1,402 Matching Annotations
  1. Nov 2018
    1. provides a conceptually new algorithm for solving a fundamental computational problem

      This "fundamental computing problem" is the problem of being able to quickly and efficiently pick out objects that are similar to one another from very large data sets.

      For example, Google needs to be able to pick out search results that are similar to what you typed—and it needs to do this very quickly. Likewise, Netflix needs to compare your watch history to that of all its other users so that it can make recommendations for what else you might like.

      These and other similar problems pop up all over the place in your everyday life. The process of solving them is called a "similarity search" and is of intense interest to computer scientists who want to make these searches faster, more efficient, and better quality.

    1. We injected Yob mRNA into nonsexed preblastoderm embryos of A. gambiaeand its sibling species A. arabiensis to assess whether ectopic Yob transcripts affect mosquito sex ratios. To control for successful injection, we coinjected a plasmid with a green fluorescent protein (GFP) expression cassette (embryos that receive sufficient nucleic acids develop into larvae transiently expressing GFP; fig. S9). Surviving individuals were sorted at the larval stage into a GFP-positive and a GFP-negative group (Fig. 3A), and at the pupal stage mosquitoes were sexed.

      In this experiment, the authors injected normal Yob transcripts into normal embryos, along with a fluorescent marker to let them know that injection was successful, and allowed the embryos to grow. Once at the larval stage, they could see the fluorescent injection marker and sorted the larvae based on expression of the marker. At the pupal state, the authors were able to sex the mosquitoes.

    2. We transfected the Sua5.1 cells with two modified Yobtranscripts containing putative nonsynonymous point mutations r.1A>C and [r.5U>A; 6U>G] and investigated the effects of the deduced amino acid changes Met1→Leu and Phe2→Stop, respectively, on dsx splicing.

      Based on what the authors found by analyzing other species, they hypothesize that Yob actually codes for a protein. They made new lab-generated transcripts of Yob with sequence changes that would result in no protein product (either removing the start codon, or adding in a premature stop codon) and transfected those transcripts in to cells to monitor dsx splicing.

    3. we evaluated the protein-coding potential of Yob by comparing its sequence to PCR-isolated orthologous sequences from Anopheles arabiensis and Anopheles quadriannulatus, two members of the Anopheles gambiae complex.

      The authors isolated sequences from closely related mosquito species and compared similarities and differences in those sequences computationally.

    4. We investigated the effect of in vitro–synthesized mRNA corresponding to the shortest, presumably mature A. gambiae Yob transcript isoform on dsxsplicing

      The authors made mRNA in the lab. The mRNA has the same sequence of what they think is the shortest, but functional Yob transcript. They are testing whether Yob has a direct effect on dsx.

    5. ectopic embryonic delivery of Yob transcripts

      The scientists artificially inserted the gene-reading Yob into the embryos of Anopheles gambiae.

    6. transcription of Yob is limited to males. Transcription begins in embryos between 2 and 2.5 hours after oviposition,

      As seen in Figure 1, they are analyzing Yob mRNA through use of a gel, not sequencing, in this experiment. The authors find that transcription of this male factor begins very early after fertilization, at the same time as other genetic markers of zygotic expression.

    7. we analyzed transcriptomes of male and female embryos (18), whose sexual identity was determined by polymerase chain reaction (PCR) (fig. S1). Separate pools of mRNA were sequenced, yielding ~500,000 Roche 454 reads from each sex.

      Because mosquito embryos look identical whether male or female, the authors had to use molecular techniques to find out the sex of each embryo. The embryos were separated based on their sexual identity (male or female) and mRNA was extracted and sequenced from the collective male or female embryos.

    1. We are sometimes asked what the result would be if we put four +'s in one gene. To answer this my colleagues have recently put together not merely four but six +'s. Such a combination is active, as expected on the basis of our theory, although sets of four or five of them are not.

      This experiment is an extension of the frameshift experiment. If the bases are read in triplets, then you'd expect that four or five extra bases would ruin the code. However, six extra bases would still encode the amino acids for the native protein (with two extra amino acid residues).

    1. FGFP data set provided a unique opportunity to perform an informed power analysis

      The authors performed a power analysis study, which allows researchers to determine the sample size required to detect an effect, due to the large number of samples with broad characteristics. They first calculated the number of samples needed to determine a difference in microbiota diversity when the cause is unknown. To do so, they need to determine the effect size (the minimum deviation that is considered significant), the significance level (the probability of determining that a condition is true given that it is false), and the power (the probability of determining that a condition is false given that it is true).

      Read more: http://www.biostathandbook.com/power.html

    1. Mitochondrial sequences as well as genotype files (in plink format) were deposited on Dryad (doi:10.5061/dryad. 8gp06).

      Genetic sequences of the modern and ancient dogs used in this study are publicly available. Check out their data on Dryad!

    2. cross coalescence rate (CCR)

      A method of estimating the time at which populations had a common ancestor, based on their genetic differences and similarities.

    3. multiple sequentially Markovian coalescent (MSMC)

      A technique that looks at the pattern of DNA sequence changes (mutations) in multiple individuals, focusing on the most recent common ancestor of any two sequences. It can provide information about the timing of shared ancestry, population sizes, population splits, and migration rates.

    4. complete (28x) genome

      Certain bases that are always present in dogs were covered by sequencing 28 times, so the whole genome is said to have been covered 28 times.

    1. SEM

      Acronym for standard error of the mean, a measure that represents how far the mean of a sample is from the estimated true population mean. It is a good estimate of how accurately your mean reflects the true population.

      To learn more about SEM: https://www.khanacademy.org/math/ap-statistics/sampling-distribution-ap/sampling-distribution-mean/v/standard-error-of-the-mean

    2. Analysis of covariance

      An analysis of covariance is used to see how two independent variables change together.

      Here, the authors used it to determine whether the response to a reward or devaluated stimuli was due to habitual learning, rather than external factors such as outcome-action knowledge, working-memory/inhibition, or the ability to learn how to discriminate during the training phase of the test.

      To learn more about analysis of covariance: http://www.lehigh.edu/~wh02/ancova.html

  2. Oct 2018
    1. To test whether activation of the NPF-NPFR pathway is rewarding per se, we trained virgin males to associate artificial activation of NPF neurons with either EA or IAA. Males expressing dTRPA1 in NPF neurons (NPF-GAL4 + UAS-dTRPA1) and the genetic controls each carrying only one of the two transgenes were trained for three 1-hour sessions at 29°C, with dTRPA1 active, interspersed with three 1-hour rest periods at 18°C, with dTRPA1 inactive (Fig. 4B).

      The final thing the researchers wanted to test was whether artificial activation of the NPF circuit was inherently rewarding. To do this, they once again relied on the dTRPA1 proteins, which can activate neurons at high temperatures. The researchers inserted this protein into neurons that were part of the NPF circuit. They then exposed the flies to one smell while keeping a high temperature (thus activating the NPF neurons). This smell became associated with a state of high NPF circuit activity. Conversely, they exposed the flies to the second smell while keeping a low temperature. This smell became associated with a state of normal/low NPF circuit activity. Finally, they placed the flies in a Y maze (as before) and checked to see if the flies would prefer the prong containing the NPF-activity paired odor, or the unpaired odor.

    2. A conditioned odor preference index (CPI) for mating was calculated by averaging preference indices for reciprocally trained groups of flies. Positive CPI values indicate conditioned preference, negative values indicate aversion.

      The researchers used time spent in the two prongs of the Y maze to develop a preference score. Per this score, a positive value would indicate that the flies preferred the odor that was associated with one of the three activities (mating, alcohol exposure, or activation of the NPF circuit). Conversely, a negative score would indicate that the flies preferred the odor that was not associated with these activities. A score of zero would indicate no obvious preference between the two.

    3. To determine whether the inverse correlation between NPF levels and ethanol preference reflects a cause-and-effect relationship, we manipulated the NPF-NPFR system genetically.

      So far, the researchers have shown that changes in NPF levels correlate with both sexual experience and alcohol preference. Specifically, a more active sexual history is correlated with an increase in NPF levels, which is correlated with a decrease in preference for alcohol (and vice versa). However, one of the most important rules to remember in science is that correlation does not equal causation. The researchers thus decided to follow up with a direct genetic manipulation of NPF in order to establish a causal relationship.

    4. To further test the strength of this conclusion, we divided a cohort of rejected-isolated males into two subgroups, one of which was left undisturbed, and the other of which was allowed to mate with virgin females for 2.5 hours immediately before testing.

      As a final follow-up experiment, the researchers took a group of sexually rejected males and split them up into two. One subgroup was left alone, and then tested for alcohol preference. The other subgroup was exposed to virgin females just before testing. As such, these males did in fact get an opportunity to mate before undergoing the alcohol preference test. If the first subgroup continued to prefer alcohol, but this preference were to disappear for the second group (who initially experienced rejection, but then were able to mate just before testing), it would indicate that the main cause for the alcohol preference was indeed sexual deprivation.

    5. We next investigated ethanol preference in males that were sexually deprived (blocked from copulating) but not exposed to the social experience of rejection.

      For the second follow-up experiment, the researchers wanted to investigate the possibility that it was not actually the lack of sex, but rather the experience of rejection by a female that was driving the virgin males to prefer alcohol.

    1. To identify nonredundant covariates of microbiome variations from our shortlist of 69 correlating factors

      A redundant covariate is a factor that either has the same value for every sample or multiple factors that are highly correlated and can be used as substitutes for one another. The researchers removed redundant covariates from their study because they can hamper interpretability of the results.

    2. forward stepwise redundancy analysis (RDA)

      This is a statistical technique used to describe linear relationships between components of dependent variables that can be explained by a set of independent variables. Here, the independent variables were the 69 covariates and the dependent variable was the microbiota variation in each sample.

    3. PCoA

      Stands for Principal Coordinate Analysis, a statistical technique used to visualize similarities between variables by representing the variance in as few axes/dimensions as possible. The arrows show the direction of the original variables and the angle between arrows gives a rough estimate of the correlation between features.

    4. 16S ribosomal RNA (rRNA) gene amplicon sequencing

      Almost all bacteria have a copy of the 16S rRNA gene. Comparing their sequences is a common tool to identify and differentiate bacteria at the species level.

      Learn more about how bacteria are identified with this resource from iBiology: https://www.ibiology.org/microbiology/genome-sequencing/

    1. We then found the top 2% of predicted nearest neighbors in m-dimensional hash space

      For the fly, the data is organized into a higher dimensional space. For LSH, it is organized in a lower dimensional space. But if the algorithms work effectively, the data should still be arranged so that similar features are near one another.

      Instead of feature vectors, the algorithms arrange items in hashes or "tags" (just like the fly brain uses a tag to represent an odor, the algorithms use a tag to represent a specific image or word). Finding the hashes that are closest to each other in this new m-dimensional space should reveal the images/words that are most similar to each other (again, if the algorithm works correctly).

      These nearby hashes are called "predicted nearest neighbors" because they predict which items in the data set are the most similar.

    2. we show analytically that sparse, binary random projections of the type in the fly olfactory circuit generate tags that preserve the neighborhood structure of input points.

      In a mathematical proof, the authors took a matrix of input odors and multiplied it by a vector of projection values to create a new matrix of Kenyon Cell values. In this new matrix, the relative positions of the input odors were approximately the same as they were in the original matrix.

      This shows that the fly algorithm preserves the distance between input points when sending information from PNs to KCs.

    1. scanning electron microscopy

      In bright field microscopy, an image is produced by bouncing light off of an object and the image is magnified by lenses. In scanning electron microscopy, an image is produced by bouncing electrons off of an object and the image is interpreted and produced by a computer. By using electrons instead of light, researchers can observe incredibly fine detail in the image created.

    2. initial microscopic imaging

      Researchers look at a sample through a bright field microscope to look for evidence of cell morphology that could be basidiomycetes.

    3. When assaying for the basidiomycete

      To take a measurement of a sample, usually meaning a biochemical or chemical measurement of a biological sample; in this case, basidiomycete yeasts.

    4. we estimated transcript abundances by mapping raw reads back to a single, pooled metatranscriptome assembly and binning

      In a biological sample that contains more than one organism, before gene expression can be evaluated, the researchers must identify which genes belong to which organism. To do this, gene expression data is mapped onto the genomes of the organisms that are known in the mixture, and genes are sorted, or binned, into groups by the genomes they match. This way the gene expression data for each known organism can be determined.

    5. We hypothesized that differential gene expression might account for the increased production of vulpinic acid in B. tortuosa.

      The authors knew that the difference between these two lichen species was poorly understood. One species produces vulpinic acid, and the other does not. Phylogenetic analysis could not identify differences in the symbionts of the two species. The authors decided to test if differential gene expression of a gene shared between the species might be the cause of the phenotypic difference.

    1. We purified the corresponding recombinant I. sakaiensis proteins (fig. S5) and incubated them with PET film at 30°C for 18 hours.

      Using genetic engineering, the authors isolated the protein strand the two organisms had in common and exposed it to a PET film without any microbes present.

    2. We collected 250 PET debris–contaminated environmental samples including sediment, soil, wastewater, and activated sludge from a PET bottle recycling site (7). Using these samples, we screened for microorganisms that could use low-crystallinity (1.9%)

      The authors collected samples from different environments where PET plastic waste was likely to be found. In this case they chose to sample near a PET recycling plant. The plant specifically recycled high-crystallinity PET. They took some dirt, water, and sludge and put the samples in an environment including low-crystallinity PET. Low crystallinity PET is more disorganized and easier to break down than polymers with higher crystallinity.

      It was ultimately one of the samples of sediment that grew bacteria. When looking at the bacteria under a microscope, the researchers were able to see that it was growing on a thin layer of PET plastic.

    3. We compared the activity of the ISF6_4831 protein with that of three evolutionarily divergent PET-hydrolytic enzymes identified from a phylogenetic tree that we constructed using published enzymes (Fig. 2C and table S2). We purified TfH from a thermophilic actinomycete (10), cutinase homolog from leaf-branch compost metagenome (LC cutinase, or LCC) (11), and F. solanicutinase (FsC) from a fungus (fig. S5) (12), and we measured their activities

      The authors used published data of proteins having similar activity as the ISF6_4831 enzyme and analyzed it using statistical tests to select three enzymes that divergently evolved—meaning that the enzymes evolved from a common ancestor and accumulated enough differences to result in the formation of a new species.

    1. Monte Carlo analysis

      This technique is used in calculations for complicated systems where changes in one variable influences many parts of the calculation. In this technique many calculations are run with each calculation having different beginning conditions. The end results are then analyzed to look at the range of possible outcomes. The error bars in parentheses in Figure 4 are determined by a Monte Carlo simulation.

      See more about Monte Carlo simulations from the Massachusetts Institute of Technology: http://news.mit.edu/2010/exp-monte-carlo-0517

    2. R2

      R-squared (\(R^2\)) values represent how close the data points are to the predictions from a model. An \(R^2\) value of 1 means that all of the data points lie on the prediction from the model. In this case, the researchers are using the solid line that represents perfect agreement between the box model calculation and the experimental data. An \(R^2\) value of 1 in this case would mean that the box model calculations and the experimental data are in perfect agreement. In general, the closer the \(R^2\) value is to 1, the closer the model and data match.

      Without the inclusion of VCPs in the model calculation the \(R^2\) value was 0.59 (Panel A of Figure 3). With the inclusion of VCPs into the model the \(R^2\) value improved to 0.94 (Panel B of Figure 3).

    3. Here, we assess the importance of VCP emissions to ambient air pollution, again using Los Angeles as a test case (Fig. 4). Los Angeles currently violates the U.S. 8-hour O3 standard,

      The air in Los Angeles, California has ozone (\(O_3\)) levels that are higher than those recommended by the Environmental Protection Agency. The amounts and types of VOCs present influences ozone levels.

    4. We therefore conclude that large underpredictions are due to missing emission sources.

      Comparisons are made between experimental data and model calculations. The model calculations are influenced by what molecules are emitted, how those emissions chemically react, and how wind and other variables cause emissions to move from one place to another. The calculations use techniques from other researchers (including this paper's co-authors Joost de Gouw and Si-Wan Kim) to take into account chemical reactions and movement of the emissions from one place to another.

      The authors then make different predictions with their model assuming different types of chemical emissions (fossil fuels and/or VCPs).

    5. we found that fossil fuel VOCs [from mobile sources and from local oil and natural gas production and distribution (36)] can only account for 61% of the mass of freshly emitted VOCs measured, and 59% of their variability

      Box model calculations were carried out under various conditions. The authors can make various assumptions about what sources are emitting VOCs into the air (mobile sources or VCPs). The results of the calculations under these various conditions are then compared to experimental data. This allows the researchers to examine what sources are important contributors to VOCs in air.

    1. boundary conditions and idealizations in the simulation

      The authors define a series of constraints for their simulation. The scientific model assumes facts about the phenomenon that make the problem easier to solve. In this case, the authors assume among others that the pore structure is homogeneous, that the vapor pressure is the same for the MOF and the condenser, and that the diffusivity of the crystal does not vary.

    2. For visualization purposes, we used a condenser with a temperature controller to maintain the temperature slightly below ambient levels but above the dew point, in order to prevent vapor condensation on the inner walls of the enclosure.

      To simplify the experiments, the author maintain the condenser at a temperature cooler than the surrounding temperature. This results in the condenser being cooler than the walls of the box, so that all the water condenses on the condenser and can be easily collected. In practice, ambient temperature is enough to harvest water without the need for additional cooling of the condenser.

    3. A solar flux (1 kW m–2, air mass 1.5 spectrum) was introduced to the graphite-coated substrate layer

      The authors simulate natural sunlight in the laboratory. The air mass is the ratio of the path length that sunlight has to travel through the atmosphere to the vertical path length (when sun is at the highest point in the sky, which corresponds to the shortest path length). An air mass of 1.5 is the condition most commonly use to quantify the efficiency of solar devices.

    4. The environmental temperature above standard ambient temperature was necessary to perform the experiments at >1 kPa

      In order to efficiently harvest water, a large difference of temperature between the MOF and the condenser is needed. In their experiment, the authors set the temperature at 35°C in order to be able to condense the vapor with a condenser at 10°C.

    5. In this simulation, MOF-801 was initially equilibrated at 20% RH, and the vapor content in the air-vapor mixture that surrounds the layer during desorption increased rapidly from 20 to 100% RH at 25°C

      The authors performed a simulation in conditions reflecting an accurate, real-life use of the MOF. The results show an increase in RH upon water desorption, followed by return to the initial 20% value upon water adsorption.

    6. the crystal diameter of MOF-801 is only ~0.6 μm

      The diameter of MOF-801 was determined by a method called scanning electron microscopy. The surface of the MOF is scanned with a beam of electrons which gives information about the surface.

      See figure S5 in the Supplementary Materials.

    7. A theoretical model was developed to optimize the design of the water-harvesting process with MOF-801, which was further validated with the experimental data.

      The authors developed a computational model to simulate the behavior of the MOF material. Through this approach, they can study the influence of the material parameters on the water harvesting properties and then confirm their result experimentally. Their goal is to screen a large number of parameters computationally to optimize the system.

    8. We carried out the adsorption-desorption experiments for water harvesting with MOF-801 at 20% RH

      The author first quantified the water harvesting capacity of the MOF. For this purpose, they use 20% relative humidity for their measurements, as it is representative of the low humidity in dry regions of the world where an efficient water-harvesting method is most needed.

    9. Therefore, to predict the prototype’s water-harvesting potential under equilibrium conditions, we extended the desorption time for the simulation

      The design of the prototype results in a slower desorption process than was considered in the initial calculations. The authors modified the parameters of their simulation to take this into account and have a model reflecting the experimental conditions.

    10. Last, a proof-of-concept MOF-801 water-harvesting prototype was built to demonstrate the viability of this approach outdoors

      To demonstrate the utility of their material in real conditions, the authors built a small device containing the MOF and a condenser and tested it outdoors.

    11. The powder was infiltrated into a porous copper foam with a thickness of 0.41 cm and porosity of ~0.95, which was brazed on a copper substrate to create an adsorbent layer

      The MOF is pressed onto a substrate made of copper metal in order to have a material that conducts heat efficiently and that can cover a large surface.

    1. Ordinary least squares (OLS) regression summary statistics (adjusted R2)

      Like PGLS, this method examines the relationship between two or more variables. However, this method does not account for phylogeny. OLS regression was used to compare with PGLS results to see if it would produce similar results.

    2. We measured differences in species’ northern and southern range limits, the warmest or coolest temperatures occupied, and their mean elevations in three periods (1975 to 1986, 1987 to 1998, and 1999 to 2010) (figs. S2 to S4) relative to a baseline period (1901 to 1974)

      To test whether climate change has impacted species ranges, the authors had to measure, for each species, the latitudes of the northern and southern range limits (and the distance between these), the warmest and coolest temperature the species were observed, and the mean elevation of the species.

      These measurements are based on the average of the five most relevant species observations in each case. For example, to find the northern range limit, the authors took the average of the five northernmost observations of the species, while to find the coolest temperatures within the species' range, they took the average of the five coolest points that the species was observed at. This helps avoid skewed results from a signal sighting.

      Check out the Supplemental Materials for more information on how the authors took all their measurements.

    1. glial fibrillary acidic protein (GFAP) promoter to target ChR2 to local astroglia

      Glial fibrillary acidic protein (GFAP) is an intermediate filament (cytoskeletal component) protein expressed by glial cells including astrocytes. Glial cells are non-neuronal cells within the central and peripheral nervous systems whose roles include providing support and protection for neurons. When a gene like ChR2 is under the control of a GFAP promoter, only those cells that express the transcription factor for GFAP will express ChR2.

    2. Optical circuit interventions were tested in rats that had been made hemiparkinsonian by injection of 6-hydroxydopamine (6-OHDA) unilaterally into the right medial forebrain bundle (MFB)

      To test whether optogenetics can be used to dissect neural circuits, the authors utilized an established model of induced Parkinson's disease. 6-OHDA is a synthetic neurotoxin that selectively destroys dopaminergic and noradrenergic neurons in the brain. When injected on one side, it can cause side-biased motor deficits. The right medial forebrain bundle is a neural pathway that passes through the hypothalamus and basal forebrain and contains a high number of dopaminergic neurons.

  3. Sep 2018
    1. Johnson-Lindenstrauss

      This is a mathematical statement that shows that you can take a small number of points in a high-dimensional space and transfer them to a lower-dimensional space while (very nearly) maintaining the distance between all the points.

      You could think of this like taking all the points on a sculpture and projecting them onto a piece of paper while keeping every point in its proper position relative to all the other points on the sculpture.

    2. Illustrative odor responses.

      Here, the authors use chemicals (ethanol, methanol, and dimethyl sulfide) to demonstrate similarity tagging/hashing.

    3. we used 20k random projections for the fly to equate the number of mathematical operations used by the fly and LSH

      As stated earlier in the article, the authors could only fairly compare the LSH and fly algorithms if each used the same number of mathematical operations.

      They determined that if they used 20k random projections in the fly algorithm (where k is the length of the output tag, or hash) then the total number of operations for the fly and LSH algorithms would be equal.

      For more detail, see the second paragraph under "Materials and Methods" in the Supplementary Materials document.

    4. we selected 1000 random query inputs from the 10,000 and compared true versus predicted nearest neighbors

      From the 10,000 vectors, the authors randomly chose 1,000 of them to be query inputs (images or words that they would test against the rest of the feature vectors to find which ones represent similar images or words).

    5. We used a subset of each data set with 10,000 inputs each, in which each input was represented as a feature vector in d-dimensional space

      They chose 10,000 vectors of length d from each data set. Each vector represented an image (SIFT, MNIST), or word (GLOVE).

    6. computed the overlap between the ranked lists of true and predicted nearest neighbors

      This is a method of measuring how well an algorithm performed. The more overlap between the two lists, the more similar they are. If they are exactly the same, this means that the algorithm performed perfectly.

      For example, it might mean that it found the closest match to all query images, or that it correctly found all the correct features that match part of an image (see picture below).

    7. determined on the basis of Euclidean distance between feature vectors

      Each feature vector contains a row of values representing features of an image or word (depending on the data set). Because the data is arranged so that similar features are near each other, measuring the straight-line (Euclidean) distance between two vectors allows the authors to determine the similarity between them.

      That is, the closer the vectors, the more similar their features. The vectors closest to one another are the "true nearest neighbors," or the images/words in the data set most similar to one another.

    1. AIC

      The Akaike information criterion (AIC) is a statistic that measures whether some models are more or less informative than others. This means that a model with a lower score for AIC is more informative than a competing statistical model and may tell us something that is meaningful in a biological sense.

      Like many other aspects of statistics, we have to be careful about blindly believing test results, so we apply our experience as scientists to make sure that numbers like the AIC score actually make biological sense!

      Click here for a more in-depth view of what AIC is, how it is calculated, and what it can (and can't) do.

    2. PGLS models

      Phylogenetic Generalized Least Squares (PGLS) is a method that accounts for phylogenetic relationships within a group of species (phylogeny reveals how closely related species are to each other). This method will determine if variables of interest are closely related.

      In this table, models of trait evolution indicated whether traits are ancestral and kept over evolutionary time. Species’ upper thermal limit (one of the traits studies here) was found to be shared by close relatives. The authors pointed out in this paper that niche conservatism (i.e. the tendency for species to keep their ancestral traits) could explain such findings.

    3. applications accounted for changes in bumblebee species’ range or thermal limits (table S3)

      Without also testing competing theories (land-use and pesticides), the authors would not be able to say how important the effects of climate change were in influencing range limits. Remember that the scientific method does not allow us to definitively prove a theory, only add to the evidence that one hypothesis is more likely than the alternatives.

    4. We investigated whether land use affected these results. Finally, we used high-resolution pesticide application data available in the United States after 1991 to investigate whether total pesticide or

      Because climate change is not the only possible explanation for changes in species' latitudinal and thermal range limits, the authors also tested other competing theories, like land-use changes and pesticides.

    1. opsin vector introduction

      To accurately target the subthalamic nucleus (STN), the authors used extracellular recordings to determine accurate position within the deep brain structure since the STN has a characteristic firing pattern. Guided by this method, accurate injection of opsin vectors to the region was accomplished.

    1. Cyclone intensities around the world are estimated

      The authors used satellite imagery and characteristic wind and moisture patterns in order to gauge the intensity of storms and hurricanes.

      This method of gauging hurricane intensity has been tested against aircraft sensor measurements and does a solid job of accurately recognizing hurricane characteristics.

    2. we conducted a comprehensive analysis of global tropical cyclone statistics for the satellite era (1970–2004)

      These authors used satellite data from 1970 to 2004 in order to examine cyclones and hurricanes in every tropical ocean basin.

      They examined: 1) number of storms and hurricanes 2) number of storm days 3) hurricane intensities

      Most of the data examined came in the form of best track data archived in hurricane warning centers.

    1. instrumental learning

      A method used to reinforce an association with a certain stimulus. Unlike classical conditioning, instrumental learning (otherwise known as operant conditioning) is active and involves a person performing behaviors that are positively or negatively reinforced. Reinforcement is also known as learning.

      The two instrumental tasks used in this study were reward and avoidance learning. Participants learned that using a pedal would allow them to avoid being shocked when presented with pictures that they had learned to associate with a shock.

    2. slip-of-action test

      A test for habitual behavior. Certain stimuli that were previously associated with a reward are devalued, meaning the reward is removed.

      Once the reward is removed, responding to a stimulus no longer makes sense. If people continue to respond, their response is considered habitual.

    3. skin conductance

      Skin conductance measures the activity of certain sweat glands, which become more active during the avoidance response (resulting in a higher skin conductance).

      It was measured 0.5 to 5 seconds after each stimulus was presented. Conductance allowed the authors to measure how the participants respond to seeing the stimulus before reacting to it and to make sure they had learned to fear the stimuli associated with a shock. This way, they knew that if participants performed poorly in the task, it was not because they did not learn to fear the stimuli.

    1. Early hominin stature reconstructions are notoriously difficult to assess: the limited number of intact long bones available in the fossil record often requires reconstruction of the long bone length from fragmentary remains, before different methods can be used to estimate the stature; the eventual results can differ according to the method employed.

      The authors based early hominin stature estimates on the length of intact fossil femurs, reconstructed femurs, and femur head diameters.

    2. For both of the described methods, mean estimates of stature and body mass for S1 were computed by averaging the estimates obtained from individual tracks (Tables 2 and 3). The average footprint length values were considered more reliable than minimum values (which from a theoretical point of view could be regarded as more representative of the foot length) for the following reasons.

      The authors used average footprint length when estimating stature and body mass. They did so to minimize overestimates and underestimates of footprint size and to compensate for poor preservation.

    3. Similarly, we estimated the body mass of the Laetoli track-makers using the 'walk only' regression equation that relates footprint area (i.e., footprint length x max. width) to body mass

      The authors used a regression equation based on walking pace as it relates to the size of the footprint and body mass. Australopithecus afarensis proportions were used rather than those of modern humans.

    4. we also computed some estimates using the foot:stature ratio known for Au. afarensis (Dingwall et al., 2013). This ratio is 0.155–0.162 (Dingwall et al., 2013), so we obtained stature estimates (Tables 2–3) predictably close to or slightly lower than the lower limit of the estimates given by the Tuttle (1987) method.

      The authors also estimated the stature of the Laetoli trackmakers using Australopithecus afarensis foot:stature ratios.

    5. Given that the foot length in H. sapiens is generally about 14% to 16% of stature (Tuttle [1987], and references therein), we computed two estimates for the Laetoli hominins assuming that their feet were, respectively, 14% and 16% of their body height (Tables 2–3). This method, however, is not fully reliable because it is based on the body proportions of modern humans, and because it does not take into account that the footprint length does not accurately reflect the foot length.

      The authors used two different methods to estimate the stature of the hominin trackmakers. One used modern Homo sapiens body proportions. This was considered to be less accurate since the body proportions of the hominids were likely different from those of modern humans.

    6. The following morphometric measures were taken on the contour maps: footprint length – maximum distance between the anterior tip of the hallux and the posterior tip of the heel; footprint max width – width across the distal metatarsal region; footprint heel width; angle of gait – angle between the midline of the trackway and the longitudinal axis of the foot; step length – distance between the posterior tip of the heel in two successive tracks; stride length – distance between the posterior tip of the heel in two successive tracks on the same side.

      The authors gathered the following data on tracks from both G and S.

    7. Data acquisition and processing (Supplementary file 4) were performed following the workflow described above for the Site S test-pits. We positioned four perimeter control points and 11 inner targets. The latter were used to model in detail six selected tracks (G2/G3–29, G1–35, G1–34, G2/3–26, G2/3–25 and G2/3–18, listed in the direction of walking)

      The authors used the same data gathering and visualization techniques on the fiberglass casts as they had used on the tracks at S.

    8. Data processing started by checking measurements in plan and height. This step is preliminary to the definition of the control point coordinates

      The authors checked their measurements and used STAR*NET software to combine their conventional observations with GPS vectors. They used the leveling observations the software provided to make 3D adjustments in the footprint images.

    9. The photographic survey was carried out by three shooting modes:

      Each test-pit was surveyed and photographed in order to determine its relationship to the other test-pits and to Site G.

    10. n the second step, the perimeter target positions were measured. We placed two rods equipped with a spherical level on successive pairs of targets and we marked points at the same height on the rods for each pair by using the water level device

      The authors carefully measured distances between targets so that the location and depth of each of the modeled footprints could be precisely determined.

    11. Each test-pit was entirely surveyed at lower resolution and then detailed 3D models of some inner portions (single prints or groups of close prints) were acquired (Figures 4–6).

      The authors made 3D models of selected footprints and sets of footprints. Models of some of the footprints were not made due to their poor state of preservation.

    1. We explored a low, moderate, and high scenario for atmospheric CO2 concentration (550, 850, and 950 ppm, respectively: EPA and IPCC 2007) and each climate driver: mean annual air temperature (+1, +2.5, and +4.2°C; IPCC 2013) and precipitation (−2, +7, and +14%; IPCC 2013) (Fig. 4).

      Here, the authors explains how they ran different scenarios in the simulation system with the data they had collected in the field, making for more realistic results.

    2. Fig. 2. Long-term daily weather data from the NCDC Royal Palm Ranger Station from 1963 to 2012. In climate change simulations weather data variability during 2000 to 2100 was based on variability at the Royal Palm weather station during 1963 to 2011 and weather data from TS and SRS in 2012.

      These data collected from 1963-2012 wereinputted into the DAYCENT system to simulate conditions during the next 100 years (from 2000-2100).

    3. Table 1. DAYCENT site characteristics for Taylor Slough (TS) and Shark River Slough (SRS). Site data was obtained from the Florida coastal Everglades Long-term Ecological Research (FCE LTER sites TS-1 and SRS-2), AmeriFlux and the literature.

      This table shows the two different sites, Taylor Slough (TS) and Shark River Slough (SRS) along with their exact location using the coordinate system, characteristics of each site, such as root:shoot ratio, soil composition, depth of roots, Nitrogen deposition along with the amount of Carbon found at each site.

    1. All islands were sampled at the completion of the growing season (August) between 2001 and 2003. We established a 30 m by 30 m plot at each of the grid crosspoints (12 to 32 per island, depending on island size) (12), within which we sampled plant species presence and cover; aboveground plant biomass; total soil N, P, and δ15N; and %N and δ15N from a common grass (in most cases Leymus mollis but in some instances Calamagrostis nutkanensis) and forb (Achillea borealis) (12).

      In this experiment, grids were designated over the island and 30 x 30 meter plots were established to sample within. In these plots the total soil nitrogen, phosphorous, amount of stable nitrogen isotope, and percentage nitrogen were sampled across grasses, forbs, dipterans, arachnids, passerines, and mollusks to determine how much of these nutrients were derived from the ocean. Organisms and soil that derive their nitrogen from a local source will have fewer amounts of nitrogen isotopes than those that get their nitrogen from higher trophic levels as is shown in Figure 3.

    2. Experimental nutrient additions to a community representative of fox-infested islands over 3 years caused a 24-fold increase in grass biomass (24.33 ± 6.05 g m–2) compared with control plots (0.51 ± 0.38 g m–2 increase; two-factor analysis of variance, F1,20 = 23.96, P < 0.001) and a rapid shift in the plant community to a grass-dominated state. In fertilized plots, grass increased from 22 (±2.7%) to 96 (±17.3%) of total plant biomass, whereas grass biomass in control plots was relatively unchanged (11.4 ± 3.0% and 12.1 ± 1.2% of total biomass at the start and end of the experiment, respectively) (12). In a parallel experiment (18), we disturbed and fertilized plots to mimic the effects of both seabird burrowing and guano addition. Here we found that disturbance negatively rather than positively affected grass biomass; the effects of fertilization alone were far greater than the joint effects of disturbance and fertilization. These results confirm the importance of nutrient limitation in these ecosystems and establish that nutrient delivery in the form of seabird guano is sufficient to explain observed differences in terrestrial plant communities between islands with and without foxes.

      In this experiment, nutrients were added to a community that represented the fox-infested islands which increased the grass biomass and also created a more grass dominated environment overall.

      The nutrient additions were meant to represent the spread of guano by the seabirds. In the fox-infested islands the seabird populations were scarce, which decreased guano accumulation. Therefore, the added nutrients represented the guano production by seabirds in the absence of seabird on the island that is fox-infested.

      These nutrient additions are similar to the nutrient states of fox-free islands and show what the changes in ecosystem could have been like during the introduction of foxes.

      In another experiment, they tried to mimic the disturbance in soil of seabirds burrowing, this negatively impacted the grass biomass and it was shown that fertilization by itself had the greatest positive effect on grass biomass and distribution. This helps explain the higher incidence of grasses in the fox-free islands.

    3. Fig. 1. The Aleutian archipelago with sample islands indicated in red (fox-infested) and blue (fox-free). Adak Island, the location of fertilization experiments, is indicated with a yellow dot.

      This figure shows the islands that were studied and their relative position on Earth. There are 9 fox-infested areas shown in red and 9 fox-free areas shown in blue, the sample size of islands is the same so the results cannot be attributed to the sample size difference. The yellow dot is the location of fertilization experiments.

    4. We use this experiment to show how differing seabird densities on islands with and without foxes affect soil and plant nutrients; plant abundance, composition, and productivity; and nutrient flow to higher trophic levels

      This is very broadly what the experiment seeks to discover through looking at fox-infested and fox-free islands. This experiment is designed to assess effects of sea bird densities on soil nutrients and composition, plant abundance, primary productivity, and the flow of nutrients from lower organisms to higher ones (i.e. from plants to herbivores to carnivores.)

    1. Physics had to be modified

      It would be more accurate to say that the models used to describe physics had to be modified.

      As physics progressed, physicists revised their theories in light of new evidence and knowledge (and they continue to do so today). Here, Einstein describes how the special theory of relativity came about, in part, to solve the problem of classical mechanics' compatibility with electromagnetic theory.

      Modern physicists consider classical mechanics an approximate theory that is useful for the study of non-quantum mechanical, low-energy particles in weak gravitational fields. It is usually the starting point for students learning physics.

  4. Aug 2018
    1. Fig. 1 High-resolution melting profiles showing the resolved Symbiodinium strain genotypes.

      Different genetic sequences melt at slightly different rates. Therefore, they can be analyzed using these melting point curves, where fluorescence is plotted on the y-axis and temperature on the x-axis.

      If the DNA strand is composed of many guanine and cytosine bases, it will take a higher temperature to break the hydrogen bonds apart as opposed to adenine and thymine bases. This is why the E2 clade fluoresced at a higher temperature than the other clades.

    2. denaturing gradient gel electrophoresis (DGGE)

      Denaturing Gradient Gel Electrophoresis (DGGE) has been used, along with yeast cultures, to analyze saliva samples of 24 adults to study the bacteria present. This study showed ample variation of bacteria among individuals. The conclusion obtained from this experiment was that there is a lack of association between yeasts and bacterial DGGE fingerprint clusters in saliva, implying a significant ecological specificity.

    3. In this study, we evaluated the effectiveness of HRMas a tool to rapidly and precisely genotype monotypic Symbiodinium populations using the internal transcribed spacer, region 2, ribosomal DNA (ITS2 rDNA).

      This study (experiment) wanted to identify the effectiveness of high-resolution melting for the genotype (genetic makeup) of the Symbiodinium. Symbiodinium are single-celled algae that can be found in the endoderm (innermost layer of cells or tissue of an embryo in early development) of tropical cnidarians such as corals, sea anemones, and jellyfish.

    4. High-resolution melting

      A technique that detects mutations, and differences in DNA samples. HRM has been used to target various microbial communities on tadpole intestines and feces. In this study, HRS targeted a short amplicon (piece of DNA or RNA that is the source of amplification or replication events) of the 16S rRNA gene, which was the sequence being tested. Along with the HRM, gel electrophoreses and DNA sequencing were also used in order to study the results more closely. All three methods revealed several types of bacteria living in a tadpole's intestines and feces.

    1. We were unable to accurately calculate pollinator importance confidence intervals as we only estimated the relative frequency data (see above).

      Since much of the calculations were done using estimations, the authors could not provide a range of values that's likely to encompass the true value. For example, when scientists say this value falls within the 95% confidence interval it means that the intervals contain the true value 95 percent of the time and fail to contain the true value the other 5 percent of the time.

    2. Foraging behaviour was categorized by following visitor movements after they visited A. berteroi flowers. Visitation frequency of the floral visitors was estimated by counting the number of visits of each of the visitor groups to A. berteroi flowers during the observation periods where at least one visitor was seen, and calculating the corresponding percentage of the total visits observed in those periods.

      In order to determine the pollination efficiency of the insects, the authors tracked how often a specific visitor groups arrived and how long each individual stayed. Then the authors calculated an “efficiency” by estimating pollen on the visitors’ mouthparts. This was calculated as the average number of pollen grains per individual visitor of each group.

    3. Pollen grains were collected from the insect bodies to see whether visitors carried A. berteroi and/or other pollen.

      The reason behind this technique is to determine what species the carriers favored.

    4. We conducted pollinator watches weekly, for 3 h per week per site (12 intervals of 15 min per day), from 9:00 am to 12:00 pm (the hours with the highest visitation rates, B. B. Roque, personal observations) during the flowering period (April–June)

      The author monitored when the pollinators came around and interacted with the plants. Reason behind this is to determine how frequently the flower will have visitors. Another reason why the author monitored the plants, was to determine the period of time when the flower had the most visitors. With both of these factors being monitored, the author can determine more or less how frequently these plants can have their pollen spread throughout an area.

    1. Total genomic DNA of fresh leaves of a single individual of C. argentata was also isolated with the DNeasy Mini Plant kit. This DNA sample was subsequently used to obtain microsatellite loci that were developed by the Georgia Genomics Facility at the University of Georgia (Athens, Georgia, USA).

      This experiment isolated the genome of an individual C. argentata sample using the same method described in the previous experiment (DNeasy). The DNA sample obtained from this group was then taken to the Georgia Genomics Facility at the University of Georgia to develop microsatellite loci which are single sequence repeats that allows researchers to see any variability in the genome of this specific plant compared to the others. The researchers then used an Illumina HiSeq 2000 to see the microsatellite markers. The process through which they obtained these microsatellite loci is not described in the paper; however, the significance of this step when comparing different samples of DNA is described in the video below: https://www.youtube.com/watch?v=9bEAJYnVVBA

    1. Finally, knowing object distance is a prerequisite (or corequisite) in the model for deconfounding size, impedance and shape, so these features would first appear in the torus and higher areas. Although this proposal is not yet based on quantitative simulation or modeling, we believe it may be a useful working hypothesis for interpreting and further exploring parts of the electrosensory nervous system.

      Here, the authors are hypothesizing that the EOD pattern incorporated into electrosensory nervous system of the electric fish uses the information of size, shape, and distance of the objects in an algorithm to process and relay information to the electric fish brain.

    2. we have focused on reconstructing quantitatively the entire pattern of currents resulting from the fish’s discharge and environment.

      The authors here talk about the premise of their experiment and how they want to take all the data that the team has collected visually and through pattern tracking and turn it into data that has numbers. That is why in the chart that follows there is system mapping for EOD of both "wave" and "pulse" fish.

    1. Table 1. Evolution of spread-relevant traits as a function of landscape patchiness.

      Table 1 shows data indicating linear relationships between variables that changed in an evolving population based on the variability of the environment. The P column explains whether the data is statistically significant, meaning it can be concluded as a relationship between data and not just due to random chance. The two values observed for each variable are Y intercept (which indicates whether the trait changed from the original population) and the slope (which indicates how much the trait changed). These values are calculated as P values to determine if they are significant.

    2. Fig. 3. Genotypes and traits at the invasion fronts.

      Figure 3 shows the initial and final genotype compositions as well as trait changes among the different conditions (A. continuous, B. gap size of four times the mean dispersal rate, C. gap size of eight times the dispersal rate, and D. gap size of 12 times the dispersal rate). The central pie chart found in each of the four graphs shows the equal frequency of genotypes in the founding population, while the other pie charts are used to represent the genotypic make-up of the top 10 leading individuals after 6 generations of spreading within the different conditions. The placement of the pie charts is based on rankings of three traits: the competitive ability of the plant (dominance of the plant in ways that do not directly affect it's ability to spread offspring), dispersal (the average distance of the farthest dispersed seed from the plant it was dispersed from), and the height of the plant.

      These values show that even as the gap size increases, the trait values hardly increased at all. Similarly, as can be seen by the color of the pinwheels, the genotype composition change between the different gap sizes was rather consistent. The only outlier is the continuous condition that has random trait values and genotype compositions.

    3. We initiated each replicate invasion in the leftmost pot of the array by sowing equal fractions of 14

      In the first experiment, the authors simulated invasion by planting 14 genotypes on one side of the pot array. The plants were allowed to proceed for several generations to follow evolution in A. thaliana. Habitat patchiness was tested by putting space between the pots at varying distances.

    1. Error bars reflect the 95% confidence interval of the mean or expert judgment

      Notice that each bar in the figure has small capped lines that extend above and below the bar. These lines represent error bars for the various VOC emission factors. Error bars reflect the experimental uncertainty in determining VOC emission factors. The error bars in this figure are 95% confidence intervals, meaning that we are 95% certain that the true value lies somewhere in between the top and bottom of the error bar. Any measurement made has uncertainty associated with it.

      In scientific communication it is important to give the value that was found and the uncertainty in that value. We typically talk about uncertainty as being the precision of the measurement, where a smaller uncertainty is a more precise measurement and a larger uncertainty is a less precise measurement.

    2. We used energy and chemical production statistics, together with near-roadway and laboratory measurements, to construct the mass balance shown in Fig. 1 (17).

      Economic data was used to estimate the mass of various chemical products used in the United States. You can see how the estimates were derived in Tables S2 and S3 of the supporting information.

      Tables S2 and S3 can be found on pages 23 and 24 of the Supplementary Materials document

    3. California has an extensive regulatory reporting program for consumer products (34), including residential and commercial uses, which we used to speciate emissions. These speciation profiles provided us with target compounds to characterize in both outdoor and indoor environments. We also accounted for industrial emissions from VCPs (e.g., degreasing, adhesives, and coatings). The reporting data are in agreement with a U.S. database of chemicals (35) used as key constituents in chemical products (table S7). The VOC speciation profiles of VCPs (table S8) are distinguishable from those of fossil fuels (table S9), although there is some overlap in species present.

      Every emission source of VOCs emits different compounds. Many databases of the different emission profiles exist. The emission profiles allow the researchers to determine what activities (driving a car, painting a house, spraying perfume, etc.) lead to the presence of different VOCs in the air. The researchers use these profiles to differentiate between sources that come from fossil fuels (e.g. driving a car) and those that come from VCPs (e.g. painting or perfume).

    4. If chemical products are an important source of urban air pollution, then their chemical fingerprint (fig. S3) should be consistent with ambient and indoor air quality measurements.

      Previous studies have measured volatile organic compounds in outdoor and indoor air around Los Angeles, California. This study is using that data to see how it agrees with model calculations when accounting for volatile organic compounds from different sources. The video below gives an overview of how atmospheric modeling helps us understand what is occurring.


      The study looks at two different environments (indoor air and outdoor air) that can influence each other.

      See Figure S4 on page 18 from the Supplementary Materials document

    5. The fraction that can be emitted to the atmosphere depends strongly on product type and use (table S4).

      Emissions from volatile chemical products was determined by looking at the composition of chemicals in a particular product and how readily those chemicals evaporate into the air. The authors compiled information from various other researchers to perform these calculations.

    6. In 2012, the amount of oil and natural gas used as fuel in the United States was ~15 times the amount used as chemical feedstocks (Fig. 1A).

      United States government data was compiled to look at the amount of oil and natural gas supplied and what these sources are used for. See Table S1 of the supporting information to see the breakdown.

      Table S1 can be found on page 22 of the Supplementary Materials document

    7. Although U.S. sales of VCPs are substantially smaller than for gasoline and diesel fuel, VOC emissions from VCPs (7.6 ± 1.5 Tg) are twice as large as from mobile sources (3.5 ± 1.1 Tg) (Fig. 1E, light green, dark green, and blue bars) because of differences in emission factors.

      The authors use different calculations to determine VOC emissions from mobile sources and VCPs. This is due to the different end uses. Oil and natural gas used for mobile sources undergo combustion that causes much of the carbon to be emitted as carbon dioxide and not VOC. VCPs are primarily used directly and remain as intact organic molecules.

    8. We consider four key pieces of evidence to support this finding: (i) energy and chemical production statistics; (ii) near-roadway measurements of transportation emissions, together with laboratory testing of chemical products; (iii) ambient air measurements away from roads; and (iv) indoor air measurements.

      Many experiments from other researchers are utilized in this study to compare model calculations with experimental data.

      The authors in this study utilize all of these different sources of information to build a better understanding of the sources of air pollution.

    1. formed on the PET film upon culturing

      After collecting the samples the authors provided the samples with water, an appropriate temperature, and a food source of PET film. They later used microscopy to observe what samples were able to use the PET film as an energy source.

    2. To explore the genes involved in PET hydrolysis in I. sakaiensis 201-F6, we assembled a draft sequence of its genome

      The authors took the DNA from Ideonella sakaiensis and created a list of the order of the DNA bases A, G, C, and T. Next, they used a computer program to compare the sequence to sequences from other organisms that were already known to degrade PET.

    3. limiting dilutions

      A lab technique that isolates a single species of bacteria from a sample with more than one species of bacteria.

    1. Fig. 2. Changing bumble bee community composition, bumble bee tongue length distributions, and tube depth distributions of visited flowers over time. (A and B) Bumble bee community composition. (C and D) Bumble bee tongue length. (E and F) Flower tube depth distribution. Bombus species abundance in alpine communities is indicated by the proportion of total foragers (15). Species are ordered by increasing tongue length [in (A), species’ names follow (18)]. Bimodality of the density functions (15) indicates that bumble bee communities contain two predominant phenotypes, short-tongued and long-tongued [(C) and (D)]. (E) and (F) show the tube depth density functions for flowers visited by, respectively, B. balteatus and B. sylvicola in the Front Range [Mount Evans and Niwot Ridge (15)]. For tongue length [(C) and (D)] and tube depth [(E) and (F)], representative density functions for simulated communities (15) are shown.

      These results suggest that bumble bee tongue lengths are decreasing and corolla tube lengths are also decreasing in response to climate change.

    2. On Pennsylvania Mountain, alpine bumble bees forage over hundreds of meters to provision their nests (28). To ask how warming has affected floral resources at this scale, we measured PFD of six bumble bee host plants from 1977–1980 and 2012–2014 in five habitats along a 400-m altitudinal span (table S5). Land surface area decreases with altitude above tree line in the Rocky Mountains (29), declining by more than an order of magnitude on Pennsylvania Mountain, where 58% of habitable terrain is found below 3800 m and only 4% above 3938 m on the summit (Fig. 3Aand table S5). Because bumble bees forage across the 400-m altitudinal range (28), we evaluated the temporal change in flower production at this landscape scale. For each habitat, we multiplied PFD (flowers per square meter) within sampling plots by surface area (square meters) to estimate of total flower production (15)

      From 2012-2014, researchers analyzed the change of flower production of six bumble bee host plants over time at a altitude of 400m. This is where the bumble bees most commonly forage. The flower abundance was measured as flowers per square meter across five different habitats. It was then compared to data from 1977-1984 to determine change over time.

    3. Climate records from Niwot Ridge show warming summer minimum temperatures over the past 56 years (27).We see similar changes on Mount Evans (R2 = 0.383, t1,52 = 5.68, P < 0.0001) and Pennsylvania Mountain (R2 = 0.341, t1,52 = 5.20, P < 0.0001) (fig. S3, A and B), where summer minimums have increased ~2°C since 1960. We used a nonlinear model to characterize the relationship between peak flower density (PFD; flowers per square meter) and summer minimum temperature.

      Over the past 56 years, climate change has caused summer temperatures at Niwot Ridge, Mount Evans, and Pennsylvania Mountain to rise. To understand the relationship between summer temperatures and flower density, four bumble bee host species were analyzed for change in average flower density between 1977 and 2014.

    4. (i) decreasing body size, (ii) coevolution with floral traits, (iii) competition from subalpine invaders, and (iv) diminishing floral resources.

      The author listed four possible processes responsible for the tongue length changes in the bees studied:

      1) Decreased body size over time has correlated to a shorter tongue - The authors compared the body size measurements with the tongue length measurements over time.

      2) Coevolution between the flowers and bees - The tube depth of the flowers were measured and compared to the tongue length of the bees over time.

      3) Competition from other bees in the same region affected tongue length - The authors compared other bee species to the bees studied and determine which species had the advantage and if these advantaged affected the other species.

      4) Diminishing floral resources - The authors analyzed the effects of lower amounts of flowers due to increased temperatures on the foraging habits of bees. They then concluded if this could have impacted tongue length.

    1. JH synthesis was analyzed

      Scientists started using fluorescent tags as a natural way to accurately detect low concentration of metabolites in insects. This method proved to be sensitive and effective and most importantly stable. Being stable was an important factor in this method because if it was not stable it would start to degrade after about 15 minutes. The new method has lasted over an hour, which is in benefit of being detectable by HPLC-FD (High-Performance Light Chromatography with Fluorescent Detection).

    2. JH and insulin regulate reproductive output in mosquitoes; both hormones are involved in a complex regulatory network, in which they influence each other and in which the mosquito's nutritional status is a crucial determinant of the network's output.

      The hypothesis is saying the mosquitoes' nutrition has an effect on "insulin sensitivity" and "juvenile hormone synthesis." Insulin is a hormone that works with the amount of glucose (sugar) in blood. A juvenile hormone is a hormone in insects that work with maturation and reproduction. So the researchers are saying that the amount of nutrition (food) the mosquitoes eat will determine the amount of these two hormones, insulin and juvenile, are produced.

      Since the researchers hypothesize that how much the mosquitoes eat determines how much insulin and juvenile hormones work, this means how much insects reproduce is also affected. So the researchers are saying if the insects eat enough, they will reproduce with better conditions than if they were not eating enough. This is because the hormones that control reproduction are controlled by the insects' nutrition.

    1. we measured the position and bearing of the fish swimming at a constant velocity with no acceleration; the position and bearing at time zero was then converted into Cartesian coordinates using the range of the center acoustic beam as a reference distance.

      Certain variables in the frame of these barracuda, as they were being detected, was used through complicated physics and mathematics to calculate their stride lengths.

    2. ensuring stimulation of the white muscle tissue

      Ensuring the white muscle tissue were stimulated by the electric pulse to contract, tighten.

    3. time from initial lure strike to landing was minimized and never exceeded 15 min.

      The time it took to hook a fish, reel it in, and brought on board was never more than 15 minutes.

    4. x-axis is percentage of total length

      The x-axis shows where along the length of each fish the contraction times were collected from, starting from the head (0%) to the tail (100%).

    5. P<0.001

      In statistics, 'P' is the p-value. The p-value is the probability that the null hypothesis is true, and if it's below .05 that usually means the null hypothesis (there is no correlation between the two variables being observed) is rejected as not true.

      In the case of this section of the paper, a statistical test was run to see if the mean temperatures of the fishes (taken from the muscles being observed) were significantly different from each other. It was found that the probability of this was so low (below .05), they were not significantly different.

      This is important because it implies that all the muscles of all the fish had close enough temperatures to each other that the researchers wouldn't have to worry about slightly different muscle temperatures making the contraction times collected in each fish faster or slower in relation to each other.

      Basically, they checked that the muscle temperatures in each fish were close enough to each other that the variable temperature would be (close enough) to being a "constant" in all the tested subjects in the experiment.

    6. forces needed to reach a certain swimming speed

      This refers primarily to effects of strong and weak water currents, as discussed in the last few annotations.

    7. Here we investigated maximum speed of sailfish, and three other large marine

      Sailfish, barracuda, little tunny, and dorado are four marine predatory fish species known for their extremely high swimming speeds.

      The researchers decided to reconsider how accurate the previously predicted estimates of each of those species' maximum swimming speeds truly is, hypothesizing that they may be over-estimations.

      They did this by using a different method than that used to make the estimates they're investigating. Their method was to measure the time it takes for every contraction of swimming muscle (that doesn't use oxygen) when it twitches.

    1. Both results suggest that chemically distinct species are more likely to co-occur. In contrast, we found a significant negative relationship between species phylogenetic distance and presence/absence co-occurrence (r = −0.16, P = 0.01). Thus, more closely related species are more likely to co-occur.

      The Mantel tests allowed the authors to determine the amount of difference between species. The test uses estimated distances to measure difference.

      In this case, it is testing chemical differences between species. The results indicated Piper species that are more chemically different are more likely to exist within the same environmental patch.

    2. Mantel test

      A Mantel test measures the correlation between two matrices. In this case the Mantel test was used to estimate the correlation between species phylogenetic distance and their co-occurrence.

      Using this test, the researchers were able to statistically verify that there is a negative relationship between how closely related two species are their co-occurrence.

    3. Pianka's index

      Pianka's Index is used for field measurements of the niches. They can measure microhabitat, resource usage, and spatial activity. The values range from 0 to 1, no resources used in common between species to complete overlap in resource use, respectively.

    4. Gotelli's c score

      Gotelli's c-score measures co-occurrence between species. A binary matrix is used to represent the absence (0) or presence (1) of a species. Each column represents a site and each row represents a species. Once the c-score is calculated a high value indicates less co-occurrence and a low values indicates there is more likely to be co-occurrence.

    5. only -NRI was significantly different from zero (-NRI, t = 1.83, df = 80, P = 0.03; -NTI, t = 0.77, df = 80, P = 0.22). In contrast, species composition within the plots was phylogenetically underdispersed. Both -NRI and -NTI were significantly different from zero (t = −5.24, df = 80, P = 0.0001 and t = −2.26, df = 80, P = 0.01, respectively; Fig. 2).

      Here, the author’s evaluated whether or not similar species tend to coexist together using the inverse nearest relative index (-NRI), which asses clusters of species, as well as the inverse nearest taxon index (-NTI), which asses cluster taxonomy of clustered species.

      Along with data obtained by the chemical dendrogram the clusters were analyzed and diversity in relation to clustering was discovered.

    6. In our community-based approach, we found that Piper species were, on average, more overdispersed with respect to their secondary chemical composition than expected

      In order to assess the dispersion of Piper species, the authors analyzed the secondary chemical composition of the plants. In plants, secondary composition are organic compounds used for plant defense. They are secondary because their absence does not cause immediate death of the plant.

      The analysis of the secondary chemical composition was performed using a chromatography technique: GC-MS. This technique does not detect all secondary metabolites but it detects most of them (86% of the metabolites).

      After the secondary metabolites were detected, a library was created for all species and the library was compared to references in the AMDIS (Automated Mass Spectral Deconvolution and Identification System -a mass spectra identification system). The library created was a mass spectra library.

      Mass spectrometry is an analytical technique that identifies molecular weights of compounds which helps in the identification process. The particular identification method allowed the researchers to assess chemical similarities between individual plants and species.

      This analysis allowed the researchers to conclude that the Piper species were more spread apart than expected when their secondary metabolites was the point of comparison.

    7. species coexistence

      In order to asses species coexistence, the amount of niche overlap needed to be found. Niche overlap occurs when two or more organismal units use the same resources. More overlap would mean that more species exploit each resource. This was done using Pianka's Index.

    8. K = 0.03

      K statistic is a statistical analysis of the phylogenetic signal based off a Brownian motion metric that determines the strength of the phylogenetic signal.

    9. Phylogenetic analysis yielded a local species phylogeny that concurs with the current phylogenetic Piper data

      Piper is a very species rich genera of flowering plants. Molecular phylogenetics has been crucial in identifying the monophyletic groups of this genus.

      The researchers amplified the ITS region and the psbJ-pet A intron of each of the sampled species they collected to conduct a phylogenetic analysis. They were able to construct a phylogenetic tree of the Piper species that were found within the patches they sampled.

    10. The hierarchical clustering showed five discrete chemical clusters

      The authors collected data regarding chemicals produced by the plants used in their experiment using a multi-step process.

      They first preserved leaves they collected within silica gel before transporting the leaves to the University of Missouri, St. Louis. There, the researchers crushed the leaves under liquid nitrogen. Next, a methanol-chloroform solution was used to extract volatile compounds along with a small addition of piperine. The authors then filtered and stored the compounds so that they could later be analyzed.

    11. The GC-MS analysis yielded more than 1,100 chromatographic features. Approximately 40% of all features were present in all Piper species (e.g., phytol, stigmasterol, sitorterol, and tocopherol).

      The authors used previously collected extracts from leaves and analyzed them using gas chromatography mass spectroscopy (GC-MS), which determines chemicals within an extract based off particular peaks given off by those chemicals. They were able to find that 40% of all Piper species contained the same types of chemicals.

    12. We sampled a total of 2,035 individuals from 27 species of Piper across the 81 sampled plots (Appendix S1: Table S1). The number of individuals present per plot was 25.2 ± 1.1 (mean ± SE; max–min = 4–51), and the number of Piper species per plot was 5.2 ± 1.4 (max–min = 3–11).

      In order to conduct the experiment, patches needed to be sampled and the average number of Piper per patch needed to be calculated. The researchers created transects located between 50 and 100 m from the trail and a large space separated each transect (more than 250 m). Then in a different transect, 81 plots of 20m in diameter were created. The standardization of the plots is important for an accurate calculation of the average number of Piper per plot.

      Piper had different sizes, so for a better standardization, only Piper that are more than 1cm in diameter were selected for calculating the mean number of Piper per plot. The researchers than identified the species of each Piper. A total of 2,035 individuals were counted. The mean per plot was calculated by dividing the number of individuals by the total number of plots. (2,035/ 81 = 25.123) The number of species per plot was calculated by determining the mean of the average of species per plot. The average was found to be 5.2 species per plot.

    13. Pianka's Index

      In the process of his research, Pianka came up with a universal formula for calculating the overlap of pairs of niches.

    1. in situ topographic measurements

      Here, in situ means "in place." Measurements were made of the actual footprint contours left by the individuals striding along the trackway.

    2. The eye-scale characteristics of the profiles exposed in the test-pits are reported here from the top downwards.

      For each test-pit, the authors analyzed the soil profile.This established how the different areas were related geologically and provided information about how to proceed with the analysis of hominin and wildlife print present.

    3. Small amounts of water were used during the excavation, in order to soften the sediment and darken its hue to better distinguish it from the surrounding tuff. The infill was finally removed by small dental tools, trying not to damage the very thin calcite film covering the original footprint surface (White and Suwa, 1987).

      The degree of fossilization of the footprints varied in different locations. In order to study the shape and structure of a footprint, soil had to be carefully removed from the depression.

    4. The preservation state of the tracks varies considerably along the trackway, depending on the depth of the Footprint Tuff from the surface.

      The trackway footprints are extremely fragile. Clearing and preserving them had to be done with the utmost care. Unlike fossil bones that are solid, the footprints are only empty spaces. Depending on the substrate, an incorrect move could cause the soil to crumble and the footprint to be destroyed. The author had to do a careful analysis of the trackway soils to be able to know how to best preserve and analyze each footprint.

    5. Following the code used for the Site G prints (Leakey, 1981), we refer to the new individual as S1 (footprint numbers S1-1–7 in L8, S1-1–4 in M9 and S1-1–2 in TP2). At the end of the September 2015 field season, we discovered one more track referable to a second individual (S2), in the SW corner of TP2

      Site G is the code used by Leakey to identify the trackway her group discovered. The three individuals leaving the footprints are referred to as G-1, G-2, and G-2. Keeping with this code system, the authors identified the new trackway area as Site S. The footprints are attributed to individuals S-1 leaving foot prints numbered S-1-1 through S-1-7 in location L8. Individual S-1 also traveled through locations M9 and TP2.

  5. Jul 2018
    1. His clear and wide ideas will for ever retain their significance as the foundation on which our modern conceptions of physics have been built.

      It is only by embracing Newton's ideas that physicists such as Einstein were able to consider exceptions and, in doing so, build on them.

      Although we don't often think about it, scientists improve on each others' ideas all the time and revise theories in light of new evidence generated by new technologies and methodologies.

    2. The interpretation seemed obvious, but classical mechanics forbade it.

      Einstein introduces the need for a concept of general relativity, which describes motion with respect to any two coordinate systems, not just inertial coordinate systems.

    3. What has nature to do with the coordinate systems that we propose and with their motions?

      Einstein's question points out that coordinate systems are not a product of nature, but a way of understanding nature. It is because of this that physicists are able to revise the tools and methodologies they use in light of new evidence.

  6. Jun 2018
    1. Fab only lightly marked the nucleus, suggesting very little KDM5B had been synthesized (Fig. 1D). Fab also colocalized and co-moved with many MCP-labeled mRNA in the cytoplasm

      After determining that neither the SM tag nor Fab would disrupt cellular processes, the authors wanted to determine how soon Fab would mark KDM5B. To do this, the authors repeated their first trial but imaged at 6 hours instead of 24.

    2. 24 hours after transfection, MCP marked mRNA in the cytoplasm and Fab marked KDM5B in the nucleus

      Preliminary testing was conducted twenty-four hours after the plasmid was transiently transferred to ensure that the methods of coupling the FLAG SM tag (that can be labeled with the anti-fluorescein antibody, anti-FLAG Fab) and the MS2 stem-loop (that can be identified with a MS2 coat protein) would not inhibit transcription, translation, or the movement of SM-KDM5B mRNAs throughout the cell.

      The results are highlighted in Fig. 1C with the SM-KDM5B protein in green and the MCP-labeled mRNAs in red.

    3. labeled with the far-red JF646 fluorophore

      JF646 produces a brighter fluorescence than other proteins.

    4. MS2 stem-loop repeat allows visualization with labeled MS2 coat protein

      MS2 is a protein from the coat of bacteriophages. It has a high affinity for "stem-loop" structures, which are hairpin-like shapes formed by DNA. The authors took advantage of this high affinity by using labeled MS2 to detect certain sequences.

    5. sensitivity and versatility of NCT make it a powerful new tool

      Scientists are always working to improve existing techniques to be more precise and versatile.

    1. indicates, for each point, the number of scenarios different from the simulated present. Yellow areas indicate when only RCP8.5 is different; orange areas denote when RCP4.5 and RCP8.5 are different; red areas indicate when RCP2.6, RCP4.5, and RCP8.5 are different; and blue circles mark areas in which the biome type at 4700 yr B.P. is different from the present biome type.

      The final map, 3H, uses a different color coding key, and shows the grid points that are different between the past, present, and future scenarios. Grid points that differ between the 4700 B.P. scenario and the present scenario are marked with blue circles. The filled dots all indicate differences in the biomes between the present and certain future scenarios. Yellow dots are grid points that are only different in the 4.0°C scenario, RCP8.5. The orange dots are different in both the RCP4.5 and RCP8.8 scenarios, and the red dots are different in the RCP2.6, RCP4.5, and RCP7.5 scenarios.

    2. simulated by the BIOME4 model for the present

      The third map, 3C, was generated by putting the current climate data into the BIOME4 model, running in "forward" mode. This does differ from the first map, based on pollen core samples. The biggest difference is that the first map shows warm mixed forest whereas the third map shows Temperate Deciduous Forest; this is likely caused by pine pollen being over-represented in the pollen core data.

    3. reconstructed from pollen for 4700 yr B.P.

      The second map, 3B, comes from putting the 4700 B.P. pollen core data (covering the years 4650–4750 B.P.) into the BIOME4 model. This time slice was picked to represent the past Holocene because it had the highest variation from the present distribution - this is the bar that extends above the 99th percentile line in Figure 2.

    4. Horizontal lines represent the 50th, 80th, 90th, and 99th percentiles of the Holocene values

      These lines, marked below in purple, provide information about the distribution of the black bars, which show the proportional biome change in past Holocene compared to the current period. Half of the values are at or below the lowest line, whereas all but 1% of the past values fall at or below the top line.

      Here's the figure with the lines more prominently marked:

    5. For the future, the forward application of the same model yields ecosystem distributions from climate projections

      To calculate future values, BIOME4 was run in its normal mode—using climate inputs to generate biome predictions. The climate inputs come from the models and time-series data found in the Coupled Model Intercomparison Project (CMIP5), which "promotes a standard set of model simulations." This way, research is done using the same underlying models, which allows for better comparisons across projects. Researchers update these models approximately every 5 years, incorporating new information.

    6. Given the confidence with which past ecosystems and climate change can be reconstructed from numerous pollen profiles, the development and validation of more reliable numerical models for the ecosystem-climate relationship have become possible. We apply such an approach to future climate conditions, using simulations from the Coupled Model Intercomparison Project phase 5 (CMIP5) for three different greenhouse gas (GHG) forcings

      The researchers use two main methods to generate the information about the Mediterranean Basin in this paper—for the past, the model uses data from pollen cores to generate information about the climate and ecosystems found over the past 10,000 years. For the future, the model uses different CO<sub>2</sub> emissions pathways to generate projections about the ecosystems and climate.

    7. The vertical bars represent the ±1 SDs provided by the reconstruction method

      The error bars for the Holocene and 1901–2009 data points are based on standard deviations. This is a different method from what the researchers used to calculate the future predicted values.

  7. May 2018
    1. Ants, particularly P. gracilis, may pose a significant threat to butterfly eggs and larvae, but butterflies have developed ways to cope with such predators

      Authors found that this species of ants are more efficient at being predators.

    2. Early instar caterpillars suffered the most damage when interacting with P. gracilis (n = 15 trials, 86.7% mortality); late instar caterpillars successfully foiled P. gracilis advances (n = 15 trials, 0% mortality).

      The ant species C. floridanus death rate is at 56.3% during this experiment. They have forced some or all instar caterpillars to leave their habitat.

    3. Exclusion experiments revealed that early instar caterpillars were vulnerable to both crawling and non-crawling predators.

      The instar caterpillars were vulnerable to be eaten, or killed by other predators.

    4. However, individual parameters (tree groups) were investigated to determine significance using non-host trees as the baseline group to compare the frequency of P. gracilis collected for each tree group.

      Each species was investigated to determine and compare each tree group.

    5. Percentage of ant species captured in pitfall traps at Elliott and Adams Keys (tree canopy, trunk, and base). Overall, 1418 total ants comprising 25 ant species were captured and identified from pitfall traps

      The experiment was conducted at Biscayne National Park and was split into three parts. The first part was using pitfall traps to collect ants at Biscayne Park near the host plants. Secondly, the host plants with known caterpillars were protected with various combinations of cages and tanglefoots. Concluding the author was keeping track of how long it take for certain species of ants to first find the caterpillars. Authors assumed that the ants would interact with caterpillars more frequently than other predators.

    6. Ants collected in pitfall traps at Elliott and Adams Keys: number of individuals of each species, and status

      All of the ants found in pitfall traps that were placed all around the canopy in the keys. 11 out of 25 species were found to be exotic and 735 out of 1418 of the total amount of ants were exotic.

    7. Pseudomyrmex gracilis and C. floridanus were more aggressive towards caterpillars in comparison to other ant species;

      After the experiment was finished, they have found that the Pseudomyrmex gracilis and C. floridanus interacted more aggressively toward the subject compared to another species.

    1. fear conditioning

      A learned association between a stimulus and negative outcomes, in this case represented by pictures associated with an electric shock.

      Learn more about fear conditioning (animal study): https://www.youtube.com/watch?v=ozkpK-nhz04

    2. appetitive learning

      Reinforcement that allows the association of a symbol with a positive outcome, in this case money. After trials, the objective was to teach participants to respond to the animal pictures associated with a reward.

    3. avoidance responses

      This task establishes an association between a neutral stimulus and a negative outcome with the hope that the repetitive task will teach participants to avoid the stimulus.

      In this study, the negative stimulus was an electric shock over four stages of learning.

    4. coefficient of determination

      The determination of the correlation coefficient.

      In this study, the authors used both Pearson (r) and Spearman (rho) correlations. Pearson correlation is used when there is an assumed normal distribution (distribution that shows symmetry around the mean), while Spearman is used when it is assumed the distribution will not be normal.

      Learn more about coefficient determination:


    5. trial and error

      The eight blocks (96 trials) where participants were tested and learned from the feedback that they received after their response to the presentation of the pictures.

    6. urine screen

      A urine screen is performed to rule out medical conditions and/or drug use. In this case, the authors used it to determine if the volunteers were appropriate for their study and to see if they tested positive for drug use for the different types of drugs involved.

    7. square-root transformation

      A test that makes it easier to analyze data that aren’t normally distributed. The authors performed this analysis to reduce skewness (effects due to asymmetry in a probability distribution) and stabilize variance from the questionnaires and demographic responses.

      To learn more about transformation: http://fmwww.bc.edu/repec/bocode/t/transint.html

    1. in human skin grafted to severe combined immunodeficiency disease mice

      In this experiment, skin from a human was grafted onto mice with depressed immune systems. This means a piece of human skin was transplanted onto mice whose immune systems could not protect them from diseases.

    2. The treatment of mouse NC cells carrying Kitl mutations with Edn3

      In this study, the cells from the neural crest of mice that carried the specific mutation Kitl were treated with endothelin 3.

    3. Treatment of quail NC cultures with Edn3

      The study described here consisted of treating quail (type of bird) neural crest cultures with Edn3. This study was in vitro, meaning the embryonic neural crest was removed and treated outside of the organism. This study showed the role that Edn3 plays in development as stated in the paper.

    1. We estimated the annual input of plastic to the ocean from waste generated by coastal populations worldwide.

      Exact numbers are not available for the mass of plastic waste entering the ocean, so the authors used a series of estimates in their calculations.

    1. the narrow strip of Mediterranean vegetation on the Libyan and Egyptian coast, which is below the spatial resolution of our climatic data

      For the reconstructions of the past Holocene ecosystems based on the pollen cores, the initial model outputs are at a lower resolution than the 0.5° by 0.5° resolution of the future projected biomes. The resolution of the reconstructions was a grid of 2° latitude and 4° longitude.

      To allow more direct comparisons between past and future, the researchers interpolated the reconstruction data to fill in a 0.5° by 0.5° grid. However, this may miss some features at scales smaller than the original gird.

    1. First, we test a range of plot sizes and shapes to determine the most accurate (least bias and greatest precision) and most efficient (accuracy per unit effort) method to estimate AGB and tree biodiversity. Second, we evaluate whether there exists a general trade-off among methods in the accuracy of information they provide for tree diversity vs. aboveground biomass estimates. Third, we analyze the extent to which different inventory methods may be appropriate among forests differing in structure and floristic composition.

      How the authors obtained AGB and tree biodiversity, evaluated trade off among tree diversity and aboveground biomass

    2. Several alternatives to 1 ha plots have been suggested, differing primarily in their sizes, shapes, and the minimum size of trees inventoried

      Table 1 shows the different types of plots used and how they differed in size, area covered, areas inventories, shape, and number of days used.

    1. (Nowak et al. 2004),

      Fundamental paper which shows how plants will uptake more CO2 when it is present in higher and higher quantities.

      At 600 parts CO2 per million parts air, plant carbon uptake is limited due to low levels of CO2 present in atmosphere.

    2. Fig. 6. Observed (solid) versus modeled (hollow) CO2 exchange rates (NEE, Reco and GEE) at TS (A) and SRS (B). Atmospheric convention is used here and positive numbers indicate a loss of C to the atmosphere.

      The solid bars show the actual data gathered. They measure the different rates of carbon exchange.

    3. Fig. 7. The effect of elevated atmospheric CO2 concentration (550 ppm, 850 ppm and 950 ppm) on cumulative (A) NEE, (B) Reco and (G) GEE, at TS. The influence of rising temperatures (1°C, 2.7°C and 4.2°C) on cumulative (B) NEE, (E) Reco and (H) GEE and shifts in seasonal and annual precipitation patterns (−2%, +7% and +14%) on cumulative (C) NEE, (F) Reco and (I) GEE. Atmospheric convention is used here and positive numbers indicate a loss of C to the atmosphere. All simulations were compared to current weather and atmospheric CO2concentration (red line).

      Figures displays projections for the effect of three climate-induced environmental changes on ecosystem processes (NEE, Reco, GEE).

      As CO2 levels and temperature increase, the ecosystem will uptake more and more CO2, and will retain more carbon.

    4. Fig. 5. Observed versus modeled soil temperature at (A) TS and (B) SRS, and soil volumetric water content (VWC) at (C) TS and (D) SRS.

      The figures show what happened to the actual data vs the data from the model. They are very close and show that the model is reliable, in other words, the data is precise.

    5. ran the model for 100 years (1) under climate change projections,

      Remember, this is a simulation! It was done on the DAYCENT simulation model.

    6. Fig. 4. Climate change scalars for (A) elevated atmospheric CO2 concentration, (B) air temperature, and (C) precipitation. The distributions for (D) minimum temperature, (E) maximum temperature and (F) precipitation were modified to match projected seasonal change.

      These charts show the different magnitudes, at differing scales of CO2, temperature, and rainfall, respectively, projected out to the year 2076.

  8. Apr 2018
    1. We computed anomaly fields using all data and compared these results with computed fields on the basis of data that did not include values exceeding 3 SDs.

      By comparing a modeled map using every data point available and comparing that map to one that excludes outliers exceeding 3 standard deviations, the authors realized they were actually excluding some ocean temperature features that occurred. Thus by changing the data cut-off to 6 standard deviations it was possible to more accurately model ocean conditions.

    2. we used a 6-SD check to flag data as not being usable in this study as compared to the 3-SD check used earlier.

      By broadening the cutoff criteria the authors of this study were able to distinguish real-life changes in ocean temperatures. Their previous data cutoff of 3 standard deviations (which are a measure of variance around a mean), was too strict and deleting real data points.

      Broadening the pool of what was considered "good" or "realistic" data to 6 standard deviations was necessary in order to properly document temperatures that actually occurred in the oceans.

    1. biological, rather than physical, processes drove reach-scale rates (table S5)

      Biological factors that drive the decomposition of carbon include the consumption and respiration from microbes, vertebrates, and in-vertebrates.

      Physical factors include temperature, flow of water, and sediment.

      The alignment of the reach-litterbag scale rates show that the bags represent the actual rates, and that the bags were not simply filtered by increased water flow or torn apart because of temperature.

    2. litterbag

      The litterbag technique can be used to measure the density lost due to composition by microbes and fungi.

      A known type and amount of litter is stuffed in the bag and is left for a certain amount of time in the stream. The bag is then later taken out and weighed while wet. The bag is allowed to dry and then the dry weight is taken.

    3. pools of benthic fine and coarse POC declined

      Benthic fine and coarse particulate organic carbon can be used as a measurement when observing loss of carbon caused by detrivores.

    4. consideration of differences among litter species and potential divergence in rates due to the degree of biological processing

      In this experiment, leaf litter from different types of trees were used. The results are in Figure 3.

      The structure and chemistry of leaves are different and this determines how fast they may decompose.

      For example, tulip poplar decomposes the quickest and has a low carbon to nitrogen ratio. Comparatively, oak decomposes slowly and has a higher carbon to nitrogen ratio.

    5. Our results generally support the use of litterbags to measure larger-scale C dynamics

      It is often necessary to use a simpler model version of a larger system because it is easier to observe.

      It is difficult to observe the effects on a whole stream, but the litter bags are observed and represent the carbon loss in streams.

    6. Higher C loss rates at the litterbag scale than the reach scale are expected, because litterbags track distinct parcels of C, whereas reaches receive additional C inputs over time.

      Litter in the litter bags have a higher concentration of carbon than the stream, which has carbon more spread out. The litter will have higher loss of carbon but generally represents reach-scales.

    7. We conducted two manipulative experiments at large spatial and temporal scales and focused our measurements on forest-derived leaf litter, because it is the most biologically active pool of terrestrial C in forest streams and is renewed annually (7).

      Two experiments were done separately to test the effects of nitrogen to phosphorus ratios on streams.

      Pre-treatment of streams include recording the levels of nutrients and typical conditions throughout a year.

      The first experiment had two watersheds with one being the control and the other having an addition of nitrogen and phosphorus to match a ratio that was decided by the scientists.

      The second experiment was done on five streams with different combinations of the nitrogen and phosphorus ratio.

    8. not been previously assessed in response to human-influenced stressors

      Although this experiment focuses on microbial and fungal effects on streams, humans sometimes also have an impact.

      These effects may include the leaking of septic tanks into bodies of water that increase human waste and phosphorus levels. Fishing and polluting also affect stream ecosystems.

    9. We measured the response of terrestrial C loss rates in whole 70- to 150-m stream reaches (tables S1 and S2).

      The experiments were conducted on streams in the Appalachian Mountains of North Carolina.

      Because of the elevation, there are little to no fish influencing the data. Mainly microbes and fungi influence the nutrient levels of the streams.