Evapotranspiration and water yield of a pine‐broadleaf forest are not altered by long‐term atmospheric [CO2] enrichment under native or enhanced soil fertility

Changes in evapotranspiration (ET) from terrestrial ecosystems affect their water yield (WY), with considerable ecological and economic consequences. Increases in surface runoff observed over the past century have been attributed to increasing atmospheric CO₂ concentrations resulting in reduced ET by terrestrial ecosystems. Here, we evaluate the water balance of a Pinus taeda (L.) forest with a broadleaf component that was exposed to atmospheric [CO₂] enrichment (ECO₂; +200 ppm) for over 17 years and fertilization for 6 years, monitored with hundreds of environmental and sap flux sensors on a half‐hourly basis. These measurements were synthesized using a one‐dimensional Richard's equation model to evaluate treatment differences in transpiration (T), evaporation (E), ET, and WY. We found that ECO₂ did not create significant differences in stand T, ET, or WY under either native or enhanced soil fertility, despite a 20% and 13% increase in leaf area index, respectively. While T, ET, and WY responded to fertilization, this response was weak (<3% of mean annual precipitation). Likewise, while E responded to ECO₂ in the first 7 years of the study, this effect was of negligible magnitude (<1% mean annual precipitation). Given the global range of conifers similar to P. taeda, our results imply that recent observations of increased global streamflow cannot be attributed to decreases in ET across all ecosystems, demonstrating a great need for model–data synthesis activities to incorporate our current understanding of terrestrial vegetation in global water cycle models.     

Cytonuclear discordance in the Florida Everglades invasive Burmese python (Python bivittatus) population reveals possible hybridization with the Indian python (P. molurus)

The invasive Burmese python (Python bivittatus) has been reproducing in the Florida Everglades since the 1980s. These giant constrictor snakes have caused a precipitous decline in small mammal populations in southern Florida following escapes or releases from the commercial pet trade. To better understand the invasion pathway and genetic composition of the population, two mitochondrial (mtDNA) loci across 1,398 base pairs were sequenced on 426 snakes and 22 microsatellites were assessed on 389 snakes. Concatenated mtDNA sequences produced six haplotypes with an average nucleotide and haplotype diversity of π = 0.002 and h = 0.097, respectively. Samples collected in Florida from morphologically identified P. bivittatus snakes were similar to published cytochrome oxidase 1 and cytochrome b sequences from both P. bivittatus and Python molurus and were highly divergent (genetic distances of 5.4% and 4.3%, respectively). The average number of microsatellite alleles and expected heterozygosity were N_A = 5.50 and H_E = 0.60, respectively. Nuclear Bayesian assignment tests supported two genetically distinct groups and an admixed group, not geographically differentiated. The effective population size (N_E = 315.1) was lower than expected for a population this large, but reflected the low genetic diversity overall. The patterns of genetic diversity between mtDNA and microsatellites were disparate, indicating nuclear introgression of separate mtDNA lineages corresponding to cytonuclear discordance. The introgression likely occurred prior to the invasion, but genetic information on the native range and commercial trade is needed for verification. Our finding that the Florida python population is comprised of distinct lineages suggests greater standing variation for adaptation and the potential for broader areas of suitable habitat in the invaded range.     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

TRAF6 Mediates Basal Activation of NF-κB Necessary for Hematopoietic Stem Cell Homeostasis

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Primary Cilium-Mediated Retinal Pigment Epithelium Maturation Is Disrupted in Ciliopathy Patient Cells

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The Black Yeasts: an Update on Species Identification and Diagnosis

Purpose of review

Black yeast-like fungi are capable of causing a wide range of infections, including invasive disease. The diagnosis of infections caused by these species can be problematic. We review the changes in the nomenclature and taxonomy of these fungi, and methods used for detection and species identification that aid in diagnosis.

Recent findings

Molecular assays, including DNA barcode analysis and rolling circle amplification, have improved our ability to correctly identify these species. A proteomic approach using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has also shown promising results. While progress has been made with molecular techniques using direct specimens, data are currently limited.

Summary

Molecular and proteomic assays have improved the identification of black yeast-like fungi. However, improved molecular and proteomic databases and better assays for the detection and identification in direct specimens are needed to improve the diagnosis of disease caused by black yeast-like fungi.

     

Behavioral and Biochemical Effects of N-Acetylcysteine in Zebrafish Acutely Exposed to Ethanol

Neurochemical Research - Alcohol hangover refers to unpleasant symptoms experienced as a direct consequence of a binge drinking episode. The effects observed in this condition are related to the...     

Effect of female group size on harem male roosting behavior of the Indian short-nosed fruit bat <Emphasis Type="Italic">Cynopterus sphinx</Emphasis>

Mate guarding has been known to incur costs and cause constraints for harem males in many polygynous species. However, the effect of female group size on the harem male’s time budget in bats has received very limited attention. The Indian short-nosed fruit bat, Cynopterus sphinx, exhibits resource defense polygyny, in which tent roosting males construct tents and defend multiple female bats. We studied the effect of female group size on three aspects of harem male behavior: social grooming by reciprocal licking, tent maintenance, and tent guarding in the mast tree Polyalthia longifolia. In the process of reciprocal licking, all the bats in the harem were drenched in saliva before emergence, and this activity was positively and significantly correlated with female group size. Once females departed for foraging, harem males remained in their respective tents at night-time between intermittent foraging bouts and engaged in tent maintenance and tent guarding. Time invested by harem male bats in tent maintenance and tent guarding were positively and significantly correlated with female group size. Harem males extended their presence in tent by utilizing tents as feeding roosts. Female group size also influenced the emergence time of harem male bats, where males with largest group emerged later than did the smallest group. Likewise, harem male with the smallest group had more time available for foraging than the male with the largest group. Findings of this study suggest that having a larger harem may indeed be costly for the males by reducing their foraging time.     

Zebrafish “personality” influences sensitivity to magnetic fields

How animals integrate different sensory information for orientation is a complex process involving interactions between a variety of internal and external factors. Due to this complexity, each component of a suite of factors is typically studied in isolation. Here, we examine how an internal factor (personality of fish) influences the response of zebrafish (Danio rerio) to the magnetic field, while swimming in a flow chamber. Our previous work demonstrated that the orientation to the water current (rheotaxis) of zebrafish individuals is influenced by variations of the magnetic field only when fish are part of a shoal. In this study, we evaluated the rheotactic behavior of 20 fish, grouped in shoals of “proactive” or “reactive” individuals, under magnetic fields of different directions. We found that the magnetic field influenced at which water speed rheotaxis was elicited in zebrafish with “reactive” personality, but not in those with “proactive” personality. These results suggest that fish personality influences response to or weighing of sensory inputs and provides some insight on the variation in behavioral responses to environmental stimuli in both laboratory and natural settings.     

The 1.25 A crystal structure of sepiapterin reductase reveals its binding mode to pterins and brain neurotransmitters.

Sepiapterin reductase catalyses the last steps in the biosynthesis of tetrahydrobiopterin, the essential co-factor of aromatic amino acid hydroxylases and nitric oxide synthases. We have determined the crystal structure of mouse sepiapterin reductase by multiple isomorphous replacement at a resolution of 1.25 A in its ternary complex with oxaloacetate and NADP. The homodimeric structure reveals a single-domain alpha/beta-fold with a central four-helix bundle connecting two seven-stranded parallel beta-sheets, each sandwiched between two arrays of three helices. Ternary complexes with the substrate sepiapterin or the product tetrahydrobiopterin were studied. Each subunit contains a specific aspartate anchor (Asp258) for pterin-substrates, which positions the substrate side chain C1'-carbonyl group near Tyr171 OH and NADP C4'N. The catalytic mechanism of SR appears to consist of a NADPH-dependent proton transfer from Tyr171 to the substrate C1' and C2' carbonyl functions accompanied by stereospecific side chain isomerization. Complex structures with the inhibitor N-acetyl serotonin show the indoleamine bound such that both reductase and isomerase activity for pterins is inhibited, but reaction with a variety of carbonyl compounds is possible. The complex structure with N-acetyl serotonin suggests the possibility for a highly specific feedback regulatory mechanism between the formation of indoleamines and pteridines in vivo.