• Energy Research

Abstract Resilience is the ability of ecosystems to maintain function while experiencing perturbation. Globally, forests are experiencing disturbances of unprecedented quantity, type, and magnitude that may diminish resilience. Early warning signals are statistical properties of data whose increase over time may provide insights into decreasing resilience, but there have been few applications to forests. We quantified four early warning signals (standard deviation, lag-1 autocorrelation, skewness, and kurtosis) across detrended time series of multiple ecosystem state variables at the Hubbard Brook Experimental Forest, New Hampshire, USA and analyzed how these signals have changed over time. Variables were collected over periods from 25 to 55 years from both experimentally manipulated and reference areas and were aggregated to annual timesteps for analysis. Long-term (>50 year) increases in early warning signals of stream calcium, a key biogeochemical variable at the site, illustrated declining resilience after decades of acid deposition, but only in watersheds that had previously been harvested. Trends in early warning signals of stream nitrate, a critical nutrient and water pollutant, likewise exhibited symptoms of declining resilience but in all watersheds. Temporal trends in early warning signals of some of groups of trees, insects, and birds also indicated changing resilience, but this pattern differed among, and even within, groups. Overall, ∼60% of early warning signals analyzed indicated decreasing resilience. Most of these signals occurred in skewness and kurtosis, suggesting ‘flickering’ behavior that aligns with emerging evidence of the forest transitioning into an oligotrophic condition. The other ∼40% of early warning signals indicated increasing or unchanging resilience. Interpretation of early warning signals in the context of system specific knowledge is therefore essential. They can be useful indicators for some key ecosystem variables; however, uncertainties in other variables highlight the need for further development of these tools in well-studied, long-term research sites.

In semi-arid regions, a major population limitation for riparian trees is seedling desiccation during the dry season that follows annual spring floods. We investigated the stress response of first-year pioneer riparian seedlings to experimental water table declines (0, 1 and 3 cm day(-1)), focusing on the three dominant cottonwood and willows (family Salicaceae) in California's San Joaquin Basin. We analyzed growth and belowground allocation response to water stress, and used logistic regression to determine if these traits had an influence on individual survival. The models indicate that high root growth (>3 mm day(-1)) and low shoot:root ratios (<1.5 g g(-1)) strongly predicted survival, but there was no evidence that plants increased belowground allocation in response to drawdown. Leaf δ(13)C values shifted most for the best-surviving species (net change of +3.5 per mil from -30.0 ± 0.3 control values for Goodding's willow, Salix gooddingii), implying an important role of increased water-use efficiency for surviving water stress. Both S. gooddingii and sandbar willow (S. exigua) reduced leaf size from controls, whereas Fremont cottonwood (Populus fremontii) sustained a 29% reduction in specific leaf area (from 13.4 to 9.6 m(2) kg(-1)). The functional responses exhibited by Goodding's willow, the more drought-tolerant species, may play a role in its greater relative abundance in dry regions such as the San Joaquin Basin. This study highlights the potential for a shift in riparian forest composition. Under a future drier climate regime or under reduced regulated river flows, our results suggest that willow establishment will be favored over cottonwood.

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub‐sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross‐section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world's vegetation.

AbstractData capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research—from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non‐forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC‐BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology—from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

AbstractIn terrestrial ecosystems, a large portion (20–80%) of the dissolved Si (DSi) in soil solution has passed through vegetation. While the importance of this “terrestrial Si filter” is generally accepted, few data exist on the pools and fluxes of Si in forest vegetation and the rate of release of Si from decomposing plant tissues. We quantified the pools and fluxes of Si through vegetation and coarse woody debris (CWD) in a northern hardwood forest ecosystem (Watershed 6, W6) at the Hubbard Brook Experimental Forest (HBEF) in New Hampshire,USA. Previous work suggested that the decomposition ofCWDmay have significantly contributed to an excess ofDSi reported in stream‐waters following experimental deforestation of Watershed 2 (W2) at theHBEF. We found that woody biomass (wood + bark) and foliage account for approximately 65% and 31%, respectively, of the total Si in biomass at theHBEF. During the decay of American beech (Fagus grandifolia) boles, Si loss tracked the whole‐bole mass loss, while yellow birch (Betula alleghaniensis) and sugar maple (Acer saccharum) decomposition resulted in a preferential Si retention of up to 30% after 16 yr. A power‐law model for the changes in wood and bark Si concentrations during decomposition, in combination with an exponential model for whole‐bole mass loss, successfully reproduced Si dynamics in decaying boles. Our data suggest that a minimum of 50% of theDSi annually produced in the soil of a biogeochemical reference watershed (W6) derives from biogenic Si (BSi) dissolution. The major source is fresh litter, whereas only ~2% comes from the decay ofCWD. Decay of tree boles could only account for 9% of the excessDSi release observed following the experimental deforestation of W2. Therefore, elevatedDSi concentrations after forest disturbance are largely derived from other sources (e.g., dissolution ofBSi from forest floor soils and/or mineral weathering).

Resilience theory is increasingly applied to the management of global change impacts. There is growing concern, however, that misapplications of resilience-based management (RBM) can sometimes lead to undesirable outcomes. We address here an inescapable conundrum in the application of resilience theory: systems will need to track environmental change, but management that aims to support adaptive capacity can introduce undesirable levels of change. We provide a framework that links concepts from novel ecosystems and resilience theory to inform management of ecosystem change. We highlight that resilience-based applications need to address risks associated with novel human impacts to improve management outcomes.

AbstractRange shifts of infectious plant disease are expected under climate change. As plant diseases move, emergent abiotic-biotic interactions are predicted to modify their distributions, leading to unexpected changes in disease risk. Evidence of these complex range shifts due to climate change, however, remains largely speculative. Here, we combine a long-term study of the infectious tree disease, white pine blister rust, with a six-year field assessment of drought-disease interactions in the southern Sierra Nevada. We find that climate change between 1996 and 2016 moved the climate optimum of the disease into higher elevations. The nonlinear climate change-disease relationship contributed to an estimated 5.5 (4.4–6.6) percentage points (p.p.) decline in disease prevalence in arid regions and an estimated 6.8 (5.8–7.9) p.p. increase in colder regions. Though climate change likely expanded the suitable area for blister rust by 777.9 (1.0–1392.9) km2into previously inhospitable regions, the combination of host-pathogen and drought-disease interactions contributed to a substantial decrease (32.79%) in mean disease prevalence between surveys. Specifically, declining alternate host abundance suppressed infection probabilities at high elevations, even as climatic conditions became more suitable. Further, drought-disease interactions varied in strength and direction across an aridity gradient—likely decreasing infection risk at low elevations while simultaneously increasing infection risk at high elevations. These results highlight the critical role of aridity in modifying host-pathogen-drought interactions. Variation in aridity across topographic gradients can strongly mediate plant disease range shifts in response to climate change.