Araptus attenuata
Applied Population Genetics Textbook Release
I will be posting portions of all 10 chapters of my upcoming textbook, Applied Population Genetics, as early draft chapters to this website over the spring semester. Read more
Methylation Structure
Here is some interesting data coming out of the Baja Araptus attenuatus project. We looked at methylation variation, localized within the genome and compared the amount of among-population variation present. The underlying idea here is that in insects, methylation is more often encountered in coding regions, and has been shown in many cases to be influencing phenotype.
Extracting Data from Rasters
This document shows you how to extract data from rasters.
Getting The Libraries
First, I’ll load in some packages to get the ability to work with raster data and to load in the Arapatus attenuatus data set (it is part of the default gstudiopackage).
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require(raster) require(gstudio) |
Loading and Cropping Rasters
We can load in the raster, and then crop it to just the are we need. These rasters were downloaded from [http://www.worldclim.org] and are much larger than the study area. This just makes it easier on the computer to not have to deal with such large areas. After cropping it, we will load in the annual precip and temperature data as well.
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tmp <- raster("alt_22.bil") e <- extent(c(-115, -109, 22, 30)) baja_alt <- crop(tmp, e) baja_temp <- crop(raster("bio1_22.bil"), e) baja_prec <- crop(raster("bio12_22.bil"), e) |
Getting Example Data from Araptus attenuatus
Now, lets grab the Araptus data and look at the data and plot out the locations.
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data(arapat) summary(arapat) |
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## Species Cluster Population ID Latitude ## CladeA: 75 CBP-C :150 32 : 19 101_10A: 1 Min. :23.1 ## CladeB: 36 NBP-C : 84 75 : 11 101_1A : 1 1st Qu.:24.6 ## CladeC:252 SBP-C : 18 Const : 11 101_2A : 1 Median :26.2 ## SCBP-A: 75 12 : 10 101_3A : 1 Mean :26.3 ## SON-B : 36 153 : 10 101_4A : 1 3rd Qu.:27.5 ## 157 : 10 101_5A : 1 Max. :29.3 ## (Other):292 (Other):357 ## Longitude LTRS WNT EN EF ## Min. :-114 01:01:147 03:03 :108 01:01 :225 : 2 ## 1st Qu.:-113 01:02: 86 01:01 : 82 01:02 : 52 01:01:219 ## Median :-112 02:02:130 01:03 : 77 02:02 : 38 01:02: 52 ## Mean :-112 02:02 : 62 03:03 : 22 02:02: 90 ## 3rd Qu.:-110 : 11 01:03 : 7 ## Max. :-109 03:04 : 8 03:04 : 6 ## (Other): 15 (Other): 13 ## ZMP AML ATPS MP20 ## : 33 08:08 : 51 05:05 :155 05:07 : 64 ## 01:01: 46 07:07 : 42 03:03 : 69 07:07 : 53 ## 01:02: 51 07:08 : 42 09:09 : 66 18:18 : 52 ## 02:02:233 04:04 : 41 02:02 : 30 05:05 : 48 ## : 23 07:09 : 14 05:06 : 22 ## 07:09 : 22 08:08 : 9 11:11 : 12 ## (Other):142 (Other): 20 (Other):112 |
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plot(arapat, zoom = 6) |
Extracting Point Data
To elevation, temperature and precipitation from the rasters for each sampling location, we need to translate them into points first. I’ll first grab the coordinate data as a data.frame.
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coords <- StrataCoordinates(arapat) |
Then we can grab them using the normal functions in the sp library.
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pts <- SpatialPoints(coords[, 2:3]) coords$elev <- extract(baja_alt, pts) coords$temp <- extract(baja_temp, pts) coords$prec <- extract(baja_prec, pts) coords |
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## Strata Longitude Latitude elev temp prec ## 1 88 -114.3 29.33 681 178 143 ## 11 9 -113.9 29.01 361 195 148 ## 20 84 -113.7 28.97 368 197 124 ## 29 175 -113.5 28.73 240 205 106 ## 36 177 -114.0 28.66 177 203 120 ## 46 173 -112.9 28.41 26 223 102 ## 56 171 -113.2 28.22 522 195 145 ## 66 89 -113.4 28.04 290 203 117 ## 76 159 -113.3 27.53 87 211 123 ## 85 SFr -113.0 27.36 305 205 107 ## 94 160 -112.5 27.40 371 212 124 ## 104 162 -112.4 27.20 248 220 119 ## 114 12 -112.7 27.18 488 202 130 ## 124 161 -113.0 27.04 29 216 91 ## 134 93 -112.0 26.95 66 230 81 ## 144 165 -112.4 26.25 NA NA NA ## 154 169 -111.4 26.21 6 238 134 ## 164 58 -111.4 26.02 12 240 129 ## 173 166 -112.1 25.91 70 224 84 ## 183 64 -111.3 25.61 397 214 245 ## 188 168 -111.2 25.56 354 217 235 ## 198 51 -111.6 25.35 80 222 140 ## 205 Const -111.7 25.02 50 217 127 ## 216 77 -110.7 24.88 52 231 193 ## 226 164 -111.5 24.75 35 213 95 ## 236 75 -110.7 24.59 21 231 166 ## 247 163 -111.0 24.21 196 219 127 ## 257 ESan -110.4 24.46 9 237 175 ## 265 153 -110.5 24.13 31 235 165 ## 275 48 -110.3 24.21 117 233 217 ## 285 156 -110.0 24.04 0 237 196 ## 291 157 -110.1 24.02 609 208 443 ## 301 73 -109.9 24.01 103 233 227 ## 311 Aqu -110.1 23.29 142 226 227 ## 319 Mat -110.1 23.09 5 234 159 ## 324 98 -109.6 23.08 75 237 233 ## 328 101 -110.6 27.91 8 249 244 ## 337 32 -109.3 26.64 18 244 337 ## 356 102 -109.1 26.38 10 245 346 |
Plotting Trend lines.
Cool, lets sort this by latitude
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coords <- coords[order(coords$Latitude), ] |
and then plot out some values to look at what is going on.
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require(ggplot2) ggplot(coords, aes(x = Latitude, y = elev)) + geom_line(color = "gray") + theme_bw() + geom_text(aes(y = elev + 10, label = Strata), color = "blue") |
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ggplot(coords, aes(x = Latitude, y = temp)) + geom_line(color = "gray") + theme_bw() + geom_text(aes(y = temp + 5, label = Strata), color = "blue") |
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ggplot(coords, aes(x = Latitude, y = prec)) + geom_line(color = "gray") + theme_bw() + geom_text(aes(y = prec + 10, label = Strata), color = "blue") |
Garrick RC, Nason JD, Fernandez-Manjaress JFF, Dyer RJ. 2013. Ecological co-associations influence species’ responses to past climatic change: an example from a Sonoran Desert bark beetle. Molecular Ecology, 22, 3345-3361.
Ecologically interacting species may have phylogeographic histories that are shaped both by features of their abiotic landscape, and by biotic constraints imposed by their co-association. The Baja California peninsula provides an excellent opportunity to examine the influence of abiotic vs. biotic factors on patterns of diversity in plant-insect species. This is because past climatic and geological changes impacted the genetic structure of plants quite differently to that of co-distributed free-living animals (e.g., herpetofauna and small mammals). Thus, ‘plant-like’ patterns should be discernible in host-specific insect herbivores. Here we investigate the population history of a monophagous bark beetle, Araptus attenuatus, and consider drivers of phylogeographic patterns in light of previous work on its host plant, Euphorbia lomelii. Based on mitochondrial and nuclear markers, we found that the evolutionary history of A. attenuatus exhibits similarities to host plant that are attributable to both biotic and abiotic processes. Southward range expansion and recent colonization of continental Sonora peninsula appear to be unique to this taxon pair, and likely reflect influences of the host plant. On the other hand, abiotic factors with landscape-level influences on suites of co-distributed taxa, such as Plio- and Pleistocene-aged marine incursions in the region, also left genetic signatures in beetle populations. Superimposed on these similarities, bark beetle-specific patterns and processes were also evident. Taken together, this work illustrates that the evolutionary history of species-specific insect herbivores may represent a mosaic of influences, including, but not limited to, those imposed by the host plant.
Garrick RC, Meadows CA, Nason JD, Cognato AI, Dyer RJ. 2009. Variable markers for a Sonoran dessert bark beetle, Araptus attenuatus Wood (Curculionidae:Scolytinae), with applications to related genera. Conservation Genetics, 10 1177-1179.
We report eight new co-dominant nuclear markers for population genetics of the bark beetle Araptus attenuatus Wood. Several loci include introns from low- copy genes, and four cross-amplify in one or more related genera. The markers show moderate levels of polymorphism (2–19 alleles per locus), and no loci showed significant deviations from Hardy–Weinberg or linkage equilibrium across both of the two populations examined, consistent with Mendelian inheritance patterns.