Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB₉₅) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB₉₅, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.     

Ecological Consequences of Animal Migration: Prey Partial Migration Affects Predator Ecology and Prey Communities

Ecosystems - Patterns of animal migration and the ecological forces that shape them have been studied for centuries. Yet ecological impacts caused by the migration, such as altered...     

Identification of Avramr1 from Phytophthora infestans using long read and cDNA pathogen-enrichment sequencing (PenSeq)

Potato late blight, caused by the oomycete pathogen Phytophthora infestans, significantly hampers potato production. Recently, a new Resistance to Phytophthora infestans (Rpi) gene, Rpi-amr1, was cloned from a wild Solanum species, Solanum americanum. Identification of the corresponding recognized effector (Avirulence or Avr) genes from P. infestans is key to elucidating their naturally occurring sequence variation, which in turn informs the potential durability of the cognate late blight resistance. To identify the P. infestans effector recognized by Rpi-amr1, we screened available RXLR effector libraries and used long read and cDNA pathogen-enrichment sequencing (PenSeq) on four P. infestans isolates to explore the untested effectors. Using single-molecule real-time sequencing (SMRT) and cDNA PenSeq, we identified 47 highly expressed effectors from P. infestans, including PITG_07569, which triggers a highly specific cell death response when transiently coexpressed with Rpi-amr1 in Nicotiana benthamiana, suggesting that PITG_07569 is Avramr1. Here we demonstrate that long read and cDNA PenSeq enables the identification of full-length RXLR effector families and their expression profile. This study has revealed key insights into the evolution and polymorphism of a complex RXLR effector family that is associated with the recognition by Rpi-amr1.     

Desert breath—How fog promotes a novel type of soil biocenosis,forming the coastal Atacama Desert’s living skin

The Atacama Desert is the driest non‐polar desert on Earth, presenting precarious conditions for biological activity. In the arid coastal belt, life is restricted to areas with fog events that cause almost daily wet–dry cycles. In such an area, we discovered a hitherto unknown and unique ground covering biocenosis dominated by lichens, fungi, and algae attached to grit‐sized (~6 mm) quartz and granitoid stones. Comparable biocenosis forming a kind of a layer on top of soil and rock surfaces in general is summarized as cryptogamic ground covers (CGC) in literature. In contrast to known CGC from arid environments to which frequent cyclic wetting events are lethal, in the Atacama Desert every fog event is answered by photosynthetic activity of the soil community and thus considered as the desert's breath. Photosynthesis of the new CGC type is activated by the lowest amount of water known for such a community worldwide thus enabling the unique biocenosis to fulfill a variety of ecosystem services. In a considerable portion of the coastal Atacama Desert, it protects the soil from sporadically occurring splash erosion and contributes to the accumulation of soil carbon and nitrogen as well as soil formation through bio‐weathering. The structure and function of the new CGC type are discussed, and we suggest the name grit–crust. We conclude that this type of CGC can be expected in all non‐polar fog deserts of the world and may resemble the cryptogam communities that shaped ancient Earth. It may thus represent a relevant player in current and ancient biogeochemical cycling.     

The two kinds of free energy and the Bayesian revolution

The concept of free energy has its origins in 19th century thermodynamics, but has recently found its way into the behavioral and neural sciences, where it has been promoted for its wide applicability and has even been suggested as a fundamental principle of understanding intelligent behavior and brain function. We argue that there are essentially two different notions of free energy in current models of intelligent agency, that can both be considered as applications of Bayesian inference to the problem of action selection: one that appears when trading off accuracy and uncertainty based on a general maximum entropy principle, and one that formulates action selection in terms of minimizing an error measure that quantifies deviations of beliefs and policies from given reference models. The first approach provides a normative rule for action selection in the face of model uncertainty or when information processing capabilities are limited. The second approach directly aims to formulate the action selection problem as an inference problem in the context of Bayesian brain theories, also known as Active Inference in the literature. We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information processing of perception.     

Technoecological analysis of energy carriers for long‐haul transportation

Long‐haul transportation demand is predicted to increase in the future, resulting in higher carbon dioxide emissions. Different drivetrain technologies, such as hybrid or battery electric vehicles, electrified roads, liquefied natural gas and hydrogen, might offer solutions to this problem. To assess their ecological and economic impact, these concepts were simulated including a weight and cost model to estimate the total cost of ownership. An evolutionary algorithm optimizes each vehicle to find a concept specific optimal solution. A model calculates the minimum investment in infrastructure required to meet the energy demand for each concept. A well‐to‐wheel analysis takes into account upstream and on‐road carbon dioxide emissions, to compare fully electric vehicles with conventional combustion engines. Investment in new infrastructure is the biggest drawback of electrified road concepts, although they offer low CO₂ emissions. The diesel hybrid is the best compromise between carbon reduction and costs.     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.     

Unfamiliar partnerships limit cnidarian holobiont acclimation to warming

Enhancing the resilience of corals to rising temperatures is now a matter of urgency, leading to growing efforts to explore the use of heat tolerant symbiont species to improve their thermal resilience. The notion that adaptive traits can be retained by transferring the symbionts alone, however, challenges the holobiont concept, a fundamental paradigm in coral research. Holobiont traits are products of a specific community (holobiont) and all its co‐evolutionary and local adaptations, which might limit the retention or transference of holobiont traits by exchanging only one partner. Here we evaluate how interchanging partners affect the short‐ and long‐term performance of holobionts under heat stress using clonal lineages of the cnidarian model system Aiptasia (host and Symbiodiniaceae strains) originating from distinct thermal environments. Our results show that holobionts from more thermally variable environments have higher plasticity to heat stress, but this resilience could not be transferred to other host genotypes through the exchange of symbionts. Importantly, our findings highlight the role of the host in determining holobiont productivity in response to thermal stress and indicate that local adaptations of holobionts will likely limit the efficacy of interchanging unfamiliar compartments to enhance thermal tolerance.