1 Data

1.1 Climate data

source("code/data_chelsa.R")
df_chelsa

## # A tibble: 8 × 4
##   site    mat   tap   vpd
##   <chr> <dbl> <dbl> <dbl>
## 1 SJ    14.6   650.  971 
## 2 DV     8.05  432. 1131.
## 3 AT    19.8   931. 1810.
## 4 ST    10.7  1118.  839.
## 5 NY    12.2  1216. 1319.
## 6 DT    10.3   984. 1211.
## 7 TP    22.1  1355. 1365 
## 8 HT    20.4  1343. 1525.

Climatologies.

source("code/data_terraclim.R")
df_terraclim

## # A tibble: 7 × 3
##   site    mat   tap
##   <chr> <dbl> <dbl>
## 1 AT    19.4   842.
## 2 DV     9.25  424 
## 3 ST    10.9  1011 
## 4 HT    21.3  1249.
## 5 NY    12.3  1198.
## 6 DT    10.2   869.
## 7 TP    22.6  1298

Annual summaries.

source("code/data_terraclim_annual.R")
df_terraclim_annual

## # A tibble: 35 × 4
##    site    mat   tap  year
##    <chr> <dbl> <dbl> <int>
##  1 AT    20.0   882   2018
##  2 DV     9.92  364.  2018
##  3 ST    11.5   955.  2018
##  4 HT    22.0  1281.  2018
##  5 NY    13.1  1720.  2018
##  6 DT    10.8  1063.  2018
##  7 TP    23.4  1579.  2018
##  8 AT    20.1   783.  2019
##  9 DV     9.61  342.  2019
## 10 ST    11.6   812.  2019
## # ℹ 25 more rows

1.2 NAB data

Read in data using tidynab package.

source("code/data_nab_read.R")

Focus on seven cities. * Exceptions: Denver pollen data are from Colorado Springs; Austin pollen data are from Georgetown; Detroit pollen data are from Sylvania.

source("code/data_nab_meta.R")
p_pollen_map

source("code/data_nab_avail.R")
p_nab_avail

View pollen phenology in study sites.

source("code/data_nab_pheno.R")
p_nab_calen

source("code/data_nab_window.R")
p_flower_window

1.3 Plant location data

Read in data previously processed with batchplanet package.

source("code/data_occ_tree_read.R")

Find family names from genus names. This step needs supervision.

source("code/data_occ_tree_taxa.R")

Map relative position of tree inventory and nab station.

source("code/data_occ_map.R")
p_nab_plant_map

Calculate distance from plants to NAB stations in the unit of km.

source("code/data_occ_dist.R")
df_distance

## # A tibble: 7 × 7
##   site  midlon midlat sitelon sitelat sitename distance
##   <chr>  <dbl>  <dbl>   <dbl>   <dbl> <fct>       <dbl>
## 1 AT     -97.7   30.3   -97.7    30.6 Austin      41.0 
## 2 DT     -83.1   42.4   -83.7    41.7 Detroit     90.7 
## 3 DV    -105.    39.7  -105.     38.9 Denver      96.5 
## 4 HT     -95.4   29.7   -95.4    29.7 Houston      3.92
## 5 NY     -73.9   40.7   -74.0    40.8 New York    10.6 
## 6 ST    -122.    47.6  -122.     47.7 Seattle      6.91
## 7 TP     -82.4   28.1   -82.4    28.1 Tampa        3.65

Prepare street map as basemap. Street shapefiles for major cities manually downloaded from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CUWWYJ. Boeing, Geoff, 2017, “U.S. Street Network Shapefiles, Node/Edge Lists, and GraphML Files”, https://doi.org/10.7910/DVN/CUWWYJ, Harvard Dataverse, V2

source("code/data_occ_road_read.R")

Map plant occurrence in a city.

source("code/data_occ_road_map.R")
p_plant_map

1.4 PlanetScope data

source("code/data_ps_snap.R")
p_ps_snap

PS data previously processed using batchplanet package.