Data Sets in SEV

Sevilleta (SEV) Long Term Ecological Research (LTER) Program

Data curated and provided by Dr. Jennifer A. Rudgers, Professor of Biology and Director of the SEV LTER Research Program at The University of New Mexico.

Arthropod

Ground Arthropod Community Survey in Grassland, Shrubland, and Woodland at the Sevilleta National Wildlife Refuge, New Mexico (1992-2004)

Abstract: This data set contains records for the numbers of selected groups of ground-dwelling arthropod species and individuals collected from pitfall traps at 4 sites on the Sevilleta NWR, including creotostebush shrubland, both black and blue grama grasslands, and a pinyon/juniper woodland. Data collections begin in May of 1989, and are represented by subsequent sample collections every 2 months. One site (Goat Draw/Cerro Montosa) was discontinued in 2001, and a new site (Blue Grama) was initiated . Only three sites, creosotebush, black grama, and blue grama were continued between 2001-2004.

Jenn used sev029_arthropod.R to create the datasets.

Arthropod metadata: http://sevlter.unm.edu/data/sev-29

Codebook: http://sevlter.unm.edu/data/sev-029/4833

Note that:

all sites were not sampled in all years
sampling of particular months was inconsistent over time. Jenn grouped the data into 2 seasons and threw out the winter samples which were only done in a subset of years.

Jenn created 2 new arthropod variables in the R code. “With lots of attention on declines in arthropod biodiversity in the news, it would be interesting to look at diversity.”

total = sum of all arthopods caught per trap
species_diversity = Shannon diversity index for arthropods caught per trap

List of attached files:

Original data: sev029_arthropop02162009.csv
Reshaped data: sev029_arthwide.csv
R code to reshape: sev029_arth.R
Matching yearly climate data: sev001_met_arth.csv
- merge by: Site, Year, each season has a separate met column
Metadata on arthropod taxon codes: sevpittaxa.xls

ID Not all datasets have an ID, but each observation should be able to be identified uniquely. In this case a combination of the variables will identify an observation uniquely (Year, Season, Site, Line, and Trap). (“Trap” is in the codebook but doesn’t seem to be in the dataframe – there are some differences because I got this directly from Jenn and the codebook is from the LTER website.)

Codebook for the original data: sev029_arthropop02162009.csv

Variables

ID
  See note above
  Year, Season, Site, Line, Trap

Year
  Label: Year
  Definition: The year in which the data were collected.
  Type: Date/time
  Date format: YYYY
  Missing values: None specified

Month
  Label: Month
  Definition: The month in which data were collected.
  Type: Nominal
  Missing values: None specified

Day
  Label: Day
  Definition: The numeric designation for the day of the month on which the measurement was taken. The numbers range from 1 to 31.
  Type: Nominal
  Missing values: None specified

Site
  Label: Site
  Definition: The location at which data was collected.
  Type: Code list
  Codes:
    B = Blue Grama grassland (only a few years, maybe exclude from analysis)
    C = Creosote shrubland
    G = Black grama grassland
    P = Pinyon-Juniper woodland
  Missing values: None specified

Line
  Label: Line
  Definition: The line of pitfall traps along which data was collected.
  Type: Nominal
  Missing values: None specified

Trap
  Label: Trap
  Definition: The individual pitfall trap which was sampled.
  Type: Nominal
  Missing values: None specified

Order
  Label: Order
  Definition: The taxonomic rank of a specimen as designated by the first two letters of the order.
  Type: Nominal
  Missing values: None specified

Family
  Label: Family
  Definition: The taxonomic rank of a specimen as designated by the first three letters of the family..
  Type: Nominal
  Missing values: None specified

Genus
  Label: Genus
  Definition: The taxonomic rank of a specimen as designated by the first three letters of the genus.
  Type: Nominal
  Missing values: None specified

Species
  Label: Species
  Definition: The taxonomic rank of a specimen as designated by the first three letters of the species.
  Type: Nominal
  Missing values: None specified

Count
  Label: Count
  Definition: The total number of individuals for each taxon per trap per date.
  Type: Code list
  Codes:
    0 = No target taxa observed.
  Missing values:
    -888 = missing

comments
  Label: comments
  Definition: A special statement related to an observation.
  Type: Code list
  Codes:
  na = not applicable
  Missing values: None specified

Reshaped, a very good place to start

Jenn writes: “I also added a reshaped dataset that summarizes by arthropod family rather than species (=taxon_code). There are many fewer zeros that way. It would be easiest to start students on this one. In addition, the level of detail to which arthropods were identified does not seem to be consistent across years, getting less precise in later years of collection, perhaps good justification for lumping at a higher taxonomic level. Hopefully these data are clean enough to analyze - I haven’t spent time with the data to know what might surface as a downstream problem…”

Reshaped data: sev029_arthwide_family.csv

Read and display data

library(tidyverse)

dat_arth_wide_f <- read_csv("arth/sev029_arthwide_family.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   Season = col_character(),
##   Site = col_character(),
##   Line = col_character()
## )

## See spec(...) for full column specifications.

dim(dat_arth_wide_f)

## [1] 385  67

#names(dat_arth_wide_f)

dat_arth_wide <- read_csv("arth/sev029_arthwide.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   Season = col_character(),
##   Site = col_character(),
##   Line = col_character()
## )
## See spec(...) for full column specifications.

dim(dat_arth_wide)

## [1] 385 513

#names(dat_arth_wide)

#dat_arth_taxon <- read_csv("sevpit.csv")
#dim(dat_arth_taxon)
#names(dat_arth_taxon)

dat_met <- read_csv("arth/sev001_met_arth.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   Site = col_character(),
##   Station.Name = col_character()
## )
## See spec(...) for full column specifications.

dim(dat_met)

## [1] 97 31

#names(dat_met)

Join `arth` data with `met`eorological climate data

dat_arth_wide_f <-
  dat_arth_wide_f %>%
  full_join(dat_met)

## Joining, by = c("Year", "Site")

dim(dat_arth_wide_f)

## [1] 450  96

#names(dat_arth_wide_f)

dat_arth_wide <-
  dat_arth_wide %>%
  full_join(dat_met)

## Joining, by = c("Year", "Site")

dim(dat_arth_wide)

## [1] 450 542

names(dat_arth_wide)

##   [1] "Year"               "Season"             "Site"              
##   [4] "Line"               "AR ACR AMP COL"     "AR AGE 1 NYM"      
##   [7] "AR AGE AGE LON"     "AR AGE AGE NYM"     "AR AGE HOL HOL"    
##  [10] "AR ANY 1 NYM"       "AR ANY ANY DIX"     "AR ANY ANY NYM"    
##  [13] "AR ANY HIB INC"     "AR CLU 1 1"         "AR CLU 1 NYM"      
##  [16] "AR CLU CLU 1"       "AR CLU NEO CHA"     "AR COR -888 -888"  
##  [19] "AR COR 1 NYM"       "AR COR CAS 1"       "AR COR CAS 3"      
##  [22] "AR COR CAS OCC"     "AR COR MER 1"       "AR DIC -888 -888"  
##  [25] "AR DIC 1 NYM"       "AR DIC CIC 1"       "AR DIC CIC 2"      
##  [28] "AR DIC CIC 4"       "AR DIC CIC NYM"     "AR DIC CLC 1"      
##  [31] "AR FIL FIL 1"       "AR FIL KUK 1"       "AR GNA -888 -888"  
##  [34] "AR GNA 1 NYM"       "AR GNA CAL CHI"     "AR GNA CAL MUM"    
##  [37] "AR GNA CES BIL"     "AR GNA CES SIN"     "AR GNA DRA 1"      
##  [40] "AR GNA DRA 2"       "AR GNA DRA ANT"     "AR GNA DRA CER"    
##  [43] "AR GNA DRA CON"     "AR GNA DRA DRO"     "AR GNA DRA LAM"    
##  [46] "AR GNA DRA LEP"     "AR GNA DRA MEX"     "AR GNA DRA MOR"    
##  [49] "AR GNA DRA MUM"     "AR GNA DRA NYM"     "AR GNA DRA ORG"    
##  [52] "AR GNA DRA PRO"     "AR GNA DRD 1"       "AR GNA DRD GOS"    
##  [55] "AR GNA DRD NEG"     "AR GNA DRD NYM"     "AR GNA DRD SAC"    
##  [58] "AR GNA DRO GOS"     "AR GNA GNA 1"       "AR GNA GNA CLA"    
##  [61] "AR GNA GNA NYM"     "AR GNA GNA SER"     "AR GNA HAP CHA"    
##  [64] "AR GNA HAP NYM"     "AR GNA HER 1"       "AR GNA HER BUB"    
##  [67] "AR GNA HER EXC"     "AR GNA HER HES"     "AR GNA HER NYM"    
##  [70] "AR GNA HER PRO"     "AR GNA MIC 1"       "AR GNA MIC GOS"    
##  [73] "AR GNA MIC LON"     "AR GNA MIC NYM"     "AR GNA MIC PAL"    
##  [76] "AR GNA MIC POR"     "AR GNA NOD UTU"     "AR GNA ZEL 1"      
##  [79] "AR GNA ZEL ANG"     "AR GNA ZEL LAS"     "AR GNA ZEL NYM"    
##  [82] "AR GNA ZEL TUO"     "AR LIN 1 NYM"       "AR LIN 1 VSP"      
##  [85] "AR LIO LIO 1"       "AR LYC -888 -888"   "AR LYC 1 1"        
##  [88] "AR LYC 1 NYM"       "AR LYC ALO KOC"     "AR LYC ALO KOH"    
##  [91] "AR LYC GEO RAF"     "AR LYC HOG 1"       "AR LYC HOG CAR"    
##  [94] "AR LYC HOG FRO"     "AR LYC HOG NYM"     "AR LYC LYC NYM"    
##  [97] "AR LYC PAR 4"       "AR LYC PAR DIS"     "AR LYC PAR NYM"    
## [100] "AR LYC PAR ORO"     "AR LYC SCH 1"       "AR LYC SCH CHI"    
## [103] "AR LYC SCH MCC"     "AR LYC SCH MIM"     "AR LYC SCH NYM"    
## [106] "AR LYC VAR GOS"     "AR LYV -888 -888"   "AR MIM MIM 1"      
## [109] "AR OXY -888 -888"   "AR OXY 1 NYM"       "AR OXY OXY APO"    
## [112] "AR OXY OXY LYN"     "AR OXY OXY SAL"     "AR OXY OXY TRI"    
## [115] "AR PHI -888 -888"   "AR PHI 1 NYM"       "AR PHI APO TEX"    
## [118] "AR PHI EBO PAR"     "AR PHI PHI KEY"     "AR PHI THA 1"      
## [121] "AR PHI THA COL"     "AR PHI TIB DUT"     "AR PHI TIB NYM"    
## [124] "AR PHO -888 -888"   "AR PHO 1 NYM"       "AR PHO PHY ENA"    
## [127] "AR PHO PSI 1"       "AR SAL -888 -888"   "AR SAL 1 1"        
## [130] "AR SAL 1 NYM"       "AR SAL HAB CLY"     "AR SAL HAB CON"    
## [133] "AR SAL HAB GER"     "AR SAL HAB NYM"     "AR SAL PEL LIM"    
## [136] "AR SAL PEL NYM"     "AR SAL PHI 1"       "AR SAL PLA ARI"    
## [139] "AR SAL SAL 1"       "AR SAL SAL NYM"     "AR SAL THI 1"      
## [142] "AR THE -888 -888"   "AR THE EUR 2"       "AR THE EUR SCR"    
## [145] "AR THE LAT HES"     "AR THE STE 2"       "AR THE STE 3"      
## [148] "AR THO -888 -888"   "AR THO 1 NYM"       "AR THO BAS 1"      
## [151] "AR THO BAS 2"       "AR THO SYN NEO"     "AR THO THO NYM"    
## [154] "AR THO TMA ANG"     "AR THO XYS 1"       "AR THO XYS 3"      
## [157] "AR THO XYS APA"     "AR THO XYS CUN"     "AR THO XYS FAC"    
## [160] "AR THO XYS GUL"     "AR THO XYS LAS"     "AR THO XYS LOC"    
## [163] "AR THO XYS MON"     "AR THO XYS NYM"     "AR THO XYS ORI"    
## [166] "AR THR -888 -888"   "AR THR 1 NYM"       "AR THR APH 1"      
## [169] "AR THR EUR NYM"     "AR THR LAT HES"     "AR TNA DRA DRO"    
## [172] "BL POL -888 -888"   "BL POL ARE ERR"     "BL POL ERE SUB"    
## [175] "CO CAR -888 -888"   "CO CAR 1 56"        "CO CAR AMA 1"      
## [178] "CO CAR AMA APA"     "CO CAR AMA CAR"     "CO CAR AMA DIS"    
## [181] "CO CAR AMA ERR"     "CO CAR AMA IDA"     "CO CAR AMA RUB"    
## [184] "CO CAR CAL OBS"     "CO CAR CAL PER"     "CO CAR CAT OPA"    
## [187] "CO CAR CIC LEM"     "CO CAR CIC PUL"     "CO CAR CIC PUN"    
## [190] "CO CAR CYM ARR"     "CO CAR CYM PUN"     "CO CAR DIC LAE"    
## [193] "CO CAR EUR GRO"     "CO CAR HAR 1"       "CO CAR HAR AMP"    
## [196] "CO CAR HAR KAT"     "CO CAR HAR PEN"     "CO CAR HAR TAD"    
## [199] "CO CAR HEL LAT"     "CO CAR LEB VIR"     "CO CAR PAS 1"      
## [202] "CO CAR PAS CAL"     "CO CAR PAS ELO"     "CO CAR PAS OBS"    
## [205] "CO CAR PIO SET"     "CO CAR PTE 1"       "CO CAR PTE LUC"    
## [208] "CO CAR RHA 3"       "CO CAR RHA DIS"     "CO CAR SCR SUB"    
## [211] "CO CRY 1 1"         "CO CUR -888 -888"   "CO CUR 1 1"        
## [214] "CO CUR API 1"       "CO CUR CIM BUC"     "CO CUR CIM CON"    
## [217] "CO CUR CLE POR"     "CO CUR CLE QUA"     "CO CUR CRO 1"      
## [220] "CO CUR CUR 1"       "CO CUR GER LEC"     "CO CUR GER TUR"    
## [223] "CO CUR MIN LAN"     "CO CUR MIN LAT"     "CO CUR NOT LIM"    
## [226] "CO CUR OPH DUN"     "CO CUR OPH GLO"     "CO CUR OPH LAT"    
## [229] "CO CUR OPH SUL"     "CO CUR OPH VIT"     "CO CUR PAD DEN"    
## [232] "CO CUR RHY BRE"     "CO CUR SAP LON"     "CO CUR SAP PUN"    
## [235] "CO CUR SCY ACU"     "CO CUR SIT CAL"     "CO CUR SIT HIS"    
## [238] "CO CUR YUC FRO"     "CO ELA -888 -888"   "CO ELA 1 1"        
## [241] "CO ELA 1 20"        "CO ELA AEO MEL"     "CO ELA CAR 1"      
## [244] "CO ELA CON ATH"     "CO ELA CTE CAR"     "CO ELA HET SOR"    
## [247] "CO ELA HOR SIM"     "CO ELA LAN SCH"     "CO ELA MEL SIM"    
## [250] "CO GLA GLA PHO"     "CO HIS 1 4"         "CO HIS ILI CAC"    
## [253] "CO HIS SAP DIS"     "CO HIS SAP PEN"     "CO HIS XER 1"      
## [256] "CO HIS XER COE"     "CO LAT 1 1"         "CO LAT 1 3"        
## [259] "CO LEI PTO TEX"     "CO MEL LYT 2"       "CO MEL NEM 1"      
## [262] "CO NIT CAR LUG"     "CO SCA 1 30"        "CO SCA 1 46"       
## [265] "CO SCA 1 51"        "CO SCA 1 57"        "CO SCA 1 60"       
## [268] "CO SCA APH 1"       "CO SCA CRE PLA"     "CO SCA DIP 1"      
## [271] "CO SCA DIP 2"       "CO SCA DIP SUB"     "CO SCA EUP IND"    
## [274] "CO SCA HOP LAT"     "CO SCA ONT 1"       "CO SCA PAR PUN"    
## [277] "CO SCA PHY RUB"     "CO SCA PHY VET"     "CO SCA PHY WIC"    
## [280] "CO STA 1 88"        "CO STA OCY ATE"     "CO STA OMA 1"      
## [283] "CO STA QUE DES"     "CO TEN -888 -888"   "CO TEN AGR RUF"    
## [286] "CO TEN ANE 1"       "CO TEN ARA DEC"     "CO TEN ARE DEC"    
## [289] "CO TEN ARG RUF"     "CO TEN BLA FOR"     "CO TEN BLA PIM"    
## [292] "CO TEN EDR 1"       "CO TEN EDR LEE"     "CO TEN EDR ROT"    
## [295] "CO TEN ELE 1"       "CO TEN ELE 2"       "CO TEN ELE CAR"    
## [298] "CO TEN ELE CAU"     "CO TEN ELE EXT"     "CO TEN ELE FUS"    
## [301] "CO TEN ELE GRA"     "CO TEN ELE HIS"     "CO TEN ELE LON"    
## [304] "CO TEN ELE OBS"     "CO TEN ELE SPO"     "CO TEN ELE SUT"    
## [307] "CO TEN ELE TEN"     "CO TEN ELE TRI"     "CO TEN EMB CON"    
## [310] "CO TEN EMB PLA"     "CO TEN EUS RET"     "CO TEN GLY SOR"    
## [313] "CO TEN GON ELA"     "CO TEN GON INF"     "CO TEN HEL CAL"    
## [316] "CO TEN LOB FUS"     "CO TEN MEG 1"       "CO TEN MEG OBL"    
## [319] "CO TEN MEG TEN"     "CO TEN MEL 1"       "CO TEN MEL COA"    
## [322] "CO TEN MET CRI"     "CO TEN MET PRU"     "CO TEN NEO PLA"    
## [325] "CO TEN STE 1"       "CO TEN STE CON"     "CO TEN STE COV"    
## [328] "CO TEN STE OBO"     "CO TEN STE SEV"     "CO TEN STR COS"    
## [331] "CO TEN TEL HIS"     "CO TEN TRI PRU"     "CO TEN TRO COS"    
## [334] "EM EM EM EM"        "HE ALY -888 -888"   "HE ALY ALY 1"      
## [337] "HE ALY STA API"     "HE CYD -888 -888"   "HE CYD 1 1"        
## [340] "HE CYD 1 2"         "HE CYD 1 NYM"       "HE CYD PAN BIL"    
## [343] "HE LYG -888 -888"   "HE LYG 1 28"        "HE LYG 1 NYM"      
## [346] "HE LYG ANT PIL"     "HE LYG EMB VIC"     "HE LYG EXP 1"      
## [349] "HE LYG GEO 2"       "HE LYG GEO 3"       "HE LYG LYG KAL"    
## [352] "HE LYG LYG NYM"     "HE LYG NYS 1"       "HE LYG NYS RAP"    
## [355] "HE LYG ORT 1"       "HE LYG OZO 1"       "HE LYG PLI 1"      
## [358] "HE LYG SPH 1"       "HE MIR EUS 1"       "HE THY 1 2"        
## [361] "HE THY GAL 1"       "HY MUT DAS KLU"     "IS ARM ARM VUL"    
## [364] "LI LIT -888 -888"   "LI LIT 1 1"         "LI LIT TAI HAR"    
## [367] "MA MAN -888 -888"   "MA MAN LIT MIN"     "MA MAN YEP SOL"    
## [370] "MA MAN YER SOL"     "MI -888 -888 -888"  "MI 1 -888 -888"    
## [373] "MI 1 1 1"           "MI MAC -888 -888"   "MI MAC MES 1"      
## [376] "MI MAC MES NEA"     "MI MEI -888 -888"   "MI MEI MAC AUR"    
## [379] "MI MES MAC AUR"     "OP -888 TRA MAR"    "OP GAG 1 1"        
## [382] "OP GAG 1 2"         "OP SCL 1 1"         "OR ACR -888 -888"  
## [385] "OR ACR 1 1"         "OR ACR 1 NYM"       "OR ACR AC NYM"     
## [388] "OR ACR ACA PIP"     "OR ACR ACR 1"       "OR ACR ACR NYM"    
## [391] "OR ACR AGE 1"       "OR ACR AGE DEO"     "OR ACR AGG DEO"    
## [394] "OR ACR AMP COL"     "OR ACR ARP CON"     "OR ACR ARP NYM"    
## [397] "OR ACR ARP PSE"     "OR ACR AUL 1"       "OR ACR AUL ELL"    
## [400] "OR ACR BAR HUM"     "OR ACR BOO ARG"     "OR ACR CIB 1"      
## [403] "OR ACR CIB PAR"     "OR ACR COR 1"       "OR ACR COR CRE"    
## [406] "OR ACR COR OCC"     "OR ACR DAC BIC"     "OR ACR DAC NYM"    
## [409] "OR ACR ERI SIM"     "OR ACR HAD TRI"     "OR ACR HEL RUF"    
## [412] "OR ACR HES VIR"     "OR ACR HIP CAP"     "OR ACR LEP INT"    
## [415] "OR ACR LEP WHE"     "OR ACR MEL 1"       "OR ACR MEL ARZ"    
## [418] "OR ACR MEL BIV"     "OR ACR MEL BOW"     "OR ACR MEL GLA"    
## [421] "OR ACR MEL LAK"     "OR ACR MEL PAC"     "OR ACR MEL SPL"    
## [424] "OR ACR MES TER"     "OR ACR MES VIR"     "OR ACR OPE OBS"    
## [427] "OR ACR PAR PAL"     "OR ACR PHL QUA"     "OR ACR PHO NEB"    
## [430] "OR ACR PSO 1"       "OR ACR PSO DEL"     "OR ACR PSO TEX"    
## [433] "OR ACR SPH EQU"     "OR ACR SYR ADM"     "OR ACR SYR MON"    
## [436] "OR ACR TRA 1"       "OR ACR TRA KIO"     "OR ACR TRI 1"      
## [439] "OR ACR TRI CAL"     "OR ACR TRI CIN"     "OR ACR TRI PAL"    
## [442] "OR ACR TRI PIS"     "OR ACR TRO FOR"     "OR ACR XAN COR"    
## [445] "OR ACR XAN MON"     "OR ACR XAN NYM"     "OR GRY -888 -888"  
## [448] "OR GRY 1 NYM"       "OR GRY CYC COM"     "OR GRY GRY 1"      
## [451] "OR GRY GRY NYM"     "OR GRY GRY PEN"     "OR GRY GRY PER"    
## [454] "OR GRY HOP BOR"     "OR GRY OEC CAL"     "OR MOG -888 -888"  
## [457] "OR MOG CYC COM"     "OR MOG HOP BOR"     "OR MOG MOG 1"      
## [460] "OR MOG MOG NYM"     "OR RHA -888 -888"   "OR RHA 1 NYM"      
## [463] "OR RHA CEU 1"       "OR RHA CEU LAM"     "OR RHA CEU NYM"    
## [466] "OR RHA CEU PAL"     "OR RHA CEU UTA"     "OR RHA DAI HAS"    
## [469] "OR RHA DAI NYM"     "OR ROM PHR ROB"     "OR STE STE 1"      
## [472] "OR STE STE FUS"     "OR STE STE NYM"     "OR TET 1 NYM"      
## [475] "OR TET ARE 1"       "OR TET CAP NYM"     "OR TET ERE BIL"    
## [478] "OR TET ERE EPH"     "OR TET ERE NYM"     "OR TET ERE SCU"    
## [481] "OR TET INS ELE"     "OR THE MAS GIG"     "PA PAR 1 1"        
## [484] "SC SCO SCO POL"     "SC SCO SCO VIR"     "SC VAE -888 -888"  
## [487] "SC VAE VAE COA"     "SL SCO -888 -888"   "SL SCO SCO POL"    
## [490] "SO ERE -888 -888"   "SO ERE 1 1"         "SO ERE 1 NYM"      
## [493] "SO ERE ARE MUM"     "SO ERE ARE PUE"     "SO ERE ERC BIL"    
## [496] "SO ERE ERE 1"       "SO ERE ERE BAJ"     "SO ERE ERE NOD"    
## [499] "SO ERE ERE NYM"     "SO ERE ERE PAL"     "SO ERE ERE SIL"    
## [502] "SO ERE ERE SIM"     "SO ERE HEM BRA"     "SO ERE HEM FRU"    
## [505] "SP SPI -888 -888"   "SP SPI 1 1"         "SP SPI ORT ORN"    
## [508] "SR VAE 1 NYM"       "SR VAE VAE COA"     "SR VAE VAE RUS"    
## [511] "UR THE MAS GIG"     "species_diversity"  "total"             
## [514] "precip.annual"      "precip.monsoon"     "precip.spring"     
## [517] "precip.winter"      "precip.intra_CV"    "events"            
## [520] "ev_size"            "extreme_size"       "CDD"               
## [523] "GDD.monsoon"        "GDD.spring"         "soil_T"            
## [526] "soil_Tso"           "soil_Tjja"          "VPDaso"            
## [529] "VPDmjj"             "GDDW.monsoon"       "avgsuT"            
## [532] "maxsuT"             "minsuT"             "extreme_CDD"       
## [535] "Station.Name"       "lat"                "long"              
## [538] "elevation"          "spring12SPEI.comp"  "spring6SPEI.comp"  
## [541] "monsoon12SPEI.comp" "monsoon6SPEI.comp"

Additional summaries

library(ggplot2)
p <- ggplot(dat_arth_wide_f, aes(x = Year, y = total, colour = Site))
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "total arthropods")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 65 rows containing non-finite values (stat_smooth).

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 2002

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 5.2085e-017

## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401

## Warning: Removed 65 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat_arth_wide_f, aes(x = Year, y = precip.annual, colour = Site))
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "Annual Precip")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 70 rows containing non-finite values (stat_smooth).

## Warning: Removed 70 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat_arth_wide_f, aes(x = Year, y = avgsuT, colour = Site))
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "Average summer temperature")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 74 rows containing non-finite values (stat_smooth).

## Warning: Removed 74 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat_arth_wide_f, aes(x = Year, y = spring12SPEI.comp, colour = Site))
p <- p + geom_hline(aes(yintercept = 0), alpha = 1/2)
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "CROSS-SITE SPEI drought index over the water year ending in May - spring (12 month integration)")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 241 rows containing non-finite values (stat_smooth).

## Warning: Removed 241 rows containing missing values (geom_point).

Small Mammals

Small Mammal Mark-Recapture Population Dynamics at Core Research Sites at the Sevilleta National Wildlife Refuge, New Mexico (1989 - present)

Abstract: This file contains mark/recapture trapping data collected from 1989-2012 on permanently established web trapping arrays at 8 sites on the Sevilleta NWR. At each site 3 trapping webs are sampled for 3 consecutive nights in spring and fall. Not all sites have been trapped for the entire period. Each trapping web consists of 145 rebar stakes numbered from 1-145. There are 148 traps deployed on each web: 12 along each of 12 spokes radiating out from a central point (stake #145) plus 4 traps at the center point. The trapping sites are representative of Chihuahuan Desert Grassland, Chihuahuan Desert Shrubland, Pinyon-Juniper Woodland, Juniper Savanna, Plains-Mesa Sand Scrub and Blue Grama Grassland.

Small Mammal metadata: http://sevlter.unm.edu/data/sev-8

Codebook: http://sevlter.unm.edu/data/sev-008/4786

As of 8/25/19: The dataset has not been updated on our website yet. I filtered the newest dataset (thru 2018) and it is attached as SEV008_mammals_Erhardt.csv

Jenn created 2 new arthropod variables:

species_diversity = Shannon Diversity Index
total = sum of all mammal individuals

The sample unit is the trapping web (web_id), which is nested in location. If you wanted to simplify, you could take the mean across the 3 webs per location, to get just one observation for each (location, season, year) combination. This would eliminate the need for a random effect of web_id.

Values in each small mammal species cell are counts of individuals per trap per night (we use 148 traps per web, usually 3 nights of trapping per (season, year) combination). That is, count of animals trapped / (148 traps * 3 nights of trap deployment) = count / 444.

Jenn removed the following taxa (AMIN, AMLE, EUDO, EUQU, SPSP) because they are “by-catch” — chipmunks and squirrels occassionally get into our rodent traps and these aren’t interesting for us.

The locations were sampled for different time periods.

If you wanted to simplify, just subset to 5pgrass and 5plarrea locations. Or, one could look at 5 of the locations from 1989-1998.

Here is a summary of years sampled by location; I filtered out some locations that are in the metadata (savanna, blue grama) because the time series was so short.

Location	Years collected
5pgrass	1989-2018
5plarrea	1989-2018
goatdraw	1992-2006
rsgrass	1989-1998
rslarrea	1989-2008
two22	1989-1998

List of attached files:

Data: SEV008_mammals_Erhardt.csv
Species codes: SEV_mammal_species_codes.xlsx
Matching yearly climate data: SEV001met_mammals.csv
- merge by: Year

ID Not all datasets have an ID, but each observation should be able to be identified uniquely. In this case a combination of the variables will identify an observation uniquely (year, location, season, night, web, and trap).

Codebook for the original data: sev029_arthropop02162009.csv

Variables

ID
  See note above
  year, location, season, night, web, trap

year
  Label: year
  Definition: The year in which data was collected.
  Type: Date/time
  Date format: YYYY
  Missing values: None specified

location
  Label: location
  Definition: site name at which trapping occurs
  Type: Code list
  Codes:
    two22 = two22
    5pgrass = five points grass
    5plarrea = five points larrea
    rsgrass = rio salado grass
    goatdraw = goat draw
    bluegram = blue grama
    savanna = savanna
  Missing values: None specified

season
  Label: season
  Definition: Season in which trapping occurs
  Type: Code list
  Codes:
    1 = Spring
    2 = Summer
    3 = Fall
  Missing values: None specified

night
  Label: night
  Definition: The night that the trapping was done
  Type: Nominal
  Missing values: None specified

web
  Label: web
  Definition: The number of the web being sampled, which can range from 1-5 depending on site and project.
  Type: Nominal
  Missing values: None specified

trap
  Label: trap
  Definition: The numbered tag on rebar adjacent to the trap
  Type: Nominal
  Missing values: None specified

recap
  Label: recap
  Definition: An indication if the animal was a recapture or not
  Type: Code list
  Codes:
    y = a recapture from that week only
    n = not a recapture
  Missing values: None specified

species
  Label: species
  Definition: Four letter code for small mammal species.
  Type: Code list
  Codes:
    amin = Ammospermophilus interpres
    amle = Ammospermophilus leucurus
    eudo = Eutamias dorsalis
    euqu = Eutamias quadrivittatus
    spsp = Spermophilus spilosoma
    spva = Spermophilus variegatus
    chin = Chetodipus intermedius
    pgfl = Perognathus flavescens
    pgfv = Perognathus flavus
    pgsp = Perognathus sp.
    dime = Dipodomys merriami
    dior = Dipodomys ordii
    disp = Dipodomys spectabilis
    dipo = Dipodomys sp.
    neal = Neotoma albigula
    nemi = Neotoma micropus
    nesp = Neotoma sp.
    onar = Onychomys arenicola
    onle = Onychomys leucogaster
    onsp = Onychomys sp.
    pebo = Peromyscus boylii
    pedi = Peromyscus difficilis
    peer = Peromyscus eremicus
    pele = Peromyscus leucopus
    pema = Peromyscus maniculatus
    petr = Peromyscus truei
    pesp = Peromyscus sp.
    remg = Reithrodontomys megalotis
    remn = Reithrodontomys montanus
    resp = Reithrodontomys sp.
    sihi = Sigmodon hispidus
    syau = Sylvilagus auduboni
    na = unknown genus and species
  Missing values: None specified

sex
  Label: sex
  Definition: Gender of animal (M or F)
  Type: Nominal
  Missing values: None specified

age
  Label: age
  Definition: Estimated from animal's weight.
  Type: Code list
  Codes:
    a = adult
    j = juvenile
  Missing values:
    na = missing

reprod
  Label: reprod
  Definition: Reproductive condition of capture
  Type: Code list
  Codes:
    l = lactating
    v = vaginal
    p = pregnant
    st = scrotal
    na = none of the former or not taken
  Missing values: None specified

mass
  Label: mass
  Definition: mass of captured rodent in grams
  Type: Physical quantity
  Unit: gram
  Maximum: Not specified
  Minimum: Not specified
  Precision: 0.5
  Missing values:
    . = missing

Read and display data

library(tidyverse)

dat_smam <- read_csv("smam/SEV008_mammals_Erhardt.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   web_id = col_character(),
##   ecosystem = col_character(),
##   habitat = col_character(),
##   location = col_character(),
##   season.fs = col_character(),
##   Station.Name = col_character()
## )

## See spec(...) for full column specifications.

dim(dat_smam)

## [1] 683  34

#names(dat_smam)

dat_met <- read_csv("smam/SEV001met_mammals.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   Station.Name = col_character()
## )
## See spec(...) for full column specifications.

dim(dat_met)

## [1] 110  35

#names(dat_met)

Join `smam` data with `met`eorological climate data

dat_smam <-
  dat_smam %>%
  full_join(dat_met)

## Joining, by = c("year", "Station.Name")

dim(dat_smam)

## [1] 727  67

names(dat_smam)

##  [1] "X1"                 "year"               "web_id"            
##  [4] "ecosystem"          "habitat"            "location"          
##  [7] "season.fs"          "CHIN"               "DIME"              
## [10] "DIOR"               "DISP"               "DIXX"              
## [13] "MUMU"               "NEAL"               "NEMI"              
## [16] "NEXX"               "ONAR"               "ONLE"              
## [19] "ONXX"               "PEBO"               "PEDI"              
## [22] "PEER"               "PELE"               "PEMA"              
## [25] "PETR"               "PEXX"               "PGFL"              
## [28] "PGFV"               "REME"               "REMO"              
## [31] "REXX"               "Station.Name"       "species_diversity" 
## [34] "total"              "Sta"                "precip.annual"     
## [37] "precip.monsoon"     "precip.spring"      "precip.winter"     
## [40] "precip.intra_CV"    "events"             "ev_size"           
## [43] "extreme_size"       "CDD"                "GDD.monsoon"       
## [46] "GDD.spring"         "soil_T"             "soil_Tso"          
## [49] "soil_Tjja"          "VPDaso"             "VPDmjj"            
## [52] "GDDW.monsoon"       "avgsuT"             "maxsuT"            
## [55] "minsuT"             "extreme_CDD"        "lat"               
## [58] "long"               "elevation"          "spring12SPEI"      
## [61] "spring6SPEI"        "monsoon12SPEI"      "monsoon6SPEI"      
## [64] "spring12SPEI.comp"  "spring6SPEI.comp"   "monsoon12SPEI.comp"
## [67] "monsoon6SPEI.comp"

Additional summaries

library(ggplot2)
p <- ggplot(dat_smam, aes(x = year, y = total, colour = ecosystem))
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "total mammals")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 44 rows containing non-finite values (stat_smooth).

## Warning: Removed 44 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat_smam, aes(x = year, y = precip.annual, colour = ecosystem))
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "Annual Precip")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 240 rows containing non-finite values (stat_smooth).

## Warning: Removed 240 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat_smam, aes(x = year, y = avgsuT, colour = ecosystem))
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "Average summer temperature")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 278 rows containing non-finite values (stat_smooth).

## Warning: Removed 278 rows containing missing values (geom_point).

library(ggplot2)
p <- ggplot(dat_smam, aes(x = year, y = spring12SPEI.comp, colour = ecosystem))
p <- p + geom_hline(aes(yintercept = 0), alpha = 1/2)
p <- p + geom_point()
p <- p + stat_smooth(se = FALSE)
p <- p + labs(title = "CROSS-SITE SPEI drought index over the water year ending in May - spring (12 month integration)")
print(p)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 563 rows containing non-finite values (stat_smooth).

## Warning: Removed 563 rows containing missing values (geom_point).

SEV Data

Erik Erhardt

9/5/2019

Data Sets in SEV

Arthropod

Reshaped, a very good place to start

Read and display data

Join `arth` data with `met`eorological climate data

Additional summaries

Small Mammals

Read and display data

Join `smam` data with `met`eorological climate data

Additional summaries

SEV Data

Erik Erhardt

9/5/2019

Data Sets in SEV

Arthropod

Reshaped, a very good place to start

Read and display data

Join arth data with meteorological climate data

Additional summaries

Small Mammals

Read and display data

Join smam data with meteorological climate data

Additional summaries

Join `arth` data with `met`eorological climate data

Join `smam` data with `met`eorological climate data