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Figure 6 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 6. Estimated area of Disenchantment Bay (km2) containing three different ice cover types: scattered (1–3 tenths), intermediate (4–6 tenths), and dense (7–10 tenths), and all types combined (i.e., ice-covered area [ICA]) from 3 May to 4 August 2002. Estimates of ice cover were averaged within grid cells (when n> 1) and the areas of cells with each type of ice cover were summed (Jansen et al. 2006)) and then scaled upward (proportionately) based on the percent of the study area that was sampled on a given day.

Figure 7 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 7. Patterns of ship movement and visitation time in Disenchantment Bay, Alaska, in 2002 (n = 56 cruise ships of 105 total during study). Shading of cells represents the cumulative time that visiting ships spent within that cell for each of three months: May, June, and July (including early August). Four distinct shades, from gray to black, reflect increasing residence: <5 min, 5–10 min, 10–20 min,>20 min, respectively. Refer to Figure 1 for geographical points and scale.

Figure 5 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 5. Standardized resource selection coefficient by harbor seals for ice cover class in Disenchantment Bay, Alaska, 3 May to 4 August 2002, for the Bernoulli part of the P1B model (i.e., for the cell-based abundance of seals). The solid circles and lines are for all seals, and the open circles and dashed lines are for mother-pup pairs. Confidence intervals (95%) are shown by the vertical bars. The thin horizontal line represents equal selection for all ice cover classes.

Figure 4 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 4. Standardized resource selection coefficient by harbor seals for ice cover class in Disenchantment Bay, Alaska, 3 May to 4 August 2002, for the Bernoulli part of the P1B model (i.e., for the cell-based spatial distribution of seals). The solid circles and lines are for all seals, and the open circles and dashed lines are for mother-pup pairs. Confidence intervals (95%) are shown by the vertical bars. The thin horizontal line represents equal selection for all ice cover classes.

Figure 3 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 3. Spatial distribution of harbor seals, ice cover zones, and maximum penetration of cruise ships on the day of surveys () and the previous day () in Disenchantment Bay, Alaska, on (A) 6 May, (B) 16 May, (C) 31 May, (D) 20 June, (E) 18 July, and (F) 4 August 2002 (figures for all 18 survey dates are available as supplemental material online). The time of day that ships reached their maximum penetration appears near the location symbol. The range of seal counts summed per grid cell is shown in three levels: small dot (<5 seals), medium dot (5–20 seals), and large dot (>20 seals). A small, overlying white dot indicates the presence of at least one mother-pup pair within that grid cell. Ice cover is represented by a gradient in cell-color shading: light gray (scattered), medium gray (intermediate), dark gray (dense). For this graphic, if cells with no ice data were bounded on three sides by cells with ice measures, the average of neighboring cells was used as an estimate. Refer to Figure 1 for geographical points and scale.

Figure 2 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 2. Counts of all seals and pups along video sampling transects (relative abundance) in Disenchantment Bay, May to August 2002. Raw counts for all seals are shown as solid circles, and raw counts for pups are shown as open circles. The thick solid curve is the fitted GAM model for all seals, with the 95% prediction intervals shown by the thinner solid lines. The thick dashed curve is the fitted GAM model for pups, with the 95% prediction intervals shown by the thinner dashed lines.

Figure 1 in Natural and human effects on harbor seal abundance and spatial distribution in an Alaskan glacial fjord

Figure 1. Map of Disenchantment Bay study areas near Yakutat, Alaska. Major tidewater glaciers are labeled. The location of the terminus of Hubbard Glacier was mapped in early June 2002 as part of this study. The extent of snow and ice-covered terrain (stippled area) was derived from a NOAA Coastal Service satellite photo taken in 1993. Icy Bay, an adjacent tidewater glacial fjord with a seal population (see Discussion), is shown for reference.

opencc-by-4.0Mar 2016View details →

Fig. 2 in Examining the relationship between flower thrips (Thysanoptera: Thripidae) spatial distribution and blueberry (Ericales: Ericaceae) flower density

Fig. 2. Graph of percentage of open blueberry flowers vs. log10 thrips per trap from the Windsor farm on 18 Mar 2010. The black line represents a regression line fitted by least squares regression.

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Fig. 1 in Examining the relationship between flower thrips (Thysanoptera: Thripidae) spatial distribution and blueberry (Ericales: Ericaceae) flower density

Fig. 1. Graphs showing percentage of open blueberry flowers vs. thrips per trap from the Inverness farm on a) 30 Jan and b) 5 Feb 2009. The black lines represent regression lines fitted by Theil regression.

Fig. 3 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea

Fig. 3: Rižana and Badaševica river hourly (QR and QB) and daily mean (Qr and Qb) discharge with related inflow concentrations of ISS (ISS and ISS) (a). Density profile (b) and wind direction and velocity applied (average wind velocity of 1.5 m s-1 221°) during the r b high-discharge event 21st June 2013 (c) (ARSO, 2014a).

Fig. 8 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea

Fig. 8: Salinity and ISS profiles after 24-hour simulation at comparison sites (P1- right, P2-center and P3-left); the event on 21st June 2013.

Fig. 4 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea

Fig. 4: ISS distribution in the surface layer on 8th June 2011: measurements (a) and model simulation – average velocities and ISS distribution (b). Spatial map of discrepancies between model and measurements (c) and ranging of cells with regard to discrepancies (d); ΔMIN and ΔMAX depict minimum and maximum discrepancy, respectively.

Fig. 7 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea

Fig. 7: ISS distribution in the surface layer on 21st June 2013: measurements (a) and model simulation – average velocities and ISS distribution (b). Spatial map of discrepancies between model and measurements (c) and ranging of cells with regard to discrepancies (d); ΔMIN and ΔMAX depict minimum and maximum discrepancy, respectively.

Fig. 2 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea

Fig. 2: Rižana and Badaševica river hourly (QR and QB) and daily mean (Qr and Qb) discharge with related inflow concentrations of ISS (ISS and ISS) (a). Density profile (b) and wind direction and velocity applied (average wind velocity of 1.7 m s-1 188°) during r b the high-discharge event 8th June 2011 (c) (ARSO, 2014a).

Fig. 6 in Spatial Distribution of Suspended Solids During Short-Term High River Discharge in the Bay of Koper, Northern Adriatic Sea

Fig. 6: Salinity and ISS profiles after 24-hour simulation at the comparison sites (P1- right, P2-center and P3-left); the event on 8th June 2011.

opencc-by-4.0Sep 2017View details →

Fig. 3 in Seasonal abundance and spatial distribution of Diaphania hyalinata (Lepidoptera: Crambidae) on yellow squash in south Florida

Fig. 3. Comparison of average daily temperature (°C) and average daily rainfall (mm) with mean abundance of total Diaphania hyalinata larvae during the 4 cropping seasons (26 May–30 Dec 2014) of yellow squash. Data on temperature and rainfall were obtained from the Florida Automated Weather Network, Homestead, Florida.

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Fig. 2 in Seasonal abundance and spatial distribution of Diaphania hyalinata (Lepidoptera: Crambidae) on yellow squash in south Florida

Fig. 2. Weekly abundance (mean ± SE per 2 leaves) of total Diaphania hyalinata larvae on yellow squash during 4 planting seasons from 26 May through 30 Dec 2014. Means topped by the same lowercase letter are not significantly different (P> 0.05) (analysis of variance and Waller–Duncan K-ratio test). Bars above and below means represent standard errors.

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Fig. 1 in Seasonal abundance and spatial distribution of Diaphania hyalinata (Lepidoptera: Crambidae) on yellow squash in south Florida

Fig. 1. Weekly abundance (mean ± SE per 2 leaves) of small (L1 + L2), medium (L3 + L4), large (L5) Diaphania hyalinata larvae on yellow squash from a) 26 May through 16 Jun, b) 18 Jul through 8 Aug, c) 1 Sep through 22 Sep, and d) 9 Dec through 30 Dec 2014. Means topped by the same uppercase letter are not significantly different (P> 0.05) between larval sizes, and means topped by the same lowercase letter are not significantly different (P> 0.05) between sampling dates (analysis of variance and Waller–Duncan K-ratio test). Bars above and below means represent standard errors.

opencc-by-4.0Oct 2019View details →

Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset

This dataset contains supplementary files uploaded as part of the above journal article. 5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few records contain negative peak delay times due to change in waveform shape from filtering, these were excluded during further calculations. The other 4 sub-datasets contain files as well as the script to generate the results of Δlog tp , κ, εparam and P(ml ) as shown in figures 7, 8, 9 and 10 respectively of the main article. Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (tp ) in 2-4 Hz, 4-8 Hz, 8-16 Hz and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude, event longitude, event depth, station code, station latitude, station longitude, tp in 2-4 Hz, tp in 4- 8 Hz, tp in 8-16 Hz and tp in 16-32 Hz in a sequential manner. Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt) written in BASH. The text files contain Δlog tp values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent node index, node latitude, node longitude, node depth and Δlog tp value in a sequential manner. The GMT script uses GSHHG coastline data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ . Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5 (Wessel et al., 2013) and above. Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt, kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in the text files represent node latitude, node longitude and κ value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution of κ using GMT-5 (Wessel et al., 2013) and above. Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt, aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain εparam values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in the text files represent node latitude, node longitude and εparam value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution of εparam using GMT-5 (Wessel et al., 2013) and above. Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt, psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain psdf (P(ml )) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in the text files represent node latitude, node longitude and P(ml ) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution of P(ml ) using GMT-5 (Wessel et al., 2013) and above.