Relieving efforts in palm‐tree tissue sampling for population genetics analyses

1. The young leaves are the main source of nucleic acids for population genetic studies in palm‐trees; however, the access to this tissue may be limited by specific features of each species. Using root tissues as an alternative source of nucleic acids could facilitate the sampling in large populations.
2. This study tests root tissue viability as an alternative nucleic acid source (root versus. leaf) and explores different protocols (tissue storage and DNA extraction methods) to obtain high‐quality DNA samples.
3. The results showed no significant differences in DNA concentration (603.7 vs. 599.1 ng/μl) and quality ratios (A260/280:2.1 vs. 1.9, and A260/230:2.1 vs. 2.0) for the comparisons of tissue source (leaf vs. root) and DNA extraction method (manual vs. kit). For tissue storage method, DNA concentration was significantly higher for root tissues stored in 70% and 90% alcohol solutions (692.8 and 822.6 ng/μl, respectively) versus those obtained from leaf tissue (603.7 ng/μl); however, for the quality parameters, no differences were found.
4. Results showed the effective potential of using root tissue as an alternative source for nucleic acids, which could facilitate population sampling of palm‐tree species for future studies, and this methodological alternative could be applied to other plant systems with similar sampling challenges.

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Evolutionary trade-offs at the Arabidopsis WRR4A resistance locus underpin alternate Albugo candida race recognition specificities

The oomycete Albugo candida causes white rust of Brassicaceae, including vegetable and oilseed crops, and wild relatives such as Arabidopsis thaliana. Novel White Rust Resistance (WRR) genes from Arabidopsis enable new insights into plant/parasite co-evolution. WRR4A from Arabidopsis accession Columbia (Col-0) provides resistance to many but not all white rust races, and encodes a nucleotide-binding, leucine-rich repeat immune receptor. Col-0 WRR4A resistance is broken by AcEx1, an isolate of A. candida. We identified an allele of WRR4A in Arabidopsis accession Øystese-0 (Oy-0) and other accessions that confers full resistance to AcEx1. WRR4A^Oy-0 carries a C-terminal extension required for recognition of AcEx1, but reduces recognition of several effectors recognized by the WRR4A^Col-0 allele. WRR4A^Oy-0 confers full resistance to AcEx1 when expressed in the oilseed crop Camelina sativa.     

Social Network Analysis Predicts Health Behaviours and Self-Reported Health in African Villages

The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.     

Thermal ecology and baseline energetic requirements of a large‐bodied ectotherm suggest resilience to climate change

1. Most studies on how rising temperatures will impact terrestrial ectotherms have focused on single populations or multiple sympatric species. Addressing the thermal and energetic implications of climatic variation on multiple allopatric populations of a species will help us better understand how a species may be impacted by altered climates.
2. We used eight years of thermal and behavioral data collected from four populations of Pacific rattlesnakes (Crotalus oreganus) living in climatically distinct habitat types (inland and coastal) to determine the field‐active and laboratory‐preferred body temperatures, thermoregulatory metrics, and maintenance energetic requirements of snakes from each population.
3. Physical models showed that thermal quality was best at coastal sites, but inland snakes thermoregulated more accurately despite being in more thermally constrained environments. Projected increases of 1 and 2°C in ambient temperature result in an increase in overall thermal quality at both coastal and inland sites.
4. Population differences in modeled standard metabolic rate estimates were driven by body size and not field‐active body temperature, with inland snakes requiring 1.6× more food annually than coastal snakes.
5. All snakes thermoregulated with high accuracy, suggesting that small increases in ambient temperature are unlikely to impact the maintenance energetic requirements of individual snakes and that some species of large‐bodied reptiles may be robust to modest thermal perturbations under conservative climate change predictions.

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Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long‐term atmospheric CO₂ concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO₂ concentrations (ΔCO₂) is estimated from the difference of CO₂ concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO₂ of ?4.75 ppm per decade (p < .05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process‐based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0 Tg C for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO₂ trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO₂ measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible.     

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.     

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.