1,078 Matching Annotations
  1. Aug 2018
    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. 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.

    2. limiting dilutions

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

    1. 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. 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.

  2. 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.

  3. 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.

  4. 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:

      http://blog.uwgb.edu/bansalg/statistics-data-analytics/linear-regression/what-does-coefficient-of-determination-explain-in-terms-of-variation/

    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.

    2. We calculate that 275 million metric tons (MT) of plastic waste was generated in 192 coastal countries in 2010, with 4.8 to 12.7 million MT entering the ocean.

      The range of 4.8 to 12.7 million Metric Tons of waste comes from uncertainty in the total amount of waste generated, the amount of that waste that is plastic, and the amount of mismanaged waste that enters the ocean.

    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.

  5. 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.

    10. flow-proportional nitrogen (N) and phosphorus (P)

      An irrigation line was used along the 70-150 meter streams to pump in liquid nutrients. The nutrients were pumped proportional to the flow of the water.

    1. using primers specific to the cDNA encoding each of these proteins, we performed RT-PCR on 7 tissues of the adult host, including those that are and those that are not dedicated to light modulation

      Using reverse transcription polymerase chain reaction (RT-PCR), the authors amplified sequences for the genes coding for opsin, arrestin, and rhodopsin kinase (RK). Transcripts were extracted from the tissues of the light organ, eye, gills, mantle, tentacle, and arm.

    2. To determine whether they also co-localize in light-organ tissues, we applied 2 independent analyses of these 3 components: localization of message by PCR and of protein by immunocytochemistry.

      The authors used two methods to discover if opsin, arrestin, and rhodopsin kinase also occur in the light organ.

      In the first method (message expression), the authors looked for the molecular signals of these proteins in tissues from the light organ, eye, gills, mantle, tentacle, and arm.

      In the second method (immunocytochemistry), the authors looked for evidence of protein production in the tissues of the light organ using confocal microscopy.

    1. A GWAS analysis with linear mixed models, controlling for age, sex, and genetic relatedness (9), identified four regions with multiple significant associations

      The authors do a Genome-Wide Association Study (GWAS) analysis, which is a study of whether any particular loci in the genome are associated with a particular phenotype (in this case, skin pigmentation levels).

      GWAS analyses look at variants (alleles) across the whole genome, rather than focusing only on regions that are thought to be associated with a phenotype. This way, new regions of the genome can be discovered as being involved in the genetic architecture of a trait.

      The authors control for age, sex, and genetic relatedness, because these are variables that could confound (confuse) the results by affecting the phenotype of interest, in this case skin pigmentation. To avoid confusion, the authors take these variables into account when they look for associations between SNPs and skin pigmentation.

    2. using the Illumina Infinium Omni5 Genotyping array

      The authors used the Illumina Infinium Omni5 Genotyping array, a specific type of SNP array.

      The array has a set of oligos (short stretches of single-stranded DNA) on a chip that is complementary to the DNA right next to the SNP of interest. DNA taken from the person being genotyped gets broken up into small pieces and put on the chip, where it will stick to the complementary oligo.

      Next, nucleotides (A, C, G, T) labeled with different color dyes get put on the chip and will bind to the DNA that is attached to the oligos. A scanner reads what color is found at each spot on the chip, which tells the investigator what nucleotide the person has at that locus—which is also known as what allele the person has.

    3. To estimate the proportion of pigmentation variance explained by the top eight candidate SNPs at SLC24A5, MFSD12, DDB1/TMEM138 and OCA2/HERC2, we used a linear mixed model with two genetic random effect terms, one based on the genome-wide kinship matrix, and the other based on the kinship matrix derived from the set of significant variants.

      The authors estimate how much pigmentation variation can be explained by their top predicted variants.

      A mixed model has both fixed effects and random effects. In this case the authors looked at random effect terms based on looking at the whole genome as well as looking at only variants that were identified as significant. The mixed model gives an estimation of how much each of the variants contributes to skin pigmentation in their study.

    4. We do not detect evidence for positive selection at MFSD12 using Tajima’s D

      Directional selection occurs when one trait is favored over others, leading to a shift in allele frequency toward the allele associated with the favored trait. This also leads to an excess of low-frequency alleles, and a negative value of Tajima's D.

      Balancing selection is a process by which multiple alleles are maintained in the population, for example when heterozygotes are at an advantage (which keeps both of their alleles at a relatively high frequency). This leads to a lack of rare alleles, and a positive Tajima's D.

      Tajima's D statistic of approximately zero indicates that the population is evolving with little or no selection.

    5. luciferase expression assays

      The luciferase expression assay is also known as the luciferase reporter assay, and is a method to detect how much expression can be driven by a particular enhancer.

      The assay is described in detail here: https://bitesizebio.com/10774/the-luciferase-reporter-assay-how-it-works/. Briefly, the authors put DNA that corresponds to the predicted, potentially causal regulatory regions into a vector that also has the luciferase gene, and then put this vector into cells. In this experiment, the authors use WM88 melanoma cells, which are a cell line grown from a patient who had melanoma.

      The luciferase gene will only get expressed (and the luciferase protein made) if the regulatory region ahead of it gets expressed in these cells. To detect whether there is luciferase in the cells, the authors break open the cells to release their proteins, add additional reagents that interact with luciferase (if it is present), and use a detector to see how much light gets emitted. More light emitted means more luciferase was present, which means that there was more expression being driven by the regulatory region.

    6. CRISPR/Cas9 was used to generate a Mfsd12 null allele in a wild-type mouse background

      The authors extend their previous experiments in mouse cells to make mice that are missing Mfsd12 in all of their cells.

      They use CRISPR/Cas9 to make a null allele of the gene, which is a nonfunctional copy of a gene due to genetic mutation. They do this in mice that are otherwise wild-type, or have typical genetic and phenotypic characteristics.

    7. We silenced expression of the mouse ortholog of MFSD12 (Mfsd12) using small hairpin RNAs (shRNAs) in immortalized melan-Ink4a mouse melanocytes derived from C57BL/6J-Ink4a−/−mice

      In this experiment, the authors use a genetic tool called shRNA (small hairpin RNAs) to turn off expression of the gene in mice that is homologous to one of the genes they identified in the previous section as being associated with skin pigmentation. An ortholog is a homologous gene from a different species.

      An shRNA is an artificial RNA molecule that is shaped like a hairpin—a strand with a tight turn as it folds back on itself. shRNAs are processed by mammalian cells into small interfering RNAs (siRNAs), which suppress gene expression of a particular gene that it is complementary to—in this case, the authors use shRNA that will silence the gene Mfsd12.

    8. using local imputation of high coverage sequencing data from a subset of 135 individuals and data from the Thousand Genomes Project (TGP)

      To perform fine-mapping, the authors need more data than they have from their SNP arrays. To get more precise data, they performed full-coverage sequencing of a small subset of people from their original populations, sequencing only the regions of the genome they identified with the GWAS. Sequencing allows researchers to discover the order of nucleotides across a given region of the genome.

      The authors also used previously published work from a data set called the Thousand Genomes Project, which looked at the sequences of the whole genome for over 1000 people.

      Local imputation uses patterns of association between SNPs in the data sets with higher coverage, as well as known patterns of linkage disequilibrium (nonrandom association of alleles in a particular population) between SNPs, to predict what alleles a person has at those regions. This means that it is possible to confidently predict the alleles for each of the >1500 people in the current study, even for loci with SNPs that aren't included in the original genotyping array.

    9. We then performed fine-mapping

      The authors use fine-mapping to narrow down the larger regions identified in their GWAS results, allowing them to identify specific SNPs that are likely to be causal, so they can study these SNPs further. Fine-mapping uses a variety of methods to pinpoint the precise location of a gene or regulatory region that is associated with variation in a phenotype.

    10. We genotyped 1,570 African individuals with quantified pigmentation levels

      The authors determined the genotypes (the set of alleles in an organism's genome) of the people for whom they had quantified skin pigmentation.

      They use a SNP array for genotyping, which is a tool that allows researchers to study small differences between genomes. A SNP (pronounced "snip") is a single nucleotide polymorphism, or a change to a single base (nucleotide) at a particular DNA locus.

    11. To identify genes affecting skin pigmentation in Africa, we used a DSM II ColorMeter to quantify light reflectance from the inner arm as a proxy for melanin levels in 2,092 ethnically and genetically diverse Africans living in Ethiopia, Tanzania, and Botswana

      The authors wanted to study a wide variety of Africans, and used a handheld battery operated tool called the DSM II ColorMeter to get a quantification of how much pigment was in each person's skin.

      The DSM II ColorMeter works by shining light at skin and detecting how much light reflects back, and at what wavelengths. They used a particular wavelength to determine how much melanin was present in the skin.

    12. Exome sequencing of an archived gr/gr DNA sample, subsequently confirmed by Sanger sequencing in an independent colony,

      The authors look at the exome (all of the protein coding parts of the genome) by gene sequencing, to see what differences exist between gr/gr mice that are gray and wild-type mice with the usual agouti color.

      They sequence a saved sample they had, and confirm that they can find the same mutation in a gr/gr mouse from a different colony, to be sure that the mutation they found in Mfsd12 wasn't a random mutation that arose in their colony.

      For confirmation of the mutation, they use Sanger sequencing of the specific region around the mutation. Sanger sequencing is a method of sequencing shorter pieces of DNA, instead of the whole exome.

    13. We targeted transmembrane domain 2 (TMD2) in the highly conserved zebrafish ortholog of mfsd12a with CRISPR/Cas9

      Here, the authors investigate the effect of losing the zebrafish ortholog of MFSD12 by using a technique called CRISPR/Cas9.

      Cas9 is an enzyme that cuts DNA, and is guided to particular DNA sequences by a guide RNA (gRNA). Researchers can use different gRNAs to make cuts to particular DNA regions in order to disrupt expression of genes.

      The authors use CRISPR/Cas9 with gRNAs targeting the part of the zebrafish ortholog mfsd12a that spans the cell membrane so that the protein will no longer function properly. They then examined the zebrafish for what impact the knockdown of the gene has had, if any.

    14. We assessed the localization of human MFSD12 isoform c (RefSeq NM_174983.4) tagged at the C terminus with the HA epitope (MFSD12-HA). By immunofluorescence microscopy,

      The authors look for what part of the cell the MFSD12 protein they're interested in is located. To find the protein, they first "tag" it at the C terminus with an HA epitope.

      The C terminus is the end of the protein. Tags are often put on the end of the protein because in some cases they are less likely to interfere with expression or function if they're at the end.

      HA stands for human influenza hemagglutinin, which is a protein on the surface of human cells. A particular part of it, the HA epitope, can be used to tag proteins in cells because it's small, so it's unlikely to interfere with protein function or location.

      An epitope is a part of a protein that is recognized by an antibody, which can be bound to fluorescent molecules so it can be visualized by a technique called immunofluorescence microscopy.

    15. Analyses of gene expression using RNA-sequencing data from 106 primary melanocyte cultures

      RNA-sequencing, or RNA-seq for short, is a method to quantify the amount of specific sequences of RNA in a sample.

      To perform RNA-seq, the authors extracted the RNA from primary melanocyte cultures (melanocytes growing in a dish after being taken from human skin). Next they made complementary DNA, or cDNA, that corresponds to each RNA fragment from the cells. The more RNA there is for a particular gene, the more cDNA there will be.

      Then, the authors sequence the cDNA, and determine how many times they detect each molecule of cDNA. The number of times they see each cDNA (the number of reads) corresponds to how much RNA for that gene was in the sample, and the authors compare the amount of RNA for different genes in different groups of people from whom they collected melanocytes.

    16. We ranked potential causal variants within each locus using CAVIAR

      Next, the authors ranked the known and predicted variants using a method called CAVIAR (CAusal Variants Identification in Associated Regions). CAVIAR is a statistical method used to determine the likely DNA variants causing differences in a trait. This method is better than previous methods used to determine causal variants, because it allows for the idea that there may be multiple causal variants in a region. This is likely to be the case for many traits, possibly including skin pigmentation.

    17. Association tests using a permutation approach indicated that, of the 35 protein-coding genes with a transcription start site within 1Mb of rs7948623, expression of DDB1 is most strongly associated with a SNP in an intron of DDB1, rs7120594, at marginal statistical significance after correction for ancestry and multiple testing (Padj = 0.06

      The authors use a statistical approach to determine which gene within 1 Mb (1 million basepairs) of a variant of interest, rs7948623, has expression levels most strongly associated with the variant.

      1 Mb is used as the distance to look for genes within because most regulatory regions act on genes that start within 1 Mb.

    18. Allele-specific expression (ASE) analysis

      The authors use allele-specific expression (ASE) analysis, a method to determine how much expression occurs from each allele at a particular locus in an individual.

      ASE looks at the amount of RNA that gets transcribed from each copy of a gene (the copy inherited from the mother and the copy inherited from the father).

      The authors use this analysis to determine whether people who are heterozygous for the allele of interest (people who have different variants in their two copies of the allele) have different levels of expression from the two copies of the gene, which would indicate that this particular variant could affect the expression level directly.

    1. The resulting database contained 4140 individuals and over 2500 herbarium vouchers.

      These numbers refer to the total sample size in the experiment.

    2. We therefore stratified our study design such that each habitat was sampled once within each of the six combinations of two topographic positions (hilltop and slope) and three taxonomic blocks, for a total of 18 samples.

      In statistics, stratification allows a researcher to divide entire populations in to subgroups.

      The authors divide the data set into hilltop and slope, and 3 differently identified groups.

    3. Paracou experimental site (5°18′N, 52°55′W), a lowland tropical rain forest near Sinnamary, French Guiana (Gourlet-Fleury et al. 2004)

      The Paracou experimental site is located near the Paracou Field Station in French Guiana.

      Read more about the field station at: https://paracou.cirad.fr/station/overview

    4. (i) How does logging disturbance affect tropical tree community structure, in terms of diversity, evenness and composition? (ii) How do the taxonomic and functional responses of tree communities to logging differ? (iii) What guidelines can we give to timber harvesters to improve biodiversity conservation in selectively logged forests?

      The objective of the study is to address whether logging affects the forest species; is there a difference between what types of species and what each species contributes to the community when logged; and how this information can be used to improve how the forest is logged.

    1. We performed a x2 test on the proportions of individuals with the A or B chloroplast haplotype per site (table 1) to verify that our type designations, which were classified according to the results of the Williams studies (Williams et al. 2005, 2007) and following their site locality sampling, were correct.

      Geiger et al. utilized the Pearson's chi-squared test (χ2), a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance, to conclude the accuracy of the type designations given.

    1. homozygosity mapping provides an important approach to dissect this heterogeneity

      Families that share a larger part of their genome are more likely to be homozygous for certain genes. Studying these families can make it easier to identify and isolate the effects of certain genes.

    2. we sequenced NHE9 in other patients with autism and epilepsy

      The authors compared the genotypes of patients who have both autism and epilepsy to determine if these patients had similar mutations to NHE9 and surrounding genes.

    3. binomial test

      A statistical measure of the probability that experimental results are due to chance. It is calculated by looking at the difference between the observed (experimental) results and the expected results (i.e. the results that would be expected if only chance were involved).

      The results of the test are reported as a P value, which is a probability. In general, a P of less than 0.05 means that the results of an experiment are statistically significant (and not a result of chance).

    4. neuronal membrane depolarization by elevated KCl

      This technique is used to study enhanced neuronal activity that may result from changes in gene expression.

      Elevated KCl (an increase in potassium outside the cell) results in a sustained depolarized state in three ways:

      1. Normally, potassium ions flow out of the cell, resulting in hyperpolarization. Elevated KCl outside the cell disrupts this gradient and slows the outward flow of potassium. At the same time potassium ions are flowing out of the cell, sodium ions are flowing into the cell. The flow of sodium ions into the cell does not slow down.
      2. The increase in the cell's membrane potential (the difference in charge on one side of the membrane versus the other) causes sodium channels to open. This allows more sodium ions into the cell and results in further depolarization.
      3. Partial activation of sodium ion channels prevents the neuron from triggering a full action potential.
    5. hippocampal neurons

      The hippocampus is a major component of the human brain and those of other vertebrates. It is part of the limbic system and has a crucial role in the consolidation of information, including short- and long-term memory and spatial navigation.

      Rat and mouse hippocampal neurons are widely used in neurobiological studies. By isolating and growing individual neurons, researchers are able to analyze properties related to cellular trafficking, cellular structure, and individual protein localization.

    6. microarray screens

      An array is a grid of DNA samples that is used to identify and map genes.

      In this experimental setup, the cDNA derived from the mRNA of known genes is immobilized. The expression pattern is then compared to the expression pattern of a gene responsible for a disease.

      https://www.youtube.com/watch?v=pWk_zBpKt_w

    7. independently identified

      Because the three genes were identified by independent screens, it increases the reliability of the authors' conclusions.

    8. screens

      A technique used to detect a mutation or abnormality and provide important information on its function. In this case, the authors screened for genes associated with autism.

      There are many different types of screens depending on a researcher's needs, and they are widely used in scientific research.

    9. two sensitive methods for detecting them

      Both the single nucleotide polymorphism array and BAC Comparative Genomic Hybridization arrays are used to detect the number of copies of a specific locus in a subject's DNA. This allows us to find out whether the locus is present on one or both chromosomes of a subject.

      To do this, control DNA and experimental DNA are labeled with different fluorescent molecules of different colors (in the picture below, the control is red and the experimental is green).

      If the experimental DNA and the control DNA are identical (i.e. the target variant is present on both chromosomes), the sample will appear orange. If the control DNA has deletions, the solution will appear red. It it has insertions, it will appear green.

    10. we reasoned that a prominent involvement of autosomal recessive genes in autism would be signaled by differences in the male-to-female (M/F) ratio of affected children in consanguineous (related) versus nonconsanguineous marriages (although recessive causes of autism may still retain some gender-specific difference in penetrance)

      Based on studies of other neurological birth defects, the authors hypothesized that differences between consanguineous and non-consanguineous families in the M/F ratio of affected children would suggest that autosomal recessive genes play a large part in incidence.

    11. The Homozygosity Mapping Collaborative for Autism (HMCA) (21) has recruited 104 families (79 simplex and 25 multiplex) from the Arabic Middle East, Turkey, and Pakistan (table S1 and fig. S1), of which 88 pedigrees (69 simplex and 19 multiplex) have cousin marriages (i.e., parental consanguinity).

      The authors selected families with one or more individuals affected by autism.

      Among those families, about half have cousin marriages, which are referred to as consanguineous pedigrees.

      A simplex family is one in which there is only one person with autism. A multiplex family has multiple people with autism.

    12. Reliability between clinician assessments was high

      In order to ensure that the results obtained from this study are reliable, the authors ensured that all of the teams were using the same clinical protocol in the same way.

    13. homozygosity mapping

      To improve the quality of results, the authors chose consanguineous families (families that share a very recent ancestor), because of their high risk of having autistic children.

      Researchers at Johns Hopkins University reasoned that families with multiple females with autism must have special genetic variants for autism that can be more easily identified than when comparing other groups.

      For more information, see the Hub at Johns Hopkins.

    14. de novo

      In this article, the authors focus on patients who inherited long deletions or additions from their parents.

      However, this is not the only approach to understanding the genetics of autism. Some look directly at the appearance of de novo single nucleotide polymorphisms (mutations to a single base).

    1. Table 4. Proboscis measurements (mean and standard error) for flower visitors to A. berteroi. Proboscis width and length with the same letters are not significantly different with Tukey comparisons.

      In this table, the author describes how the width and length of the insects proboscis play a role in pollination.

      This chart was made to determine the differences in proboscis.

    2. We then used monofilament nylon fishing line of four different diameters (4 lb, 0.20 mm diameter; 6 lb, 0.23 mm diameter; 8 lb, 0.28 mm diameter and 25 lb, 0.53 mm diameter) inserted into single flowers to simulate flower visits.

      Fishing line was used to simulate repeatedly the collecting of nectar. This could then be used to see which line (stand-in for proboscis) collected the most pollen, and if it correlated with thickness.

    3. Table 1. Flower visitors of A. berteroi observed in the study sites, and presence/absence of pollen on the proboscis.

      In this table, the author describes various interactions the visitor encountered with plants.

    4. Angadenia berteroi bears large, showy, yellow, tubular flowers. They have the typical complex floral arrangement

      This species that will be studied shows complex floral arrangements.

    1. The Homozygosity Mapping Collaborative for Autism (HMCA) (21) has recruited 104 families (79 simplex and 25 multiplex) from the Arabic Middle East, Turkey, and Pakistan (table S1 and fig. S1), of which 88 pedigrees (69 simplex and 19 multiplex) have cousin marriages (i.e., parental consanguinity)

      The authors selected families with one or more individuals affected by autism.

      Among those families, about half have cousin marriages, which are referred to as consanguineous pedigrees.

      A simplex family is one in which there is only one person with autism. A multiplex family has multiple people with autism.

    1. we tested six sgRNAs targeting enhanced green fluorescent protein (EGFP)

      The authors used sgRNAs to target enhanced green fluorescent protein (EGFP) cells.

      The EGFP cells allow the cell to express fluorescent color. sgRNAs target these cells and destroy them. In cells where the sgRNA removed the EGFP gene, there is no fluorescent color.

  6. Mar 2018
    1. The two known populations of this species were included in our study, and we sampled 18–30 individuals per population (Table 1). All of the individuals (18) from the Dominican Republic population were included in the analyses. Leaf material was fast dried in the field using Drierite (W. A. Hammond Drierite Co. Ltd., Xenia, Ohio, USA), subsequently this material was used for DNA isolations using the DNeasy Mini Plant kit (QIAGEN, Venlo Limburg, The Netherlands).

      For the first part of their experiment, the authors gathered samples of 18-30 plants from two known populations of C. jimenezii. They then dried the leaf material of the Dominican Republic sample, using Drierite (an all-purpose drying agent most commonly used in storage spaces as it dries the air in the atmosphere). The DNA from these populations was isolated from the dried material using the DNeasy Mini Plant kit; the procedure was similar to the one described in the following video https://www.youtube.com/watch?v=rY-Qbrw9KMc

    2. The high levels of genetic differentiation detected within C. jimenezii raise questions whether these two populations can be treated as different varieties/subspecies

      In this study, the researchers found some questions as to whether the two species of C. jimenezii could be grouped with the same taxa.

      The authors used molecular tools which have found a large amount of differentiation. This was not what the authors hoped and in turn it was concluded that they were to remain in separate taxa.

    3. Fig 2. Principal coordinate and STRUCTURE (K = 2) analyses of DNA microsatellite data for the two known populations of Coccothrinax jimenezii. The scatter diagram shows PCO values along the first two coordinates with their respective percentages of variance. Inset in upper right corner shows results yielded by STRUCTURE. Color and box sizes indicate the cluster type of each individual and the number of plants sampled per site. The vertical lines indicate the probability that each individual belongs to an inferred cluster. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

      This figure describes the 2 populations of Coccothrinax Jimenezii and it shows the results that are yielded by the processing computer software called STRUCTURE that is used for analysis of microsattelites.

    4. The Bayesian clustering program STRUCTURE v.2.3.4

      In this experiment the authors used The Bayesian clustering program STRUCTURE v.2.3.4 as a way to estimate the underlying genetic structure among populations.

    5. A Monte Carlo Markov chain method was applied with 100,000 iterations, a burn-in of 10,000 and the significance level set at P < 0.001. Principal coordinate analysis (PCO) among all the individuals included in our study was computed with GenAlEx based on the algorithm developed by Orloci (1978) after conversion of the individual-by-individual genetic distance matrix, as defined by Smouse and Peakall (1999), to a covariance matrix and data standardization.

      This experiment was conducted with the purpose of producing a scatter diagram that summarized the original multidimensional data set and revealed the presence of groups. They also used GenAIEx to analyze the principal coordinate among all individuals in the study. This experiment also allowed them to detect the genetic structure of populations projected in a continuous space.

    6. Tests for Hardy–Weinberg Equilibrium (HWE) and the U test (Rousset and Raymond, 1995) for heterozygote excess or deficiency were run with GenePop v. 4.2 (Raymond and Rousset, 1995, 2008) using 10,000 Monte Carlo Markov chain iterations (Guo and Thompson, 1992).

      This experiment was done with GenePop v. 4.2 using 10,000 Monte Carlo Markov chain interactions. Throughout this experiment the researcher tested for Hardy–Weinberg Equilibrium, which says that the genotype frequencies in a population tend to remain constant throughout generations if no evolutionary influences are present. They also used GenePop v.4.2. to do a U test and to look for heterozygote excess or deficiency.

    7. The program Micro-Checker v. 2.2.3 (Van Oosterhout et al., 2004) was used to evaluate the presence of null alleles and allelic dropouts, employing 3000 randomizations. Descriptive statistics

      This experiment was carried out with the purpose of identifying null alleles which are those that express the same trait whether they are homozygous or heterozygous, and allelic dropouts which are copies of a locus that are not amplified by the primer. This part of the experiment was completed through the use of Micro-Checker v. 2.2.3.

    8. PERL script program PAL_FINDER_v0.02.03 to identify potential 4 bp microsatellite repeat elements among the reads (Castoe et al., 2012; Slashdot Media, San Jose, California, USA). Among these SSR loci we selected six that cross-amplified with samples of C. jimenezii and that were polymorphic with different samples of this species.

      This experiment was conducted using the PERL script program PAL_FINDER v0.02.03 and the 100bp Illumina sequences from the previous experiment. This experiment resulted in the identification of potential 4bp microsatellite repeat elements. Of these microsatellite repeats, only six were selected which had different phenotypes than C.jimenezii.

    1. he peptide was a mixture of phosphorylated and unphosphorylated serine in position #357, so that antibodies might be generated to both the activated and non-activated forms of the molecule

      Proteins in the phototransduction cascade can be in either an "on" (activated) or "off" (inactivated) state. The authors created antibodies to bind to either state to ensure that they would be able to detect the protein regardless of what form it was in.

    2. Using the cDNA sequences, we first used NCBI BLASTX analyses to identify the closest matches to the derived amino acid sequences

      Basic Local Alignment Search Tool (BLASTX) is a search algorithm developed by the National Center for Biotechnology Information (NCBI)

      For more information about BLASTX, go to: NCBI: BLAST

    3. RACE primers (Table S2) were constructed from the EST sequences identified as having similarity to a transcript of interest.

      Primers are short, single-stranded sequences of DNA used to amplify RNA sequences of interest using polymerase chain reaction (PCR). Amplifying a sequence means exponentially increasing the number of copies.

    4. Retinal tissues of E. scolopes were used as a positive control for the cross reactivity of these antibodies in regions known to produce these proteins

      A positive control is used to verify that your experiment is working the way you want.

      In this study, the authors tested their immunocytochemical test by using it on tissues that they already knew contained the three proteins.

    5. we used antibodies raised against cephalopod opsin, arrestin, and rhodopsin kinase (25, 26) to localize the protein in host tissues.

      The authors created antibodies against the three proteins so they could detect them in specific tissues using immunocytochemistry.

      They incubated squid eyes and light organs with the antibodies and stained with secondary antibodies that were tagged with a fluorescent molecule.

      The stained eyes and light-organs were mounted on microscope slides and viewed with a confocal microscope. A fluorescent signal identified the presence of the proteins.

    6. we performed electroretinograms (ERGs) (21, 22), which extracellularly record photoreceptor membrane potential, of both eyes and light organs of juvenile E. scolopes.

      Electroretinograms (ERGs) measure the change in voltage across cells when exposed to a light stimulus. The authors compared the response of photoreceptive tissues in both the eyes and light organs of juvenile Euprymna.

      Electrical signals of rhabdomeric photoreceptors typically respond by depolarizing, that is, becoming more negative.

      Electrical signals of ciliary photoreceptors typically respond by hyperpolarizing, that is becoming less negative.

    7. We identified 11 such cDNAs (Fig. 2; Table S1), including the gene encoding the visual pigment opsin itself, as well as molecules involved in subsequent activation and deactivation of the cascades.

      The authors looked for evidence that the light organ produces proteins similar to those found in the eye that could generate a phototransduction cascade.

      The authors identified 11 sequences that code for proteins similar to those responsible for phototransduction in the eye.

    8. tissues that modulate the intensity and direction of symbiont light emission

      The authors determined that light organs have tissues that are similar to the eye: a lens that is able to focus light, reflective tissue that is able to reflect light, and an iris that can control the amount of light entering or leaving the light organ.

    1. Much greater variation is associated with differences among species within a taxonomic group than between taxonomic groups (Fig. 2 and table S2)

      The latitudinal range shift variation among species within four typical taxonomic groups was evaluated in Figure 2. This was compared to the overall variation among all the latitude taxonomic groups in the meta-analysis.

      The authors also calculated the percentage of species in each taxonomic group which moved in a direction opposite to that expected based on temperature change.

    2. nearly as many studies of observed latitudinal changes fall above as below the observed = expected line in Fig. 1A

      If species are tracking climate change, then the observed range shifts should equal the expected range shifts. The authors did a chi-square (goodness-of-fit) statistical test to measure how well this 1:1 relationship describes the data.

    3. To estimate the expected shifts, we calculated the distances in latitude (kilometers) and elevation (meters) that species in a given region would have been required to move to track temperature changes and thus to experience the same average temperature at the end of the recording period as encountered at the start (18) (table S1)

      This correlation test examined the relationship between observed range shifts and expected range shifts (based on temperature change patterns).

      For an explanation of how the expected range shifts were calculated, please see the notes for Table S1.

    4. mean latitudinal shift versus average temperature increase

      The authors conducted a statistical correlation test to determine if there is a linear correlation (relationship) between range shifts and temperature changes.

    5. whereas the rates of range shift that we found were significantly greater [N = 22 species groups × regions, one-sample ttest versus 6.1 km decade−1, t = 3.99, P = 0.0007 for latitude; N = 30 groups × regions, one-sample t test versus 6.1 m decade−1, t = 3.49, P = 0.002 for elevation

      The authors did a one-sample t test to compare their range shift rates to those in Reference 14.

    6. We considered N = 23 taxonomic group × geographic region combinations for latitude, incorporating 764 individual species responses, and N = 31 taxonomic group × region combinations for elevation, representing 1367 species responses. For the purpose of analysis, the mean shift across all species of a given taxonomic group, in a given region, was taken to represent a single value (for example, plants in Switzerland or birds in New York State; table S1)

      The authors conducted the current study to determine if there is a positive trend between observed range shifts and climate warming. They performed a meta-analysis of a very large set of data for diverse species and geographic regions.

      The data used in the meta-analysis are summarized in Tables S1a and S1b. Rates of range shift and statistical analyses were calculated as described in the notes for Table S1.

    7. evidence has previously fallen short of demonstrating a direct link between temperature change and range shifts

      One goal of this meta-analysis was to demonstrate this direct link between temperature change and range shifts.

    1. difference plots clearly show the distinct profiles obtained for the mixtures

      Fluorescence is used to determine the profiles of the DNA mixtures between the Symbiodinium types.

    2. an alternative technique to rapidly and accurately genotype monotypic Symbiodinium populations.

      In this experiment, a faster and alternative method was used in order to accurately genotype the monotypic (one type) Symbiodinium populations. This method was compared to those with DGGE profiles, where they extracted the gel bands and sequenced them based on region 2 of the ribosomal DNA transcribed spacer.

    3. The touchdown protocol consisted of an initial denaturing step at 92 "C for 3 min, 21 cycles at 92 "C for 30 s, 62 "C for 40 s, and 72 "C for 30 s, decreasing each cycle 0.5 "C, followed by 15 cycles with a 52 "C annealing step and a final extension at 72 "C for 10 min.

      One PCR cycle consists of denaturing, annealing, and extension steps. Denaturing means that the double-stranded DNA is separating into single-stranded DNA. Annealing refers to primers being added to the DNA strand. The extension step is when Taq polymerase adds dNTPs to the annealed primer.

    4. PCR amplification had the following conditions

      The PCR amplifications exhibited a set of conditions in order to compare the cultures of a HRM analysis to that of a DGGE analysis.

    5. For each genotype, one of the samples was used as the reference genotype, while the other was treated as an ‘unknown.’

      In order to determine if HRM is accurate, onr of the genotypes was used as the sample and the other was used as an unknown to identify if the template concentration affects accuracy of genotyping.

    6. To understand potential limitations of the HRM technique, three tests were conducted.

      Three different tests were conducted for HRM:

      1. Tested out if the template concentration affected the accuracy.
      2. DNA from Symbiodinium types of the same clade were combined to identify if it will change melting profiles.
      3. DNA from Symbiodinium types of the all clades were combined to identify if it will change melting profiles.
    7. The rest of the samples were assigned to a genotype based on the percentage of confidence

      The amplifications were compared based on the best percentage confidence (accuracy) of the specific culture ( HRM vs. DGGE). This was done through a software. In order to calculate the error, the square of the difference was taken between the fluorescence of each reading sample and reference genotype.

    8. DNA extractions were carried out using DNeasy# Plant Mini kit (Qiagen)

      A standardized kit was used to extract each culture's DNA, where two replications of the DNA were obtained.

      The ITS2 (a specific gene in a ribosome) was then amplified.

    9. Symbiodinium cultures of clades A–E were obtained from Dr Scott Santos

      The experiment consisted of cultures of the Symbiodinium clades A-E for growing marine algae; the media was replaced (only half of it) with fresh medium every month. They were set at a constant temperature of 25˚C and were exposed under a constant light intensity for 12 hours light and a 12 hour dark period.

    1. Therefore, a considerable volume of the electric fish brain is devoted to electrosensory processing. For the computational algorithms proposed above to be involved in electrolocation, they must have a plausible neural implementation in the fish’s nervous system. We propose one such projection onto the neural networks in the electric fish brain.

      Electric fish are able to receive signals and information from emission of electroreceptors in their environment passing through their skin. After the contact of the electroreceptors and the external stimuli, information is relayed in a pattern to the electro-receptory organs of the fish. Since this is an essential part of their way of life, the authors know a large amount of neurons and brain matter are involved in this process. Therefore, scientists hypothesized that in order to ensure this sensory information is relayed efficiently and quickly to the brain of the electric fish, there must be an algorithm used by the neural networks in the fish in order for this process to occur.

    2. The scanning or probing movements have also been hypothesized to help recognize object features.

      The movements that these electric fish use allows for the recognition of an object's features. Evidence from Heiligenberg's stimulation show that when electric fish bend their tails it increases the spatial contrast and makes it easier to distinguish an object's features. While Bacher's 3-D model showed that fish's tail bending help show a clear difference between the object's location and its shape. The BEM stimulation showed that electric fish control their movement in order to regulate their electrosensory system input by demonstrating a stable image of the rostral body which is thought to help the fish to distinguish features.

    3. To address these limitations and to simulate exploratory behaviors further, we built a complementary three-dimensional BEM electric fish model.

      The boundary element numerical method (BEM) simulator was built by the authors to remedy an issue presented by their standard three-dimensional simulator (color-coded mapping of a stationary fish): only the proximal (close to the center/point of origin) side of the fish's body could be digitized since secondary effects of the body could not be accounted for on the EOD. The BEM simulator addressed this problem by allowing objects under analysis to be bent or randomly shaped via nodes placed on their surfaces, thus giving the BEM simulator the ability to analyze secondary effects and other regions of the fish's body.

    4. This allows the fish to swim equally well forwards or backwards and to hold the body in an arc around objects (Bastian, 1986; Toerring and Belbenoit, 1979) while maintaining rigid control over the electroreceptive surfaces. Presumably, by keeping the detector array in a fixed orientation with respect to field generation, this controlled body motion reduces the number of variables that must be taken into account to interpret electrosensory information.

      The author is explaining here that the fish have a certain mechanism in their body that allows them to move much more fluently, whether forwards or backwards, and allows them to wrap around objects while not compromising the information they get from their electroreceptors. The mechanism that allows them to do this is done by keeping their bodies rigid and only moving their dorsal or ventral fin.

    5. To date we have mapped the EODs of three gymnotiform wave species and seven gymnotiform pulse species. These maps, for the first time, clearly illustrate the full spatiotemporal structure of the EOD (Fig. 1). In the majority of species, the EOD waveforms vary greatly with location, revealing considerably more complex patterns than were previously appreciated.
      • "Wave" fish have longer discharges when it comes to EOD's and continuous charges, "pulse" fish however have silent intervals between discharges.
      • This experiment, as the author illustrated above, allows for the statistical analysis of the EOD discharges rather than the collective qualitative data that stood in its place.
      • With this experiment, the waveforms created by the EOD's are affected by their location and proximity to other objects, and although in the charts both fish have many similarities, it seems the further the fish, the easier it becomes to tell the wavelength between both fish apart.
      • Thanks to this study, it is no longer difficult to explain the results of this experiment, previously there were no numbers to support claims, but now the authors have helped to provide some numerical values to this type of research.
    6. We have developed a powerful system for mapping EOD potentials and electric field vectors in three dimensions.

      Electric organ discharge (EOD) potentials, which are generated by electrodes located near the head and tail of an electric fish, are typically difficult to analyze due to variations among different species and the inconsistent geometry of the EOD spatial patterns. Thus, to overcome this obstacle, the authors devised a system for the visual representation of EOD potentials to facilitate analysis. This was achieved by the creation of a robotic arm that recorded the EOD of an immobile electric fish at multiple positions around its body. The motionless state of the fish also enabled the authors to capture the exact times of numerous EODs. Each EOD measurement collected from the arm was then digitally processed and converted into a color-coded map overlaid on a diagram of the fish's body for further research/analysis in the phenomenon known as electrolocation.

    1. To determine whether observed trends in the timing of phenological events were associated with summer temperature trends, we tested the relationship between (βDOY_x_YEAR) with the summer temperature trend (βDOY_x_TAIRSUMMER) for each species at each subsite using linear mixed models, with site, subsite (nested within site) and species as random factors in JMP.

      Statistical analysis such as linear mixed models were used to demonstrate if the trends in the timing of phenological events were associated with the temperature during the summer.

    2. Because site effects such as latitude, elevation and species traits have strong influences on the calendar date that a phenological event occurs, we did not use the DOY associated with a phenological event as a direct measure of phenological response. Instead, we used two types of measures that are largely independent of the site-specific properties (table 2). First, we calculated the TDD from snowmelt until the occurrence of the phenological event for each species-plot combination. This measure reflects the amount of heat accumulated from snowmelt until the phenological event was observed. Second, for each species-subsite combination, we calculated the slopes (β) of the relationships between the timing of the phenological event (represented as DOY or TDD) and the calendar year or site temperature (measured as air temperature of the spring or summer).

      Two types of measures were used to calculate the time of phenological events. One of them was thaw degree days (TDD) which was able to show how much heat was produced from snowmelt until the phenological response occurred. The other measure was done by comparing the slopes of each species-subsite. The slopes represented the time of the phenological event and the year or temperature it took place. One of the advantages of this measure is that it shows the comparison between species and sites.

    3. To evaluate potential differences in species responses among locations, sites were categorized into climatic zones as in previous syntheses [11,22,23]: high Arctic, low Arctic or alpine. Subsites were categorized as dry, moist or wet, where dry refers to plant communities on well-drained, mineral soils typically located on ridges, moist refers to sites with some soil drainage, and wet refers to plant communities with water tables frequently near or above the surface.

      The different locations used was categorized based on climate zones which were high Arctic, low Arctic, and alpine. They were also categorized as dry, moist, or wet.

    4. Consequently, we used mean temperatures of month combinations (spring = April–May and summer = June–August temperatures) as the basis of the temperature analysis.

      The average temperature for spring and summer times within a year were used to analyze temperature change throughout the experiment.

    5. Mean monthly air temperatures for the months preceding the growing season and months of the growing season (April–August) were calculated for each study site each year for comparison with plant phenology.

      The plant phenology was compared with the mean monthly air temperatures.

    6. A cubic spline interpolation

      Used to show the most accurate average of the temperatures.

    7. The weather dataset was based on data collected at the sites

      The data used for the weather was gathered from the specific site. However, in some places such as Finse, Norway, where the weather data was not available for a period of time, the averages from previous years was used.

    8. A priority-ranking lumping scheme that accounted for differences in plant morphology was used to consolidate phenological variables, although for a given species at a subsite, the phenological definitions were consistent over time.

      The plants were ranked based on the way they looked.

    9. Flowering and leafing stages were compiled from most sites at an observation resolution of one to two times per week.

      The plants' flowers and leafs were measured once or twice a week.

    10. Our approach was to use long-term trends and interannual variability across the ITEX control plots to evaluate change in plant phenology in relation to temperature.

      Plant phenology changes were measured using trends and interannual variations.

    1. Pollination success was quantified for these same flowers by recording the fruit production of visited flowers on the potted plants, maintained in the greenhouse. We placed at least 15 plants per day, each with one to three open flowers, over 20 days of observations, using a new set of plants each day. We recorded a total of 69 visits to over 400 flowers observed in this potted plant placement experiment. Pollination efficiency of each visitor group was assessed by comparing the percentage of visited flowers that produced fruit after a single visit.

      The success rate of the pollination was determined by recording the fruit produced by visited flowers. 15 potted plants having 1-3 open flowers were studied over 20 days, and a new set of plants was used every day. 69 total visits to over 400 flowers were observed throughout the course of the experiment.

    2. To determine the most effective pollinator, we placed 15 greenhouse-grown potted plants in the field to quantify pollination success at Site 3, the site with the highest visitation frequency (B. B. Roque, personal observations). On 20 different days during the flowering period, we compared the qualitative effectiveness of the different pollinator groups by allowing a single visit to individual flowers on the potted plants. Flowers that were ready to open prior to observation periods were bagged, while in bud, to exclude visitors. At the time of observation (from 9:00 am to 12:00 pm), bags were removed and flowers exposed to foraging insects. For a specific flower, pollinator visits were restricted to a single visit by one individual from one of the four groups of pollinators. After visitation, a flower was labelled (by pollinator group) and bagged to exclude subsequent visitors.

      For this experiment, the authors wanted to determine which was the best pollinator in relation to effectiveness. They grew 15 plants in the greenhouse and then placed them at the site with the highest number of visitors. Over 20 days, a single visit by pollinators was allowed for each individual flower. Bags were placed on the flowers whenever they were not being studied so as to avoid pollinator visits.

    3. To examine a possible relationship between line thickness and pollen deposition, we hand pollinated fresh flowers using fishing line of four different diameters. Each thickness was inserted into a fresh flower to the bottom of the corolla tube to collect pollen (as above); we then stained entire length of the fishing line with methylene blue to stain the adhering pollen grains and introduced the stained portion into another new fresh flower.

      In this experiment, fresh flowers were pollinated using four fishing lines of different diameters to simulate different proboscis diameters. The fishing lines were inserted into the floral tube in order to collect pollen. Then, the length of the fishing lines were stained blue. The fishing lines were re-inserted and the pollen that attached were stained blue. The same fishing lines were then inserted into other fresh flowers.

    4. To quantify the efficiency of each visitor group, we estimated pollen on the visitor mouthparts as the average number of pollen grains per individual visitor of each group.

      The authors counted average number of pollen grains per group, as well as time spent at each flower, and the behavior of the pollinator after leaving the flower.

      All of these metrics are important to pollination efficiency.

    5. We haphazardly selected plants with open flowers for each of the intervals.

      The authors use the word 'haphazardly' to indicate randomness in the sampling of the flowers. It is important to have randomness to avoid bias.

    6. The flowers have no notable fragrance, and offer viscous nectar as a pollinator reward, with the sugar concentration of the nectar ranging from 30 to 67 %

      This species has no fragrance and uses nectar as a reward with 30-67% sugar concentration as the reward.

    1. individuals at the leading edge and back of the invasions were genotyped, and traits of all 14 genotypes were measured.

      The authors aimed to determine both the genotype and the phenotype of the plants to determine which traits (phenotype) contributed to successful invasion, and how the diversity of the plants at the leading edge changes with gap size (determined by number of genotypes present). Comparing the leading edge and the back allows for determining the effect of invasion on population evolution.

    2. separating individual pots of suitable habitat by gaps that were 0 (continuous landscapes), 4, 8, or 12 times the mean dispersal distance.

      The question of how evolution occurs in patchy landscapes was addressed by testing both evolving and non-evolving populations in environments in which there was space between the habitable spaces in order to grow. Gaps simulated barriers in landscape which may require certain traits to overcome.

    3. In evolving populations, the resulting plants produced seeds, which dispersed across the array (assisted via a simulated rain event), constituting the next generation of the population (Fig.

      One of the questions addressed in the study is how evolution affects the spreading of plants. To test this, plants in the evolving condition were allowed to spread seeds, and then the seeds grew into the next generation. In the non-evolving condition, the new seedlings were replaced with seeds randomly drawn from the source population. This difference allows for determining if the traits the seeds carry with them are important for how fast they spread.

    4. spreading through continuous and fragmented landscapes, each consisting of a linear array of rectangular pots

      The experiments were designed to mimic real-life invasion using a one dimensional (linear) model. Pots filled with soil are places where seeds can grow, while empty pots act as barriers to invasion, resembling a fragmented landscape. The setup allows for the researchers to have precise control over as many variables as possible, and the linear array makes tracking of invasion progress simpler.

    1. To test whether neuronal responses to tickling are also modulated by such conditions, we tickled rats in both control (Fig. 3A, left) and anxiogenic settings, such as under bright illumination and on an elevated platform (Fig. 3A, right).

      The authors wanted to test whether ticklishness in rats is mood dependent, as it is in humans.

      To do this, they used bright lights and height to trigger anxiety in the rats. They then recorded USVs and the neuron firing rate while tickling the rats under these conditions.

    2. (ventral tickling, 4.45 ± 0.28 Hz; ventral gentle touch, 2.58 ± 0.21 Hz; n = 16, P < 0.001; mean ± SEM, paired t test).

      SEM is the standard error of the mean. It is related to standard deviation and provides information about the likelihood that the studied sample represents the whole population.

      A paired t-test is used to compare two measurements (in this case ventral gentle touch vs. ventral tickling) to see if they are significantly different. A P-value of 0.05 or less is an indicator of statistical significance.

    3. rats rapidly approached the tickling hand, and tickling induced unsolicited jumps accompanied by 50-kHz USVs (Freudensprünge, “joy jumps”; movie S2), which can be seen in joyful subjects in various mammalian species (14–16). We visually categorized spectrograms of an extensive set of USVs (34,140 calls) into modulated, trill, combined, and miscellaneous call types (Fig. 1Band fig. S1).

      The authors tickled rats while recording their behaviors and vocalizations.

      A spectrogram is a visual representation of sound waves. It provides quantitative information about the pitch and volume of the rat calls.

      By looking at spectrograms the researchers sorted the vocalizations into three categories.

    1. Hominin and non-hominin tracks were recognised in four test-pits at Site S, namely L8, M9, TP2 and M10.

      The authors made a series of test-pits in order to follow the predicted direction of the footprints.

    2. Consequently, we focused our interpretations on the more appropriate predictions inferred from the relationship between foot size and body dimensions in Australopithecus

      The authors used the size and shape of the footprints to infer body mass, stature, and walking speed for four of the individuals (S1, G1,G2, and G3)

    1. Fig. 4. Relative advantage of generalizing as a function of flower density and the proportion of deep flowers in the community. Outcomes with flight speed of 0.5 m s−1 are shown (15). The generalist is favored when its relative advantage is >1 (pink shading).

      The influences of floral density and the abundance of flowers featuring deep corollas are used to identify the advantage that a generalist (short-tongued) bumble bee would have over a specialist (long-tongued) bumble bee where bees have the same flight speed. Pink areas denote conditions that favor generalists.

      This model was adapted from a model that explained the effects of floral density on the composition of pollinators. The means by which the model that was adapted from to produce this model can be found at:

      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3825104/

      A list of materials and methods used to produce this figure is found at:

      http://science.sciencemag.org/content/sci/suppl/2015/09/23/349.6255.1541.DC1/Miller-Struttmann-SM.pdf

    2. Fig. 3. Change in flower abundance at landscape and local scales along a 400-m altitudinal gradient on Pennsylvania Mountain. (A) Map showing areas where PFD decreased (1.95 km2), is stable (1.29 km2), and increased (0.10 km2). Unshaded (excluded) areas contain cliff, talus, mining disturbance, and subalpine forest. (B) PFD (mean >± SE) for plots in krummholz (KRUM); tundra slopes (SLOPE); wet meadow (SWALE), false summit (FSUMMIT); and summit (SUMMIT) habitats (N = 6 species; F4,385 = 5.55, P = 0.0002). Asterisks indicate significant differences at P < 0.05. (C) Total flower production (in millions) is the product of total surface area for (A) each habitat (table S5) (15) and (B) mean PFD.

      These results show that the peak flower density of the habitat of the bumble bees is mostly decreasing, causing more competition for available nectar to ensue among the bees.

  7. Feb 2018
    1. In 2012–2014, we resurveyed bumble bee visitation on Mount Evans and Niwot Ridge in accordance with historical observations (18). Despite a 10-fold difference between past (n = 4099 visits observed) and present (n = 519 visits observed) collection effort, surveys indicate that resident bumble bees have broadened their diet. Resampling historical visitation data to match present collection effort reveals that foraging breadth (Levin’s niche breadth) (15) increased from 2.61 to 7.01 for B. balteatus [z score (Z) = 28.48, P < 0.0001] and 2.09 to 5.07 for B. sylvicola (Z = 19.78, P < 0.0001). Bumble bees have added flowers with shorter and more variable tube depth to their diet (B. balteatus: F1,1997 = 7554, P < 0.0001; B. sylvicola: F1,1997 = 64,851, P < 0.0001) (Fig. 2, E and F, and table S3).

      The researchers analyzed differences in bumble bee diets on Mount Evans and Niwot Ridge, between the 1960s and presently around 2011. Fewer bee visits were recorded during the more recent survey, but it appears that the bees have an expanded range of plants in their diet. This diet includes flowers that have shorter and more varied depths.

    2. We measured the change in tongue length of B. balteatus and B. sylvicola using specimens collected from 1966–1980 and 2012–2014 in the central Rocky Mountains (15). These two species historically comprised 95 to 99% of bumble bees at our high-altitude field sites (16–18). B. balteatus workers were collected from three geographically isolated locations: Mount Evans (39°35.033′N, 105°38.307′W), Niwot Ridge (40°3.567′N, 105°37.000′W), and Pennsylvania Mountain (39°15.803′N, 106°8.564′W).

      B. balteatus and B. sylvicola are the most common bumble bee species at the three research sites in the central Rocky Mountains. The sites are Mount Evans, Niwot Range, and Pennsylvania Mountain. To analyze the change in tongue length over time of both species at the sites, recent specimens from 2012-2014 were compared to older specimens from 1966-1980.

    3. Although the climate change impacts on phenological and spatial overlap of mutualists are well known, the role of climate change in generating functional discrepancies between them is less understood. Using historical data, we show that reduced flower abundance in bumble bee host-plants at the landscape scale has accompanied recent warming, leading to evolutionary shifts in foraging traits of two alpine bumble bee species

      It is known that climate change is impacting compatibility of mutualistic species. However, it is unknown which specific mechanisms of the mutualistic relationship are being affected. Using historical data the authors measured flower abundance shifts due to climate change and their effects on foraging behaviors by the bees studied.

    1. we restricted this analysis to break periods.

      The authors wanted to look at the relationship between neural activity and USV production. However, they needed to remove the possibility that both of these things are independent responses to tickling.

      For this reason, they carried out the experiments during breaks, when USVs and increased neuronal activity were occurring, but tickling was not a factor.

    2. Our recordings revealed that USVs and neuronal activity in the trunk cortex are modulated in a similar way by tickling and anxiogenic conditions. We wondered whether tickling-evoked USVs and neuronal responses to tickling are causally linked. We therefore aligned neuronal firing to the onsets of USVs (Fig. 4, A and B).

      The authors observed that USV production and neuron firing change in the same ways in response to tickling.

      In this set of experiments they wanted to see if there was also a causal relationship. In other words, is the neuronal activity triggering the production of sounds or do they just happen to occur at the same time?

    1. In this paper, we report a novel set of hominin tracks discovered at Laetoli in the new Site S, comparing it to a reappraisal of the original evidence. The new tracks can be referred to two different individuals moving in the same direction and on the same palaeosurface as those documented at Site G.

      The authors of this study analyzed the new set of footprints found at the Laetoli site. Their goal was to compare their results with earlier work in determining stature, morphology, and degree of sexual dimorphism of the individuals leaving the tracks.

    1. climate protection

      This paper investigates a means to optimize carbon storage by understanding the diversity of the mechanics and use of plant life as carbon sinks in order to lower atmospheric CO2.

    2. Thus, for each of the two grassland experiments, we used data collected from each plot to estimate parameters for a model that projects soil C accumulation over a 50-year time frame

      The author used data collected from each plot to produce a fraction to estimate the carbon accumulation in the soil, on the surface (0 to 20 cm) and at a deeper level ranging from 20 to 100 cm, over time. Using that fraction, the author created an equation that would estimate the amount of carbon accumulating in the soil over a 50-year period.

    3. E141, called BioCON, started in 1997 to explore how plant communities respond concurrently to three forms of environmental change: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity (45).

      In an experiment conducted by P. B. Reich and others in 1997, the effect of increased nitrogen deposits, increased atmospheric CO2 levels, and decreased species richness on plant communities was observed. The author used only the data from the CO2 concentrations and Nitrogen treatments for this research study. With this data the author was able to calculate carbon storage in the BioCON plots because plants store carbon through the accumulation of biomass. CO2 and nitrogen are vital components in the chemical pathways plants use to add on biomass.

    4. represented C4 grasses, C3 grasses, legumes, and other forbs. The species composition of plots was chosen by separate random draws of 1, 2, 4, 8, or 16 plant species from a pool of 18 species, with each level replicated in 30 or more plots.

      The author used C4 grasses, C3 grasses, legumes, and other forbs, in this experiment, as a common variable between all experimental plots. The author mixed the native species, to the American grassland, and a variety of 18 other plant species. The author randomly selected varying levels of species richness, for each plot, by introducing 1, 2, 4, 8, or 16 different plant species. Each mixture of species richness was replicated in 30 or more plots to produce multiple trials of the same level of species richness to produce an average.

    5. Marginal carbon was computed for each of the 15 incremental steps in species richness described in the modeled data, from S = 1 to S = 2, S = 2 to S = 3, …, to S = 15 to S= 16, bounded by the actual data from the experiments where S varied from 1 to 16 in experimental plots.

      The marginal increase of carbon was estimated for each increasing level of species richness. The model data calculated from equation (2) was bounded by the actual data collected from the experiment in each plot.

    6. For each bootstrapped iteration for each year, the parameter values a and b were used to estimate total soil and ecosystem C content for plots ranging from 1 to 16 species

      The equation above was used by the author to calculate the total amount of carbon, in the soil and ecosystem, between the varying species richness in each plot. The a and b values were parameters calculated, by equation 1, to measure the amount of carbon in the plants and the soil for a 50-year simulation. Using the parameters of a and b and the different amount of specie richness (ranging from 1 to 16), in each plot, the total amount of carbon in the ecosystem was determined.

    7. In the end, the Metropolis-Hastings algorithm generates a distribution of accepted I and kvalues wherein their means coincide with the minimal value of J (that is, the best fit to the observed data). Soil C content for each plot was projected to 50 years using the mean parameter estimates I and k and measured C0 for both depths (that is, soil C at start of the experiment).

      The author used the equation above, the Metropolis-Hastings algorithm, to calculate the difference between the observed and modeled data of carbon accumulation in the soil for each year the carbon soil data was provided. The equation generated a variety of accepted i and k values. The author then calculated the average i and k values with the smallest J value (best fit for observed data). Using the average i and k values that were calculated, the author was able to find the amount of carbon accumulated, at both the surface and deeper level of the soil, over a 50-year period. The author assumes the calculations to be within a 30% accuracy of the actual amount of carbon in the soil.

    8. For E141, soil C data were available for each plot at the beginning of the experiment (year 0) and after 5 and 10 years of experimental treatment (0- to 60-cm depth); we scaled soil C data to 0- to 100-cm depth for E141 using soil C depth distributions for E120 (20-cm increments to 1-m depth) to determine the proportion of C in the top meter of soil contained in the 60- to 100-cm depth, and then we used this proportion with observed soil C data for each plot to estimate total C to 1 m.

      For the BioCON experiment, the author used soil carbon data collected from each plot at the beginning of the experiment and the 5 and 10 years after the introduction of varying species richness (from a 0 to 60 cm depth). The author then estimated the amount of carbon in the soil, from 0 to 100 cm depth, by using carbon soil depth distribution data, for 20 cm increments up to a 1 meter depth. Using the data from the BigBio experiment, the author developed a proportion that helped determine the amount of carbon in the soil up to a meter depth for the BiCON plots.