habitat loss resulting from deforestation and land-use change. Although reserves are considered the cornerstones of conservation efforts in the tropics, their efficacy remains equivocal. One question that remains unresolved is whether leakage - the unanticipated displacement of deforestation from inside reserves into the unrestricted zones just beyond a reserve's administrative boundary - is common around tropical forest reserves, or whether the zones are acting as buffers between the protected area and the outside world. To resolve this question, we used the Landsat-derived Global Forest Change dataset to estimate deforestation rates between 2000 and 2012 inside and outside of 60 nature reserves spread across the tropics. Deforestation rates inside reserves (within 5km of the administrative boundary) were generally lower than those immediately outside the reserves (i.e. in buffer zones 0-10km from the boundary), suggesting that reserves are effective at protecting forests. We hypothesised that leakage would result in greater deforestation rates in reserve buffer zones than in the broader reserve landscapes, but such a pattern was observed in only five African sites, suggesting that leakage does not often occur on the edge of established reserves. However, roughly 80% of reserves experienced deforestation rates that increased gradually from their interiors to the outer periphery of their buffer zones. Thus, while leakage may not be a pervasive phenomenon around tropical reserves worldwide, tropical reserves are often losing their buffer zones, resulting in increased isolation that could have ramifications for ecosystem services provisioning and tropical conservation strategies.We thank the Keasbey Memorial Foundation for their support of G.V.L.’s program of study at Cambridge. We thank Bill Laurance and Mark Mulligan for helpful discussions about the work. We also thank Marion Pfeifer, Xiuzhi Chen, Jung-Tai Chao, Mark Balman, Antonio Trabucco, Megan MacDowell, Beatriz Beisiegel, Joseph Wright, and Patrick Jansen (see Table A.1) for generously providing the shapefiles of various reserve boundaries required for this study.This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.envres.2015.11.00

Getting the biggest birch for the bang: restoring and expanding upland birchwoods in the Scottish Highlands by managing red deer

High deer populations threaten the conservation value of woodlands and grasslands, but predicting the success of deer culling, in terms of allowing vegetation to recover, is difficult. Numerical simulation modeling is one approach to gain insight into the outcomes of management scenarios. We develop a spatially explicit model to predict the responses of Betula spp. to red deer (Cervus elaphus) and land management in the Scottish Highlands. Our model integrates a Bayesian stochastic stage-based matrix model within the framework of a widely used individual-based forest simulation model, using data collected along spatial and temporal gradients in deer browsing. By initializing our model with the historical spatial locations of trees, we find that densities of juvenile trees (3 m) height tiers over 30 years, but regeneration also requires suitable ground cover for seedling establishment. Densities of adult seed sources did not influence regeneration, nor did an active management scenario where we altered the spatial configuration of adults by creating “woodland islets”. Our results show that managers interested in maximizing tree regeneration cannot simply reduce deer densities but must also improve ground cover for seedling establishment, and the model we develop now enables managers to quantify explicitly how much both these factors need to be altered. More broadly, our findings emphasize the need for land managers to consider the impacts of large herbivores rather than their densities

Stabilizing effects of diversity on aboveground wood production in forest ecosystems: linking patterns and processes.

Both theory and evidence suggest that diversity stabilises productivity in herbaceous plant communities through a combination of overyielding, species asynchrony and favourable species interactions. However, whether these same processes also promote stability in forest ecosystems has never been tested. Using tree ring data from permanent forest plots across Europe, we show that aboveground wood production is inherently more stable through time in mixed-species forests. Faster rates of wood production (i.e. overyielding), decreased year-to-year variation in productivity through asynchronous responses of species to climate, and greater temporal stability in the growth rates of individual tree species all contributed strongly to stabilising productivity in mixed stands. Together, these findings reveal the central role of diversity in stabilising productivity in forests, and bring us closer to understanding the processes which enable diverse forests to remain productive under a wide range of environmental conditions.This is the author's accepted manuscript and will be under embargo until the 13th of October 2015. The final version will be published by Wiley in Ecology Letters. The 'Early View' article is available online here: http://onlinelibrary.wiley.com/doi/10.1111/ele.12382/abstract

\u3ci\u3eCorrigendum\u3c/i\u3e (Russo et al. 2007): A Re-Analysis of Growth–Size Scaling Relationships of Woody Plant Species

Russo et al. (2007) tested two predictions of the Metabolic Ecology Model (Enquist et al. 1999, 2000) using a data set of 56 tree species in New Zealand: (i) the rate of growth in tree diameter (dD/dt) should be related to tree diameter (D) as dD/dt = βDα and (ii) tree height (H) should scale with tree diameter as H(D) = γDδ, where t is time, β and γ are scaling coefficients that may vary between species, and α and δ are invariant scaling exponents predicted to equal 1/3 and 2/3, respectively (Enquist et al. 1999, 2000). To this end, Russo et al. (2007) used maximum likelihood methods to estimate α and δ and their two-unit likelihood support intervals. As noted in our original manuscript, the growth–diameter scaling exponent and coefficient covary, complicating the estimation of confidence intervals. We now recognize that the method we used to estimate support intervals (using marginal support intervals with the nuisance parameters fixed) underestimates the breadth of the interval and that the support intervals, properly estimated, should account for the variability in all parameters (Hilborn & Mangel 1997). This can be done in several ways. For example, the Hessian matrix can be used to estimate the standard deviation for each parameter, assuming asymptotic normality. Alternatively, one can systematically vary the parameter for which the interval is being estimated, re-estimate the Maximum likelihood estimates (MLEs) for the other parameters, and take the support interval to be the values of the target parameter that result in log likelihoods that are two units away from the maximum (Edwards 1992; Hilborn & Mangel 1997). A third and more direct approach to comparing data with prediction is to use the likelihood ratio test (LRT), which explicitly tests if a model with a greater number of parameters provides a significantly better fit to the data than a simpler model in which some parameters are fixed at predicted values (Hilborn & Mangel 1997; Bolker in press). Here, we re-analyze our data using LRTs, present a table revising Tables 1 and 2 from Russo et al. (2007), and reevaluate whether there is statistical support for the predictions of the Metabolic Ecology Model that we tested in Russo et al. (2007). We used LRTs to test, respectively, whether a model in which a,or d, was estimated at its MLE had a significantly greater likelihood than did a model with α = 1/3, or δ = 2/3, for the growth–diameter and height–diameter scaling relationships

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

Efectos del clima y la estructura del rodal sobre procesos de mortalidad en los bosques ibéricos.

Herrero A & Zavala MA, editores (2015) Los Bosques y la Biodiversidad frente al Cambio Climático: Impactos, Vulnerabilidad y Adaptación en España. Ministerio de Agricultura, Alimentación y Medio Ambiente, Madrid.Peer Reviewe

Drivers of aboveground wood production in a lowland tropical forest of West Africa:teasing apart the roles of tree density, tree diversity, soil phosphorus, and historical logging

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Wiley.1. Tropical forests currently play a key role in regulating the terrestrial carbon cycle and abating climate change by storing carbon in wood. However, there remains considerable uncertainty as to whether tropical forests will continue to act as carbon sinks in the face of increased pressure from expanding human activities. Consequently, understanding what drives productivity in tropical forests is critical. 2. We used permanent forest plot data from the Gola Rainforest National Park (Sierra Leone) – one of the largest tracts of intact tropical moist forest in West Africa – to explore how (i) stand basal area and tree diversity, (ii) past disturbance associated with past logging and (iii) underlying soil nutrient gradients interact to determine rates of aboveground wood production (AWP). We started by statistically modelling the diameter growth of individual trees and used these models to estimate AWP for 142 permanent forest plots. We then used structural equation modelling to explore the direct and indirect pathways which shape rates of AWP. 3. Across the plot network, stand basal area emerged as the strongest determinant of AWP, with densely packed stands exhibiting the fastest rates of AWP. In addition to stand packing density, both tree diversity and soil phosphorus content were also positively related to productivity. By contrast, historical logging activities negatively impacted AWP through the removal of large trees, which contributed disproportionately to productivity. 4. Synthesis. Understanding what determines variation in wood production across tropical forest landscapes requires accounting for multiple interacting drivers – with stand structure, tree diversity and soil nutrients all playing a key role. Importantly, our results also indicate that logging activities can have a long-lasting impact on a forest’s ability to sequester and store carbon, emphasizing the importance of safeguarding old-growth tropical forests.This study was funded through a grant from the Cambridge Conservation Initiative Collaborative Fund entitled “Applications of airborne remote sensing to the conservation management of a West African National Park”. T.J. was funded in part through NERC grant NE/K016377/1. A.C.S. was funded in part through a grant from the Percy Sladen Memorial Fund

Tracking shifts in forest structural complexity through space and time in human‐modified tropical landscapes

Habitat structural complexity is an emergent property of ecosystems that directly shapes their biodiversity, functioning and resilience to disturbance. Yet despite its importance, we continue to lack consensus on how best to define structural complexity, nor do we have a generalised approach to measure habitat complexity across ecosystems. To bridge this gap, here we adapt a geometric framework developed to quantify the surface complexity of coral reefs and apply it to the canopies of tropical rainforests. Using high‐resolution, repeat‐acquisition airborne laser scanning data collected over 450 km2 of human‐modified tropical landscapes in Borneo, we generated 3D canopy height models of forests at varying stages of recovery from logging. We then tested whether the geometric framework of habitat complexity – which characterises 3D surfaces according to their height range, rugosity and fractal dimension – was able to detect how both human and natural disturbances drive variation in canopy structure through space and time across these landscapes. We found that together, these three metrics of surface complexity captured major differences in canopy 3D structure between highly degraded, selectively logged and old‐growth forests. Moreover, the three metrics were able to track distinct temporal patterns of structural recovery following logging and wind disturbance. However, in the process we also uncovered several important conceptual and methodological limitations with the geometric framework of habitat complexity. We found that fractal dimension was highly sensitive to small variations in data inputs and was ecologically counteractive (e.g. higher fractal dimension in oil palm plantations than old‐growth forests), while rugosity and height range were tightly correlated (r = 0.75) due to their strong dependency on maximum tree height. Our results suggest that forest structural complexity cannot be summarised using these three descriptors alone, as they overlook key features of canopy vertical and horizontal structure that arise from the way trees fill 3D space. Keywords: Forest disturbance, LiDAR, logging, recovery, remote sensing, structural complexit