[ECOLOG-L] Time-series fields of ocean chemistry
Hi all, Very specific question here: Does anyone have any knowledge of existing fields of ocean chemistry (e.g., pCO2, NO3, O2, DIC..) in the Central Pacific? We're looking to validate the output of a biogeochemical model around the main Hawaiian Islands, so a time-series would be ideal. We already have point data from HOT and Station Aloha. Hopes aren't high, but I'll take any leads- thanks!
[ECOLOG-L] Stable alternatives to R package microbiomeSeq?
Hi all, Cross-posted on Stack Overflow: https://stackoverflow.com/questions/48413669/stable-alternative-for-microbiomeseq I'm teaching a class for R users in the biological sciences. Most students are geneticists and I'd like to offer them some exercises exploring microbial community analysis in R. I'm aware of the package microbiomeSeq, but I know it isn't in stable release yet. Please send any suggestions my way- thanks!
[ECOLOG-L] Working with NetCDF data in R
Hi all,
I posted to the listserv a little over a month ago soliciting tips about
working with .nc file formats in R. THANK YOU to everyone who replied! I
couldn't reply to everyone because my inbox was overflowing. As a thank you
for your help, I thought I'd post the code I ended up using that worked well
for me:
library(ncdf4)
nc = nc_open('C:/Users/Lindsay/Downloads/HIIG_winter_uvel_var_050.nc') #
change this pathway for your needs
str(nc) # examine nc file structure
v1 = nc$var[[1]] # identify your variable of interest by indexing the nc file
u = ncvar_get(nc, v1) # u is a matrix of values for your v1
lon = v1$dim[[1]]$vals # dim[[1]] is lon
lat = v1$dim[[2]]$vals # dim[[2]] is lat
# Using deconstruct function c() to turn matrices into numeric class output
uvals <- c(u)
uvalsdf50w <- as.data.frame(uvals)
# Now, need to match lat (Latitude) and lon (Longitude) to data pts
# same lat applied to 7 uvals at a time
lat1list <- c(lat[1],lat[1],lat[1],lat[1],lat[1],lat[1],lat[1])
# ...and so on, repeat up to...
lat12list <- c(lat[12],lat[12],lat[12],lat[12],lat[12],lat[12],lat[12])
# Next: lon applied to 7 uvals
# 1 column and 84 rows in uvalsdf
colnames(uvalsdf50w) <- "uwinter50"
uvalsdf50w$Lat <- c(lat1list, lat2list, lat3list, lat4list, lat5list,
lat6list, lat7list, lat8list, lat9list, lat10list, lat11list, lat12list)
uvalsdf50w$Lon <- c(lon,lon,lon,lon,lon,lon,lon,lon,lon,lon,lon,lon)
head(uvalsdf50w)
# uwinter50 Lat Lon
#1 0.02767440 20.85938 -157.4055
#2 0.02629073 20.85938 -157.3671
#3 0.02462446 20.85938 -157.3286
# Now we have an easy-to-read df of values matched to coords.
Hope this is useful!
[ECOLOG-L] Working with netCDF files in R
Hi all, I'm new to the netCDF file format, but I need to rapidly familiarize myself. In attempting to manipulate IOOS data (specifically, http://oos.soest.hawaii.edu/thredds/ncss/hioos/roms_assim/hiog/ROMS_Oahu_Regional_Ocean_Model_Assimilation_best.ncd/dataset.html ) in R, I've found that online resources are helpful only to a point, as netCDF files are variable- one snippet of code isn't very transferable. Since I can't see the data in a netCDF file like I could in a table, I'm not sure how to best learn how to work with these files in R. I need to analyze years of data across a swath of area, so pulling one set of coordinates at a time is not efficient. Does anyone have advice on how to transform a netCDF file into a TIFF or csv? Or has anyone pulled IOOS data in the past? Thanks for your advice!
[ECOLOG-L] FOLLOW-UP- ArcGIS problem: points to polygon spatial join crash
Hi all,
Thanks to each and every one of you who replied to my call for help
regarding my issue with ArcMap crashing in the middle of a spatial join
between a point file and a polygon shapefile. Some of the replies I received
included the following suggestions:
- The Near tool, Near 3D tool, or Generate near table tool
- Modification of the "guts" of ArcMap, including disabling background
processing so that a progress bar appears and coding the join into ArcMap
vs. just pressing a button (i.e., using Python)
-Splitting the dataset into chunks and processing each individually
-Using the resources at https://gis.stackexchange.com/
I ended up working around the instability of ArcMap by continuing my
analyses in R. Thanks to everyone who suggested this as an option, and
thanks to everyone who replied to offer their expertise! Here is a sample of
the code I used in R; I hope it helps someone in the future who encounters
this issue.
# lpb2 is my points file
coords <- cbind(lpb2$Long, lpb2$Lat)
coordsSP <- SpatialPoints(coords, proj4string=CRS(as.character(NA)))
crs.geo <- CRS("+init=EPSG:32604") # WGS 84 / UTM zone 4N
proj4string(coordsSP) <- crs.geo # define projection system of our data
is.projected(coordsSP)
summary(coordsSP)
df<- lpb2[3:6] # Make covariates their own df
SPdf <- SpatialPointsDataFrame(coordsSP,df)
summary(SPdf)
# Read in my polygon
Molokai <- readOGR(dsn = 'Molokai', layer = 'Molokai') # Made folder named
Molokai and placed my Molokai shp in there
# Calculate distances from each point
Dist <- gDistance(SPdf, Molokai, byid=T)
class(Dist)
tDist <- t(Dist) # transpose the matrix
colnames(tDist)[1]<- 'Dist'
lpb2 <- cbind(lpb2, tDist)
[ECOLOG-L] ArcGIS problem: points to polygon spatial join crash
Hi all, I'm working in ArcGIS 10.1 and occasionally 10.4. I'm encountering a repeat issue with an attempted spatial join between a set of ~5 million points to a polygon. I want the distance between the points to the closest edge of the polygon. My join has worked for some files on the lower side (~4 million), but ArcMap keeps crashing around 4.4 - 4.7 million. What gives? These crashes are consistent across machines: a 16 GB RAM desktop, 16 GB RAM laptop, and 4 GB RAM desktop. Any advice or workarounds?
[ECOLOG-L] High-resolution current/wave estimates for Hawaii
Hi ECOLOG-ers, I am seeking high-resolution estimates of significant wave height and/or current velocities for the main Hawaiian Islands, specifically around Molokai (around Penguin Bank and Pailolo Channel). By "high-res", I mean anything finer than the 4 x 4 km ROMS-modeled current estimates and 0.5 x 0.5 km SWAN-modeled wave height estimates that I'm using now. These estimates can be modeled values or sourced directly from buoys. Any leads appreciated. Mahalo nui loa!
[ECOLOG-L] Conflicting results for spatial autocorrelation tests
Hi all, I hope this isn't an inappropriate use of the listserv. Perhaps there are some spatial ecologists reading who could share some insight: I am working with an imbalanced (5%/95%) presence/absence dataset. I've created 25x25 m. raster cells that are categorized '1' or '0'. My Geary's C result (0.993) and Moran's I result (0.0045) are each nearly ideal- the expectation for each, respectively, would be 1 and 0 in the absence of spatial autocorrelation. I also created a spherical semivariogram which displays pretty constant semivariance across all distances, save for a few outliers near the nugget and sparsely dotted across a few other sections. These outliers near the nugget do suggest there may be some autocorrelation present, but I'm really not sure- we're talking perhaps 2-3 dozen dots in relation to about 10,000 others. My question is, why would my Moran's I and Geary's C statistics indicate very strongly against extant autocorrelation if there truly is some remaining in the data? Should I weigh each result (semivariogram, MI, GC) equally, or trust one result more than the others? Any insight would be sincerely appreciated. My personal email is lindsaymveazey [at] gmail dot com. Thanks!
Re: [ECOLOG-L] "The Audacity of Graduate School"
As one of many hopeful individuals trying to find an open program in which to begin an advanced degree, I'd also like to point out the pitiful state of scientific funding in North America. The current NSERC funding success rate is below 8%, and the NSF success rate hovers around 20%. Additionally, in my discussions with students of all levels, both current and (hopefully) prospective, I've noticed that funding has essentially dried up for M.Sc candidates, and is not much better for Ph.D candidates. I'm wondering if any subscribers have recommendations for programs abroad, like MESPOM, that welcome foreign students instead of stack the deck against their entry. Dr. Dossey, thank you for a well written submission that rings all too true.
